A chromatograph detection data positioning method, system, terminal and storage medium

Abstract

The present application relates to the technical field of chromatography, and discloses a chromatograph detection data positioning method, system, terminal and storage medium, the method comprising: performing multi-scale filtering on original chromatographic data stream to obtain clinical sample ion flow data; performing baseline fitting on the clinical sample ion flow data based on a healthy population background control area to obtain reference baseline data; performing offset event preliminary screening on the clinical sample ion flow data, and taking the screened offset event as candidate chromatographic peak starting point data; performing end convergence analysis on the clinical sample ion flow data to obtain candidate chromatographic peak end point data; performing clinical significance correlation labeling on the clinical sample ion flow data based on the candidate chromatographic peak starting point data and the candidate chromatographic peak end point data to obtain targeted peak position data; and performing co-elution separation verification on the targeted peak position data to obtain chromatographic peak positioning report data; the present application can improve the efficiency of chromatograph detection data positioning.

Description

Technical Field

[0001] This invention relates to the field of chromatographic analysis technology, and in particular to a method, system, terminal, and storage medium for locating chromatographic detection data. Background Technology

[0002] In the process of processing clinical chromatograph test data, existing technologies are unable to efficiently separate noise and purify signals from the raw chromatographic data stream, and cannot accurately distinguish between matrix noise spectrum and effective ion flow signal. This results in a large amount of broad-spectrum matrix trend interference and narrow-spectrum random noise remaining in the ion flow data of clinical samples, which leads to poor basic data quality for subsequent data processing and directly affects the accuracy of overall test data positioning.

[0003] Existing technologies lack systematic technical solutions for key aspects such as baseline fitting, peak selection, and endpoint determination. They fail to fully integrate background control information from healthy individuals to establish scientific reference standards, lack a continuous and effective monitoring mechanism for identifying shift events, and do not thoroughly address interference from drug metabolism tailing and co-elution of endogenous substances. This results in significant deviations between the chromatographic peak initiation and endpoint positioning, and insufficient accuracy in labeling the clinical relevance of targeted peak data. Ultimately, this affects the reliability and efficiency of chromatographic peak positioning reports. Therefore, improving the efficiency of chromatographic data positioning has become an urgent problem to be solved. Summary of the Invention

[0004] This disclosure provides a method, system, terminal, and storage medium for locating chromatograph detection data.

[0005] In a first aspect, this disclosure provides a method for locating chromatograph detection data, including:

[0006] S1. Perform multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block;

[0007] S2. Based on the baseline control area of ​​healthy individuals in the clinical detection block, perform baseline fitting on the ion flow data of the clinical sample to obtain the reference baseline data of the clinical detection block;

[0008] S3. Based on the reference baseline data, perform initial screening of offset events on the ion flow data of the clinical samples, and use the screened offset events as candidate chromatographic peak start data for the clinical detection block;

[0009] S4. Based on the candidate chromatographic peak initiation data, perform endpoint convergence analysis on the ion flow data of the clinical sample to obtain the candidate chromatographic peak endpoint data of the clinical detection block;

[0010] S5. Based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, perform clinical significance association annotation on the clinical sample ion flow data to obtain the target peak position data of the clinical detection block;

[0011] S6. Perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

[0012] In a preferred embodiment, the step of performing multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block includes:

[0013] Matrix noise spectrum analysis was performed on the raw chromatographic data stream of the clinical detection block to obtain the noise characteristic data of the clinical detection block;

[0014] Based on the noise characteristic data, broadband matrix trend separation is performed on the original chromatographic data stream to obtain the baseline contour data of the clinical detection block;

[0015] Narrow-spectrum random noise stripping is performed on the raw chromatographic data stream to obtain the noise residual data of the clinical detection block;

[0016] Based on the baseline profile data and the noise residual data, the coupling interference of the original chromatographic data stream is synchronously eliminated to obtain the decoupled ion flow signal data of the clinical detection block;

[0017] Ion flow stability calibration is performed on the decoupled ion flow signal data to obtain the clinical sample ion flow data of the clinical detection block.

[0018] In a preferred embodiment, the baseline fitting of the ion flow data of the clinical samples based on the healthy population background control area of ​​the clinical detection block to obtain reference baseline data of the clinical detection block includes:

[0019] Background signals are extracted from the baseline control area of ​​the healthy population in the clinical testing block to obtain the sample reference area data of the clinical testing block;

[0020] Based on the sample reference area data, local trend tracking is performed on the non-peak areas in the ion flow data of the clinical sample to obtain the dynamic background trend data of the clinical detection block;

[0021] Background interference correction is applied to the dynamic baseline trend data to obtain the baseline trajectory data of the clinical detection block;

[0022] The baseline trajectory data is smoothed and connected to obtain the reference baseline data for the clinical detection block.

[0023] In a preferred embodiment, the step of performing initial screening of shift events on the clinical sample ion flow data based on the reference baseline data, and using the screened shift events as candidate chromatographic peak initiation data for the clinical detection block, includes:

[0024] Based on the sensitivity requirements of the clinical detection block and the fluctuation characteristics of the reference baseline data, a continuous offset judgment rule for the clinical detection block is established.

[0025] Based on the continuous offset determination rule, the signal deviation between the clinical sample ion flow data and the reference baseline data is continuously monitored to obtain candidate deviation segment data of the clinical detection block;

[0026] The starting boundary of the deviation segment data is traced to obtain the candidate chromatographic peak starting point data of the clinical detection block.

[0027] In a preferred embodiment, the step of performing endpoint convergence analysis on the ion current data of the clinical sample based on the candidate chromatographic peak initiation data to obtain the candidate chromatographic peak endpoint data of the clinical detection block includes:

[0028] The drug metabolism tailing effect of the candidate chromatographic peak initiation data is evaluated to obtain the signal attenuation segment of the clinical detection block;

[0029] Based on the signal attenuation region, the endogenous substance co-elution interference is identified in the ion flow data of the clinical sample to obtain the mixed signal region of the clinical detection block;

[0030] Signal regression trajectory tracking is performed on the mixed signal segment to obtain the dynamic trajectory data of the clinical detection block;

[0031] The endpoint compliance of the dynamic trajectory data is verified to obtain the candidate chromatographic peak endpoint data of the clinical detection block.

[0032] In a preferred embodiment, the step of performing clinical significance association annotation on the clinical sample ion flow data based on the candidate chromatographic peak initiation data and the candidate chromatographic peak endpoint data to obtain the target peak position data of the clinical detection block includes:

[0033] Peak regions are extracted from the candidate chromatographic peak start data and the candidate chromatographic peak end data to obtain the complete characteristic peak signal segment of the clinical detection block;

[0034] The complete characteristic peak signal segment is mapped to the clinical diagnostic knowledge base of the clinical detection block to obtain the verification characteristic peak data of the clinical detection block;

[0035] Based on the preset disease biomarker associations, diagnostic orientation analysis is performed on the verification feature peak data to obtain the pathological significance identifier of the clinical detection block;

[0036] The formula for calculating the comprehensive index of clinical significance in the pathological significance markers is as follows:

[0037] ;

[0038] In the formula, The clinical significance composite index, The retention time deviation between the verification feature peak data and the clinical diagnostic knowledge base. The relative standard deviation of the preset retention time. The target peak area is defined in the verification feature peak data. The target peak height is defined in the verification feature peak data. This is the preset reference peak area. The preset reference peak height, The strength of the response signal in the verification feature peak data. The noise intensity of the noise residual data in the clinical detection block. These are pre-defined clinically relevant factors;

[0039] The pathological significance markers were reviewed and verified to obtain the target peak data of the clinical detection blocks.

[0040] In a preferred embodiment, the step of co-eluting and verifying the targeted peak position data to obtain the chromatographic peak localization report data of the clinical detection block includes:

[0041] The targeted peak data were co-eluted to obtain the peak segments to be validated in the clinical detection block;

[0042] The component separation degree of the peak segment to be verified is verified to obtain independent component peak segment data of the clinical detection block;

[0043] The chromatographic peak location information of the independent component peak segment data is structured and integrated to obtain the chromatographic peak location report data of the clinical detection block.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This invention refines the raw chromatographic data stream through multi-scale filtering, effectively separating matrix noise from effective ion flow signals, simultaneously eliminating coupling interference and calibrating ion flow stability, resulting in higher purity and stronger reliability of the obtained clinical sample ion flow data. At the same time, baseline fitting is performed based on the background control area of ​​healthy individuals, and through local trend tracking and background interference correction, the reference baseline data is made to better fit the actual clinical testing scenario, providing accurate data support for subsequent peak position positioning.

[0046] 2. This invention achieves accurate initial screening of offset events by establishing a persistent offset judgment rule, and completes endpoint convergence analysis by combining drug metabolism tail assessment and co-elution interference identification, ensuring the accuracy of candidate chromatographic peak start and endpoint data. Then, through clinical diagnostic knowledge base mapping and pathological significance analysis, the target peak data has clear clinical relevance attributes. Finally, through co-elution separation verification and structured integration, the generated chromatographic peak positioning report data is more comprehensive and standardized, significantly improving the accuracy of chromatograph detection data positioning and clinical application value. Attached Figure Description

[0047] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0048] Figure 1 The flowchart of a chromatograph detection data localization method according to Embodiment 1 of the present invention is shown;

[0049] Figure 2 This diagram shows a functional block diagram of a chromatograph detection data positioning system according to Embodiment 2 of the present invention;

[0050] Figure 3 The diagram shows the structural composition of a terminal implementing the chromatograph detection data localization method according to Embodiment 3 of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0052] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0053] Example 1

[0054] Figure 1 This is a schematic flowchart illustrating a method for locating chromatograph detection data according to an embodiment of this disclosure. Figure 1 As shown, a method for locating chromatograph detection data includes:

[0055] S1. Perform multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block;

[0056] In this embodiment of the invention, the step of performing multi-scale filtering on the raw chromatographic data stream of the clinical detection block to obtain the clinical sample ion flow data of the clinical detection block includes:

[0057] Matrix noise spectrum analysis was performed on the raw chromatographic data stream of the clinical detection block to obtain the noise characteristic data of the clinical detection block;

[0058] Based on the noise characteristic data, broadband matrix trend separation is performed on the original chromatographic data stream to obtain the baseline contour data of the clinical detection block;

[0059] Narrow-spectrum random noise stripping is performed on the raw chromatographic data stream to obtain the noise residual data of the clinical detection block;

[0060] Based on the baseline profile data and the noise residual data, the coupling interference of the original chromatographic data stream is synchronously eliminated to obtain the decoupled ion flow signal data of the clinical detection block;

[0061] Ion flow stability calibration is performed on the decoupled ion flow signal data to obtain the clinical sample ion flow data of the clinical detection block.

[0062] The raw chromatographic data stream of the clinical testing block was comprehensively acquired. This data stream contains continuous time-signal intensity correspondence data. The entire data stream was split into several continuous and non-overlapping segments with each segment as a fixed time block of 0.1 seconds. The signal variation pattern was analyzed segment by segment, with a focus on observing the recurring fluctuation patterns in 10 consecutive adjacent segments. Fluctuation intervals that are not related to the effective signal of the clinical sample were identified. The time distribution range, signal fluctuation amplitude, frequency of occurrence, duration of a single fluctuation, and time interval between adjacent fluctuations of these fluctuation intervals were recorded in detail. All recorded information was classified and organized according to time sequence and fluctuation characteristics to form the noise characteristic data of the clinical testing block.

[0063] Based on the clearly defined temporal distribution range, signal fluctuation amplitude, and fluctuation pattern of the broad-spectrum matrix noise in the noise characteristic data, the raw chromatographic data stream is scanned segment by segment from the beginning to the end. The length of each scan segment is set to 1 second. The fluctuation amplitude and distribution position of each signal point in the scan segment are compared point by point with the broad-spectrum matrix noise parameters in the noise characteristic data. As long as the fluctuation attribute of the signal point completely matches the broad-spectrum matrix noise characteristics, the signal point is classified as a broad-spectrum matrix trend signal. After all scan segments are processed, all classified broad-spectrum matrix trend signals are extracted separately. Linear interpolation is used to connect these signal points continuously in time order to fill the gaps between signal points and form a smooth curve with a corresponding signal value at each time point, which is the baseline contour data of the clinical detection block.

[0064] One hundred consecutive signal points were selected from the original chromatographic data stream after excluding obvious abnormal fluctuations. The average amplitude of these 100 signal points was calculated, and 10% of this average was set as the threshold for narrow-spectrum random noise amplitude. At the same time, the criteria for judging signal change patterns were set: there is no fixed trend of change within three consecutive signal points and the fluctuations are not repeatable. Each signal point in the original chromatographic data stream was screened one by one. Signal points that meet both the threshold amplitude and the criteria for judging signal change patterns were identified as narrow-spectrum random noise. All the screened narrow-spectrum random noise signals were extracted and organized in chronological order in the original data stream to form noise residual data of the clinical detection block containing the timestamp and amplitude information of each noise signal.

[0065] The timestamp of each signal point in the raw chromatographic data stream is precisely matched with the signal values ​​of the same timestamp in the baseline profile data and noise residual data. The matched baseline profile data signal value is added to the noise residual data signal value to obtain the coupling interference superposition component of the signal point at that timestamp. The actual signal intensity of the signal point in the raw chromatographic data stream is subtracted from the corresponding coupling interference superposition component to complete the coupling interference elimination of a single signal point. All signal points in the raw chromatographic data stream are processed in the same way to ensure that the common interference of broad-spectrum matrix noise and narrow-spectrum random noise is eliminated from each signal point, and only the effective ion current signal related to the clinical sample is retained. After processing, all signal points are arranged in chronological order to obtain the decoupled ion current signal data of the clinical detection block.

[0066] The preset ion flow signal stability fluctuation standard is: the absolute value of the difference between the amplitude of the subsequent signal point and the amplitude of the previous signal point, divided by the amplitude of the previous signal point, should not exceed 0.5%. Starting from the initial signal point of the decoupled ion flow signal data, the amplitude changes of adjacent signal points are checked point by point to see if they meet the standard. If a signal point exceeds the stability fluctuation standard, the three signal points that meet the standard before and the three signal points that meet the standard after that signal point are extracted, and the average amplitude of these six signal points is calculated. This average value is used to replace the amplitude of the signal point that exceeds the standard. After replacement, the amplitude changes of the signal point and its adjacent signal points are checked again to see if they meet the stability standard, until all signal points meet the requirements. After comprehensive inspection and adjustment, the clinical sample ion flow data of the clinical detection block is formed.

[0067] The beneficial effects are as follows: By performing matrix noise spectrum analysis on the raw chromatographic data stream of the clinical detection block, after accurately capturing noise characteristics, targeted broad-spectrum matrix trend separation and narrow-spectrum random noise stripping are performed. This effectively separates and clarifies baseline profile data and noise residual data. Then, by combining the two types of data, coupling interference in the raw data stream is eliminated simultaneously, ensuring that the decoupled ion current signal data retains only the effective signals relevant to the clinical sample. Subsequently, ion current stability calibration is used to further standardize the signal fluctuation state. The final clinical sample ion current data has high purity, high stability, and high accuracy, providing a reliable and high-quality data foundation for subsequent processing steps such as baseline fitting, candidate chromatographic peak screening, and targeted peak labeling, ensuring the efficient advancement of the overall chromatograph detection data positioning process.

[0068] S2. Based on the baseline control area of ​​healthy individuals in the clinical detection block, perform baseline fitting on the ion flow data of the clinical sample to obtain the reference baseline data of the clinical detection block;

[0069] In this embodiment of the invention, the baseline fitting of the ion flow data of the clinical sample based on the healthy population background control area of ​​the clinical detection block to obtain the reference baseline data of the clinical detection block includes:

[0070] Background signals are extracted from the baseline control area of ​​the healthy population in the clinical testing block to obtain the sample reference area data of the clinical testing block;

[0071] Based on the sample reference area data, local trend tracking is performed on the non-peak areas in the ion flow data of the clinical sample to obtain the dynamic background trend data of the clinical detection block;

[0072] Background interference correction is applied to the dynamic baseline trend data to obtain the baseline trajectory data of the clinical detection block;

[0073] The baseline trajectory data is smoothed and connected to obtain the reference baseline data for the clinical detection block.

[0074] Continuous signal data from a pre-defined baseline control area for healthy individuals within the clinical testing block are collected. This control area is a region corresponding to a specific testing period that has been verified to be free of clinically targeted components. The preset signal stability criterion is that the intensity change ratio between two adjacent signal points does not exceed 0.3%. The signal data of this control area are screened point by point, and all signal segments that continuously meet the stability criterion and have a length of not less than 5 seconds are selected. The start time, end time, and corresponding signal intensity value of each signal segment are recorded. These records are integrated in chronological order to form the sample reference area data of the clinical testing block containing complete and stable baseline signal information.

[0075] Using the average signal intensity of the sample reference area data as a benchmark, a peak area determination criterion is preset: if the signal intensity is more than 3 times the benchmark and 5 consecutive signal points show a gradual upward trend, it is determined to be a peak area. All other areas are defined as non-peak areas. The ion flow data of clinical samples are scanned throughout the entire time period to determine the specific time range of all non-peak areas. Each non-peak area is divided into several continuous analysis segments with a duration of 1 second. The signal change trend within each segment is compared with the signal change trend of the corresponding time period in the sample reference area data. The direction of signal rise or fall, rate of change, and duration of each analysis segment are recorded. The recorded results of all analysis segments are connected and integrated in chronological order to form dynamic background trend data of the clinical detection block that can reflect the overall change law of the signal in the non-peak area.

[0076] A preset background interference threshold is set: if the rate of change of any signal point in the dynamic background trend data exceeds twice the average rate of change of the corresponding time period in the sample reference area data, then the signal segment consisting of that signal point and one signal point before and after it is determined to be a background interference segment. All background interference segments in the dynamic background trend data are identified one by one. For each interference segment, three undisturbed normal signal points before and after it are extracted. The average rate of change and signal intensity gradient of these six normal signal points are calculated. According to the average rate of change and gradient, the intensity of each signal point in the interference segment is adjusted so that the adjusted signal segment can seamlessly connect with the normal signals before and after. After all interference segments are corrected, the background trajectory data of the clinical detection block is formed.

[0077] Preset smooth transition judgment criteria: The signal intensity difference between the endpoints of two adjacent analysis segments in the baseline trajectory data does not exceed 0.2%. The endpoint difference is calculated for each adjacent analysis segment in the baseline trajectory data. If the difference exceeds the standard, three transition signal points are inserted between the two endpoints. The intensity of each transition signal point is distributed in a linear proportion from the previous endpoint to the next endpoint, so that the signal intensity of the adjacent analysis segment smoothly transitions from one endpoint to the other. After the smoothing of all adjacent segments is completed, a reference baseline data for clinical testing blocks that covers the entire time period of clinical testing and has continuous signals without abrupt fluctuations is formed.

[0078] The beneficial effects are as follows: by accurately extracting the background signal from the healthy population background control area of ​​the clinical testing block, sample reference area data with stable properties is obtained, providing a reliable reference benchmark for subsequent baseline fitting. Based on this reference data, targeted local trend tracking is carried out on the non-peak areas in the ion flow data of clinical samples, accurately capturing the signal change patterns in the non-peak areas to form dynamic background trend data. Then, background interference in the dynamic background trend data is identified and corrected through clear judgment criteria to ensure the authenticity and purity of the background trajectory data. Finally, abrupt fluctuations between data segments are eliminated through smoothing and connection processing to obtain continuous, stable reference baseline data that closely matches the actual background state of clinical testing. This data can accurately reflect the background change characteristics of clinical testing, providing accurate baseline reference for subsequent steps such as initial screening of offset events and candidate chromatographic peak positioning, effectively ensuring the overall accuracy and scientific nature of the chromatograph detection data positioning.

[0079] S3. Based on the reference baseline data, perform initial screening of offset events on the ion flow data of the clinical samples, and use the screened offset events as candidate chromatographic peak start data for the clinical detection block;

[0080] In this embodiment of the invention, the step of performing preliminary screening of shift events on the ion flow data of the clinical samples based on the reference baseline data, and using the screened shift events as candidate chromatographic peak start data for the clinical detection block, includes:

[0081] Based on the sensitivity requirements of the clinical detection block and the fluctuation characteristics of the reference baseline data, a continuous offset judgment rule for the clinical detection block is established.

[0082] Based on the continuous offset determination rule, the signal deviation between the clinical sample ion flow data and the reference baseline data is continuously monitored to obtain candidate deviation segment data of the clinical detection block;

[0083] The starting boundary of the deviation segment data is traced to obtain the candidate chromatographic peak starting point data of the clinical detection block.

[0084] Based on the sensitivity requirements of the clinical testing block for the target component, which are specified as accurately capturing the signal intensity corresponding to the lowest concentration of the target component, the signal intensity difference of all adjacent sampling points in the reference baseline data is first extracted. The sampling interval is the fixed sampling period preset by the chromatograph. The intensity differences of all adjacent points are listed in sequence, and extreme outliers are removed. The remaining differences are summed and divided by the number of remaining differences to obtain the baseline average fluctuation amplitude of the reference baseline data. Then, the lowest target signal intensity corresponding to the clinical sensitivity requirement is converted into a specific deviation amplitude standard. That is, the deviation amplitude corresponding to the lowest target signal intensity is verified to be twice the baseline average fluctuation amplitude. At the same time, based on the need for chromatograph signal response speed and to avoid misjudgment due to transient interference, the duration standard is set to a continuous signal deviation state of not less than 0.3 seconds. This duration ensures that the captured signal deviation is a valid signal deviation caused by the target component rather than random interference. The deviation amplitude standard and the duration standard are incorporated together to form the continuous deviation judgment rule of the clinical testing block. The rule specifies that only when the signal deviation amplitude reaches twice the baseline average fluctuation amplitude and is continuously maintained for not less than 0.3 seconds can it be considered a valid deviation event.

[0085] Using the chromatograph's sampling time as a benchmark, the clinical sample ion flow data and the reference baseline data are precisely aligned at the microsecond level according to the sampling timestamps to ensure that the timestamps of each sampling point are completely consistent. From the beginning to the end of the data, the difference between the signal intensity of the clinical sample ion flow data and the corresponding sampling point signal intensity of the reference baseline data is calculated point by point in the sampling order to obtain the signal deviation difference at each time point. After calculating the deviation difference of each sampling point, it is immediately determined whether the difference meets the deviation amplitude standard in the continuous deviation judgment rule. If it does, the duration timing is started, and the duration of sampling points that meet the amplitude requirement is continuously accumulated from that sampling point. If the deviation difference of a certain sampling point does not meet the standard, the timing is reset to zero and restarted. Only when the sampling points in a certain time period continuously meet the deviation amplitude requirement and the cumulative duration reaches 0.3 seconds is the time period marked as a complete deviation segment. The precise start timestamp, end timestamp, signal deviation difference of each sampling point in the segment, and signal change trend are recorded for each deviation segment. All the marked deviation segment information is organized one by one in chronological order to form candidate deviation segment data of clinical detection blocks containing complete deviation characteristics.

[0086] For each candidate deviation segment, starting from the sampling point corresponding to the starting timestamp of that segment, the sampling points are traced forward point by point in the negative direction of the time axis according to the chromatograph's sampling interval. The tracing process does not exceed 1 second to avoid meaningless tracing. The signal deviation difference of each traced sampling point is calculated one by one, and it is determined whether the difference meets the deviation amplitude standard in the continuous deviation judgment rule. When the first sampling point whose deviation difference does not meet the standard is traced, the tracing is stopped immediately, and the next sampling point of the sampling point that does not meet the requirement is determined as the starting boundary of the deviation segment. Then, it is verified whether the three consecutive sampling points after the starting boundary sampling point all meet the deviation amplitude requirement to ensure the accuracy of the starting boundary. The precise timestamp, signal intensity and corresponding deviation difference of the starting boundary are recorded. The starting boundary information of all candidate deviation segments is arranged in chronological order, and each starting boundary is labeled with the corresponding deviation segment number to achieve association. Finally, the candidate chromatographic peak starting point data of the clinical detection block are summarized and organized.

[0087] The beneficial effect is that a scientific and reasonable continuous deviation judgment rule is established based on the sensitivity requirements of clinical detection blocks and the fluctuation characteristics of reference baseline data. This provides a clear and practical standard for the effective identification of signal deviations. According to this rule, continuous and uninterrupted monitoring of the signal deviation between clinical sample ion flow data and reference baseline data can accurately capture the valid deviation segments that meet the requirements, forming complete and true candidate deviation segment data. By tracing the origin of the deviation segment data, the initial position of each deviation segment can be accurately located, resulting in candidate chromatographic peak starting point data with precise positioning and clear attributes. This data provides a reliable starting point reference for the subsequent determination of candidate chromatographic peak endpoints and the labeling of target peak positions, effectively improving the overall accuracy and standardization of chromatographic peak positioning.

[0088] S4. Based on the candidate chromatographic peak initiation data, perform endpoint convergence analysis on the ion flow data of the clinical sample to obtain the candidate chromatographic peak endpoint data of the clinical detection block;

[0089] In this embodiment of the invention, the step of performing endpoint convergence analysis on the ion current data of the clinical sample based on the candidate chromatographic peak initiation data to obtain the candidate chromatographic peak endpoint data of the clinical detection block includes:

[0090] The drug metabolism tailing effect of the candidate chromatographic peak initiation data is evaluated to obtain the signal attenuation segment of the clinical detection block;

[0091] Based on the signal attenuation region, the endogenous substance co-elution interference is identified in the ion flow data of the clinical sample to obtain the mixed signal region of the clinical detection block;

[0092] Signal regression trajectory tracking is performed on the mixed signal segment to obtain the dynamic trajectory data of the clinical detection block;

[0093] The endpoint compliance of the dynamic trajectory data is verified to obtain the candidate chromatographic peak endpoint data of the clinical detection block.

[0094] The peak apex, corresponding to the starting point data of the candidate chromatographic peak, is used as the initial reference. The peak apex is determined as follows: Starting from the starting point of the candidate chromatographic peak, the signal intensity change of the ion flow data of the clinical sample is tracked. The chromatograph has a fixed sampling interval of 0.01 seconds. When the signal intensity of three consecutive sampling points shows an upward trend, and the signal intensity of the fourth sampling point decreases for the first time, the third sampling point is determined as the peak apex. A preset criterion for judging the drug metabolism tailing effect is established. The threshold of this criterion is set based on the common decay rate of drug metabolism tailing: the signal intensity after the peak apex must show a continuous downward trend, the signal intensity difference between two adjacent sampling points is the difference between the previous and subsequent sampling points, the result is negative, and the absolute value of this difference does not exceed 5% of the peak apex signal intensity. At the same time, the cumulative duration of consecutive sampling points that meet this condition is not less than 0.2 seconds, corresponding to twenty sampling points, to avoid misjudgment due to instantaneous fluctuations. Starting from the peak, the signal changes of the ion flow data of the clinical sample are monitored point by point in the sampling order. The signal intensity difference between each sampling point and the previous sampling point is calculated to determine whether it meets the requirements of the decreasing trend and amplitude. Once a sampling point does not meet the requirements, the monitoring is stopped immediately. All sampling points that have met the requirements in the previous consecutive time sequence are integrated to form a continuous signal segment. This segment completely retains the timestamp, signal intensity and decreasing amplitude information of each sampling point compared with the previous sampling point, which is the signal attenuation segment of the clinical detection block.

[0095] First, calculate the average rate of decrease in the signal attenuation section. Specifically, extract the signal strength decrease amplitude of all adjacent sampling points within the attenuation section (i.e., the intensity of the previous sampling point minus the intensity of the next sampling point), remove extreme values ​​exceeding 80% of the midpoint of all decrease amplitudes, sum the remaining decrease amplitudes, and divide by the number of remaining decrease amplitudes to obtain the average rate of decrease in the signal attenuation section. This rate serves as the benchmark for judging normal attenuation. A preset condition for judging interference from co-elution of endogenous substances is established: the absolute value of the change in signal strength at a sampling point within the attenuation section compared to the previous sampling point exceeds three times the average rate of decrease, and the direction of signal change at that sampling point is inconsistent with the overall decreasing trend of the three consecutive preceding sampling points, i.e., an increase or irregular fluctuation occurs. This threshold is set based on the characteristic that interference from endogenous substances easily leads to signal abrupt changes. All sampling points within the signal attenuation zone are screened sequentially. The signal change amplitude of each sampling point compared with the previous sampling point is calculated. The average rate of decline and trend requirements are compared, and all sampling points that meet the interference judgment criteria are marked. At the same time, the two sampling points before and after each marked sampling point are included in the interference influence range. Since the interference signal usually affects adjacent sampling points, these consecutive sampling points are integrated into a complete signal segment. All such signal segments are arranged in chronological order in the signal attenuation zone. Each segment is marked with the precise timestamps of the start and end of the interference, the number of signal fluctuations in the interference segment, and the maximum fluctuation amplitude. Together, they form the mixed signal segment of the clinical detection block.

[0096] The signal intensity range of the reference baseline data is used as the regression benchmark. This range is determined as follows: The signal intensity of all sampling points in the reference baseline data is calculated. After removing the highest and lowest 10% of values, the maximum and minimum values ​​of the remaining signal intensity are taken to form the reference baseline signal intensity confidence interval. Each sampling point within the mixed signal segment is analyzed sequentially over time. The signal intensity value of each sampling point is recorded, and the difference between this value and the upper and lower limits of the reference baseline signal intensity confidence interval is calculated to determine whether the signal intensity change direction is towards or away from the interval. Simultaneously, the rate of change of signal intensity between this sampling point and the previous sampling point is calculated. Signal points with stable rates of change and continuous regression towards the reference baseline confidence interval are selected, while abnormal fluctuations caused by endogenous interference, such as sudden increases or decreases in signal intensity or exceeding the confidence interval by more than 10%, are removed. All retained valid signal points are concatenated sequentially over time, and the regression progress of each signal point, the change in the difference with the confidence interval, the duration of continuous regression, and the rate of change are recorded synchronously. This forms dynamic trajectory data of the clinical detection block that fully reflects the actual process of signal regression from a mixed interference state to the baseline.

[0097] First, calculate the average signal strength of the reference baseline data: remove the highest and lowest 10% of the signal strength values ​​from the reference baseline data, sum the remaining 80% of the signal strength values, and divide by the number of remaining signal points to obtain the average signal strength of the reference baseline. A preset endpoint compliance verification standard is set: the signal strength of a sampling point in the dynamic trajectory data must fall within ±0.3% of this average signal strength. This threshold is set based on the stable fluctuation range of the reference baseline to ensure that the endpoint is close to the background level, and the signal strength of the next four consecutive sampling points remains within this range, corresponding to 0.04 seconds, which meets the chromatograph signal stability response time. Starting from the beginning of the dynamic trajectory data, check the signal strength of each sampling point in the sampling sequence to see if it meets the above standard. When the first sampling point that meets the condition of "falling within the range and the subsequent four sampling points also meeting the condition" is found, it is determined as the preliminary endpoint. Further verification was performed on all sampling points within 0.1 seconds after the preliminary endpoint, corresponding to ten sampling points. If the signal intensity of these sampling points did not exceed ±0.3% and there was no rebound to exceed the range, then the preliminary endpoint was confirmed as the final endpoint of the candidate chromatographic peak. The precise timestamp, signal intensity, and difference from the average signal intensity of the reference baseline were recorded for this endpoint. The endpoint information corresponding to all candidate chromatographic peaks was summarized one by one in chronological order of their starting data to form candidate chromatographic peak endpoint data for a clinical detection block containing detailed attributes of each endpoint.

[0098] The beneficial effects include evaluating the effect of drug metabolism tailing on candidate chromatographic peak initiation data, accurately locating signal attenuation segments that conform to metabolic patterns, providing a clear analytical range for subsequent interference identification, and conducting endogenous substance co-elution interference identification based on these signal attenuation segments. This effectively distinguishes normal attenuation signals from interference signals, separates the interfered mixed signal segments, and avoids interference factors affecting the accuracy of endpoint determination. Signal regression trajectory tracking of mixed signal segments can capture the true trend of signal regression to the baseline, forming dynamic trajectory data that reflects the essential characteristics of the signal. By verifying the endpoint conformity of the dynamic trajectory data, the endpoint determination criteria can be strictly controlled to ensure the authenticity and stability of candidate chromatographic peak endpoints. The final candidate chromatographic peak endpoint data is accurately located and clearly defined, providing reliable endpoint support for subsequent targeted peak labeling and chromatographic peak location report generation, further improving the overall quality and clinical reference value of chromatographic detection data location.

[0099] S5. Based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, perform clinical significance association annotation on the clinical sample ion flow data to obtain the target peak position data of the clinical detection block;

[0100] In this embodiment of the invention, the step of performing clinical significance association annotation on the ion flow data of the clinical sample based on the candidate chromatographic peak initiation data and the candidate chromatographic peak endpoint data to obtain the target peak position data of the clinical detection block includes:

[0101] Peak regions are extracted from the candidate chromatographic peak start data and the candidate chromatographic peak end data to obtain the complete characteristic peak signal segment of the clinical detection block;

[0102] The complete characteristic peak signal segment is mapped to the clinical diagnostic knowledge base of the clinical detection block to obtain the verification characteristic peak data of the clinical detection block;

[0103] Based on the preset disease biomarker associations, diagnostic orientation analysis is performed on the verification feature peak data to obtain the pathological significance identifier of the clinical detection block;

[0104] The formula for calculating the comprehensive index of clinical significance in the pathological significance markers is as follows:

[0105] ;

[0106] In the formula, The clinical significance composite index, The retention time deviation between the verification feature peak data and the clinical diagnostic knowledge base. The relative standard deviation of the preset retention time. The target peak area is defined in the verification feature peak data. The target peak height is defined in the verification feature peak data. This is the preset reference peak area. The preset reference peak height, The strength of the response signal in the verification feature peak data. The noise intensity of the noise residual data in the clinical detection block. These are pre-defined clinically relevant factors;

[0107] The pathological significance markers were reviewed and verified to obtain the target peak data of the clinical detection blocks.

[0108] Using the precise timestamp corresponding to the starting point data of the candidate chromatographic peak as the starting boundary and the precise timestamp corresponding to the ending point data of the candidate chromatographic peak as the ending boundary, all continuous signal points between these two boundaries are extracted one by one according to the sampling order of the chromatograph. Each signal point is fully recorded with its timestamp, signal intensity value, intensity difference from the previous signal point, and direction of change. At the same time, the peak position within the signal segment is marked, which is the signal point corresponding to the maximum signal intensity. All extracted signal points and marking information are arranged in chronological order to form a complete characteristic peak signal segment of the clinical detection block containing the complete signal characteristics of the chromatographic peak.

[0109] The clinical diagnostic knowledge base stores clinically validated disease-related characteristic peak information, including the standard retention time range, standard peak area range, standard peak height range, and corresponding disease types. The actual retention time (the time span from start to finish), actual peak area (the integral value of the intensity of all signal points within the signal segment), and actual peak height (the signal intensity at the peak apex) of the complete characteristic peak signal segment are compared one by one with the corresponding parameters of each standard characteristic peak in the knowledge base. Matching criteria are set: the actual retention time falls within ±0.1 seconds of the standard retention time range, the actual peak area falls within ±5% of the standard peak area range, and the actual peak height falls within ±5% of the standard peak height range. Standard characteristic peaks that meet these matching criteria are selected. The complete characteristic peak signal segment is then associated and integrated with the matched standard characteristic peak information, recording the associated disease type, the degree of matching of standard parameters, and other information to form the verification characteristic peak data for the clinical detection block.

[0110] The pre-defined disease biomarker associations clarify the correspondence logic between different combinations of characteristic peak parameters and specific diseases. For example, a peak with a specific retention time and a specific peak area / peak height ratio both point to a certain type of disease. Based on this association, the parameters in the verification characteristic peak data are decomposed and analyzed to check whether they meet the biomarker parameter combination requirements corresponding to the target disease. At the same time, a diagnostic indicative threshold is set: if the verification characteristic peak data matches three or more parameters of the disease biomarker, specifically including retention time, peak area, peak height, and trend, and no parameter exceeds twice the matching range, it is considered to have clear diagnostic indicativeness. The specific disease type pointed to by the verification characteristic peak data is determined, the association priority is sorted by the number of matching items, and the clinical reference value level is divided according to the accuracy of parameter matching. This information is structured and integrated to form the pathological significance identifier of the clinical detection block.

[0111] Pathological significance marker verification standards were established: all diagnostic parameters were included and within the set range; the associated disease types were supported by corresponding clinical case data; and cross-interference with other diseases was excluded. The cross-interference criterion was that the parameter matching with other disease markers did not exceed one. Each pathological significance marker was checked one by one according to this standard. For markers with questionable parameter matching found during the verification process, the association between the clinical diagnostic knowledge base and disease markers was re-compared. After confirming that there was no deviation, the markers were retained. For markers with cross-interference, the signal separation between the target peak and the interfering peak was further verified. The separation criterion was that the time interval between the two peak apexes exceeded 0.2 seconds and the signal intensity did not overlap, ensuring that the interference was eliminated. After comprehensive verification, the pathological significance markers that met the standards were associated and integrated with the corresponding complete characteristic peak signal segment data, candidate chromatographic peak start and end data to form the target peak position data of the clinical detection block.

[0112] The retention time deviation is obtained by comparing the retention time of the verification feature peak data with the corresponding standard feature peak in the clinical diagnostic knowledge base. Specifically, it is obtained by extracting the actual retention time of the verification feature peak data and the standard retention time of the standard feature peak matched in the clinical diagnostic knowledge base, calculating the difference between the two and taking the absolute value.

[0113] The relative standard deviation of retention time is a fixed value preset based on industry standards for clinical testing and a large amount of experimental validation data. This value can reflect the normal fluctuation range of retention time allowed in clinical testing, ensuring that the assessment of retention time deviation has a unified standard.

[0114] The target peak area is calculated by integrating the signal strength of the complete characteristic peak signal segment in the verification characteristic peak data. Specifically, the intensity values ​​of all signal points in the signal segment are accumulated in chronological order, and the sum is the target peak area.

[0115] The target peak height is the maximum signal strength within the complete characteristic peak signal segment extracted directly from the verification characteristic peak data. The value corresponding to this maximum value is the target peak height.

[0116] The reference peak area is a preset fixed value derived from clinically recognized standard sample test data. It is a representative peak area benchmark value determined after multiple repeated tests.

[0117] The reference peak height is a preset fixed value, which is also based on clinically recognized standard sample test data. It is a stable and valuable peak height benchmark value selected after multiple repeated tests.

[0118] The response signal strength is the average strength of all signal points within the complete characteristic peak signal segment extracted from the verification characteristic peak data. It is calculated by summing the strength values ​​of all signal points within the signal segment and then dividing by the total number of signal points.

[0119] The noise intensity is derived from the noise residual data of the clinical test block. Specifically, the intensity values ​​of all signal points in the noise residual data are extracted, and the average value of these intensity values ​​is calculated. This average value is the noise intensity.

[0120] Clinical association factors are preset fixed values, determined based on the degree of association between different disease types and characteristic peaks. Through statistical analysis of a large number of clinical case data, the values ​​of association factors corresponding to different diseases are clarified, ensuring that the assessment of the clinical significance of characteristic peaks meets actual diagnostic needs.

[0121] The significance of this formula is to quantitatively evaluate the clinical significance of the verification characteristic peak data by integrating multiple indicators. By combining the matching accuracy of retention time, the advantage of the characteristic peak's intensity and area relative to the reference benchmark, the degree of separation between signal and noise, and the closeness of its association with clinical diseases, a comprehensive result that can fully reflect the clinical diagnostic reference value of the characteristic peak is obtained.

[0122] The calculation process of this formula organically combines various scattered evaluation indicators, avoiding the limitations of single indicator evaluation, making the obtained pathological significance markers more scientific and reliable, providing a quantitative basis for subsequent verification of pathological significance markers, ensuring that the final target peak data can accurately point to clinical diagnostic needs, and enhancing the practical value of chromatographic detection data in clinical applications.

[0123] The beneficial effects are as follows: by extracting peak regions from the start and end points of candidate chromatographic peaks, all signal information of characteristic peaks is completely preserved, forming complete characteristic peak signal segments. This provides a comprehensive data foundation for clinical significance association annotation. Mapping this signal segment to the clinical diagnostic knowledge base enables precise matching between characteristic peak data and clinical standard information, resulting in verification characteristic peak data with clinical reference attributes. Based on the preset disease biomarker association relationship, diagnostic orientation analysis is carried out, giving the verification characteristic peak data clear pathological significance and forming highly targeted pathological significance labels. By verifying the pathological significance labels, matching deviations and cross-interferences are eliminated, ensuring the authenticity and reliability of the labels. The final targeted peak position data has completeness, clinical relevance, and accuracy, providing high-quality data support for subsequent co-elution separation verification and chromatographic peak positioning report generation, effectively improving the clinical diagnostic reference value and application reliability of chromatographic detection data.

[0124] S6. Perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

[0125] In this embodiment of the invention, the step of co-eluting and separating the targeted peak data to obtain the chromatographic peak localization report data of the clinical detection block includes:

[0126] The targeted peak data were co-eluted to obtain the peak segments to be validated in the clinical detection block;

[0127] The component separation degree of the peak segment to be verified is verified to obtain independent component peak segment data of the clinical detection block;

[0128] The chromatographic peak location information of the independent component peak segment data is structured and integrated to obtain the chromatographic peak location report data of the clinical detection block.

[0129] Signal curve analysis was performed on each peak in the target peak data. The co-elution judgment criteria were preset: the signal curve of the peak has two or more slope change points on both sides of the peak apex, or the total width of the peak exceeds 0.5 seconds. The signal curve of each target peak was checked point by point, the slope change rate was recorded and the total width of the peak was calculated. Peaks that meet any judgment criteria were completely extracted, including their starting point data, ending point data, all signal points and slope change records, and arranged in chronological order to form the peak segments to be verified in the clinical detection block.

[0130] The preset component separation verification standard is as follows: the time interval between the peaks of potential adjacent components is not less than 0.1 seconds, and the maximum signal intensity of the signal intensity curves of the two peaks in the overlapping area does not exceed 20% of the signal intensity of their respective peaks. The signal curve of each peak in the peak segment to be verified is segmented and analyzed. The signal intensity change is tracked from the peak start point to identify the potential component peak boundaries corresponding to all slope change points. The temporary start point and temporary end point of each potential component peak are determined. The time interval between the peaks of adjacent potential component peaks and the degree of signal overlap are calculated to verify whether they meet the separation standard. Potential component peaks that meet the standard are determined as independent component peaks. The precise start point, end point, peak peak, signal intensity curve and separation verification results of each independent component peak are recorded and integrated to form the independent component peak segment data of the clinical detection block.

[0131] The data of independent component peak segments are sorted according to the time of peak appearance. The structured information dimensions of each independent component peak are defined, including peak number, start time stamp, end time stamp, peak vertex time stamp, peak vertex signal intensity, corresponding pathological significance identifier, resolution verification result, and confirmation information of no co-elution interference. The above information for each independent component peak is checked and supplemented one by one to ensure that there is no missing or incorrect information. The structured information of all independent component peaks is arranged in a unified format to form chromatographic peak localization report data of clinical detection blocks containing complete localization information, verification results, and clinical relevance information.

[0132] Example 2

[0133] like Figure 2 As shown in the figure, this embodiment also provides a functional block diagram of a chromatograph detection data positioning system.

[0134] The chromatograph detection data localization system 100 described in this embodiment can be installed in a terminal. Depending on the functions implemented, the chromatograph detection data localization system 100 may include a multi-scale filtering module 101, a baseline fitting module 102, a shift event initial screening module 103, an endpoint convergence analysis module 104, a target peak position labeling module 105, and a co-elution verification module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the terminal processor and perform a fixed function, stored in the terminal's memory.

[0135] In this embodiment, the functions of each module / unit are as follows:

[0136] The multi-scale filtering module 101 is used to perform multi-scale filtering on the raw chromatographic data stream of the clinical detection block to obtain the clinical sample ion flow data of the clinical detection block.

[0137] The baseline fitting module 102 is used to perform baseline fitting on the ion flow data of the clinical sample based on the background control area of ​​the healthy population in the clinical detection block, so as to obtain the reference baseline data of the clinical detection block.

[0138] The offset event screening module 103 is used to perform offset event screening on the clinical sample ion flow data based on the reference baseline data, and to use the screened offset events as candidate chromatographic peak start data for the clinical detection block.

[0139] The endpoint convergence analysis module 104 is used to perform endpoint convergence analysis on the ion flow data of the clinical sample based on the candidate chromatographic peak initiation data, so as to obtain the candidate chromatographic peak endpoint data of the clinical detection block.

[0140] The targeted peak labeling module 105 is used to perform clinical significance association labeling on the ion flow data of the clinical sample based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, so as to obtain the targeted peak data of the clinical detection block.

[0141] The co-elution verification module 106 is used to perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

[0142] In detail, each module in the chromatograph detection data positioning system 100 described in this embodiment of the invention uses the same technical means as the chromatograph detection data positioning method described in Embodiment 1 and Embodiment 2, and can produce the same technical effect, which will not be repeated here.

[0143] Example 3

[0144] like Figure 3 As shown, this embodiment also provides a computer terminal, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a chromatograph detection data localization program.

[0145] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the terminal, connecting various components of the terminal via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a chromatograph detection data localization program) and calls data stored in the memory 11 to perform various functions of the terminal and process data.

[0146] The memory 11 includes at least one type of medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the terminal, such as the portable hard drive of the terminal. In other embodiments, the memory 11 can also be an external storage device of the terminal, such as a plug-in portable hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, FlashCard, etc., equipped on the terminal. Furthermore, the memory 11 can include both internal storage units and external storage devices of the terminal. The memory 11 can be used not only to store application software and various types of data installed on the terminal, such as the code of a chromatograph detection data positioning program, but also to temporarily store data that has been output or will be output.

[0147] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0148] The communication interface 13 is used for communication between the aforementioned terminal and other terminals, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the terminal and other terminals. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the terminal and to display a visual user interface.

[0149] The figure only shows a terminal with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the terminal and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0150] For example, although not shown, the terminal may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, or any other components. The terminal may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0151] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0152] The chromatograph detection data localization program stored in the memory 11 of the terminal is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0153] S1. Perform multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block;

[0154] S2. Based on the baseline control area of ​​healthy individuals in the clinical detection block, perform baseline fitting on the ion flow data of the clinical sample to obtain the reference baseline data of the clinical detection block;

[0155] S3. Based on the reference baseline data, perform initial screening of offset events on the ion flow data of the clinical samples, and use the screened offset events as candidate chromatographic peak start data for the clinical detection block;

[0156] S4. Based on the candidate chromatographic peak initiation data, perform endpoint convergence analysis on the ion flow data of the clinical sample to obtain the candidate chromatographic peak endpoint data of the clinical detection block;

[0157] S5. Based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, perform clinical significance association annotation on the clinical sample ion flow data to obtain the target peak position data of the clinical detection block;

[0158] S6. Perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

[0159] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0160] Furthermore, if the modules / units integrated into the terminal are implemented as software functional units and sold or used as independent products, they can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0161] In the several embodiments provided by this invention, it should be understood that the disclosed terminals, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0162] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0163] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0164] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0165] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for locating chromatograph detection data, the method comprising: The method includes: S1. Perform multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block; S2. Based on the baseline control area of ​​healthy individuals in the clinical detection block, perform baseline fitting on the ion flow data of the clinical sample to obtain the reference baseline data of the clinical detection block; S3. Based on the reference baseline data, perform initial screening of offset events on the ion flow data of the clinical samples, and use the screened offset events as candidate chromatographic peak start data for the clinical detection block; S4. Based on the candidate chromatographic peak initiation data, perform endpoint convergence analysis on the ion flow data of the clinical sample to obtain the candidate chromatographic peak endpoint data of the clinical detection block; S5. Based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, perform clinical significance association annotation on the clinical sample ion flow data to obtain the target peak position data of the clinical detection block; S6. Perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

2. A method for locating chromatographic data as recited in claim 1, wherein, The process of performing multi-scale filtering on the raw chromatographic data stream of the clinical testing block to obtain the clinical sample ion flow data of the clinical testing block includes: Matrix noise spectrum analysis was performed on the raw chromatographic data stream of the clinical detection block to obtain the noise characteristic data of the clinical detection block; Based on the noise characteristic data, broadband matrix trend separation is performed on the original chromatographic data stream to obtain the baseline contour data of the clinical detection block; Narrow-spectrum random noise stripping is performed on the raw chromatographic data stream to obtain the noise residual data of the clinical detection block; Based on the baseline profile data and the noise residual data, the coupling interference of the original chromatographic data stream is synchronously eliminated to obtain the decoupled ion flow signal data of the clinical detection block; Ion flow stability calibration is performed on the decoupled ion flow signal data to obtain the clinical sample ion flow data of the clinical detection block.

3. A method for locating chromatography data as recited in claim 1, wherein, The baseline control area for healthy individuals based on the clinical detection block involves baseline fitting of the ion flow data of the clinical samples to obtain reference baseline data for the clinical detection block, including: Background signals are extracted from the baseline control area of ​​the healthy population in the clinical testing block to obtain the sample reference area data of the clinical testing block; Based on the sample reference area data, local trend tracking is performed on the non-peak areas in the ion flow data of the clinical sample to obtain the dynamic background trend data of the clinical detection block; Background interference correction is applied to the dynamic baseline trend data to obtain the baseline trajectory data of the clinical detection block; The baseline trajectory data is smoothed and connected to obtain the reference baseline data for the clinical detection block.

4. A method for locating chromatography data as recited in claim 1, wherein, The process of performing initial screening of shift events on the ion flow data of the clinical samples based on the reference baseline data, and using the screened shift events as candidate chromatographic peak initiation data for the clinical detection block, includes: Based on the sensitivity requirements of the clinical detection block and the fluctuation characteristics of the reference baseline data, a continuous offset judgment rule for the clinical detection block is established. Based on the continuous offset determination rule, the signal deviation between the clinical sample ion flow data and the reference baseline data is continuously monitored to obtain candidate deviation segment data of the clinical detection block; The starting boundary of the deviation segment data is traced to obtain the candidate chromatographic peak starting point data of the clinical detection block.

5. The method for locating chromatograph detection data as described in claim 1, characterized in that, The endpoint convergence analysis of the ion flow data of the clinical samples to obtain the candidate chromatographic peak endpoint data of the clinical detection block includes: The drug metabolism tailing effect of the candidate chromatographic peak initiation data is evaluated to obtain the signal attenuation segment of the clinical detection block; Based on the signal attenuation region, the endogenous substance co-elution interference is identified in the ion flow data of the clinical sample to obtain the mixed signal region of the clinical detection block; Signal regression trajectory tracking is performed on the mixed signal segment to obtain the dynamic trajectory data of the clinical detection block; The endpoint compliance of the dynamic trajectory data is verified to obtain the candidate chromatographic peak endpoint data of the clinical detection block.

6. A method for locating chromatography data as recited in claim 1, wherein, The step of performing clinical significance association annotation on the ion flow data of the clinical samples based on the candidate chromatographic peak initiation data and the candidate chromatographic peak endpoint data to obtain the target peak position data of the clinical detection block includes: Peak regions are extracted from the candidate chromatographic peak start data and the candidate chromatographic peak end data to obtain the complete characteristic peak signal segment of the clinical detection block; The complete characteristic peak signal segment is mapped to the clinical diagnostic knowledge base of the clinical detection block to obtain the verification characteristic peak data of the clinical detection block; Based on the preset disease biomarker associations, diagnostic orientation analysis is performed on the verification feature peak data to obtain the pathological significance identifier of the clinical detection block; The formula for calculating the comprehensive index of clinical significance in the pathological significance markers is as follows: ； In the formula, The clinical significance composite index, The retention time deviation between the verification feature peak data and the clinical diagnostic knowledge base. The relative standard deviation of the preset retention time. The target peak area is defined in the verification feature peak data. The target peak height is defined in the verification feature peak data. This is the preset reference peak area. The preset reference peak height, The strength of the response signal in the verification feature peak data. The noise intensity of the noise residual data in the clinical detection block. These are pre-defined clinically relevant factors; The pathological significance markers were reviewed and verified to obtain the target peak data of the clinical detection blocks.

7. A method for locating chromatography data as recited in claim 1, wherein, The co-elution separation verification of the targeted peak position data to obtain the chromatographic peak localization report data of the clinical detection block includes: The targeted peak data were co-eluted to obtain the peak segments to be validated in the clinical detection block; The component separation degree of the peak segment to be verified is verified to obtain independent component peak segment data of the clinical detection block; The chromatographic peak location information of the independent component peak segment data is structured and integrated to obtain the chromatographic peak location report data of the clinical detection block.

8. A chromatograph detection data positioning system characterized by, The system for implementing the chromatograph detection data localization method according to claim 1 includes: A multi-scale filtering module is used to perform multi-scale filtering on the raw chromatographic data stream of the clinical detection block to obtain the clinical sample ion flow data of the clinical detection block; The baseline fitting module is used to perform baseline fitting on the ion flow data of the clinical sample based on the background control area of ​​the healthy population in the clinical detection block, so as to obtain the reference baseline data of the clinical detection block. The offset event screening module is used to perform offset event screening on the ion flow data of the clinical sample based on the reference baseline data, and to use the screened offset events as candidate chromatographic peak start data for the clinical detection block. The endpoint convergence analysis module is used to perform endpoint convergence analysis on the ion flow data of the clinical sample based on the candidate chromatographic peak initiation data, so as to obtain the candidate chromatographic peak endpoint data of the clinical detection block. The targeted peak labeling module is used to perform clinical significance association labeling on the ion flow data of the clinical sample based on the candidate chromatographic peak start data and the candidate chromatographic peak end data, so as to obtain the targeted peak data of the clinical detection block; The co-elution verification module is used to perform co-elution separation verification on the target peak position data to obtain the chromatographic peak positioning report data of the clinical detection block.

9. A terminal comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, causes the terminal to perform the method of any one of claims 1 to 8. The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A storage medium having stored thereon a computer program, characterized in that When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

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