Test data identification method and system based on sample analysis

Abstract

This application relates to a test data identification method and system based on sample analysis. The method includes: parsing a test dataset to be identified obtained from a sample analysis instrument to obtain test parameters associated with each test data point; integrating and processing all the test parameters associated with the test data to obtain parameter features; determining a target test parameter from the test parameters based on the parameter features; and identifying the test data associated with the target test parameter as quality control data. The technical solution provided by this application can effectively identify quality control data from the test data to be identified, avoiding the mixing of quality control data and sample data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a test data identification method and system based on sample analysis. Background Technology

[0002] A sample analyzer is an instrument used to detect the concentration of unknown substances in patient bodily fluid samples (such as blood samples). Test results are typically displayed on the analyzer as a data sequence including the instrument serial number, sample number, test item, and result. The analyzer can then transmit the results to the hospital laboratory's Laboratory Information Management System (LIS) and a cloud data platform that is connected to the analyzer.

[0003] To ensure the testing quality of sample analysis instruments, calibration and quality control procedures must be performed regularly. Calibrators and quality control samples differ from patient body fluid samples with unknown analyte concentrations; they are typically standard reference materials prepared by the sample analysis instrument manufacturer or a third-party vendor, meaning the concentration of the analyte they contain is known. During calibration, calibrators are used to adjust the analyzer's testing parameters. During quality control, quality control samples are used to measure the analyzer's testing quality. Sample analyzers typically have dedicated calibration and quality control procedures. These procedures assign unique identifiers to calibrators and quality control samples, allowing the data analysis unit (DAU) of the analyzer to easily distinguish between calibration and quality control test data. The DAU can be deployed, for example, on a cloud data platform to statistically analyze the analyzer's testing performance.

[0004] However, in some cases, laboratories do not wish to use dedicated procedures for calibration and quality control, especially dedicated quality control procedures. In such situations, laboratory personnel may number quality control samples as if they were ordinary patient samples, mixing them into the testing queue. In this case, the quality control test data has the exact same data structure as the patient sample test data. When the data analysis unit analyzes the collected test data, it becomes difficult to distinguish between the actual patient sample test data and the quality control test data, potentially negatively impacting the data analysis unit's evaluation of the sample analysis instrument's testing performance. Summary of the Invention

[0005] To address or partially address the problems existing in related technologies, this application provides a test data identification method and system based on sample analysis.

[0006] The first aspect of this application provides a test data identification method based on sample analysis, comprising: parsing a test dataset to be identified obtained from a sample analysis instrument to obtain test parameters associated with each of the test data; integrating and processing all the test parameters associated with the test data to obtain parameter features; determining a target test parameter from the test parameters according to the parameter features, and identifying the test data associated with the target test parameter as quality control data.

[0007] This application addresses the test dataset to be identified by utilizing the parameter characteristics of the test parameters in the quality control data and the differences between the test parameters and the sample data. This enables the effective identification of quality control data from the test dataset, effectively avoiding the simultaneous mixing of quality control data and sample data in the test data, and providing reliable data support for the performance analysis of sample analysis instruments.

[0008] In an optional implementation, determining the target test parameter from the test parameters based on the parameter features includes: comparing a given feature reference value with the value of the parameter feature; and determining the target test parameter from the test parameters based on the comparison result.

[0009] In an optional implementation, after identifying the test data associated with the target test parameter as quality control data, the method further includes updating the given feature reference value.

[0010] In an optional implementation, the test parameters include instrument identifier, sample identifier, test item, and test time, and the parameter features include time distribution; the integration and processing of all test parameters associated with the test data to obtain parameter features includes: traversing the parsed test dataset; and calculating the time frequencies corresponding to the same instrument identifier, the same sample identifier, and the same test item in the parsed test dataset based on the test time corresponding to each of the instrument identifier, sample identifier, and test item.

[0011] In an optional implementation, the test parameters further include test results, and the parameter features further include test result stability; the step of integrating and processing all the test parameters associated with the test data to obtain parameter features further includes: based on the test results corresponding to the instrument identifier, sample identifier, and test item, solving for the degree of concentration and the degree of dispersion of the test results corresponding to the same instrument identifier, the same sample identifier, and the same test item in the parsed test dataset.

[0012] In an optional implementation, the step of determining the target test parameter from the test parameters based on the parameter characteristics and identifying the test data associated with the target test parameter as quality control data includes: determining whether the solved time frequency, test result concentration, and test result dispersion meet the set conditions with their respective corresponding feature reference values; using the instrument identifier, sample identifier, or test item corresponding to the time frequency, test result concentration, and test result dispersion that all meet the set conditions as the target instrument identifier, target sample identifier, or target test item; and using the test data corresponding to the test results that are associated with the target instrument identifier, the target sample identifier, and the target test item as quality control data.

[0013] In an optional implementation, after identifying the test data associated with the target test parameter as quality control data, the method further includes: updating the feature reference values ​​corresponding to the time frequency and the concentration of test results, wherein the feature reference values ​​corresponding to the time frequency and the concentration of test results are updated according to a memory factor.

[0014] In an optional implementation, the test parameters include test attributes, and the test attributes include repeated testing; after obtaining the test parameters associated with each of the test data, and before integrating all the test parameters associated with the test data, the method further includes: determining that the test attribute of the test data is repeated testing; determining that the test data is repeated test data, and deleting the test data.

[0015] A second aspect of this application provides a sample analysis system, comprising: at least one sample analysis instrument; a server communicatively connected to all of the sample analysis instruments; and a test data identification module running on the server, the test data identification module being configured to implement the method described above when executed.

[0016] A third aspect of this application provides a test data identification system based on sample analysis, including a data acquisition terminal and a processing terminal; the data acquisition terminal includes a sample analysis instrument; the processing terminal is communicatively connected to the data acquisition terminal and is configured to implement the method described above when executed.

[0017] A fourth aspect of this application provides an electronic device, including: a processor; and a memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method described above.

[0018] The fifth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of a vehicle, causes the processor to perform the method described above.

[0019] A sixth aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described above.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0021] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments of this application taken in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of this application.

[0022] Figure 1 This is a flowchart illustrating the test data identification method based on sample analysis shown in this application; Figure 2 This is another flowchart illustrating the test data identification method based on sample analysis shown in this application; Figure 3 This is a schematic diagram of the sample analysis system shown in this application; Figure 4 This is a schematic diagram of the structure of the test data recognition system based on sample analysis shown in this application; Figure 5 This is a schematic diagram of the structure of the electronic device shown in this application. Detailed Implementation

[0023] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In related technologies, quality control data and sample data are often mixed together. When quality control data and sample data are not effectively distinguished, the quality control data can interfere with the analysis of sample data (patient sample results), thereby affecting the performance analysis results of medical equipment.

[0027] To address the aforementioned issues, this application provides a test data identification method based on sample analysis, which can effectively identify quality control data from the test data to be identified, thus avoiding the mixing of quality control data and sample data.

[0028] The following will describe in detail the test data identification method based on sample analysis according to the embodiments of this application with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart illustrating the test data identification method based on sample analysis shown in this application.

[0030] See Figure 1 This application discloses a test data identification method based on sample analysis, which mainly includes steps S101 to S103.

[0031] Step S101: Analyze the test dataset to be identified obtained from the sample analysis instrument to obtain the test parameters associated with each test data.

[0032] The sample analysis instrument refers to the instrument used to detect and analyze test samples to obtain the test results. Specifically, it can be a biochemical analyzer, an immunoassay analyzer, a blood analyzer, etc. It should be noted that the test dataset to be identified can originate from the same instrument or from multiple different instruments. Specifically, the test dataset consists of test data acquired from one or more instruments within a first time period. To facilitate the processing of the test data, this first time period can be divided into multiple second time periods. After the quality control data corresponding to the i-th second time period is processed, the test dataset corresponding to the (i+1)-th second time period is then processed.

[0033] Considering that the data structures of quality control data and sample data are completely identical, both involving test parameters such as instrument identification (instrument serial number), sample identification (sample number), test items, test attributes, test results, and test time, it is difficult to distinguish between sample data and quality control data when acquiring test data over a period of time. In step S101, the test data to be identified is parsed, and the instrument serial number, sample number, test items, test attributes, test results, and test time in each piece of test data are analyzed.

[0034] Step S102: Integrate and process the test parameters associated with all test data to obtain parameter characteristics.

[0035] Specifically, for the test dataset, assuming the presence of quality control data, the parsed test parameters can be integrated to extract two types of parameter features: time distribution and test result stability. In this embodiment, different instrument serial numbers, different sample numbers, or different test items will correspond to diverse test results, test times, and other parameters. Therefore, integrating the parsed test parameters involves comprehensively analyzing these parameters to obtain parameter characteristics that reflect the individual characteristics of each instrument, sample, and item.

[0036] The parameter features include time distribution, and the test parameters related to the time distribution include instrument identifier, sample identifier, test item, and test time. In at least one embodiment, step S102 may include: traversing the parsed test dataset; and calculating the time frequencies corresponding to the same instrument identifier, the same sample identifier, and the same test item in the parsed test dataset according to the test time corresponding to each of the instrument identifier, sample identifier, and test item.

[0037] It should be noted that within a testing cycle, the quality control time frequency t = quality control duration / testing market. Therefore, for the time cycle corresponding to the acquired test dataset, the quality control time frequency corresponding to each different instrument identifier can be calculated based on the instrument identifier and its testing time. Similarly, for the time cycle corresponding to the acquired test dataset, the quality control time frequency corresponding to each different sample identifier can be calculated based on the sample identifier and its testing time, and the quality control time frequency corresponding to each different test can be calculated based on the test item and its testing time. For example, suppose there are instruments a, b, and c, and instrument a has a quality control time frequency t. a Instrument b has a quality control time frequency t b Instrument C has a quality control time frequency t c .

[0038] Furthermore, the parameter features also include test result stability. Test parameters related to test result stability include instrument identifier, sample identifier, test item, and test result. In at least one embodiment, step S102 may further include: based on the test results corresponding to the instrument identifier, sample identifier, and test item, calculating the concentration and dispersion of test results for the same instrument identifier, sample identifier, and test item in the parsed test dataset. Test result stability is reflected in the concentration and dispersion of test results. The concentration can be the average value, or alternatively, the median. The dispersion can be the coefficient of variation, or alternatively, the standard deviation. In this embodiment, the concentration is represented by the average value, and the dispersion by the coefficient of variation. Therefore, within the time period corresponding to the acquired test dataset, the average value and coefficient of variation of the test results for the same instrument identifier can be obtained from all test results corresponding to that instrument identifier. Similarly, the average value and coefficient of variation of the test results for each sample identifier, and the average value and coefficient of variation of the test results for each test item, can also be obtained.

[0039] Step S103: Based on the parameter characteristics, determine the target test parameter from the test parameters, and identify the test data associated with the target test parameter as quality control data.

[0040] In step S103, determining the target test parameter from the test parameters based on parameter characteristics includes: comparing the given feature reference value with the value of the parameter characteristic; and determining the target test parameter from the test parameters based on the comparison result. Specifically, the test parameter is determined to be the target test parameter by the parameter characteristic value calculated in step S102 satisfying a set condition with the corresponding feature reference value, for example, the parameter characteristic value being within the allowable deviation of the corresponding reference value or within the corresponding reference interval. In at least one embodiment, step S103 may include: determining whether the solved time frequency, test result concentration, and test result dispersion satisfy set conditions with their respective corresponding feature reference values; using the instrument identifier, sample identifier, or test item corresponding to the time frequency, test result concentration, and test result dispersion that all satisfy the set conditions as the target instrument identifier, target sample identifier, or target test item; and using the test data corresponding to the test results associated with the target instrument identifier, target sample identifier, and target test item as quality control data. Among them, satisfying the three set conditions can be used to determine that the time frequency and the concentration of the test results obtained are both within the allowable deviation of their respective corresponding feature reference values, and that the dispersion of the test results obtained is within the corresponding feature reference value range.

[0041] For ease of understanding, consider instruments a and b, samples A and B, and project 1 and project 2. First, calculate the time frequency, average value, and coefficient of variation for each of the six parameters: instrument a, instrument b, sample A, sample B, project 1, and project 2. For the time frequency, determine if the time frequency of each parameter is within the allowable deviation of a given time frequency reference value. Select the parameters within the allowable deviation; assume that instrument a, sample B, project 1, and project 2 meet the requirements. Similarly, for the average value, select the six parameters that meet the requirements; for the coefficient of variation, select the five parameters that meet the requirements. Then, from the selected parameters, choose instrument a, sample B, project 1, and project 2 that simultaneously meet the time frequency, average value, and coefficient of variation requirements. The test results corresponding to instrument a, sample B, and project 1 are the quality control data, and the test results corresponding to instrument a, sample B, and project 2 are also the quality control data.

[0042] To ensure the accuracy of quality control data identification, the test data from the sample analysis instrument may be obtained from either the initial test or repeated tests. To ensure the accuracy of subsequent results, duplicate data can be removed, i.e., duplicate test data can be filtered out. Information regarding whether a test is repeated can be parsed from the test data as a test attribute. In at least one embodiment, test parameters include test attributes, which include initial test and repeated test attributes. After obtaining the test parameters associated with each test data point, before integrating all the test parameters associated with the test data, the process may further include: determining that the test attribute of the test data is a repeated test; and deleting the test data if it is determined to be repeated test data.

[0043] To adapt to changes in the characteristics of the test data, timely updates to the feature reference values ​​can make the recognition results more accurate. In a preferred embodiment, after step S103, a step S104 is also included. Step S104 may include updating the given feature reference values.

[0044] Furthermore, step S104 may also include: updating the feature reference values ​​corresponding to the time frequency and the concentration of test results, wherein the feature reference values ​​corresponding to the time frequency and the concentration of test results are updated according to the memory factor.

[0045] Taking time frequency t1 as an example, under the premise that time frequency t1 is valid, the initially given time frequency reference value is T0, with an allowable deviation of x. t1 satisfies the mathematical inequality formula T0·(1-x)<t1<T0·(1-x). The time frequency reference value T0 is then updated to T1, where the update formula is T1=α·T0+ (1-α)·t1, and α is the memory factor. Therefore, the time frequency update formula can be T... i =α·T i-1 +(1-α)·t i , where i is a positive integer, and T i Let t be the time frequency reference value corresponding to the i-th test dataset. i This represents the actual time frequency corresponding to the test dataset acquired in the i-th iteration. Each instrument, sample, and item has its own time frequency, and updating each instrument, sample, and item individually allows for the dynamic determination of new time frequency reference values.

[0046] Regarding the average of the test results, let the initial given reference value be the average of the results. The allowable deviation is d, and the mean of the first series of test results is d. ,when The quality control results can be considered valid at this point, and then the average value of the results can be used as a reference. Updated to The update formula is: In the formula, α is the memory factor. Therefore, the update formula for the reference value of the result mean can be: , where i is a positive integer, and in the formula, This is the reference value for the mean of the results corresponding to the i-th test dataset. This represents the mean of the actual results corresponding to the i-th test dataset. Similarly, each instrument, sample, and item has its own mean result, and a new reference value for the mean result can be dynamically determined by updating each instrument, sample, and item individually.

[0047] The updates of the reference values ​​for time frequency and average test results both depend on historical reference values. It should be noted that α represents the weight of historical reference values ​​in the calculation of new reference values. The larger the memory factor, the greater the influence of historical reference values ​​on new reference values. Introducing a weighted algorithm based on historical reference values ​​can make the recognition results more accurate.

[0048] The coefficient of variation (COP) of the test results is the ratio of the standard deviation to the mean, used to compare the dispersion of different datasets. The reference range for the COP is set at 8% to 20%. The mean of the first series of consecutive test results is... Given the standard deviation sd1, the coefficient of variation of the test results is calculated as cv1 = sd1 / When cv1 is between 8% and 20%, the quality control results are considered valid.

[0049] This application embodiment targets the test dataset to be identified. By utilizing the parameter characteristics of the test parameters of the quality control data and the differences between the test parameters and the sample data, the quality control data can be effectively identified from the test dataset to be identified. This effectively avoids the simultaneous mixing of quality control data and sample data in the test data, and provides reliable data support for the performance analysis of the sample analysis instrument.

[0050] The test data identification method based on sample analysis of this application will be described in more detail below with reference to the accompanying drawings.

[0051] See Figure 2 In some implementations, embodiments of this application provide a test data identification method based on sample analysis, including: Step S201: Obtain a large amount of test data from multiple sample analysis instruments, and parse the test data to obtain test parameters such as instrument serial number, sample number, test item, test attribute, test result, and test time, then proceed to step S202; Step S202: Based on the order of the test time, continuously write the test data into the pending queue according to the instrument serial number, sample number, and test item, and proceed to step S203; Step S203: Query the pending queue and determine whether the instrument list is empty. If yes, proceed to step 204; otherwise, proceed to step S205. Step S204: Determine if there is quality control data. If yes, proceed to step 213; otherwise, proceed to step S206. Step S205: Query the queue to be processed and determine whether the sample number list is empty. If it is, go to step 203; otherwise, go to step S207. Step S206: Report a warning of missing quality control result data, then proceed to step S213; Step S207: Query the pending queue and determine whether the project list is empty. If it is, proceed to step 205; otherwise, proceed to step S208. Step S208: After filtering out the retested test data according to the test attributes, calculate the time frequency of a single instrument, a single sample number, and a single item, and determine whether it is within the allowable deviation of the time frequency reference value. If yes, proceed to step 209; otherwise, proceed to step S210. Step S209: Calculate the mean of a single instrument, a single sample number, and a single item, and determine whether it is within the allowable deviation of the mean reference value. If yes, proceed to step 211; otherwise, proceed to step S210. Step S210: Identify this instrument, this sample number, and the test results under this item as sample result data, and proceed to step S203; Step S211: Calculate the coefficient of variation for a single instrument, a single sample number, and a single item, and determine whether it is within the reference range of the coefficient of variation. If yes, proceed to step S212; otherwise, proceed to step S210. Step S212: Identify this instrument, this sample number, and the test results under this project as quality control data, update the time frequency reference value and the result mean reference value, set whether there is quality control data to yes, and go to step S203. Step S213: End.

[0052] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a sample analysis system and corresponding embodiments.

[0053] Figure 3 This is a schematic diagram of the sample analysis system shown in this application.

[0054] See Figure 3 This application discloses a sample analysis system, which includes: at least one sample analysis instrument 301; a server 302 communicatively connected to all sample analysis instruments 301; and a test data identification module 3020 running on the server 302, the test data identification module 3020 being configured to implement the method as described above when executed.

[0055] In this embodiment, the test data identification module 3020 running on the server 302 is configured to acquire test data from at least one sample analysis instrument 301 to facilitate the execution of the above method embodiments. The specific manner in which the test data identification module 3020 performs its operations has been described in detail in the embodiments related to this method, and will not be elaborated further here.

[0056] Figure 4 This is a schematic diagram of the structure of the test data recognition system based on sample analysis shown in this application.

[0057] See Figure 4 This application provides a test data identification system based on sample analysis, including a data acquisition terminal 4010 and a processing terminal 4020; the data acquisition terminal 4010 includes a sample analysis instrument; the processing terminal 4020 is connected to the data acquisition terminal 4010 and is configured to implement the method described above when executed.

[0058] In this embodiment, the test data identification system includes three subsystems: a medical device subsystem 401, an Internet of Things (IoT) subsystem 403, and a cloud platform subsystem 402. The medical device subsystem 401 mainly consists of multiple acquisition terminals 4010, each of which may include a sample analysis instrument. Users generate test data using the sample analysis instrument and store this test data in the acquisition terminal 4010. After acquiring the test data generated by the sample analysis instrument through the acquisition terminals 4010, the IoT subsystem 403 transmits the big data back to the cloud platform subsystem 402 via the IoT. The cloud platform subsystem 402 is equipped with a processing terminal 4020. The processing terminal 4020 cleans and processes the collected big data, classifying it into calibration data, control data, and sample data. This allows for different analysis methods to be applied to different types of data, and provides early warnings based on the analysis structure.

[0059] In the processing terminal 4020, the test big data is parsed, and the parameters such as instrument serial number, sample number, test item, test attribute, test result and test time in each test data are analyzed; based on the initial test or retest in the test attribute, the test data of repeated tests are filtered out; the time frequency, mean of results and coefficient of variation of the same instrument serial number, the same sample number and the same test item are calculated; based on the reference values ​​of time frequency, mean of results and coefficient of variation of results, the quality control data is screened out.

[0060] Figure 5 This is a schematic diagram of the structure of the electronic device shown in this application.

[0061] See Figure 5 The electronic device 500 includes a memory 501 and a processor 502.

[0062] Processor 502 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0063] Memory 501 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 502 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 501 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some implementations, memory 501 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0064] The memory 501 stores executable code, which, when processed by the processor 502, can cause the processor 502 to execute part or all of the methods described above.

[0065] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0066] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of a server (or server, etc.), causes the processor to perform part or all of the steps of the above-described method according to this application.

[0067] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the methods described in the above embodiments.

[0068] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments described above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0069] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0070] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for identifying test data based on sample analysis, characterized in that, include: The test dataset to be identified, obtained from the sample analysis instrument, is parsed to obtain the test parameters associated with each of the test data. The test parameters associated with all the test data are integrated and processed to obtain parameter characteristics; Based on the parameter characteristics, a target test parameter is determined from the test parameters, and the test data associated with the target test parameter is identified as quality control data.

2. The method according to claim 1, characterized in that, The step of determining the target test parameter from the test parameters based on the parameter characteristics includes: Compare the given feature reference value with the value of the parameter feature; The target test parameters are determined from the test parameters based on the comparison results.

3. The method according to claim 2, characterized in that, After identifying the test data associated with the target test parameter as quality control data, the method further includes: The given feature reference value is updated.

4. The method according to claim 1 or 2, characterized in that, The test parameters include instrument identification, sample identification, test items, and test time, and the parameter characteristics include time distribution; the integration and processing of all the test data associated with the test parameters to obtain parameter characteristics includes: Traverse the parsed test dataset; Based on the test times corresponding to the instrument identifier, sample identifier, and test item, the time frequencies corresponding to the same instrument identifier, same sample identifier, and same test item in the parsed test dataset are calculated.

5. The method according to claim 4, characterized in that, The test parameters also include test results, and the parameter characteristics also include the stability of the test results; The process of integrating and processing the test parameters associated with all the test data to obtain parameter features also includes: Based on the test results corresponding to the instrument identifier, sample identifier, and test item, the degree of concentration and the degree of dispersion of the test results corresponding to the same instrument identifier, same sample identifier, and same test item in the parsed test dataset are calculated.

6. The method according to claim 5, characterized in that, The step of determining the target test parameter from the test parameters based on the parameter characteristics, and identifying the test data associated with the target test parameter as quality control data, includes: Determine whether the time frequency, the concentration of test results, and the dispersion of test results obtained from the solution meet the set conditions with their respective corresponding feature reference values; The instrument identifier, sample identifier, or test item corresponding to the time frequency, test result concentration, and test result dispersion that all meet the set conditions will be used as the target instrument identifier, target sample identifier, or target test item. The test data corresponding to the test results that are associated with the target instrument identifier, the target sample identifier, and the target test item are used as quality control data.

7. The method according to claim 6, characterized in that, After identifying the test data associated with the target test parameter as quality control data, the method further includes: The feature reference values ​​corresponding to the time frequency and the concentration of test results are updated, wherein the feature reference values ​​corresponding to the time frequency and the concentration of test results are updated according to the memory factor.

8. The method according to claim 1, characterized in that, The test parameters include test attributes, and the test attributes include repeated testing; after obtaining the test parameters associated with each of the test data, and before integrating all the test parameters associated with the test data, the process further includes: The test attribute of the test data is determined to be a repeated test; If the test data is determined to be duplicate test data, delete the test data.

9. A sample analysis system, characterized in that, include: At least one sample analysis instrument; A server that communicates with all the aforementioned sample analysis instruments; as well as, A test data identification module running on the server is configured to implement the method as described in any one of claims 1-8 when executed.

10. A test data recognition system based on sample analysis, characterized in that, Includes data acquisition terminals and processing terminals; The data acquisition terminal includes a sample analysis instrument; The processing terminal is communicatively connected to the acquisition terminal and is configured to implement the method as described in any one of claims 1-8 when executed.

Images