CN-121998488-A - Laboratory quality control data management method

CN121998488ACN 121998488 ACN121998488 ACN 121998488ACN-121998488-A

Abstract

The invention discloses a laboratory quality control data management method, which relates to the technical field of big data management, and comprises the steps of obtaining laboratory quality control data, and classifying according to grain class to obtain a plurality of quality control data sets; the method comprises the steps of carrying out trend consistency analysis on a plurality of detection category data in a quality control data sequence to obtain category credibility, carrying out category integrity analysis on the quality control data sequence to obtain integrity credibility, screening to obtain quality control data sequences of a plurality of identical storage places, carrying out transverse trend consistency analysis to obtain transverse credibility, weighting and calculating the category credibility, the integrity credibility and the transverse credibility, obtaining comprehensive credibility, carrying out credibility grading and grading storage on the quality control data sequence, and carrying out laboratory quality control data management. The invention solves the technical problem of poor quality control data management effect in the prior art.

Inventors

JI LANYANG
XU CHUNFENG
LUO YAN
YANG YIMING
CHEN LIPING
HAO JINGBO
FU YAO
LIN SHUANG

Assignees

黑龙江省粮食质量安全监测和技术中心

Dates

Publication Date: 20260508
Application Date: 20260115

Claims (10)

1. A method of laboratory quality control data management, comprising: Acquiring laboratory quality control data, classifying according to grain types, and acquiring a plurality of quality control data sets, wherein each quality control data set comprises a plurality of quality control data sequences; Carrying out trend consistency analysis on a plurality of detection category data in the quality control data sequence to obtain category credibility; Carrying out category integrity analysis on the quality control data sequence to obtain integrity credibility; Screening and obtaining the quality control data sequences of a plurality of identical storage sites, and carrying out transverse trend consistency analysis on the data of the same category of the quality control data sequences to obtain transverse credibility; And weighting and calculating the credibility of the category, the integrity credibility and the transverse credibility, acquiring comprehensive credibility, grading the credibility of the quality control data sequence, grading and storing the quality control data sequence based on the credibility grading, and managing the laboratory quality control data.
2. The laboratory quality control data management method according to claim 1, wherein laboratory quality control data is obtained and classified by grain class, and a plurality of quality control data sets are obtained, wherein each of the quality control data sets includes a plurality of quality control data sequences, comprising: Acquiring laboratory quality control data, wherein the laboratory quality control data comprises grain class, grain storage places, detection time and a plurality of detection class data; and classifying the laboratory quality control data based on the grain class to obtain a plurality of quality control data sets, wherein each quality control data set comprises a plurality of quality control data sequences, and each quality control data sequence comprises grain class, grain storage place and a plurality of detection class data of a group of samples of one grain class in one monitoring.
3. The method of claim 1, wherein performing trend consistency analysis on a plurality of inspection category data in the quality control data sequence to obtain category credibility comprises: obtaining a category trend analysis model; inputting the quality control data sequence into the category trend analysis model, obtaining a conflict coefficient, and calculating 1 minus the conflict coefficient to obtain category credibility.
4. A laboratory quality control data management method according to claim 3, wherein obtaining a category trend analysis model comprises: Acquiring a sample quality control data sequence and acquiring category trend conflict rules, wherein the category trend conflict rules comprise trend conflict category pairs and trend conflict importance; marking the sample quality control data sequence based on the category trend conflict rule to obtain a sample conflict coefficient; and constructing a category trend analysis model, taking a sample quality control data sequence as input, taking a sample conflict coefficient as supervision, and training the category trend analysis model until convergence.
5. The method of claim 1, wherein performing a category integrity analysis on the quality control data sequence to obtain an integrity confidence level comprises: based on a plurality of quality control data sequences, performing high-frequency category analysis to obtain a high-frequency category set; Based on the high-frequency category set, combining the requisite category set to obtain a complete category set; and calculating the integrity ratio of the category in the quality control data sequence in the complete category set, and obtaining the integrity credibility.
6. The method of claim 5, wherein performing high frequency category analysis based on a plurality of quality control data sequences to obtain a set of high frequency categories, comprises: acquiring a quality control data sequence as an initial control data sequence, and extracting a plurality of detection categories of the initial control data sequence; Randomly selecting one detection category in the initial control data sequence as a first detection category, traversing the residual quality control data sequence, calculating the deviation of the first detection category, counting the number of sequences with the deviation of the first detection category smaller than a preset first deviation threshold, and calculating a first high-frequency coefficient of the first detection category, wherein the deviation of the first detection category comprises data acquisition standard deviation and data precision deviation; When the first high-frequency coefficient is larger than a preset high-frequency coefficient threshold value, randomly selecting one detection category in the initial control data sequence as a second detection category, calculating to obtain a second detection category deviation, and obtaining a second high-frequency coefficient until high-frequency coefficient calculation of all detection categories is completed; When the first high-frequency coefficient is smaller than or equal to a preset high-frequency coefficient threshold value, randomly selecting one from the rest of quality control data sequences as the initial control data sequence, and re-acquiring a first detection category; When all high-frequency coefficients in the initial control data sequence are larger than a preset high-frequency coefficient threshold value, taking the detection category in the initial control data sequence as a high-frequency category, and adding the high-frequency category into the high-frequency category set.
7. The method of claim 1, wherein screening the quality control data sequences for a plurality of identical storage sites, performing a lateral trend consistency analysis on the same category of data for the plurality of quality control data sequences, and obtaining lateral confidence comprises: screening the quality control data sequences based on the storage places to obtain the same-quality control data sequences; Screening the same-quality control data sequence based on the detection time to obtain a similar quality control data sequence; And carrying out transverse trend consistency analysis on the plurality of similar quality control data sequences to obtain transverse credibility.
8. The method of claim 7, wherein performing a lateral trend consistency analysis on a plurality of said proximate quality control data sequences to obtain lateral confidence comprises: acquiring environmental commonality characteristics based on a plurality of the similar quality control data sequences; and carrying out common characteristic deviation analysis on a plurality of similar quality control data sequences based on the environmental common characteristic to acquire transverse credibility, wherein the transverse credibility is acquired based on the category with the largest common characteristic deviation.
9. The method of claim 1, wherein weighting the category trustworthiness, the integrity trustworthiness, and the lateral trustworthiness to obtain a comprehensive trustworthiness comprises: based on expert evaluation, acquiring a credibility weight set; And adopting the credibility weight set to carry out weighted calculation on the category credibility, the integrity credibility and the transverse credibility to obtain comprehensive credibility.
10. The laboratory quality control data management method according to claim 1, wherein the laboratory quality control data management is performed by performing hierarchical storage based on the confidence level hierarchy, comprising: based on grain class, acquiring a credibility grading threshold; Based on the credibility grading threshold, carrying out credibility grading on the quality control data sequence to obtain a credibility grade; And based on the credibility level, carrying out hierarchical storage and application scene marking on the quality control data sequence.

Description

Laboratory quality control data management method Technical Field The invention relates to the technical field of big data management, in particular to a laboratory quality control data management method. Background The laboratory generates mass and multi-source heterogeneous quality control data in daily quality monitoring of grains, the data cover different grain types, storage places, detection time, moisture, impurities, fatty acid values, mycotoxins and other detection categories, and an important basis for continuously tracking the storage quality and the safety state of the grains is formed. However, in the face of such a huge and complicated data flow, the prior art mainly focuses on the acquisition and input, the structured storage and the basic query statistics of data, and lacks a deep evaluation mechanism of the intrinsic quality and the credibility of the data, so that the quality control data of the current laboratory is large in quantity and uneven in quality, the credibility is vague, and the accuracy of subsequent data analysis, the timeliness of risk early warning and the reliability of data driving decisions are severely restricted. Disclosure of Invention The application provides a laboratory quality control data management method which is used for solving the technical problem of poor quality control data management effect in the prior art. In view of the above, the present application provides a laboratory quality control data management method, the method comprising: Acquiring laboratory quality control data, classifying according to grain types, and acquiring a plurality of quality control data sets, wherein each quality control data set comprises a plurality of quality control data sequences; Carrying out trend consistency analysis on a plurality of detection category data in the quality control data sequence to obtain category credibility; Carrying out category integrity analysis on the quality control data sequence to obtain integrity credibility; Screening and obtaining the quality control data sequences of a plurality of identical storage sites, and carrying out transverse trend consistency analysis on the data of the same category of the quality control data sequences to obtain transverse credibility; And weighting and calculating the credibility of the category, the integrity credibility and the transverse credibility, acquiring comprehensive credibility, grading the credibility of the quality control data sequence, grading and storing the quality control data sequence based on the credibility grading, and managing the laboratory quality control data. One or more technical schemes provided by the application have at least the following technical effects or advantages: The application provides a laboratory quality control data management method, which is characterized in that internal category trend consistency analysis, category integrity analysis and co-location data transverse trend consistency analysis are carried out on a quality control data sequence, and the quality control data sequence is weighted and fused into comprehensive credibility to carry out hierarchical management, so that the inherent quality and reliability multidimensional quantitative evaluation capability of laboratory quality control data is obviously improved. Compared with the traditional method, the technical scheme provided by the application obviously enhances the intelligent level of the data management process and the reliability of the data management result, and achieves the technical effect of improving the data management effect from the source. Drawings In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Fig. 1 is a schematic flow chart of a laboratory quality control data management method according to an embodiment of the present application. Fig. 2 is a schematic diagram of a process for acquiring a high-frequency category set in a laboratory quality control data management method according to an embodiment of the present application. Detailed Description The application provides a laboratory quality control data management method which is used for solving the technical problem of poor quality control data management effect in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skille