CN117033355A - Laboratory data validity analysis method based on big data - Google Patents
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- 238000011156 evaluation Methods 0.000 claims description 23
- 230000007613 environmental effect Effects 0.000 claims description 9
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Abstract
The application discloses a laboratory data validity analysis method based on big data, which relates to the technical field of laboratory data analysis, and comprises the following steps of firstly, establishing a test database and setting test items, and storing the test items into the big database to serve as test mode items; step two, collecting and numbering laboratory data; collecting and numbering laboratory instrument information in a laboratory, wherein single laboratory data is used as a test subject item; step three, obtaining test mode items from a test database, and retrieving required test names from the test mode items; step four, acquiring a test scheme, and respectively storing data information and test results in the test process of the corresponding test scheme to form test subject data; fifthly, analyzing the test subject data; and step six, evaluating the validity of the test subject data. The application can maintain the laboratory in time and ensure better effectiveness for the subsequent test results.
Description
Technical Field
The application relates to the technical field of laboratory data analysis, in particular to a laboratory data validity analysis method based on big data.
Background
With the development of society, technical support of Internet big data is added in training of laboratory tests for students, and in particular, database technology is mature, so that great help is brought to management of experimental data.
The prior art has the following defects: the deviation of the experimental process is difficult to be marked in the experimental process, and the experimental stability of a laboratory is difficult to be known according to the deviation analysis of the experimental result, so that the follow-up data validity assurance of the experimental process in the laboratory cannot be guaranteed.
Disclosure of Invention
The application aims to provide a laboratory data validity analysis method based on big data, which aims to solve the defects in the background technology.
In order to achieve the above object, the present application provides the following technical solutions: the laboratory data validity analysis method based on big data comprises the steps of firstly, establishing a test database, setting test items, and storing the test items into the big database to serve as test mode items;
step two, collecting and numbering laboratory data; collecting and numbering laboratory instrument information in a laboratory, wherein single laboratory data is used as a test subject item;
step three, acquiring test mode items from a test database, retrieving required test names from the test mode items, and making and implementing a test scheme according to data in the test items;
step four, acquiring a test scheme, and respectively storing data information and test results in the test process of the corresponding test scheme to form test subject data;
analyzing the test subject data, and carrying out data comparison by combining the same test subjects in the test subject items;
and step six, evaluating the validity of the test subject data.
In a preferred embodiment, the test pattern item is obtained by:
constructing an Internet test database, and setting test items according to a test subject, wherein the test item content comprises a test name, a test process and a test result;
according to the test item, correspondingly formulating a test standard result as a verification item;
when the test item is in a fluctuation result state, the test item is used as a test standard result according to a rule;
when the test item is in a fixed result state, taking the fixed result as a test standard result;
the test items are stored in a test database in the form of identities of test pattern items, and test names are used as the subjects of the test pattern items and are embodied in the form of a list.
In a preferred embodiment, the test subject items are obtained by:
acquiring the grade, position and name of a laboratory, and further numbering the laboratory to form a test subject item, wherein the grade, position and name of the test subject item and the laboratory are all in a binding state;
instrument information in a laboratory is acquired, wherein the instrument information comprises names, functions and states, instrument information items are further obtained, the instrument information items are correspondingly input into test subject items, and finally the test subject items are stored into a test database.
In a preferred embodiment, the test protocol is formulated in the following manner:
the method comprises the steps of referring to test main body information, obtaining information of test mode items from a test database, obtaining corresponding test items according to retrieval, obtaining a test process from the test items, and making a test scheme according to the test process;
expanding the flow of the test process, and simultaneously displaying operation index data in the flow to form a test operation strip; copying a plurality of test operation strips and changing operation index data to form a plurality of different test processes, thereby being used as a plurality of test schemes;
according to the information of a plurality of test schemes, respectively selecting the used instruments to form a test instrument group corresponding to the number of the test schemes;
the determined test protocol is stored with the test instrument cluster in a test database.
In a preferred embodiment, the test subject data is formed in the following manner:
acquiring a test scheme and test subject item information from a test database, and binding the test scheme to the test subject item information according to a laboratory to form a test group;
quantification of test substances: flow determination using standard amounts of material or detection of quantitative material or assays performed according to the assay protocol;
acquiring instruments in the test instrument group, detecting the states of the test instruments, automatically calibrating the instruments, and preparing test work;
performing verification measurement on the test instrument before use, and performing test of the test group under the condition of determining that the function of the test instrument is correct;
according to the test group information, further testing the test schemes one by one, monitoring the operation indexes of the test process in real time, prompting and correcting when data deviation occurs, and meanwhile, collecting data of prompting problems and test links with data deviation, and corresponding the data acquisition problems and the test links to the test schemes, so as to finally obtain test data results;
and obtaining a plurality of test data results through a plurality of test schemes, respectively and correspondingly binding the test data results to the test schemes in the test group to form test subject data, and further storing the test subject data into a test database.
In a preferred embodiment, the test subject data is analyzed in the following manner:
obtaining information in test subject data, and comparing a plurality of test data results with corresponding verification items to obtain effective data in the test data results;
comparing the same test subject data in the same test subject item to obtain a state mark of effective data in test data results;
the marking mode is as follows:
when invalid data appear, acquiring test subject data information and analyzing reasons of the invalid data, wherein the reasons are respectively as follows: marking test subject data with invalid data by human factors and environmental factors;
the same test subject data of the same test subject item is used as a test reference item;
and comparing the data of the test reference items in different test subject items to obtain validity analysis data of a plurality of test subject items.
In a preferred embodiment, the test reference item validity evaluation index of the test subject item is obtained in the following manner:
obtaining a test reference item to obtain a test reference item validity evaluation index of a test subject item, wherein the calculation formula is as follows:
wherein,test reference item effectiveness evaluation index, n, for test subject item z The number of times that the subject data was tested; n is n r Representing the number of times of occurrence of invalid data caused by human factors, wherein alpha is the weight coefficient influenced by human factors, n s Representing the number of occurrences of invalid data due to environmental factors, the beta environmental factor affecting the weighting factor, additionally, alpha<β。
In a preferred embodiment, the test subject item effectiveness evaluation index is obtained by:
acquiring validity analysis data among a plurality of test subject items so as to obtain validity evaluation indexes of the test subject items;
wherein θ z For the test subject item effectiveness evaluation index, E is a laboratory grade evaluation coefficient, E increases with the laboratory grade, and the higher the laboratory grade is, the higher the stability requirement of the laboratory is represented, and the test subject item effectiveness evaluation index threshold is set.
In the technical scheme, the application has the technical effects and advantages that:
1. the application has a plurality of test schemes under the same kind of test data, can compare and reference the validity of the data and analyze the problem, greatly improves the accuracy of data analysis, and performs data statistics on the same kind of test data, thereby having better historical data reference performance;
2. the application can analyze reasons of validity of the test and mark invalidity of test data, and can learn influence of the test chamber on the validity of the test data according to the validity analysis data of the test reference item and the plurality of test subject items, so that the application can maintain the laboratory in time and ensure better validity of subsequent test results.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In embodiment 1, referring to fig. 1, the method for analyzing the validity of laboratory data based on big data in this embodiment includes the following steps:
step one, establishing a test database and setting test items, and further storing the test items into a large database to serve as test mode items;
constructing an Internet test database, and setting test items according to a test subject, wherein the test item content comprises a test name (the name of the test subject), a test process (an operation flow and an operation index of a test, and the operation index data correspond to the test process and are marked one by one) and a test result;
according to the test item, correspondingly formulating a test standard result as a verification item;
when the test item is in a state of a variation result (a test standard result which can be obtained through a formula), the test item is used as the test standard result according to rules;
when the test item is in a fixed result state (the test standard result is a fixed value or a value which is reduced and increased in the same proportion), the test item is taken as the test standard result according to the fixed result;
the test items are stored in a test database in the form of identities of test mode items, and test names are used as the subjects of the test mode items and are embodied in a list form;
step two, collecting and numbering laboratory data; collecting and numbering laboratory instrument information in a laboratory, wherein single laboratory data is used as a test subject item;
acquiring the grade, position and name of a laboratory, and further numbering the laboratory to form a test subject item, wherein the grade, position and name of the test subject item and the laboratory are all in a binding state;
acquiring instrument information in a laboratory, wherein the instrument information comprises names, functions and states (the use performance state of an instrument), further acquiring instrument information items, correspondingly inputting the instrument information items into a test subject item, and finally storing the test subject item into a test database;
step three, acquiring test mode items from a test database, retrieving required test names from the test mode items, and making and implementing a test scheme according to data in the test items;
the formulation mode of the test scheme is as follows:
the method comprises the steps of referring to test main body information, obtaining information of test mode items from a test database, obtaining corresponding test items according to retrieval, obtaining a test process from the test items, and making a test scheme according to the test process;
expanding the flow of the test process, and simultaneously displaying operation index data in the flow to form a test operation strip; copying a plurality of test operation strips and changing operation index data to form a plurality of different test processes, thereby being used as a plurality of test schemes;
according to the information of a plurality of test schemes, respectively selecting the used instruments to form a test instrument group corresponding to the number of the test schemes;
determining a test scheme and a test instrument group to store in a test database together;
step four, acquiring a test scheme, and respectively storing data information and test results in the test process of the corresponding test scheme to form test subject data;
acquiring a test scheme and test subject item information from a test database, and binding the test scheme to the test subject item information according to a laboratory to form a test group;
quantification of test substances: flow determination using standard amounts of material or detection of quantitative material or assays performed according to the assay protocol;
acquiring instruments in the test instrument group, detecting the states of the test instruments, automatically calibrating the instruments, and preparing test work;
performing verification measurement on the test instrument before use, and performing test of the test group under the condition of determining that the function of the test instrument is correct;
according to the test group information, further testing the test schemes one by one, monitoring the operation indexes of the test process in real time, prompting and correcting when data deviation occurs, and meanwhile, collecting data of prompting problems and test links with data deviation, and corresponding the data acquisition problems and the test links to the test schemes, so as to finally obtain test data results;
obtaining a plurality of test data results through a plurality of test schemes, respectively and correspondingly binding the test data results into the test schemes in the test group to form test subject data, and further storing the test subject data into a test database (used as historical data for reference analysis of the subsequent identical test data results);
the method has the advantages that various test schemes are provided under the same test data, the data effectiveness can be compared and referenced, the problems can be analyzed, the accuracy of data analysis is greatly improved, the same test data are subjected to data statistics, and the method has good historical data reference performance;
analyzing the test subject data, and carrying out data comparison by combining the same test subjects in the test subject items;
obtaining information in test subject data, and comparing a plurality of test data results with corresponding verification items to obtain effective data in the test data results;
comparing the same test subject data in the same test subject item to obtain a state mark of effective data in test data results;
the marking mode is as follows:
when invalid data appears, acquiring test subject data information, and analyzing the reasons of the invalid data (carrying out data analysis in combination with the positions which are prompted and corrected when data deviation appears), wherein the data are respectively as follows: human factors and environmental factors (laboratory instrument influence) (laboratory tests are not influenced by laboratory construction environments and only consider laboratory instrument influence), marking test subject data with invalid data;
the same test subject data of the same test subject item is used as a test reference item;
comparing the data of the test reference items in different test subject items to obtain validity analysis data of a plurality of test subject items;
and step six, evaluating the validity of the test subject data.
In example 2, referring to fig. 1, test reference items are obtained to obtain test reference item validity evaluation indexes of test subject items, and the calculation formula is as follows:
wherein,test reference item effectiveness evaluation index, n, for test subject item z The number of times that the subject data was tested;n r representing the number of times of occurrence of invalid data caused by human factors, wherein alpha is the weight coefficient influenced by human factors, n s Representing the number of occurrences of invalid data due to environmental factors, the beta environmental factor affecting the weighting factor, additionally, alpha<Beta, n s And n r The smaller the number of +.>The larger the number of (a) is, the higher the effectiveness of the test representing the subject item;
acquiring validity analysis data among a plurality of test subject items so as to obtain validity evaluation indexes of the test subject items;
wherein θ z For the test subject item effectiveness evaluation index, E is the laboratory grade evaluation coefficient, E increases with the laboratory grade, the higher the laboratory grade (the laboratory instrument quality represents the laboratory grade), the higher the laboratory stability requirement, the test subject item effectiveness evaluation index threshold is set, and the test subject item effectiveness evaluation index threshold is set at θ z When the test subject item effectiveness evaluation index is smaller than the threshold value, namely, the test subject item effectiveness evaluation index represents that the laboratory test has great instability, laboratory maintenance (replacement or maintenance of an instrument) is required;
the method can analyze reasons of validity of the test and mark invalidity of test data, and can learn influence of the test chamber on the validity of the test data according to validity analysis data of the test reference item and a plurality of test subject items, so that the method can timely maintain the test chamber and guarantee good validity of subsequent test results.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A laboratory data validity analysis method based on big data, which is characterized by comprising the following steps:
step one, establishing a test database and setting test items, and further storing the test items into a large database to serve as test mode items;
step two, collecting and numbering laboratory data; collecting and numbering laboratory instrument information in a laboratory, wherein single laboratory data is used as a test subject item;
step three, acquiring test mode items from a test database, retrieving required test names from the test mode items, and making and implementing a test scheme according to data in the test items;
step four, acquiring a test scheme, and respectively storing data information and test results in the test process of the corresponding test scheme to form test subject data;
analyzing the test subject data, and carrying out data comparison by combining the same test subjects in the test subject items;
and step six, evaluating the validity of the test subject data.
2. The big data based laboratory data validity analysis method of claim 1, wherein: the acquisition mode of the test mode item is as follows:
constructing an Internet test database, and setting test items according to a test subject, wherein the test item content comprises a test name, a test process and a test result;
according to the test item, correspondingly formulating a test standard result as a verification item;
when the test item is in a fluctuation result state, the test item is used as a test standard result according to a rule;
when the test item is in a fixed result state, taking the fixed result as a test standard result;
the test items are stored in a test database in the form of identities of test pattern items, and test names are used as the subjects of the test pattern items and are embodied in the form of a list.
3. A laboratory data validity analysis method based on big data according to claim 2, characterized in that: the acquisition mode of the test subject item is as follows:
acquiring the grade, position and name of a laboratory, and further numbering the laboratory to form a test subject item, wherein the grade, position and name of the test subject item and the laboratory are all in a binding state;
instrument information in a laboratory is acquired, wherein the instrument information comprises names, functions and states, instrument information items are further obtained, the instrument information items are correspondingly input into test subject items, and finally the test subject items are stored into a test database.
4. A laboratory data validity analysis method based on big data according to claim 3, wherein: the formulation mode of the test scheme is as follows:
the method comprises the steps of referring to test main body information, obtaining information of test mode items from a test database, obtaining corresponding test items according to retrieval, obtaining a test process from the test items, and making a test scheme according to the test process;
expanding the flow of the test process, and simultaneously displaying operation index data in the flow to form a test operation strip; copying a plurality of test operation strips and changing operation index data to form a plurality of different test processes, thereby being used as a plurality of test schemes;
according to the information of a plurality of test schemes, respectively selecting the used instruments to form a test instrument group corresponding to the number of the test schemes;
the determined test protocol is stored with the test instrument cluster in a test database.
5. The big data based laboratory data validity analysis method of claim 4, wherein: the test subject data are formed in the following manner:
acquiring a test scheme and test subject item information from a test database, and binding the test scheme to the test subject item information according to a laboratory to form a test group;
quantification of test substances: flow determination using standard amounts of material or detection of quantitative material or assays performed according to the assay protocol;
acquiring instruments in the test instrument group, detecting the states of the test instruments, automatically calibrating the instruments, and preparing test work;
performing verification measurement on the test instrument before use, and performing test of the test group under the condition of determining that the function of the test instrument is correct;
according to the test group information, further testing the test schemes one by one, monitoring the operation indexes of the test process in real time, prompting and correcting when data deviation occurs, and meanwhile, collecting data of prompting problems and test links with data deviation, and corresponding the data acquisition problems and the test links to the test schemes, so as to finally obtain test data results;
and obtaining a plurality of test data results through a plurality of test schemes, respectively and correspondingly binding the test data results to the test schemes in the test group to form test subject data, and further storing the test subject data into a test database.
6. The big data based laboratory data validity analysis method of claim 5, wherein: the test subject data analysis mode is as follows:
obtaining information in test subject data, and comparing a plurality of test data results with corresponding verification items to obtain effective data in the test data results;
comparing the same test subject data in the same test subject item to obtain a state mark of effective data in test data results;
the marking mode is as follows:
when invalid data appear, acquiring test subject data information and analyzing reasons of the invalid data, wherein the reasons are respectively as follows: marking test subject data with invalid data by human factors and environmental factors;
the same test subject data of the same test subject item is used as a test reference item;
and comparing the data of the test reference items in different test subject items to obtain validity analysis data of a plurality of test subject items.
7. The big data based laboratory data validity analysis method of claim 6, wherein: the test reference item validity evaluation index of the test subject item is obtained by the following steps:
obtaining a test reference item to obtain a test reference item validity evaluation index of a test subject item, wherein the calculation formula is as follows:
wherein,test reference item effectiveness evaluation index, n, for test subject item z The number of times that the subject data was tested; n is n r Representing the number of times of occurrence of invalid data caused by human factors, wherein alpha is the weight coefficient influenced by human factors, n s Representing the number of occurrences of invalid data due to environmental factors, the beta environmental factor affecting the weighting factor, additionally, alpha<。
8. The big data based laboratory data validity analysis method of claim 7, wherein: the test subject item effectiveness evaluation index is obtained in the following manner:
acquiring validity analysis data among a plurality of test subject items so as to obtain validity evaluation indexes of the test subject items;
wherein θ z For the test subject term effectiveness evaluation index, ε is the laboratory rating scale factor, and ε increases as the laboratory rating increases.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20200400697A1 (en) * | 2019-06-24 | 2020-12-24 | Roche Diagnostics Operations, Inc. | Method of operating an analytical laboratory |
| CN113962569A (en) * | 2021-10-26 | 2022-01-21 | 冰山松洋生物科技(大连)有限公司 | Biological laboratory integrated management system based on cloud service |
| CN115795319A (en) * | 2022-12-06 | 2023-03-14 | 贵州电网有限责任公司 | Test item detection method and related device based on CNAS detection laboratory |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200400697A1 (en) * | 2019-06-24 | 2020-12-24 | Roche Diagnostics Operations, Inc. | Method of operating an analytical laboratory |
| CN113962569A (en) * | 2021-10-26 | 2022-01-21 | 冰山松洋生物科技(大连)有限公司 | Biological laboratory integrated management system based on cloud service |
| CN115795319A (en) * | 2022-12-06 | 2023-03-14 | 贵州电网有限责任公司 | Test item detection method and related device based on CNAS detection laboratory |
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