CN106777356A - A kind of data analysing method based on LIMS systems - Google Patents
A kind of data analysing method based on LIMS systems Download PDFInfo
- Publication number
- CN106777356A CN106777356A CN201710037162.6A CN201710037162A CN106777356A CN 106777356 A CN106777356 A CN 106777356A CN 201710037162 A CN201710037162 A CN 201710037162A CN 106777356 A CN106777356 A CN 106777356A
- Authority
- CN
- China
- Prior art keywords
- data
- client
- analysis
- database
- method based
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
Abstract
The invention discloses a kind of data analysing method based on LIMS systems, comprise the following steps：S1, data acquisition：In multiple client installation data acquisition module, relevant database and non-relational database are equipped with the data acquisition module, the relevant database is used to store detection data, the non-relational database is used for the collection of inspection data, start the client, the data acquisition module also voluntarily starts accordingly, by the data of data collecting module collected client；S2, imports：The data that the data acquisition module automatic regular polling detection client is produced, and judge whether the data of client produce change.The present invention can be applied in every field, and can be played a significant role in the work such as food and medicine inspection, certification and accreditation, standard, website, administration, and further lifting inspection industrial application of information technology level is had a very important role.
Description
Technical field
The present invention relates to data analysis technique field, more particularly to a kind of data analysing method based on LIMS systems.
Background technology
According to the requirement of state food drug surveilance general bureau, inspection Information System is set up, realize calibration authority certainly
The work of commencing business of dynamicization, procedure, and realize the inter-agency information sharing interconnected of inspections at different levels.In foundation
Inspection institute, the two-stage Monitoring Data center of food and medicine inspection body of each province, country, province and district city three-level monitor informatization system are real
Data exchange work between existing multilevel system, while building and application system supporting basic running environment, data resource, application
Support platform, standard criterion system etc..Everything all promotes food and medicine to check business service and statistical information and supervision
Reported to network electronic from reporting by hand, changed from statistical report form to service data flat-bed format.
With food and medicine checking information platform and the progressively Erecting and improving of operation system, accumulation progressively is largely enriched
Statistical information big data resource.Meanwhile, with the rise of big data technology, to we provide a kind of new data analysis
Method, no longer place one's entire reliance upon random sampling, by big data can with analysis mining go out that small data cannot extract it is valuable
Information, serves socio-economic development.
Application of the big data in food and medicine inspection industry is realized, the system business data branch of magnanimity is first had to
Hold, secondly need to realize the business intelligence based on big data and data mining, the construction of data analysis system.By business intelligence
And data mining, the construction of data analysis system, enrich extension information resource database and further improve data excavation and
Analysis.It is food and medicine supervision, food and medicine inspection grasp in time on this basis by unified data sharing analysis platform
Various statistical informations provide help, for managerial decision is provided according to supporting, for public information searching platform provides data supporting.
The application of big data technology is exactly the system such as mining analysis, statistics on demand, the searching platform of data for realizing data
Data supporting, for managerial decision provide according to support.It is envisioned that in the near future, the data of food and medicine can all be concentrated
To a unified data platform, the assay of food and medicine, historical data trend analysis is all possible to immediate inquiring report
And early warning, which forms the perfect long-acting oversight mechanism of food and medicine, ensure people's diet drug safety.
Big data technology apply food and drug safety supervision area application will widely, currently only by
The obtained data of existing sampling system sampling monitoring, are not converted into the information that can be directly provided to the public mostly,
If realizing conversion, the data volume produced by that can be bigger.If to service towards the public, it is meant that needs are built bigger
Platform, the data to magnanimity build information model, so as to be provided with food and drug safety targetedly information service to the public, and
Service is dynamic, rather than static state.
Development of the big data in food and medicine inspection field has very big relation with information network platform and information service, I
Can make full use of existing network platform, such as microblogging, wechat, by playing an active part in for societal forces, it is common build or
Food and drug safety information service platform is provided to the public.
The bottleneck of big data application development, is on the one hand technical bottleneck：Big data how is allowed to process more convenient, quick, more
It is close to the users, it is easier to go to realize or go operation；On the other hand, the application and treatment of big data, its core is operation layer
Planning of science activities.In mass data, not all information is all useful, it is necessary to continuous processing and refinement, form information
The application value of resource competence exertion big data.
The lasting wound of the communication technology with computer technology, internet as representative and the sensing technology with Internet of Things as representative
New and extensive use makes the digitization ability of the mankind and scope Rapid Expansion.The data volume of in-house generation can it is measured and
Record it is more and more, and our measurements to things, phenomenon etc., record also more frequent and careful.
Nowadays, this expansion let us see it is a large amount of from macroscopic view to it is microcosmic, from nature to the observation of society, calculate, propagate
All magnanimity, various data are quickly being produced Deng instrument and equipment and activity.The various instruments in such as laboratory, sensor, into
As various scientific research apparatus and device, analogy method, intelligent terminal and various applications etc. such as equipment, sequenators, these are all caused
Scientific research field has been pulled to unprecedented ' big data ' epoch.
The sharp increase of mass data, certainly will allow quantitative change to cause qualitative change, and ever-increasing data trigger people's thinking and behavior mould
The change of formula, and in field of scientific study, this is also by directly for Model of Scientific Research brings greatly change.After experimental science, theory
The 4th kind of research paradigm, i.e. ' data-intensive science ' are occurred in that after science, computational science, as the big data epoch under it is new
Pattern.
Big data brings major opportunity for scientific research, such as when the data to be utilized increase, people can be with
Do many things that cannot be completed on the basis of small data.Popular says, science big data is the magnanimity in Scientific Engineering research
Data.Big data " it is ubiquitous, and contain huge economic worth ", it is both one the one of Multidisciplinary Integration and intersects
Section, is also influenceing and is changing more scientific domains in turn.For example, using big data phase can be carried out by historical data
The modeling and analysis of experiment are closed, best resource is configured, researcher is effectively guided, so as to reduce R＆D costs, research and development is improved
Efficiency.
LIMS (LIMS), it is made up of computer hardware and application software, can complete experiment
Collection, analysis, report and the management of number of chambers evidence and information.LIMS is based on LAN, specifically designed for laboratory
Integrated environment and design, be one and include signal collecting device, data communication software, database management language interior efficient
Integrated system.Centered on laboratory, by the operation flow in laboratory, environment, personnel, instrument and equipment, mark thing standard liquid, chemistry examination
Agent, standard method, books and reference materials, file record, research and development management, project management, customer account management etc. factor are organically combined.
It centered on laboratory, by the operation flow in laboratory, environment, personnel, instrument and equipment, mark thing standard liquid, chemistry
Reagent, standard method, books and reference materials, file record, research and development management, project management, customer account management etc. impact analysis data
Factor combines, using advanced computer networking technology, database technology and standardized laboratory room managing thought,
Composition one comprehensively, the management system of specification, for realize analyze data online dispatch, analyze data automatic data collection, quick distribution,
Information sharing, analysis report is with no paper, quality certification system is smoothly implemented, cost is strictly controlled, personnel's quantizing examination, laboratory
Managerial skills each side such as integrally improve and provide technical support, are the letters for connecting laboratory, workshop, Quality Control Department's door and client
Breath platform, while introduce advanced mathematical statistics technology, such as variance analysis, correlation and regression analysis, significance test, accumulation and
Control figure, sampling inspection etc., assist functional department to find and control the key factor of influence product quality.
It is well known that big data is not simple the fact that be big data, and most important reality is to big number
According to being analyzed, many intelligence, deep, valuable information only could be obtained by analysis.It is so increasing
Using being related to big data, and the attribute of these big datas, including quantity, speed, diversity etc. is all to present big data
Ever-increasing complexity, so the analysis method of big data is just particularly important in big data field, it may be said that be to determine
The whether valuable deciding factor of final information.
The content of the invention
Based on the technical problem that background technology is present, the present invention proposes a kind of data analysis side based on LIMS systems
Method.
A kind of data analysing method based on LIMS systems proposed by the present invention, comprises the following steps：
S1, data acquisition：In multiple client installation data acquisition module, relationship type number is equipped with the data acquisition module
According to storehouse and non-relational database, the relevant database is used to store detection data, and the non-relational database is used for
The collection of inspection data, starts the client, and the data acquisition module is also voluntarily started, adopted by data accordingly
Collection module gathers the data of client；
S2, imports：The data that data acquisition module automatic regular polling detection client is produced, and judge that the data of client are
No to produce change, in this way, the client data that will be collected is pre-processed, and is directed into distributed storage cluster；
S3, statistics, analysis：By statistical analysis module the mass data in distributed storage cluster is carried out common analysis and
Classifying Sum, and judge whether analyzed and classified per data one by one, analysis and classification results storage are deposited in distribution
Accumulation, forms analysis result；
S4, excavates：Analysis result is extracted to computing module, the calculating based on various algorithms is carried out by computing module, and open
Dynamic prediction module carries out high-level data analysis, and shows.
Preferably, the relevant database and non-relational database are equipped with database user name and database is close
Code.
Preferably, the client is Web, App or sensor.
Preferably, the non-relational database is Redis or MongoDB.
Preferably, the prediction module uses Kmeans and NaiveBayes.
Preferably, after the prediction module carries out high-level data analysis, set up model and bring model into new data,
So as to predict the data in future.
In the present invention, first start client, data acquisition module is also voluntarily started, adopted by data acquisition module accordingly
Collect the data of client, when excavating, the algorithm of various data minings is based on different data type and form, more science
The characteristics of showing data and possess in itself, and go deep into inside data, excavate generally acknowledged data value.By the calculation of data mining
Method can faster process big data, improve the efficiency of data analysis and process, and final predictive analysis is excavated from big data
Go out feature, by the model of setting up of science, just new data can be brought into by model afterwards, so that the data in future are predicted,
The present invention can be applied in every field, and in the work such as food and medicine inspection, certification and accreditation, standard, website, administration
Can play a significant role, further lifting inspection industrial application of information technology level is had a very important role.
Specific embodiment
The present invention is made with reference to specific embodiment further explain.
A kind of data analysing method based on LIMS systems proposed by the present invention, comprises the following steps：
S1, data acquisition：In multiple client installation data acquisition module, relationship type number is equipped with the data acquisition module
According to storehouse and non-relational database, the relevant database is used to store detection data, and the non-relational database is used for
The collection of inspection data, starts the client, and the data acquisition module is also voluntarily started, adopted by data accordingly
Collection module gathers the data of client；
S2, imports：The data that data acquisition module automatic regular polling detection client is produced, and judge that the data of client are
No to produce change, in this way, the client data that will be collected is pre-processed, and is directed into distributed storage cluster；
S3, statistics, analysis：By statistical analysis module the mass data in distributed storage cluster is carried out common analysis and
Classifying Sum, and judge whether analyzed and classified per data one by one, analysis and classification results storage are deposited in distribution
Accumulation, forms analysis result；
S4, excavates：Analysis result is extracted to computing module, the calculating based on various algorithms is carried out by computing module, and open
Dynamic prediction module carries out high-level data analysis, and shows.
In the present invention, the relevant database and non-relational database is equipped with database user name and database is close
Code.The client is Web, App or sensor.The non-relational database is Redis or MongoDB.The prediction
Module uses Kmeans and NaiveBayes.After the prediction module carries out high-level data analysis, model is set up and by model
New data are brought into, so as to predict the data in future.
" big data " epoch, allow inspection " big data " it is living get up be the trend of economic progress.Using " big number
According to " premise, being will be for data be reduced weight, relevance according to business, with reference to database technology, by between disparate databases
Redundant data is rejected, and data silo is eliminated even with integrating or rebuilding, and allows the inspection " big data " to be in best form
Reveal and.
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto,
Any one skilled in the art the invention discloses technical scope in, technology according to the present invention scheme and its
Inventive concept is subject to equivalent or change, should all be included within the scope of the present invention.
Claims (6)
1. a kind of data analysing method based on LIMS systems, it is characterised in that comprise the following steps：
S1, data acquisition：In multiple client installation data acquisition module, relationship type number is equipped with the data acquisition module
According to storehouse and non-relational database, the relevant database is used to store detection data, and the non-relational database is used for
The collection of inspection data, starts the client, and the data acquisition module is also voluntarily started, adopted by data accordingly
Collection module gathers the data of client；
S2, imports：The data that data acquisition module automatic regular polling detection client is produced, and judge that the data of client are
No to produce change, in this way, the client data that will be collected is pre-processed, and is directed into distributed storage cluster；
S3, statistics, analysis：By statistical analysis module the mass data in distributed storage cluster is carried out common analysis and
Classifying Sum, and judge whether analyzed and classified per data one by one, analysis and classification results storage are deposited in distribution
Accumulation, forms analysis result；
S4, excavates：Analysis result is extracted to computing module, the calculating based on various algorithms is carried out by computing module, and open
Dynamic prediction module carries out high-level data analysis, and shows.
2. a kind of data analysing method based on LIMS systems according to claim 1, it is characterised in that the relationship type
Database and non-relational database are equipped with database user name and database password.
3. a kind of data analysing method based on LIMS systems according to claim 1, it is characterised in that the client
It is Web, App or sensor.
4. a kind of data analysing method based on LIMS systems according to claim 1, it is characterised in that the non-relation
Type database is Redis or MongoDB.
5. a kind of data analysing method based on LIMS systems according to claim 1, it is characterised in that the prediction mould
Block uses Kmeans and NaiveBayes.
6. a kind of data analysing method based on LIMS systems according to claim 1, it is characterised in that the prediction mould
After block carries out high-level data analysis, set up model and bring model into new data, so as to predict the data in future.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710037162.6A CN106777356A (en) | 2017-01-19 | 2017-01-19 | A kind of data analysing method based on LIMS systems |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710037162.6A CN106777356A (en) | 2017-01-19 | 2017-01-19 | A kind of data analysing method based on LIMS systems |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106777356A true CN106777356A (en) | 2017-05-31 |
Family
ID=58944065
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710037162.6A Pending CN106777356A (en) | 2017-01-19 | 2017-01-19 | A kind of data analysing method based on LIMS systems |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106777356A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108932352A (en) * | 2018-09-26 | 2018-12-04 | 江苏曲速教育科技有限公司 | Distributed cache system based on statistical server |
| CN108983994A (en) * | 2018-06-11 | 2018-12-11 | 山东省海洋资源与环境研究院 | Marine organisms identification intelligent input and normalization output system in a kind of LIMS system |
| CN109327543A (en) * | 2018-11-21 | 2019-02-12 | 科大智能电气技术有限公司 | A kind of implementation method of internet of things equipment data transmission |
| CN110751451A (en) * | 2019-09-11 | 2020-02-04 | 北京戴纳实验科技有限公司 | Laboratory big data management system |
| CN112151134A (en) * | 2020-09-11 | 2020-12-29 | 哈尔滨灵迅医药科技有限公司 | Clinical research data management platform and method based on big data model |
| CN112651708A (en) * | 2020-12-21 | 2021-04-13 | 深圳科诺医学检验实验室 | Detection data processing method, device and equipment based on LIMS (laser induced mechanical Spectroscopy) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130262497A1 (en) * | 2012-04-02 | 2013-10-03 | Caterpillar Inc. | Inspection Tool |
| CN105243285A (en) * | 2015-11-10 | 2016-01-13 | 广州西麦科技股份有限公司 | Big data health forecast system |
| CN105761011A (en) * | 2016-02-24 | 2016-07-13 | 北京信立方科技发展股份有限公司 | Laboratory resource management system based on cloud platform |
| CN106157099A (en) * | 2016-09-05 | 2016-11-23 | 广东聚联电子商务股份有限公司 | A kind of user's click information bonusing method based on big data |
-
2017
- 2017-01-19 CN CN201710037162.6A patent/CN106777356A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130262497A1 (en) * | 2012-04-02 | 2013-10-03 | Caterpillar Inc. | Inspection Tool |
| CN105243285A (en) * | 2015-11-10 | 2016-01-13 | 广州西麦科技股份有限公司 | Big data health forecast system |
| CN105761011A (en) * | 2016-02-24 | 2016-07-13 | 北京信立方科技发展股份有限公司 | Laboratory resource management system based on cloud platform |
| CN106157099A (en) * | 2016-09-05 | 2016-11-23 | 广东聚联电子商务股份有限公司 | A kind of user's click information bonusing method based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 邹雄 等: "《临床检验仪器（第2版）》", 31 August 2015 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108983994A (en) * | 2018-06-11 | 2018-12-11 | 山东省海洋资源与环境研究院 | Marine organisms identification intelligent input and normalization output system in a kind of LIMS system |
| CN108932352A (en) * | 2018-09-26 | 2018-12-04 | 江苏曲速教育科技有限公司 | Distributed cache system based on statistical server |
| CN109327543A (en) * | 2018-11-21 | 2019-02-12 | 科大智能电气技术有限公司 | A kind of implementation method of internet of things equipment data transmission |
| CN110751451A (en) * | 2019-09-11 | 2020-02-04 | 北京戴纳实验科技有限公司 | Laboratory big data management system |
| CN112151134A (en) * | 2020-09-11 | 2020-12-29 | 哈尔滨灵迅医药科技有限公司 | Clinical research data management platform and method based on big data model |
| CN112651708A (en) * | 2020-12-21 | 2021-04-13 | 深圳科诺医学检验实验室 | Detection data processing method, device and equipment based on LIMS (laser induced mechanical Spectroscopy) |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106777356A (en) | A kind of data analysing method based on LIMS systems | |
| Zhang et al. | A machine learning framework for assessing post-earthquake structural safety | |
| Cobo et al. | An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field | |
| Eltouny et al. | Large‐scale structural health monitoring using composite recurrent neural networks and grid environments | |
| Dahiya et al. | Gradient boosting-based regression modelling for estimating the time period of the irregular precast concrete structural system with cross bracing | |
| Zhang et al. | Teaching evaluation algorithm based on grey relational analysis | |
| Chen et al. | Identifying the most suitable machine learning approach for a road digital twin | |
| Zhang | Automatic underground space security monitoring based on BIM | |
| Jiang et al. | Improvement of random forest by multiple imputation applied to tower crane accident prediction with missing data | |
| He et al. | A synthetical analysis method of measuring technology convergence | |
| CN112418571A (en) | Method and device for enterprise environmental protection comprehensive evaluation | |
| CN106355537A (en) | Smart analysis method and system for interrelated cases | |
| Guo et al. | An ensemble learning with sequential model-based optimization approach for pavement roughness estimation using smartphone sensor data | |
| Altomare et al. | Using clouds for smart city applications | |
| Schumacher | CSU Mobile Radiosonde Data. Version 1.0 | |
| Si et al. | An empirical study on the performance evaluation of scientific data sharing platforms in China | |
| CN113626285A (en) | Model-based job monitoring method and device, computer equipment and storage medium | |
| CN114201328A (en) | Fault processing method and device based on artificial intelligence, electronic equipment and medium | |
| CN112860822A (en) | Comprehensive analysis method for land resource bearing capacity based on geographical national situation view angle | |
| CN207232961U (en) | A kind of system that can be tested in real time to watermark protocol data | |
| Marinelli et al. | Investigation of earthmoving trucks deterioration using discriminant analysis | |
| Tu | Analysis and prediction method of student behavior mining based on campus big data | |
| Pei et al. | Machine learning and deep learning | |
| Heng et al. | Assessment and governance of industrial internet maturity in the building materials industry using the entropy weight method and factor analysis | |
| Aziz et al. | Using quantitative approaches to enhance construction performance through data captured from mobile devices |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170531 |
|
| RJ01 | Rejection of invention patent application after publication |