CN113538027A - Product parameter analysis matching system based on big data - Google Patents
Product parameter analysis matching system based on big data Download PDFInfo
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Abstract
The application discloses a product parameter analysis matching system based on big data, which comprises a data acquisition module, a computer module, a database, a control module and a big data cloud computing module which are sequentially connected; the data acquisition module can acquire data according to past health detection reports, historical browsing and purchasing record behaviors of the user; the computer module converts the data acquired by the data acquisition module into a computer language after calculating; the database stores computer language converted by the data acquired by the data acquisition module through a computer; the control module can call the information stored in the database and send the data in the database to the big data cloud computing module; and the big data cloud computing module is used for receiving the data to be computed, computing to obtain incremental data, computing an incremental output result of the big data cloud computing, and determining a final computing result according to the incremental output result and an original output result of the big data cloud computing.
Description
Technical Field
The invention relates to a product parameter analysis matching system based on big data.
Technical Field
In the traditional parameter matching technology based on the historical information of the client, the matching performance and effect are not ideal because the rules and the parameters are fixed; and the matching technology based on real-time big data does not form a mature framework. The existing big data matching analysis calculation usually depends on the analysis result of the big data, and the existing big data matching analysis calculation usually does not have real-time performance and pertinence. In addition, the original data sampling precision, the difference of the statistical method and the modeling structural error can cause the analysis to be wrong, and in addition, different use scenes can bring completely different results.
Disclosure of Invention
In order to solve the problems in the prior art, the invention discloses a product parameter analysis matching system based on big data, which comprises: the system comprises a data acquisition module, a computer module, a database, a control module and a big data cloud computing module which are connected in sequence; wherein the content of the first and second substances,
the data acquisition module can acquire data according to past health detection reports, historical browsing and purchasing record behaviors of the user;
the computer module is used for converting the data acquired by the data acquisition module into a computer language after calculation;
the database is used for storing a computer language converted by the computer from the data acquired by the data acquisition module;
the control module can call the information stored in the database and send the data in the database to the big data cloud computing module;
the big data cloud computing module receives data to be computed, performs computing to obtain incremental data and computes an incremental output result of big data cloud computing, and determines a final computing result according to the incremental output result and an original output result of the big data cloud computing.
Further, the big data cloud computing module uses at least one of complete input and complete output of necessary data, and uses an increment transfer rule to describe each computing step by taking the data as granularity, wherein the increment transfer rule is a computing rule for computing the increment output of each computing step according to the increment input of each computing step and the necessary data required to be stored in each computing step, and the necessary data required to be stored in each computing step is stored according to the increment transfer rule of each computing step when complete computation or increment computation is performed; and determining a final calculation result according to the increment output result and the original output result of the big data cloud calculation.
Furthermore, the data acquisition module adopts a crawler to acquire data, filters the data through a preset data filtering rule, and takes the filtered data as the data acquired by the data acquisition module.
Further, the control module includes a web server through which data in the plurality of databases can be linked to provide larger data.
Further, the control module uses an operating system which is a Linux operating system.
Further, the database, the control module and the big data cloud computing module jointly adopt a distributed computing architecture, and the distributed computing architecture framework comprises: the system comprises a Linux operating system, an Apache network server and a MySQL database, wherein Perl, PHP or Python programming languages are used for forming products and adopting open source software.
Has the advantages that:
in the product parameter analysis and matching, data acquisition can be carried out according to past behaviors of a user such as historical browsing and purchase records, the acquired data is converted into a computer language after calculation, the computer language is stored in a database and then is sent to cloud computing, and the cloud computing carries out computing processing. Promote product parameter analysis precision through big data, it is concrete, adopt the frame based on big data matching technique, this frame is easily built, adopts the distributed architecture, supports horizontal extension, and the performance is excellent, and for industry mainstream system, throughput has promoted 41%, and the matching delay has reduced more than 25%, combines real-time data and historical data analysis, and the matching result is more accurate, and the machine learning algorithm is joined in marriage to the rear end, can initiatively optimize the matching effect.
Detailed Description
In a specific implementation, an embodiment of the product parameter analysis matching system based on big data according to the present application includes: the system comprises a data acquisition module, a computer module, a database, a control module and a big data cloud computing module which are connected in sequence;
wherein the content of the first and second substances,
the data acquisition module can acquire behavior data such as past health detection reports, historical browsing, purchase records and the like of the user;
the computer module is used for converting the data acquired by the data acquisition module into a computer language after calculation;
the database is used for storing a computer language converted by the computer from the data acquired by the data acquisition module;
the control module can call the information stored in the database and send the data in the database to the big data cloud computing module;
the big data cloud computing module receives data to be computed, performs computing to obtain incremental data and computes an incremental output result of big data cloud computing, and determines a final computing result according to the incremental output result and an original output result of the big data cloud computing.
The big data cloud computing module uses at least one of complete input and complete output of necessary data, uses an increment transfer rule for describing each computing step by taking the data as granularity, the increment transfer rule is a computing rule for computing the increment output of each computing step according to the increment input of each computing step and the necessary data required to be stored in each computing step, and the necessary data required to be stored in each computing step is stored according to the increment transfer rule of each computing step when complete computing or increment computing is carried out; and determining a final calculation result according to the increment output result and the original output result of the big data cloud calculation.
The data acquisition module adopts the crawler to acquire data, filters the data through a preset data filtering rule, and takes the filtered data as the data acquired by the data acquisition module. The crawler is also called a web spider, and a web robot, which is a program or script for automatically capturing web information according to a certain rule. Other less commonly used names are ants, automatic indexing, simulation programs, or worms.
The control module includes a web server through which data in the plurality of databases can be linked to provide larger data.
The control module uses an operating system as a Linux operating system. The Linux operating system is a Unix-like operating system which is free to use and spread freely, and is a multi-user, multi-task, multi-thread and multi-CPU supporting operating system based on POSIX and Unix. With the development of the internet, Linux is supported by software enthusiasts, organizations and companies all over the world. Besides maintaining strong development momentum on the server aspect, the method has great progress on personal computers and embedded systems. The user can not only intuitively obtain the implementation mechanism of the operating system, but also modify and perfect Linux according to the self requirement, so that the Linux is maximally adapted to the requirement of the user. The Linux is not only stable in system performance, but also open-source software. The core firewall component has high performance and simple configuration, and ensures the safety of the system. In many enterprise networks, in order to pursue speed and security, Linux is not only used as a server by network operation and maintenance personnel, but also can be used as a server as well as a network firewall, which is a great highlight of Linux. The Linux has the characteristics of open source codes, no copyright, more technical community users and the like, the open source codes enable users to freely cut, and the Linux has high flexibility, powerful functions and low cost. Especially, the embedded network protocol stack in the system can realize the function of the router through proper configuration. These characteristics make Linux an ideal development platform for developing routing switching equipment.
The database, the control module and the big data cloud computing module adopt a distributed computing architecture together, and the distributed computing architecture framework comprises: the system comprises a Linux operating system, an Apache network server and a MySQL database, wherein Perl, PHP or Python programming languages are used for forming products and adopting open source software.
Compared with the Java/J2EE architecture, the distributed computing framework has the characteristics of rich Web resources, light weight, safety and the like, and has the advantages of universality, cross-platform performance and high performance compared with the NET architecture of Microsoft. Meanwhile, a large-scale parallel (MPP) database, a distributed database and the like can be processed in a cloud computing mode, and a large amount of accurate processing parameters can be rapidly distributed.
In the product parameter analysis and matching process, data collection can be performed according to past behaviors of a user such as historical browsing and purchase records, the collected data are converted into computer languages after calculation, the computer languages are stored in a database and then sent to cloud computing, and the cloud computing performs computing processing. Promote product parameter analysis precision through big data, it is concrete, adopt the frame based on big data matching technique, this frame is easily built, adopts the distributed architecture, supports horizontal extension, and the performance is excellent, and for industry mainstream system, throughput has promoted 41%, and the matching delay has reduced more than 25%, combines real-time data and historical data analysis, and the matching result is more accurate, and the machine learning algorithm is joined in marriage to the rear end, can initiatively optimize the matching effect.
It should be noted that the above-mentioned embodiments are only examples of the present invention, and it should be understood that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principle and spirit of the present invention, so as to obtain other embodiments, which should also be within the scope of the present invention as defined in the appended claims.
Claims (6)
1. Product parameter analysis matching system based on big data, its characterized in that includes: the system comprises a data acquisition module, a computer module, a database, a control module and a big data cloud computing module which are connected in sequence;
wherein the content of the first and second substances,
the data acquisition module can acquire data according to past health detection reports, historical browsing and purchasing record behaviors of the user;
the computer module is used for converting the data acquired by the data acquisition module into a computer language after calculation;
the database is used for storing a computer language converted by the computer from the data acquired by the data acquisition module;
the control module can call the information stored in the database and send the data in the database to the big data cloud computing module;
the big data cloud computing module receives data to be computed, performs computing to obtain incremental data and computes an incremental output result of big data cloud computing, and determines a final computing result according to the incremental output result and an original output result of the big data cloud computing.
2. The big-data-based product parameter analyzing and matching system according to claim 1, wherein the big-data cloud computing module uses the necessary data including at least one of complete input and complete output, uses an increment transfer rule for describing each computing step by data as granularity, the increment transfer rule is a computing rule for calculating the increment output of each computing step according to the increment input of each computing step and the necessary data required to be saved in each computing step, and the necessary data required to be saved in each computing step is saved according to the increment transfer rule of each computing step when complete computation or increment computation is performed; and determining a final calculation result according to the increment output result and the original output result of the big data cloud calculation.
3. The big-data-based product parameter analyzing and matching system as claimed in claim 1, wherein the data collecting module collects data by using a crawler, filters the data by using a preset data filtering rule, and uses the filtered data as the data collected by the data collecting module.
4. The big-data based product parameter analyzing and matching system of claim 1, wherein the control module comprises a web server through which data in a plurality of the databases can be linked to provide larger data.
5. The big data based product parameter analyzing and matching system according to claim 1 or 4, wherein the control module uses the operating system being a Linux operating system.
6. The big-data based product parameter analyzing and matching system according to claim 1, wherein the database, the control module and the big-data cloud computing module jointly adopt a distributed computing architecture, and the distributed computing architecture framework comprises: the system comprises a Linux operating system, an Apache network server and a MySQL database, wherein Perl, PHP or Python programming languages are used for forming products and adopting open source software.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107590681A (en) * | 2017-08-16 | 2018-01-16 | 佛山市高研信息技术有限公司 | A kind of big data analysis system |
| CN108334599A (en) * | 2018-01-31 | 2018-07-27 | 佛山市聚成知识产权服务有限公司 | A kind of analysis system based on big data |
| CN108446306A (en) * | 2018-01-31 | 2018-08-24 | 佛山市聚成知识产权服务有限公司 | A kind of processing equipment of big data |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107590681A (en) * | 2017-08-16 | 2018-01-16 | 佛山市高研信息技术有限公司 | A kind of big data analysis system |
| CN108334599A (en) * | 2018-01-31 | 2018-07-27 | 佛山市聚成知识产权服务有限公司 | A kind of analysis system based on big data |
| CN108446306A (en) * | 2018-01-31 | 2018-08-24 | 佛山市聚成知识产权服务有限公司 | A kind of processing equipment of big data |
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Application publication date: 20211022 |