CN114510526A - Online numerical control exhibition method - Google Patents
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Abstract
The invention discloses an on-line numerical control exhibition method, which comprises the steps of firstly collecting data of various data sources through a data collection module, converting the data into files or messages to be transmitted backwards, transmitting the data to distributed storage or transmitting the data to a downstream data processing program in real time through a data dump module, cleaning the data, processing and converting formats and contents, sorting the data in a grading way and loading the data to a data warehouse module through an ETL module, accessing and managing the data through a data warehouse module, recording and restricting meanings and formats of the data in the data warehouse through a metadata management module, controlling the life cycle and the data quality of the data, executing various analysis sentences or codes through an analysis engine module to complete analysis tasks, extracting the data from the database according to analysis and mining targets, and organizing the data into a wide table suitable for an analysis and mining algorithm through the ETL module, and then, mining is carried out by using data mining software, so that the data mining efficiency is improved to a certain extent.
Description
Technical Field
The invention belongs to the technical field of big data processing, and particularly relates to an online numerical control exhibition method.
Background
The visiting exhibition is accompanied by the limitation of time and space, which is inconvenient, a system capable of visiting the exhibition on line exists in the related technology, the big data analysis and mining technology is usually the extraction of the value of mass data which is complicated and complicated, and the most valuable place is predictive analysis, namely, data scientists can be helped to better understand data through data mining modes such as data visualization, statistical pattern recognition and data description, and a predictive decision is obtained according to the data mining result.
However, the traditional data mining software can only support small-scale data processing on a single computer generally, and the traditional data analysis mining is limited by the limitation that the sampling mode is generally adopted to reduce the data analysis scale and reduce the data mining efficiency; therefore, an online numerical control exhibition method is provided to solve the existing problems.
Disclosure of Invention
The invention aims to provide an online numerical control exhibition method, which solves the problems in the background technology through the cooperation of a data collection module, a data unloading module, an ETL module, a data warehouse module, a metadata management module, an analysis engine module, an operation management and scheduling module and a resource allocation and scheduling module.
In order to achieve the purpose, the invention adopts the following technical scheme:
an on-line numerical control exhibition method comprises the following steps:
s1, collecting data of various data sources through a data collection module, converting the data into files or messages and transmitting the files or messages;
s2, the data is transferred to the distributed storage in fixed time or transferred to the downstream data processing program in real time through the data unloading module;
s3, cleaning data, processing and converting format and content, sorting data by grades, and loading to a data warehouse module through an ETL module;
s4, accessing and managing the data through the data warehouse module;
s5, recording and restricting the meaning and format of data in the data warehouse through the metadata management module, and controlling the life cycle and quality of the data;
s6, executing various analysis sentences or codes through the analysis engine module to complete the analysis task;
s7, analyzing the management and timing scheduling of the job through a job management and scheduling module;
and S8, effectively coordinating and distributing the cluster resources under the scene of simultaneous operation of multiple jobs through the resource distribution and scheduling module.
Preferably, in step 1, the data collection module comprises a data collector, the data collector comprises a temperature and humidity sensor, a dust concentration sensor and a pressure sensor, and the temperature and humidity sensor, the dust concentration sensor and the pressure sensor are respectively and electrically connected with the data unloading module.
Preferably, the data unloading module in step 2 includes a cloud server, and the data stored by the cloud server includes imported form data, filled-in personnel data, and data collected by the data collector.
Preferably, the ETL module in step 3 performs operations of parsing, extracting and cleaning the received data, wherein the extracting is because the acquired data may have various structures and types, and the data extracting process can help us convert these complex data into a single or easily processed configuration, so as to achieve the purpose of rapid analysis and processing; cleansing is for large data and is not all valuable, and some data is not of interest.
Preferably, the data warehouse module in step 4 is used for storing the collected data by using a memory for storing and managing the big data, establishing a corresponding database, managing and calling, developing a reliable distributed file system, storing energy efficiency optimization, calculating and integrating storage, removing redundancy of the big data, and storing the big data with high efficiency and low cost.
Preferably, the metadata management module in step 5 includes a program host, and the program host extracts data from the database according to the analysis mining target, organizes the data into a wide table suitable for an analysis mining algorithm through the ETL module, and then performs mining by using data mining software.
Preferably, both ends of the program host are respectively and electrically connected with a monitoring camera and a weather real-time monitor, the monitoring camera is used for monitoring a picture interface, and the weather real-time monitor is used for monitoring past weather data and predicting future weather data.
Preferably, the program host, when in use, comprises the following steps:
a1, starting a program, inputting an account password set by the current program, clicking to log in, and starting a data control interface after the program is a moment;
a2, whether the setting of the program password configures text setting or not;
a3, entering a numerical control main interface, wherein the numerical control interface is divided into a plurality of layouts, and each layout is responsible for different data display;
a4, switching each layout by clicking an upper button of the interface;
a5, clicking a camera icon to enter a monitoring picture;
and A6, contacting the left and right small triangles, and popping up jump buttons of the detailed numerical control layout and the subarea numerical control panel.
Preferably, the resource allocation and scheduling module in step 8 includes an internet of things layer, an edge computing layer and a cloud computing layer, where the internet of things layer is a sensor and a processor that sense, measure and collect raw data according to application requirements, process a large amount of data locally or upload the data to a computing node; the edge computing layer is positioned at the edge of the Internet and close to a data source, edge computing nodes are connected with the Internet of things, the edge nodes can communicate with each other, abstract computing and storage functions are provided, the edge nodes are divided into computing and gateway nodes, the computing nodes comprise control, computing and communication modules and are responsible for receiving and processing application requests, and the gateway nodes comprise expected rating and application allocation units and are responsible for evaluating the priority of the application requests and allocating processing nodes for the applications; the cloud computing layer is characterized in that the computing and storage capacity of the cloud computing center is higher than that of a fog node, and the service of centralized computing and storage with high polymerization degree is provided.
Preferably, in step 6, the analysis engine module performs data mining according to a main model, outputs a result set, and performs appropriate optimization, simplification, and grid-based reinforcement according to implementation requirements and conditions of a physical computing environment, wherein the main model includes a data model in the category of machine learning, deep learning, or reinforcement learning; in step 7, the job management and scheduling module includes adding, deleting, modifying, checking and modifying history of the job, setting scheduling timing and executing an engine when analyzing the management of the job.
Compared with the prior art, the on-line numerical control exhibition method provided by the invention has the following advantages:
the invention mainly collects data of various data sources through a data collection module, converts the data into files or messages to be transmitted backwards, transmits the data to a distributed storage or transmits the data to a downstream data processing program in real time through a data dump module, cleans the data, processes and converts formats and contents, sorts the data in a grading way through an ETL module, loads the data to a data warehouse module, accesses and manages the data through the data warehouse module, records and restricts meanings and formats of the data in the data warehouse through a metadata management module, controls life cycle and data quality of the data, executes various analysis statements or codes through an analysis engine module to finish analysis tasks, analyzes management and timing scheduling of jobs through a job management and scheduling module, and under the scene of simultaneous operation of multiple jobs through a resource allocation and scheduling module, resources of the cluster are effectively coordinated and distributed, data are extracted from a database according to an analysis mining target, then the data are organized into a wide table suitable for an analysis mining algorithm through an ETL module, and then data mining software is used for mining, so that the data mining efficiency is improved to a certain extent.
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FIG. 1 is a schematic structural diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an online numerical control exhibition method, which comprises the following steps:
s1, collecting data of various data sources through a data collection module, converting the data into files or messages and transmitting the files or messages;
the data collection module comprises a data collector, the data collector comprises a temperature and humidity sensor, a dust concentration sensor and a pressure sensor, the temperature and humidity sensor, the dust concentration sensor and the pressure sensor are respectively electrically connected with the data unloading module, and the temperature and humidity, the dust concentration and the pressure are collected through the temperature and humidity sensor, the dust concentration sensor and the pressure sensor.
S2, the data is transferred to the distributed storage in fixed time or transferred to the downstream data processing program in real time through the data unloading module;
the data unloading module comprises a cloud server, and the data stored by the cloud server comprises imported form data, filled personnel data and data collected by the data collector.
S3, cleaning data, processing and converting format and content, grading and sorting data, and loading to data warehouse module through ETL module;
the ETL module is used for completing the operations of analyzing, extracting and cleaning received data, wherein the extraction is because the acquired data can have various structures and types, and the data extraction process can help people convert the complex data into a single or convenient-to-process configuration so as to achieve the purpose of rapid analysis and processing; cleansing is for large data and is not all valuable, and some data is not of interest.
S4, accessing and managing the data through the data warehouse module;
the data warehouse module is used for storing the collected data by a storage device for big data storage and management, establishing a corresponding database, managing and calling, developing a reliable distributed file system, storing energy efficiency optimization, calculating and integrating storage, removing redundancy of big data, and storing the big data with high efficiency and low cost.
S5, recording and restricting the meaning and format of data in the data warehouse through the metadata management module, and controlling the life cycle and quality of the data;
the metadata management module comprises a program host, wherein the program host extracts data from a database according to an analysis mining target, organizes the data into a wide table suitable for an analysis mining algorithm through an ETL module, and then performs mining by using data mining software;
the two ends of the program host are respectively and electrically connected with a monitoring camera and a weather real-time monitor, the monitoring camera is used for monitoring a monitoring picture interface, and the weather real-time monitor is used for monitoring past weather data and predicting future weather data;
when the program host is used, the method comprises the following steps:
a1, starting a program, inputting an account password set by the current program, clicking to log in, and starting a data control interface after the program is a moment;
a2, whether the setting of the program password configures text setting or not;
a3, entering a numerical control main interface, wherein the numerical control interface is divided into a plurality of layouts, and each layout is responsible for different data display;
a4, switching each layout by clicking an upper button of the interface;
a5, clicking a camera icon to enter a monitoring picture;
and A6, contacting the left and right small triangles, and popping up jump buttons of the detailed numerical control layout and the subarea numerical control panel.
S6, executing various analysis sentences or codes through the analysis engine module to complete the analysis task;
the analysis engine module performs data mining according to a main model, outputs a result set, and performs appropriate optimization, simplification and grid strengthening according to implementation requirements and conditions of a physical computing environment, wherein the main model comprises a machine learning, deep learning or strengthened learning category data model.
S7, analyzing the management and timing scheduling of the job through a job management and scheduling module;
the job management and scheduling module comprises a job adding and deleting modification module, a modification history checking module, a scheduling timing setting module and an execution engine when analyzing the management of the job.
S8, effectively coordinating and distributing the cluster resources under the scene of simultaneous operation of multiple jobs through a resource distribution and scheduling module;
the resource allocation and scheduling module comprises an internet of things layer, an edge computing layer and a cloud computing layer, wherein the internet of things layer is formed by sensing, measuring and collecting original data by a sensor and a processor according to application requirements, and locally processing a large amount of data or uploading the data to a computing node; the edge computing layer is positioned at the edge of the Internet and is close to a data source, edge computing nodes are connected with the Internet of things, the edge nodes can communicate with each other, computing and storage function abstraction is provided, the edge nodes are divided into computing and gateway nodes, the computing nodes comprise control, computing and communication modules and are responsible for receiving and processing application requests, and the gateway nodes comprise expected rating and application allocation units and are responsible for evaluating the priority of the application requests and allocating processing nodes for the applications; the cloud computing layer is that the computing and storage capacity of the cloud computing center is stronger than that of a fog node, and provides centralized computing and storage services with high polymerization degree.
In summary, data of various data sources are collected through a data collection module, the data are converted into files or messages and transmitted backwards, the data are transmitted to a distributed storage or transmitted to a downstream data processing program in real time through a data dump module, the data are cleaned, processed and converted in format and content, sorted in a grading manner and loaded to a data warehouse module through an ETL module, the data are accessed and managed through the data warehouse module, the meaning and format of the data in the data warehouse are recorded and restricted through a metadata management module, the life cycle and the data quality of the data are controlled, the data are extracted from a database according to an analysis mining target, then a wide table suitable for an analysis mining algorithm is used through the ETL module, then mining is carried out through data mining software, certain data mining efficiency is improved, various analysis statements or codes are executed through an analysis engine module, and completing analysis tasks, analyzing management and timing scheduling of the jobs through the job management and scheduling module, and effectively coordinating and allocating resources of the cluster through the resource allocation and scheduling module under the scene of simultaneous operation of multiple jobs.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (10)
1. An on-line numerical control exhibition method is characterized in that: the method comprises the following steps:
s1, collecting data of various data sources through a data collection module, converting the data into files or messages and transmitting the files or messages;
s2, the data is transferred to the distributed storage in fixed time or transferred to the downstream data processing program in real time through the data unloading module;
s3, cleaning data, processing and converting format and content, sorting data by grades, and loading to a data warehouse module through an ETL module;
s4, accessing and managing the data through the data warehouse module;
s5, recording and restricting the meaning and format of data in the data warehouse through the metadata management module, and controlling the life cycle and quality of the data;
s6, executing various analysis sentences or codes through the analysis engine module to complete the analysis task;
s7, analyzing the management and timing scheduling of the job through a job management and scheduling module;
and S8, effectively coordinating and distributing the cluster resources under the scene of simultaneous operation of multiple jobs through the resource distribution and scheduling module.
2. An online numerical control exhibition method according to claim 1, characterized in that: in the step 1, the data collection module comprises a data collector, the data collector comprises a temperature and humidity sensor, a dust concentration sensor and a pressure sensor, and the temperature and humidity sensor, the dust concentration sensor and the pressure sensor are respectively and electrically connected with the data unloading module.
3. An online numerical control exhibition method according to claim 1, characterized in that: the data unloading module in the step 2 comprises a cloud server, and the data stored by the cloud server comprises imported form data, filled personnel data and data acquired by a data collector.
4. An in-line numerically controlled exhibition method according to claim 1, characterized in that: in the step 3, the ETL module completes the operations of analyzing, extracting and cleaning the received data, wherein the extraction is that the acquired data may have various structures and types, and the data extraction process can help us convert the complex data into a single or conveniently processed configuration so as to achieve the purpose of rapid analysis and processing; cleansing is for large data and is not all valuable, and some data is not of interest.
5. An online numerical control exhibition method according to claim 1, characterized in that: and 4, the data warehouse module stores the collected data by using a storage for storing and managing the big data, establishes a corresponding database, manages and calls the database, develops a reliable distributed file system, optimizes energy efficiency, stores the data by integrating calculation and calculation, removes redundancy of the big data, and stores the big data with high efficiency and low cost.
6. An online numerical control exhibition method according to claim 1, characterized in that: and 5, the metadata management module comprises a program host, the program host extracts data from the database according to the analysis mining target, organizes the data into a wide table suitable for an analysis mining algorithm through an ETL module, and then performs mining by using data mining software.
7. An online numerical control exhibition method according to claim 6, characterized in that: the monitoring camera is connected with a monitoring picture interface, and the weather real-time monitor is used for monitoring past weather data and predicting future weather data.
8. An in-line numerical control exhibition method according to claim 7, characterized in that: when the program host is used, the method comprises the following steps:
a1, starting a program, inputting an account password set by the current program, clicking to log in, and starting a data control interface after the program is a moment;
a2, whether the setting of the program password configures text setting or not;
a3, entering a numerical control main interface, wherein the numerical control interface is divided into a plurality of layouts, and each layout is responsible for different data display;
a4, switching each layout by clicking an upper button of the interface;
a5, clicking a camera icon to enter a monitoring picture;
and A6, contacting the left and right small triangles, and popping up jump buttons of the detailed numerical control layout and the subarea numerical control panel.
9. An online numerical control exhibition method according to claim 1, characterized in that: in step 8, the resource allocation and scheduling module comprises an internet of things layer, an edge computing layer and a cloud computing layer, wherein the internet of things layer is a sensor and a processor which sense, measure and collect original data according to application requirements, and processes a large amount of data locally or uploads the data to a computing node; the edge computing layer is positioned at the edge of the Internet and close to a data source, the edge computing nodes are connected with the Internet of things, the edge nodes can communicate with each other to provide computing and storage function abstraction, and are divided into computing and gateway nodes, wherein each computing node comprises a control module, a computing module and a communication module and is responsible for receiving and processing an application request, and each gateway node comprises an expected rating and application allocation unit and is responsible for evaluating the priority of the application request and allocating processing nodes to the application; the cloud computing layer is characterized in that the computing and storage capacity of the cloud computing center is higher than that of a fog node, and the service of centralized computing and storage with high polymerization degree is provided.
10. An online numerical control exhibition method according to claim 1, characterized in that: step 6, the analysis engine module performs data mining according to a main model, outputs a result set, and performs appropriate optimization, simplification and gridding reinforcement according to the implementation requirements and the conditions of the physical computing environment, wherein the main model comprises a machine learning, deep learning or reinforcement learning category data model; and 7, when analyzing the management of the job, the job management and scheduling module comprises the steps of adding, deleting, modifying, checking and modifying history of the job, setting scheduling timing and executing an engine.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220245274A1 (en) * | 2021-02-03 | 2022-08-04 | Cloudhedge Technologies Private Limited | System and method for detection of patterns in application for application transformation and applying those patterns for automated application transformation |
| CN115314514A (en) * | 2022-08-03 | 2022-11-08 | 江苏南工科技集团有限公司 | General module of thing networking data acquisition and conversion |
| CN115508649A (en) * | 2022-10-12 | 2022-12-23 | 广东浩博特科技股份有限公司 | Darkroom acousto-optic test system |
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- 2022-01-12 CN CN202210032301.7A patent/CN114510526A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220245274A1 (en) * | 2021-02-03 | 2022-08-04 | Cloudhedge Technologies Private Limited | System and method for detection of patterns in application for application transformation and applying those patterns for automated application transformation |
| CN115314514A (en) * | 2022-08-03 | 2022-11-08 | 江苏南工科技集团有限公司 | General module of thing networking data acquisition and conversion |
| CN115508649A (en) * | 2022-10-12 | 2022-12-23 | 广东浩博特科技股份有限公司 | Darkroom acousto-optic test system |
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