CN114996525A - Big data analysis method and system - Google Patents
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Abstract
The invention discloses a big data analysis method and a big data analysis system, which relate to the technical field of big data and comprise the following steps: s1, starting a system, collecting data and marking time by the system; s2, the system classifies the collected data; and S3, the system performs effectiveness screening on each type of data. The data classification unit is used for classifying the data, the data with different correlations are respectively sorted into different collections, the data processing efficiency is improved, the data screening unit is used for judging the effectiveness of the data, the data is subjected to effectiveness analysis processing, the effective data is reserved, the ineffective data is deleted, the interference of the ineffective data is reduced, the occupied space of useless storage is reduced, the data analysis efficiency is improved, the accuracy of the data analysis result is improved, the data prediction unit is used for performing simulation prediction on the data, the future data is subjected to simulation prediction, and the future change trend of the data is conveniently analyzed.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a big data analysis method and a big data analysis system.
Background
The big data is huge and massive data which is derived from English big data, direct translation is huge data, the big data can be captured, managed and analyzed in reasonable time to become beneficial data for helping enterprise operation decision, the big data has great influence on enterprise operation development, the capture and management of huge data information is not important, the important thing is that the huge data is analyzed, meaningful data is specialized, information with positive result on the enterprise is obtained by analysis, in other words, if the big data is compared with an industry, the key for realizing profitability of the industry is that the processing capacity of the data is improved, and the value increment of the data is realized through processing, namely, the most important part.
In the existing method, for analysis of big data, data information of the big data is usually directly adopted for direct analysis, and the obtained analysis result is prone to have a large error.
Disclosure of Invention
The present invention is directed to a method and a system for analyzing big data, so as to solve the problems mentioned in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a big data analysis method and a big data analysis system comprise the following steps:
s1, starting a system, collecting data and marking time by the system;
s2, the system classifies the collected data;
s3, the system carries out effectiveness screening on each type of data;
s4, the system predicts and simulates the change trend of the data according to the effective data;
s5, establishing a data table and a two-line time coordinate curve chart for the collected effective data and the predicted data;
and S6, storing the data table and the data graph into corresponding cells of the database respectively.
Further, in step S2, the collected data are classified according to their relevance, and relevant data are obtained and arranged into a data set: a ═ a 1 ，a 2 ，a 3 ...a n }，B＝{b 1 ，b 2 ，b 3 ...b n }，C＝{c 1 ，c 2 ，c 3 ...c n }，D＝{d 1 ，d 2 ，d 3 ...d n }...。
Further, in step S3, the data is processed as follows:
for example: data set a ═ a 1 ，a 2 ，a 3 ...a n And (6) setting ai as any data in the data set A, and obtaining a data mean value of the data set:
a i and a Are all made of The ratio of (A) to (B) is:
when 1/2<Z a <2 hour, data a i Reserving data for valid data;
when Z is a 1/2 or Z is less than or equal to a When the number is more than or equal to 2, the data a i For invalid data, delete processing is performed
...
For example: data set D ═ D 1 ，d 2 ，d 3 ...d n And D, di is any data in the data set D, and the data mean value of the data set is obtained:
d i and d Are all made of The ratio of (A) to (B) is:
when 1/2<Z d <2 hour, data d i Reserving data for valid data;
when Z is d 1/2 or Z is less than or equal to d When the value is more than or equal to 2, the data d i Deleting the invalid data;
the data is validated and invalid data is deleted quickly, so that the accuracy of the data is improved, the interference of the invalid data is reduced, and the occupied space of useless storage is reduced.
Further, in step S4, the data is subjected to simulation prediction as follows:
let the predicted temperature dataset be E ═ K 1 ，K 2 ，K 3 ...K n }, set K i Any data is predicted data;
with the latest 100 data as the prediction reference,
further, in the step S5, the steps of creating the data table of the valid data and the data predicted by the simulation and creating the two-line time coordinate graph are as follows:
establishing an effective data coordinate graph by taking the data time as an X axis of the coordinate graph and taking the data value as a Y axis, and connecting the effective data by using a curve to obtain an effective data change curve graph;
establishing a predicted data coordinate graph by taking the data time as an X axis of the coordinate graph and the predicted data value as a Y axis, and connecting the predicted data by using a curve to obtain a predicted data change curve graph;
the two curves are combined in one curve graph, so that a double-line time coordinate curve graph can be obtained, the data is more visual and strong in visualization, and the actual change curve of the data and the change curve of the predicted data can be intuitively known.
The system comprises: cloud platform, data acquisition module, time mark module, data analysis module, database and intelligent terminal, the cloud platform is used for carrying out high in the clouds formula management to the system, data acquisition module is used for gathering the big data and obtains, and the time mark module is used for carrying out the time mark to data, data analysis module is used for integrating the processing to data analysis, and the database is used for classified storage data and provides system data for cloud platform and intelligent terminal, and intelligent terminal is used for control system and looks over system data, the input of cloud platform is connected with data acquisition module, time mark module, data analysis module, database and intelligent terminal's output respectively, the output of cloud platform is connected with data acquisition module, time mark module, data analysis module, database and intelligent terminal's input respectively.
Further, the cloud platform comprises a data receiving and sending unit, a central processing unit and a storage unit, wherein the data receiving and sending unit is used for receiving and sending data, the central processing unit is used for integrating, analyzing and processing the data, and the storage unit is used for temporarily storing the data.
Further, the data analysis module comprises a data classification unit, a data screening unit and a data prediction unit, wherein the data classification unit is used for classifying and screening out relevant data to obtain a set of required data, the data screening unit is used for judging the validity of the data and deleting invalid data to obtain reserved valid data, and the data prediction unit is used for predicting the data.
Further, the database includes a data table unit for storing a table of valid data and predicted data and a data graph unit for storing a two-line time coordinate graph of the valid data and the predicted data.
Furthermore, the intelligent terminal comprises an input unit and a display unit, wherein the input unit is used for controlling system switching and data modification, and the display unit is used for displaying data tables and data graphs in a database of the system.
Compared with the prior art, the invention has the following beneficial effects:
1. the big data analysis method and system classify data through the data classification unit, respectively arrange different sets of data with different correlations, improve data processing efficiency, utilize the data screening unit to carry out validity judgment on the data, carry out validity analysis processing on the data, retain valid data, delete invalid data, reduce invalid data interference, reduce useless storage occupation space, improve data analysis efficiency, improve the accuracy of data analysis results, utilize the data prediction unit to carry out simulation prediction on the data, carry out simulation prediction on future data, and facilitate the analysis of future change trends of the data.
2. The method and the system for analyzing the big data utilize an X axis taking data time as a coordinate graph, establish an effective data coordinate graph taking a data value as a Y axis, connect the effective data by using a curve to obtain an effective data change curve graph, establish a predicted data coordinate graph taking the data time as the X axis of the coordinate graph and the predicted data value as the Y axis, connect the predicted data by using a curve to obtain a predicted data change curve graph, combine the predicted data change curve graph and the two curves in one curve graph, so that a double-line time coordinate curve graph can be obtained.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic view of the overall operation of the present invention;
fig. 2 is a schematic diagram of the connection of the module structure of the system of the present invention.
In the figure: 1. a cloud platform; 11. a data transmitting/receiving unit; 12. a central processing unit; 13. a storage unit; 2. a data acquisition module; 3. a time stamping module; 4. a data analysis module; 41. a data classification unit; 42. a data screening unit; 43. a data prediction unit; 5. a database; 51. a data table unit; 52. a data graphic unit; 6. an intelligent terminal; 61. an input unit; 62. a display unit.
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. 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 big data analysis method and system shown in fig. 1-2 comprise the following steps:
s1, starting a system, collecting data and marking time by the system;
s2, the system classifies the collected data;
s3, the system carries out effectiveness screening on each type of data;
s4, the system predicts and simulates the change trend of the data according to the effective data;
s5, establishing a data table and a two-line time coordinate curve chart for the collected effective data and the predicted data;
s6, respectively storing the data table and the data graph into corresponding units of a database;
in step S2, the collected data are classified according to their relevance, and relevant data are obtained and arranged into a data set: a ═ a 1 ，a 2 ，a 3 ...a n }，B＝{b 1 ，b 2 ，b 3 ...b n }，C＝{c 1 ，c 2 ，c 3 ...c n }，D＝{d 1 ，d 2 ，d 3 ...d n }...；
In step S3, the data is processed as follows:
for example: data set a ═ a 1 ，a 2 ，a 3 ...a n And (6) setting ai as any data in the data set A, and obtaining a data mean value of the data set:
a i and a Are all made of The ratio of (A) to (B) is:
when 1/2<Z a <2 hour, data a i Reserving data for valid data;
when Z is a 1/2 or Z is less than or equal to a When the data is more than or equal to 2, the data a i For invalid data, delete processing is performed
...
For example: data set D ═ D 1 ，d 2 ，d 3 ...d n And D, D is any data in the data set D, and the data mean value of the data set is obtained:
d i and d Are all made of The ratio of (A) to (B) is:
when 1/2<Z d <2 hour, data d i Reserving data for valid data;
when Z is d 1/2 or Z is less than or equal to d When the value is more than or equal to 2, the data d i Deleting the invalid data;
the data is validated, invalid data is deleted quickly, the accuracy of the data is improved, the interference of the invalid data is reduced, and the occupied space of useless storage is reduced;
in step S4, the data is subjected to simulation prediction as follows:
let the predicted temperature dataset be E ═ K 1 ，K 2 ，K 3 ...K n }, set K i Any data is predicted data;
taking the latest 100 data as a prediction reference,
in step S5, the steps of creating a data table of the valid data and the data predicted by the simulation and creating a two-line time coordinate graph are as follows:
establishing an effective data coordinate graph by taking the data time as an X axis of the coordinate graph and taking the data value as a Y axis, and connecting the effective data by using a curve to obtain an effective data change curve graph;
establishing a predicted data coordinate graph by taking the data time as an X axis of the coordinate graph and the predicted data value as a Y axis, and connecting the predicted data by using a curve to obtain a predicted data change curve graph;
the two curves are combined in one curve graph, so that a double-line time coordinate curve graph can be obtained, the data is more visual and strong in visualization, and the actual change curve of the data and the change curve of the predicted data can be intuitively known.
The system comprises: cloud platform 1, data acquisition module 2, time mark module 3, data analysis module 4, database 5 and intelligent terminal 6, cloud platform 1 is used for carrying out high in the clouds formula management to the system, data acquisition module 2 is used for gathering the big data and obtains, and time mark module 3 is used for carrying out the time mark to data, data analysis module 4 is used for integrating the processing to data analysis, and database 5 is used for categorised storage data and provides system data for cloud platform 1 and intelligent terminal 6, and intelligent terminal 6 is used for control system and looks over system data, the input of cloud platform 1 is connected with data acquisition module 2, time mark module 3, data analysis module 4, database 5 and intelligent terminal 6's output respectively, the output of cloud platform 1 respectively with data acquisition module 2, time mark module 3, data analysis module 4, The database 5 is connected with the input end of the intelligent terminal 6;
the cloud platform comprises a data transceiving unit 11, a central processing unit 12 and a storage unit 13, wherein the data transceiving unit 11 is used for transceiving data, the central processing unit 12 is used for integrating, analyzing and processing the data, and the storage unit 13 is used for temporarily storing the data.
The data analysis module 4 includes a data classification unit 41, a data screening unit 42 and a data prediction unit 43, the data classification unit 41 is configured to classify and screen out relevant data to obtain a set of required data, the data screening unit 42 is configured to judge validity of the data and delete invalid data to obtain retained valid data, and the data prediction unit 43 is configured to predict the data;
the database 5 comprises a data table unit 51 and a data graph unit 52, wherein the data table unit 51 is used for storing tables of valid data and predicted data, and the data graph unit 52 is used for storing two-line time coordinate graphs of the valid data and the predicted data;
the intelligent terminal 6 comprises an input unit 61 and a display unit 62, wherein the input unit 61 is used for controlling system switches and data modification, and the display unit 62 is used for displaying data tables and data graphs in a database of the system.
The working principle of the invention is as follows:
referring to the attached drawings 1-2 of the specification, data are classified by a data classification unit 41, data with different correlations are respectively arranged into different sets, data processing efficiency is improved, data validity judgment is performed by a data screening unit 42, data are subjected to validity analysis processing, valid data are reserved, invalid data are deleted, invalid data interference is reduced, useless storage occupation space is reduced, data analysis efficiency is improved, accuracy of data analysis results is improved, data are subjected to simulation prediction by a data prediction unit 43, future data are subjected to simulation prediction, future change trend of the data is conveniently analyzed, an effective data coordinate graph is established by using data time as an X axis of a coordinate graph and data values are used as Y axes, the valid data are connected by curves to obtain an effective data change curve graph, the data time is used as the X axis of the coordinate graph, the predicted data value is used as the Y axis to establish a predicted data coordinate graph, the predicted data is connected through a curve to obtain a predicted data change curve graph, the predicted data change curve graph and the predicted data change curve graph are combined in one curve graph, namely a two-line time coordinate curve graph can be obtained, the data is more visual and strong in visualization, the actual change curve of the data and the change curve of the predicted data can be visually known, the data form unit 51 and the data graphic unit 52 are used for storing the data form and the graph respectively, data storage and management are facilitated, and system data obtaining and watching are facilitated.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
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 changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A big data analysis method and a system are characterized in that: the method comprises the following steps:
s1, starting a system, collecting data and marking time by the system;
s2, the system classifies the collected data;
s3, the system carries out effectiveness screening on each type of data;
s4, the system predicts and simulates the change trend of the data according to the effective data;
s5, establishing a data table for the collected effective data and the predicted data and establishing a two-line time coordinate curve chart;
and S6, storing the data table and the data graph into corresponding cells of the database respectively.
2. The big data analysis method and system according to claim 1, wherein: in step S2, the collected data are classified according to their relevance, and relevant data are obtained and arranged into a data set: a ═ a 1 ，a 2 ，a 3 ...a n }，B＝{b 1 ，b 2 ，b 3 ...b n }，C＝{c 1 ，c 2 ，c 3 ...c n }，D＝{d 1 ，d 2 ，d 3 ...d n }...。
3. The big data analysis method and system according to claim 2, wherein: in step S3, the data is processed as follows:
for example: data set a ═ a 1 ，a 2 ，a 3 ...a n And (6) setting ai as any data in the data set A, and obtaining a data mean value of the data set:
a i and a Are all made of The ratio of (A) to (B) is:
when 1/2<Z a <2 hour, data a i Reserving data for valid data;
when Z is a 1/2 or Z is less than or equal to a When the data is more than or equal to 2, the data a i For invalid data, delete processing is performed
...
For example: data set D ═ D 1 ，d 2 ，d 3 ...d n And D, di is any data in the data set D, and the data mean value of the data set is obtained:
d i and d Are all made of The ratio of (A) to (B) is:
when 1/2<Z d <2 hour, data d i Reserving data for valid data;
when Z is d 1/2 or Z is less than or equal to d When the value is more than or equal to 2, the data d i Deleting the invalid data;
the data is validated and invalid data is deleted quickly, so that the accuracy of the data is improved, the interference of the invalid data is reduced, and the occupied space of useless storage is reduced.
4. The big data analysis method and system according to claim 3, wherein: in step S4, the data is subjected to simulation prediction as follows:
let the predicted temperature dataset be E ═ K 1 ，K 2 ，K 3 ...K n }, set K i Any data is predicted data;
with the latest 100 data as the prediction reference,
5. the big data analysis method and system according to claim 1, wherein: in step S5, the steps of creating a data table of the valid data and the data predicted by simulation and creating a two-line time coordinate graph are as follows:
establishing an effective data coordinate graph by taking the data time as an X axis of the coordinate graph and taking the data value as a Y axis, and connecting the effective data by using a curve to obtain an effective data change curve graph;
establishing a predicted data coordinate graph by taking the data time as an X axis of the coordinate graph and the predicted data value as a Y axis, and connecting the predicted data by using a curve to obtain a predicted data change curve graph;
the two curves are combined in one curve graph, so that a double-line time coordinate curve graph can be obtained, the data is more visual and strong in visualization, and the actual change curve of the data and the change curve of the predicted data can be intuitively known.
6. The big data analysis method and system according to claim 1, wherein: the system comprises: the cloud platform comprises a cloud platform (1), a data acquisition module (2), a time marking module (3), a data analysis module (4), a database (5) and an intelligent terminal (6), wherein the cloud platform (1) is used for carrying out cloud management on the system, the data acquisition module (2) is used for acquiring and obtaining big data, the time marking module (3) is used for carrying out time marking on the data, the data analysis module (4) is used for analyzing and integrating the data, the database (5) is used for storing the data in a classified mode and providing system data for the cloud platform (1) and the intelligent terminal (6), the intelligent terminal (6) is used for controlling the system and checking the system data, the input end of the cloud platform (1) is respectively connected with the output ends of the data acquisition module (2), the time marking module (3), the data analysis module (4), the database (5) and the intelligent terminal (6), the output end of the cloud platform (1) is connected with the input ends of the data acquisition module (2), the time marking module (3), the data analysis module (4), the database (5) and the intelligent terminal (6) respectively.
7. The big data analysis method and system according to claim 1, wherein: the cloud platform comprises a data transceiving unit (11), a central processing unit (12) and a storage unit (13), wherein the data transceiving unit (11) is used for transceiving data, the central processing unit (12) is used for integrating, analyzing and processing the data, and the storage unit (13) is used for temporarily storing the data.
8. The big data analysis method and system according to claim 1, wherein: the data analysis module (4) comprises a data classification unit (41), a data screening unit (42) and a data prediction unit (43), wherein the data classification unit (41) is used for classifying and screening out relevant data to obtain a set of required data, the data screening unit (42) is used for judging the validity of the data and deleting invalid data to obtain reserved valid data, and the data prediction unit (43) is used for predicting the data.
9. The big data analysis method and system according to claim 1, wherein: the database (5) comprises a data table unit (51) and a data graph unit (52), wherein the data table unit (51) is used for storing tables of valid data and predicted data, and the data graph unit (52) is used for storing a two-line time coordinate graph of the valid data and the predicted data.
10. The big data analysis method and system according to claim 1, wherein: the intelligent terminal (6) comprises an input unit (61) and a display unit (62), wherein the input unit (61) is used for controlling system switching and data modification, and the display unit (62) is used for displaying data tables and data graphs in a database of the system.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116156775A (en) * | 2023-04-19 | 2023-05-23 | 圆周率半导体(南通)有限公司 | Method for improving etching uniformity based on big data analysis |
| CN116627959A (en) * | 2023-07-26 | 2023-08-22 | 合肥思迈科技有限公司 | Method for clearing operation history data of movable ring equipment of machine room |
-
2022
- 2022-06-08 CN CN202210640926.1A patent/CN114996525A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116156775A (en) * | 2023-04-19 | 2023-05-23 | 圆周率半导体(南通)有限公司 | Method for improving etching uniformity based on big data analysis |
| CN116627959A (en) * | 2023-07-26 | 2023-08-22 | 合肥思迈科技有限公司 | Method for clearing operation history data of movable ring equipment of machine room |
| CN116627959B (en) * | 2023-07-26 | 2023-10-13 | 合肥思迈科技有限公司 | Method for clearing operation history data of movable ring equipment of machine room |
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