CN110674302B - Intelligent data classification and cooperation method based on big data analysis - Google Patents
Intelligent data classification and cooperation method based on big data analysis Download PDFInfo
- Publication number
- CN110674302B CN110674302B CN201910944836.XA CN201910944836A CN110674302B CN 110674302 B CN110674302 B CN 110674302B CN 201910944836 A CN201910944836 A CN 201910944836A CN 110674302 B CN110674302 B CN 110674302B
- Authority
- CN
- China
- Prior art keywords
- data
- label
- evaluation
- classification
- background
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/55—Clustering; Classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to a data intelligent classification and cooperation method based on big data analysis, which comprises the following steps that S100, in the industrial control process of an enterprise, data of industrial control are collected from three dimensions of set data, operation data and evaluation data through a collection end and are sent to a background end, an evaluation label is added after the set data are collected, the set label is added after the operation data are collected, and the operation label is added after the evaluation data are collected; s200, after receiving the data at the background end, intercepting and replacing the label of the received data, replacing the evaluation label with a setting label, replacing the setting label with an operation label, and replacing the operation label with the evaluation label. The label of the invention is convenient for classification cooperation after receiving data, reduces repeated data and improves the accuracy of data classification.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data intelligent classification and cooperation method based on big data analysis.
Background
With the rapid development of the internet, the network office of an enterprise is more and more common, and the amount of data generated thereby is also huge, and some enterprises also set branches or subsidiaries in different places, and the data of the branches or subsidiaries needs to be converged and summarized with the enterprise, and then the enterprise manages the total data, for example, the industrial control data of the enterprise, the format, type, size and the like of the data can be uniformly specified by the enterprise, so as to facilitate the subsequent management of the enterprise.
At present, for some industries, data of different enterprises need to be integrated, and industrial guidance information is obtained by analyzing data of a plurality of enterprises, but data from different enterprise data sources are difficult to unify, and wrong guidance information is easily obtained by analyzing the non-unified data.
Disclosure of Invention
The invention aims to provide a data intelligent classification and collaboration method based on big data analysis, so as to collaborate data from different data sources.
The intelligent data classification and collaboration method based on big data analysis comprises the following steps:
s100, in the industrial control process of an enterprise, acquiring industrial control data from three dimensions of set data, operation data and evaluation data through an acquisition end and sending the industrial control data to a background end, adding an evaluation label after the set data is acquired, adding the set label to the acquired operation data, and adding the operation label to the acquired evaluation data;
s200, after receiving the data at the background end, intercepting and replacing the label of the received data, replacing the evaluation label with a setting label, replacing the setting label with an operation label, and replacing the operation label with the evaluation label.
The beneficial effect of this method scheme is: the acquired various data are sent by adding the transposed tags, so that the data are confused, the probability that the data are accurately identified after being intercepted is reduced, the data safety is improved, the tags are replaced after the data are received at the background end, the tags are convenient to classify after receiving the data, the confused tags are automatically added when the data are acquired, the tags are automatically returned in the subsequent process, and the data classification is more intelligent while the data safety is improved.
Further, in the content S100, the field picture of the industrial control is collected through the collection terminal while data is collected, and the field picture is added with the actual tag and sent to the background terminal.
The beneficial effects are that: the data acquisition and the field picture verification are carried out simultaneously, and the industrial control process is verified through the field picture, so that enterprises can conveniently collect corresponding propaganda information.
Further, the content S300 is included, the cloud network end acquires the live picture and the operation data of the background end, the live picture is matched with the function picture transmitted by the shopping network through the cloud network end, and when the matching is successful, the transmission of the function picture is suspended.
The beneficial effects are that: the on-site pictures and the functional pictures are matched, the on-site pictures are stolen if the matching is successful, the transmission of the functional pictures is stopped at the moment, the on-site pictures are prevented from being stolen, the publicity of products which are not really famous is reduced, repeated pictures on a network can be reduced, and the cooperativity of the on-site pictures during network transmission is improved, so that an enterprise is guided to actively record the on-site pictures in the industrial production process, and the publicity effect of products per se is improved.
Further, when the matching is successful, the cloud network side acquires the address information of the successfully matched functional picture, and sends the functional picture and the address information to the background side.
The beneficial effects are that: the address information of the functional pictures is sent to the background end, so that the enterprise can know someone stealing the picture in time, and the enterprise can monitor the pirating situation in the data classification process conveniently.
And further comprising S400, matching the data of the three dimensions pairwise by the background end, and adding an abnormal label to the data which fails to be matched and storing the data when the data of any two dimensions fails to be matched.
The beneficial effects are that: the data of the three dimensions are matched pairwise, abnormal labels are added for storage when the matching fails, the data with abnormal dimensions are separated from normal data, and classification analysis of different data is facilitated.
Further, in the content S400, when the data matching of any two dimensions is successful, time information is added to the successfully matched data and the successfully matched data is stored.
The beneficial effects are that: and the successfully matched data is added with time information and then stored, so that the data category can be visually distinguished.
Further, in the content S100, when data is transmitted, a single set data, a continuous operation data, and a single evaluation data are transmitted in a group.
The beneficial effects are that: the operation data generated by the same set data and the evaluation data aiming at the same set data are sent in groups, so that the industrial control effect brought by the set data can be visually distinguished, and the disorder caused by different set data corresponding to different operation data is avoided.
Further, in the content S200, the background end averages the continuous running data, the background end determines whether the average value is within a threshold range, when the average value deviates from the threshold range, the background end determines whether each running data is within the threshold range, and when at least one running data is outside the threshold range, the background end stores the group of data adding part tags.
The beneficial effects are that: the data with partial abnormity of the operation data are separately stored, so that analysis errors caused by mixing of the data with abnormity in the data without abnormity are avoided.
Further, the set data is matched with a pre-stored limited range through a background terminal, and when the set data is out of the limited range, the set data is added with an error label for storage.
The beneficial effects are that: whether the set data is correct is judged firstly, then an error label is added for storage, and other data are not judged directly when the set data is in error, so that the program is saved.
Further, when the set data is located outside the limited range, the back end acquires the personnel information in the evaluation data and adds the personnel information as an error label.
The beneficial effects are that: when the set data is wrong, the personnel information is added as an error label, so that the responsible person can be visually seen, and the follow-up responsibility tracing is facilitated.
Drawings
FIG. 1 is a flow chart of a first embodiment of a big data analysis-based intelligent data classification and collaboration method of the present invention;
fig. 2 is a front view of a steering mechanism in a second embodiment of the intelligent data classification and collaboration method based on big data analysis according to the present invention.
Detailed Description
The following is a more detailed description of the present invention by way of specific embodiments.
Reference numerals in the drawings of the specification include: the device comprises a supporting plate 1, a rotating shaft 2, a driven gear 3, a driving gear 4, a motor 5 and a rotating bearing 6.
Example one
The intelligent data classification and collaboration method based on big data analysis, as shown in fig. 1, includes the following contents:
s100, in the industrial control process of an enterprise, acquiring industrial control data from three dimensions of set data, operation data and evaluation data through an acquisition end and sending the industrial control data to a background end, wherein the industrial control data acquisition can be realized by using the conventional NORCO North China industrial control system, the data is acquired through an acquisition module with the model of C-4017+, an evaluation tag is added after the set data is acquired, a set tag is added after the operation data is acquired, an operation tag is added after the evaluation data is acquired, the tag can be represented in a digital sequence form, for example, the evaluation tag is represented as 0104, the set tag is represented as 0203, the operation tag is represented as 0502, single set data, continuous operation data and single evaluation data are sent in groups, the field picture of the industrial control is acquired through the acquisition end at the same time of acquiring the data, the field picture is added with an actual tag and sent to the background end, the data transmission between the acquisition end and the background end can be carried out in a bus mode;
s200, after receiving data at a background end, intercepting and replacing a tag of the received data, replacing an evaluation tag with a set tag, replacing the set tag with an operation tag, replacing the operation tag with the evaluation tag, averaging continuous operation data through the background end, wherein the operation data can be power, rotating speed and the like during operation, judging whether the average value is within a threshold range by the background end, judging whether each operation data is within the threshold range by the background end when the average value deviates from the threshold range, and storing a part of tags of the group of data by the background end when at least one operation data is outside the threshold range;
s300, acquiring a controlled field picture through an acquisition end and sending the controlled field picture to a background end, wherein the field picture can be acquired through a camera, the field picture and running data of the background end are acquired through a cloud network end, the background end and the cloud network end can communicate with the Internet, the field picture is matched with a function picture transmitted by a shopping network through the cloud network end, the function picture can be a display picture during network shopping, particularly a production and processing process record aiming at some organic or green products, when the matching is successful, the transmission of the function picture is suspended, the address information of the successfully matched function picture is acquired through the cloud network end, and the function picture and the address information are sent to the background end;
s400, matching the set data with a pre-stored limited range through a background terminal, when the set data is out of the limited range, adding an error label to the set data for storage, acquiring personnel information in the evaluation data by the background terminal to serve as the error label for addition, matching the data of three dimensions in pairs through the background terminal, when the data of any two dimensions fails to be matched, adding an abnormal label to the data which fails to be matched, storing the data which fails to be matched, and adding time information to the data which succeeds in matching and storing the data.
This embodiment one sends the various data of gathering through adding the label after the transposition, with this chaotic data, reduce the probability after being accurately discerned after the data is intercepted, and change the label after the data is received to the background end, the classification after the label is convenient for receive the data is in coordination, match scene picture and function picture, match success promptly the scene picture stolen, stop the conveying of function picture this moment, prevent that the scene picture is stolen, reduce the product publicity of mismatching, and guide the enterprise to initiatively take notes the scene picture in the industrial production process, avoid being used for the publicity by other people's stealing picture, reduce false publicity and the product publicity of mismatching, in order to improve the publicity effect of self product.
Example two
The difference from the first embodiment is that, as shown in fig. 2, during data acquisition of industrial control, a produced workpiece is scanned in a turning manner through a turning mechanism, the turning mechanism comprises a support plate 1, the workpiece is placed on the support plate 1 for scanning, a rotating shaft 2 is welded at the bottom of the support plate 1, a driven gear 3 is connected to the middle of the rotating shaft 2 in a key manner, a rotating bearing 6 is connected to the end portion of the rotating shaft 2 in a key manner, the rotating bearing 6 can be mounted on other support frames, the driven gear 3 is meshed with a driving gear 4, the diameter of the driving gear 4 is smaller than that of the driven gear 3, the rotating speed of the support plate 1 is reduced, the driving gear 4 is connected with a support shaft, a motor 5 is mounted at the bottom of the support shaft, the motor 5 can use a servo motor of an existing SM130-100-15 FB model, and further comprises a scanning module, the scanning module is fixed on either side of the overlap 1 of the support plate and is located in the direction of forward projection of the workpiece, the scanning module is used for scanning the side image of the workpiece, the scanning module can use an image sensor of an existing TCD1209D model, the scanning module can use the existing processing module, the existing I7-620M processor, the existing gray scale image processing module, the gray scale module can be used for obtaining the gray scale module, the gray scale processing module, the gray scale module can be used for processing module, the gray scale processing module.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (7)
1. A data intelligent classification and cooperation method based on big data analysis is characterized by comprising the following contents:
s100, in the industrial control process of an enterprise, acquiring industrial control data from three dimensions of set data, operation data and evaluation data through an acquisition end and sending the industrial control data to a background end, adding an evaluation label after acquiring the set data, adding the set label to the acquired operation data, adding the operation label to the acquired evaluation data, acquiring field pictures of the industrial control through the acquisition end while acquiring the data, adding an actual label to the field pictures and sending the actual labels to the background end, and sending single set data, continuous operation data and single evaluation data in groups when sending the data;
s200, after receiving data at the background end, intercepting and replacing a label of the received data, replacing an evaluation label with a set label, replacing the set label with an operation label, and replacing the operation label with the evaluation label;
s300, the cloud network end acquires the live picture and the operation data of the background end, the live picture is matched with the function picture transmitted by the shopping network through the cloud network end, and when the matching is successful, the transmission of the function picture is suspended.
2. The intelligent big data analysis-based data classification and collaboration method according to claim 1, wherein: and when the matching is successful, the cloud network end acquires the address information of the successfully matched functional picture and sends the functional picture and the address information to the background end.
3. The intelligent big data analysis-based data classification and collaboration method according to claim 1, wherein: and S400, matching the data of the three dimensions pairwise by the background end, and adding an abnormal label to the data which fails to be matched and storing the abnormal label when the data of any two dimensions fails to be matched.
4. The intelligent big data analysis-based data classification and collaboration method according to claim 3, wherein: in the content S400, when data matching of any two dimensions is successful, time information is added to the successfully matched data and the successfully matched data is stored.
5. The intelligent big data analysis-based data classification and collaboration method according to claim 1, wherein: in the content S200, the background end averages the continuous running data, the background end determines whether the average value is within a threshold range, and when the average value deviates from the threshold range, the background end determines whether each running data is within the threshold range, and when at least one running data is outside the threshold range, the background end stores the set of data adding part of the tags.
6. The intelligent big data analysis-based data classification and collaboration method according to claim 3, wherein: firstly, the set data is matched with a pre-stored limited range through a background terminal, and when the set data is out of the limited range, the set data is added with an error label for storage.
7. The intelligent big data analysis-based data classification and collaboration method according to claim 6, wherein: and when the set data is positioned outside the limited range, the background terminal acquires the personnel information in the evaluation data and adds the personnel information as an error label.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910944836.XA CN110674302B (en) | 2019-09-30 | 2019-09-30 | Intelligent data classification and cooperation method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910944836.XA CN110674302B (en) | 2019-09-30 | 2019-09-30 | Intelligent data classification and cooperation method based on big data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110674302A CN110674302A (en) | 2020-01-10 |
| CN110674302B true CN110674302B (en) | 2020-07-14 |
Family
ID=69078824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910944836.XA Active CN110674302B (en) | 2019-09-30 | 2019-09-30 | Intelligent data classification and cooperation method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110674302B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106534171A (en) * | 2016-12-02 | 2017-03-22 | 全球能源互联网研究院 | Security authentication method and device, and terminal |
| CN106934436A (en) * | 2017-04-19 | 2017-07-07 | 中国铁道科学研究院标准计量研究所 | Railway freight metering security information scene calculates read-write system and method |
| CN109982309A (en) * | 2019-03-19 | 2019-07-05 | 湘潭大学 | Building microgrid electricity consumption data secure transmission technique based on RFID certification and Hybrid Encryption |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104639543A (en) * | 2015-01-29 | 2015-05-20 | 南京三宝科技股份有限公司 | Method for checking legality of collected data of sensor based on radio frequency identification tag ID (identity) |
| US10198676B2 (en) * | 2016-06-30 | 2019-02-05 | Sampleserve, Inc. | System and method for managing sample collection data and documentation |
| CN107292133B (en) * | 2017-05-18 | 2021-06-04 | 深圳中兴网信科技有限公司 | Artificial intelligence confusion technical method and device |
| CN109660526A (en) * | 2018-12-05 | 2019-04-19 | 国网江西省电力有限公司信息通信分公司 | A kind of big data analysis method applied to information security field |
| CN109714362B (en) * | 2019-02-19 | 2021-06-08 | 重庆邮电大学 | Lightweight industrial wireless network security data fusion method |
| CN109938722B (en) * | 2019-03-05 | 2022-02-18 | 安徽华米信息科技有限公司 | Data acquisition method and device, intelligent wearable device and storage medium |
-
2019
- 2019-09-30 CN CN201910944836.XA patent/CN110674302B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106534171A (en) * | 2016-12-02 | 2017-03-22 | 全球能源互联网研究院 | Security authentication method and device, and terminal |
| CN106934436A (en) * | 2017-04-19 | 2017-07-07 | 中国铁道科学研究院标准计量研究所 | Railway freight metering security information scene calculates read-write system and method |
| CN109982309A (en) * | 2019-03-19 | 2019-07-05 | 湘潭大学 | Building microgrid electricity consumption data secure transmission technique based on RFID certification and Hybrid Encryption |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110674302A (en) | 2020-01-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10878372B2 (en) | Method, system and device for association of commodities and price tags | |
| US11704631B2 (en) | Analyzing images and videos of damaged vehicles to determine damaged vehicle parts and vehicle asymmetries | |
| US8448859B2 (en) | System and method for preventing cashier and customer fraud at retail checkout | |
| US20150168727A1 (en) | Pointer tracking for eye-level scanners and displays | |
| CN105973245A (en) | Method and device for updating online map by using unmanned vehicle | |
| US10354137B2 (en) | Robust monitoring gauges | |
| CN104881770A (en) | Express bill information identification system and express bill information identification method | |
| CN110910147A (en) | Agricultural product quality traceability system and method | |
| CN111640265B (en) | Method, system and equipment for positioning card retaining and account error of self-service equipment | |
| CN110674302B (en) | Intelligent data classification and cooperation method based on big data analysis | |
| CN113283347B (en) | Assembly job guidance method, device, system, server and readable storage medium | |
| US8348146B1 (en) | System and method for determining product authenticity | |
| EP2822709B1 (en) | Method of tracking postal items to measure the travel time in a postal sorting network | |
| EP4352605A1 (en) | Methods, systems and devices for implementing remote services | |
| CN115965887A (en) | Automatic control system and method applied to container information detection and distribution | |
| KR20200138968A (en) | A system which manages the use time of child-care institution | |
| CN116385336B (en) | Deep learning-based weld joint detection method, system, device and storage medium | |
| US20220188749A1 (en) | Smart logistics label system and method of using the same | |
| US20240104495A1 (en) | System and method for tracking inventory inside warehouse with put-away accuracy using machine learning models | |
| US20220201442A1 (en) | Method and apparatus for enhancing the value of vehicular data using v2x communications | |
| US20240020995A1 (en) | Systems and methods for automated extraction of target text strings | |
| CN202512599U (en) | Automatic logistics system using trigger forwarding device | |
| CN116029318A (en) | Automatic code scanning method, device, equipment and storage medium | |
| CN116957444A (en) | Logistics management method and device based on order, medium and electronic equipment | |
| US20130329043A1 (en) | Transmissions of images in a remote recognition system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |