CN108875390A - A kind of shared economic data processing method in community - Google Patents
A kind of shared economic data processing method in community Download PDFInfo
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- CN108875390A CN108875390A CN201810575019.7A CN201810575019A CN108875390A CN 108875390 A CN108875390 A CN 108875390A CN 201810575019 A CN201810575019 A CN 201810575019A CN 108875390 A CN108875390 A CN 108875390A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a kind of communities to share economic data processing method, includes the following steps：A, local memory storage is transmitted to after being filtered noise reduction process to shared economic data source；B, classification processing is carried out to shared economic data source, obtains sorted data packet；C, data packet compressed, backed up, and be uploaded to backup server；D, compressed data packet is encrypted again；E, encrypted data packet is analyzed using the parser of selection, obtains analysis result data；F, analysis result background server is uploaded to show, the processing method that the present invention uses is at low cost, leaking data can be effectively prevented, while can also realize acquisition, classification, compress backup, encryption, the analysis to data, improves the high efficiency of data processing.
Description
Technical field
The present invention relates to economic data processing technology field, economic data processing method is shared by specially a kind of community.
Background technique
Shared economy, generally refers to obtain definite remuneration as the main purpose, based on stranger and there are the article rights to use
The new economic model of the one kind temporarily shifted.Its essence is to integrate unused article under line, labour, education and medical care resource.Have
Also say that shared economy is that people's justice enjoys social resources, respectively pay and be benefited in different ways, it is common obtain it is economical
Bonus.Such share is more to be realized by internet as medium.
Currently, the shared economic treatment of community generally passes through backstage artificial treatment, treatment effeciency is low, and Information Security
It can be poor.
Summary of the invention
The purpose of the present invention is to provide a kind of communities to share economic data processing method, to solve in above-mentioned background technique
The problem of proposition.
To achieve the above object, the present invention provides the following technical solutions：A kind of shared economic data processing method in community, packet
Include following steps：
A, local memory storage is transmitted to after being filtered noise reduction process to shared economic data source；
B, classification processing is carried out to shared economic data source, obtains sorted data packet；
C, data packet compressed, backed up, and be uploaded to backup server；
D, compressed data packet is encrypted again；
E, encrypted data packet is analyzed using the parser of selection, obtains analysis result data；
F, analysis result background server is uploaded to show.
Preferably, classification method is as follows in the step B：
A, reading attributes vector data, and obtain multiple default classification centers of processing data；
B, according to multiple default classification centers, classify to processing data, obtain post-classification comparison data；
C, according to post-classification comparison data, multiple annexable calculating tasks are established；
D, the annexable calculating task is calculated using multiple computational threads, and calculated result is merged
Operation；
E, default classification center is modified and is saved according to the calculated result after merging；And according to default classification
Center, revised cluster centre and amendment number of operations, determine data classification processing result.
Preferably, the data packet encryption method of the step D is as follows：
A, an encryption key is generated, and encryption key is generated into one group of sub-key according to pre-defined rule；
B, the data packet of preservation is packaged at random and generates multiple block of plaintext data；
C, its progress of a corresponding sub- key pair is chosen according to the size of each block of plaintext data and according to pre-defined rule
Cryptographic calculation, to obtain multiple ciphertext block datas；
D, multiple ciphertext block datas are collectively formed to the ciphertext data of output, i.e., the encryption of complete paired data packet.
Preferably, data backup is specially data acquisition unit in the step C：The shared economic data of storage is acquired,
And the data of acquisition are transmitted to data parsing unit；Data parsing unit：The data of data acquisition unit acquisition are solved
Analysis, and data are decomposed into multiple data segments；Data segment taxonomy database：Storing data categorised regulation, and parsed according to data
The data segment of unit decomposition automatically updates categorised regulation；Central processing unit：Data segment after calling data parsing unit to decompose, and
The traversal queries in data segment taxonomy database, the class categories of the data segment, and according to the class categories of inquiry by this number
According to the data segment progress sequence that in section storage to corresponding data storage server, central processing unit decomposes data parsing unit
Coding, and the coding is stored to coding storage server；Shared data storage server：Preservation is classified as shared data section
Data.
Compared with prior art, the beneficial effects of the invention are as follows：
(1) processing method that uses of the present invention is at low cost, can effectively prevent leaking data, while can also realize pair
Acquisition, classification, compress backup, encryption, the analysis of data, improve the high efficiency of data processing.
(2) data classification method that the present invention uses can reduce overall computation complexity and improve the stabilization of calculating
Property, and data general condition analysis ability is strong, and the Fast Classification suitable for mass data is handled, and further improves the essence of data classification
True property.
(3) the data packet encryption method that uses of the present invention have encryption intensity height, arithmetic speed block, calculating overhead
The advantages that small, expansible optimization, it is ensured that the safety of data.
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is data classification flow chart of the present invention；
Fig. 3 is data encryption flow chart of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, the present invention provides a kind of technical solution：A kind of shared economic data processing method in community, including with
Lower step：
A, local memory storage is transmitted to after being filtered noise reduction process to shared economic data source；
B, classification processing is carried out to shared economic data source, obtains sorted data packet；
C, data packet compressed, backed up, and be uploaded to backup server；
D, compressed data packet is encrypted again；
E, encrypted data packet is analyzed using the parser of selection, obtains analysis result data；
F, analysis result background server is uploaded to show.
As shown in Fig. 2, classification method is as follows in step B：
A, reading attributes vector data, and obtain multiple default classification centers of processing data；
B, according to multiple default classification centers, classify to processing data, obtain post-classification comparison data；
C, according to post-classification comparison data, multiple annexable calculating tasks are established；
D, the annexable calculating task is calculated using multiple computational threads, and calculated result is merged
Operation；
E, default classification center is modified and is saved according to the calculated result after merging；And according to default classification
Center, revised cluster centre and amendment number of operations, determine data classification processing result.
Wherein, in step d, when calculation processing, computer first pre-processes data object to be processed, completes data
The grouping of object, then in calculating group data object similarity matrix, and according to similarity size merge generate new data pair
As record merges generating process and deletes legacy data object simultaneously.The data classification method that the present invention uses can reduce overall meter
It calculates complexity and improves the stability of calculating, and data general condition analysis ability is strong, the Fast Classification suitable for mass data
Processing, further improves the accuracy of data classification.
As shown in figure 3, the data packet encryption method of step D is as follows：
A, an encryption key is generated, and encryption key is generated into one group of sub-key according to pre-defined rule；
B, the data packet of preservation is packaged at random and generates multiple block of plaintext data；
C, its progress of a corresponding sub- key pair is chosen according to the size of each block of plaintext data and according to pre-defined rule
Cryptographic calculation, to obtain multiple ciphertext block datas；
D, multiple ciphertext block datas are collectively formed to the ciphertext data of output, i.e., the encryption of complete paired data packet.
Wherein, encryption key can also be both randomly generated by manually entering, and be generated under specific item with public key encryption；
Encryption key Key is made of Key1, one group of sub-key such as Key2...Keyn, and password grouping is according to certain rule generation
Key；Encryption key preferably uses 16 byte cryptograms, and the value of every byte cryptograms is between 1-7FH, in the present invention, such as can
8 sub-keys are generated, then key total amount there will be 2 64 power kinds to combine；In step b, emergency data is beaten at random in communication
It wraps (being packaged into block of plaintext data), the size of packet is random (between 1-64 byte).According to the size of packet, according to certain
Rule choose one of sub-key current packet encrypted, the size and cipher controlled that Encryption Algorithm is wrapped；This hair
The data packet encryption method of bright use has that encryption intensity height, arithmetic speed block, the overhead of calculating are small, expansible optimize etc.
Advantage, it is ensured that the safety of data.
In the present invention, data backup is specially data acquisition unit in step C：The shared economic data of storage is acquired, and
The data of acquisition are transmitted to data parsing unit；Data parsing unit：The data of data acquisition unit acquisition are parsed,
And data are decomposed into multiple data segments；Data segment taxonomy database：Storing data categorised regulation, and according to data parsing unit
The data segment of decomposition automatically updates categorised regulation；Central processing unit：Data segment after calling data parsing unit to decompose, and in number
According to traversal queries in section taxonomy database, the class categories of the data segment, and according to the class categories of inquiry by the data section
It stores on corresponding data storage server, the data segment carry out sequence mark that central processing unit decomposes data parsing unit
Code, and the coding is stored to coding storage server；Shared data storage server：Save the number for being classified as shared data section
According to.The data back up method that the present invention uses can be realized the high efficiency back-up processing to data, be convenient for subsequent inquiry.
In conclusion the processing method that the present invention uses is at low cost, leaking data can be effectively prevented, while can also
It realizes to the acquisition of data, classification, compress backup, encryption, analysis, improves the high efficiency of data processing.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (4)
1. economic data processing method is shared by a kind of community, it is characterised in that：Include the following steps：
A, local memory storage is transmitted to after being filtered noise reduction process to shared economic data source；
B, classification processing is carried out to shared economic data source, obtains sorted data packet；
C, data packet compressed, backed up, and be uploaded to backup server；
D, compressed data packet is encrypted again；
E, encrypted data packet is analyzed using the parser of selection, obtains analysis result data；
F, analysis result background server is uploaded to show.
2. economic data processing method is shared by a kind of community according to claim 1, it is characterised in that：In the step B
Classification method is as follows：
A, reading attributes vector data, and obtain multiple default classification centers of processing data；
B, according to multiple default classification centers, classify to processing data, obtain post-classification comparison data；
C, according to post-classification comparison data, multiple annexable calculating tasks are established；
D, the annexable calculating task is calculated using multiple computational threads, and behaviour is merged to calculated result
Make；
E, default classification center is modified and is saved according to the calculated result after merging；And according in default classification
The heart, revised cluster centre and amendment number of operations, determine data classification processing result.
3. economic data processing method is shared by a kind of community according to claim 1, it is characterised in that：The step D's
Data packet encryption method is as follows：
A, an encryption key is generated, and encryption key is generated into one group of sub-key according to pre-defined rule；
B, the data packet of preservation is packaged at random and generates multiple block of plaintext data；
C, according to the size of each block of plaintext data and according to the corresponding sub- key pair of pre-defined rule selection, it is encrypted
Operation, to obtain multiple ciphertext block datas；
D, multiple ciphertext block datas are collectively formed to the ciphertext data of output, i.e., the encryption of complete paired data packet.
4. economic data processing method is shared by a kind of community according to claim 1, it is characterised in that:In the step C
Data backup is specially data acquisition unit：The shared economic data of storage is acquired, and the data of acquisition are transmitted to data solution
Analyse unit；Data parsing unit：The data of data acquisition unit acquisition are parsed, and data are decomposed into multiple data
Section；Data segment taxonomy database：Storing data categorised regulation, and automatically updated point according to the data segment that data parsing unit is decomposed
Class regulation；Central processing unit：Data segment after calling data parsing unit to decompose, and traverse and look into data segment taxonomy database
It askes, the class categories of the data segment, and the data section is stored to corresponding data storage according to the class categories of inquiry and is taken
It is engaged on device, the data segment carry out sequence coding that central processing unit decomposes data parsing unit, and the coding is stored to coding
Storage server；Shared data storage server：Save the data for being classified as shared data section.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810575019.7A CN108875390A (en) | 2018-06-06 | 2018-06-06 | A kind of shared economic data processing method in community |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810575019.7A CN108875390A (en) | 2018-06-06 | 2018-06-06 | A kind of shared economic data processing method in community |
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| Publication Number | Publication Date |
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| CN108875390A true CN108875390A (en) | 2018-11-23 |
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| CN201810575019.7A Withdrawn CN108875390A (en) | 2018-06-06 | 2018-06-06 | A kind of shared economic data processing method in community |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109729170A (en) * | 2019-01-09 | 2019-05-07 | 武汉巨正环保科技有限公司 | A kind of cloud computing data backup of new algorithm and restoring method |
| CN110322326A (en) * | 2019-07-09 | 2019-10-11 | 程新宇 | A kind of economic big data sharing method of geography based on ArgGis |
| CN112214771A (en) * | 2020-09-10 | 2021-01-12 | 绍兴无相智能科技有限公司 | Information analysis method and device based on big data and computer readable storage medium |
-
2018
- 2018-06-06 CN CN201810575019.7A patent/CN108875390A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109729170A (en) * | 2019-01-09 | 2019-05-07 | 武汉巨正环保科技有限公司 | A kind of cloud computing data backup of new algorithm and restoring method |
| CN110322326A (en) * | 2019-07-09 | 2019-10-11 | 程新宇 | A kind of economic big data sharing method of geography based on ArgGis |
| CN112214771A (en) * | 2020-09-10 | 2021-01-12 | 绍兴无相智能科技有限公司 | Information analysis method and device based on big data and computer readable storage medium |
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Application publication date: 20181123 |