CN116305220B - Big data-based resource data processing method and system - Google Patents
Big data-based resource data processing method and system Download PDFInfo
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- CN116305220B CN116305220B CN202310560318.4A CN202310560318A CN116305220B CN 116305220 B CN116305220 B CN 116305220B CN 202310560318 A CN202310560318 A CN 202310560318A CN 116305220 B CN116305220 B CN 116305220B
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- 238000003672 processing method Methods 0.000 title claims description 10
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000013500 data storage Methods 0.000 claims abstract description 9
- 230000011218 segmentation Effects 0.000 claims abstract description 7
- 231100000279 safety data Toxicity 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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Classifications
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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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- 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
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a method and a system for processing resource data based on big data. The method comprises the following steps: the cloud platform acquires client resource data; extracting security data from the resource data, calculating security performance of a corresponding client, and selecting a corresponding distributed processing node according to the security performance of the client; performing field segmentation on the resource data, performing deformation processing on the segmented resource data to obtain deformed fields, and transmitting the deformed fields to corresponding distributed processing nodes for distributed storage; and recording and storing the log. The invention stores the client data with different security levels by classified distributed processing nodes, and carries out security processing on the data stored in the classified distributed mode, thereby ensuring the security of the resource data storage and improving the processing efficiency of the resource data.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a resource data processing method and system based on big data.
Background
Cloud computing platforms, also referred to as cloud platforms, refer to services that provide computing, networking, and storage capabilities based on hardware resources and software resources. Cloud computing platforms can be divided into 3 classes: a storage type cloud platform mainly used for data storage, a computing type cloud platform mainly used for data processing and a comprehensive cloud computing platform taking both computing and data storage processing into consideration.
The resource data processing comprises the steps of collecting, analyzing and storing the resource data, and for the storage of the resource data, the data classification statistics is generally carried out on the received resource data, and the effects of uniform collection and the like are achieved. In the existing data storage method, data is often directly stored in a free storage space, and the data itself is not considered, so that the safety of the data in the prior art is not a problem considered in the storage stage. The invention provides a resource data processing method and system based on big data, which are used for carrying out security processing on the data and further guaranteeing the security of the data.
Disclosure of Invention
The invention provides a resource data processing method based on big data, which comprises the following steps:
the cloud platform acquires client resource data;
extracting security data from the resource data, calculating security performance of a corresponding client, and selecting a corresponding distributed processing node according to the security performance of the client;
performing field segmentation on the resource data, performing deformation processing on the segmented resource data to obtain deformed fields, and transmitting the deformed fields to corresponding distributed processing nodes for distributed storage;
and recording and storing the log.
The large data-based resource data processing method is characterized in that the client resource data comprises client attribute data and security data, the client security performance is calculated according to the security data, and the client attribute data is stored in the distributed processing nodes.
The resource data processing method based on big data, wherein the data stored by the server in each distributed processing node has a security level, and is stored according to the security of the data.
According to the resource data processing method based on big data, a plurality of security levels are set for the security of the data in the cloud platform, and each level has a corresponding preset level range value; after the client security is calculated, determining the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes from the security levels, and distributing the resource data to the distributed processing nodes for storage.
The resource data processing method based on big data, wherein the distributed processing nodes storing the client resource data and the corresponding allocation factors thereof are recorded in the storage log.
The invention also provides a resource data processing system based on big data, which comprises: cloud platform, customer end and distributed processing node;
the cloud platform acquires client resource data;
the cloud platform extracts security data from the resource data, calculates security performance of a corresponding client, and selects a corresponding distributed processing node according to the security performance of the client;
the cloud platform performs field segmentation on the resource data, performs deformation processing on the segmented resource data to obtain deformed fields, and sends the deformed fields to corresponding distributed processing nodes for distributed storage;
the cloud platform records and stores the log.
The big data-based resource data processing system as described above, wherein the client resource data includes client attribute data and security data, the client security performance is calculated from the security data, and the client attribute data is stored in the distributed processing nodes.
The resource data processing system based on big data as described above, wherein the data stored by the server in each distributed processing node has a security level, and is stored according to the security of the data.
The resource data processing system based on big data, as described above, wherein a plurality of security levels are set for the security of the data in the cloud platform, and each level has a corresponding preset level range value; after the client security is calculated, determining the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes from the security levels, and distributing the resource data to the distributed processing nodes for storage.
A big data based resource data processing system as described above wherein the distributed processing nodes storing the client resource data and their corresponding allocation factors are recorded in a storage log.
The beneficial effects achieved by the invention are as follows: the invention stores the client data with different security levels by classified distributed processing nodes, and carries out security processing on the data stored in the classified distributed mode, thereby ensuring the security of the resource data storage and improving the processing efficiency of the resource data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flowchart of a method for processing resource data based on big data according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a resource data processing system based on big data according to a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a first embodiment of the present invention provides a method for processing resource data based on big data, including:
step 110, the cloud platform acquires client resource data;
step 120, extracting security data from the resource data, calculating security performance of the corresponding client, and selecting the corresponding distributed processing node according to the security performance of the client;
the client resource data received by the cloud platform comprises resource attribute data and safety data, and the safety data determines the subsequent storage mode of the cloud platform on the resource data.
Specifically, the formula is adoptedComputing the security of the client, wherein +.>For the security of the client +.>Whether the address of the client is an illegal address field of the cloud platform, if not, the address of the client is +.>Is a number of 1, and is not limited by the specification,if yes, ->Is 0; />The method comprises the steps that the association factor of the client and the cloud platform is given when the client registers on the cloud platform; />The method comprises the steps that i security data of a client comprises a hardware security level, a software security level and a network security level, wherein the value of i is 1 to n, and n is the total number of the security data; />The influence weight of the ith security factor on the security of the client is given.
Setting a plurality of security levels for the security of data in a cloud platform, wherein each level has a corresponding preset level range value, such as an A level, a B level, a C level and a D level, for example, the A level is highest, a client with a security attribute value exceeding a preset value Rsmax can acquire/store, the B level is next time, a client with a security attribute value of 0-Rsmax can acquire/store, the C level is next time, a client with a security attribute value of Rsmin-Rsmax (Rsmax > Rsmin > 0) can acquire/store, and the D level is lowest, and a client with a security attribute value of 0-Rsmin can acquire/store.
After the client security is calculated, the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes can be determined from the levels, and the resource data is distributed to the distributed processing nodes for storage. Preferably, the processing node may be a plurality of virtual machines/physical machines registered on the cloud platform, or may be a plurality of servers in a server cluster, which is not limited herein.
In this embodiment of the present application, after the client a uploads data to the cloud platform, the cloud platform calculates that the security performance of the client a may be stored in processing nodes below the B level, for example, processing nodes found by the processing nodes are B1, B2, B3, B4, and B5, but in the processing nodes B1 and B2 of the present application, certain limits are set on storing data, so when the present application performs distributed processing node storage, after finding out storable processing nodes, the open condition of these processing nodes on the client data is calculated respectively.
Specifically, the formula is adoptedCalculating the openness of the distributed processing nodes to the client data, wherein->Representing the openness of the distributed processing nodes to the client data; e=2.718; n represents the number of times the client data is stored in the processing node; />Representing the amount of data the client stores in the processing node,/for>Representing the total memory of the processing node, +.>Representing client security, ++>Representing the security level of the processing node.
And selecting a plurality of processing nodes with higher opening degree for the client data from the processing nodes to store the client resource data.
130, field segmentation is carried out on the resource data, deformation processing is carried out on the segmented resource data to obtain deformed fields, and the deformed fields are sent to corresponding distributed processing nodes for distributed storage;
specifically, in data storage, for example, data to be stored is X, which is divided into N fields, respectivelyThe storage mode is that each field is subjected to the following operation: for->Fields are formulated->Calculation of->Is thatStorage data after field deformation, +.>Storing data for a source->Denoted as->The field sets the allocation factor. The field is +_after morphing>And->And all are sent to the corresponding distributed processing nodes for storage.
Step 140, recording a storage log;
in particular, a distributed processing node storing client resource data and its corresponding allocation factor are recorded in this storage log。
Example two
As shown in fig. 2, a second embodiment of the present invention provides a resource data processing system based on big data, including: cloud platform 21, clients 22, and distributed processing nodes 23. Wherein:
the cloud platform 21 acquires the resource data of the client 22; extracting security data from the resource data, calculating security performance of the corresponding client 22, and selecting a corresponding distributed processing node 23 according to the security performance of the client 22;
the client resource data received by the cloud platform 21 includes resource attribute data and security data, and the security data determines a subsequent storage manner of the cloud platform for the resource data.
Specifically, the formula is adoptedComputing the security of the client, wherein +.>For the security of the client +.>Whether the address of the client is an illegal address field of the cloud platform, if not, the address of the client is +.>1, if yes, then->Is 0; />The method comprises the steps that the association factor of the client and the cloud platform is given when the client registers on the cloud platform; />The method comprises the steps that i security data of a client comprises a hardware security level, a software security level and a network security level, wherein the value of i is 1 to n, and n is the total number of the security data; />The influence weight of the ith security factor on the security of the client is given.
Setting a plurality of security levels for the security of data in a cloud platform, wherein each level has a corresponding preset level range value, such as an A level, a B level, a C level and a D level, for example, the A level is highest, a client with a security attribute value exceeding a preset value Rsmax can acquire/store, the B level is next time, a client with a security attribute value of 0-Rsmax can acquire/store, the C level is next time, a client with a security attribute value of Rsmin-Rsmax (Rsmax > Rsmin > 0) can acquire/store, and the D level is lowest, and a client with a security attribute value of 0-Rsmin can acquire/store.
After the client security is calculated, the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes can be determined from the levels, and the resource data is distributed to the distributed processing nodes for storage. Preferably, the processing node may be a plurality of virtual machines/physical machines registered on the cloud platform, or may be a plurality of servers in a server cluster, which is not limited herein.
In this embodiment of the present application, after the client a uploads data to the cloud platform, the cloud platform calculates that the security performance of the client a may be stored in processing nodes below the B level, for example, processing nodes found by the processing nodes are B1, B2, B3, B4, and B5, but in the processing nodes B1 and B2 of the present application, certain limits are set on storing data, so when the present application performs distributed processing node storage, after finding out storable processing nodes, the open condition of these processing nodes on the client data is calculated respectively.
Specifically, the formula is adoptedCalculating the openness of the distributed processing nodes to the client data, wherein->Representing the openness of the distributed processing nodes to the client data; e=2.718; n represents the number of times the client data is stored in the processing node; />Representing the amount of data the client stores in the processing node,/for>Representing the total memory of the processing node, +.>Representing client security, ++>Representing the security level of the processing node.
And selecting a plurality of processing nodes with higher opening degree for the client data from the processing nodes to store the client resource data.
The cloud platform 21 performs field segmentation on the resource data, performs deformation processing on the segmented resource data to obtain deformed fields, and sends the deformed fields to the corresponding distributed processing nodes 23 for distributed storage; recording and storing a log;
specifically, in data storage, for example, data to be stored is X, which is divided into N fields, respectivelyThe storage mode is that each field is subjected to the following operation: for->Fields are formulated->Calculation of->Is thatStorage data after field deformation, +.>Storing data for a source->Denoted as->Field arrangementA fixed distribution factor, a distribution factorCan randomly set to satisfy->May also be->. The field is +_after morphing>And->And all are sent to the corresponding distributed processing nodes for storage.
Recording in the storage log a distributed processing node storing client resource data and its corresponding allocation factor。
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (8)
1. A big data-based resource data processing method, comprising:
the cloud platform acquires client resource data;
extracting security data from the resource data, calculating security performance of a corresponding client, and selecting a corresponding distributed processing node according to the security performance of the client;
performing field segmentation on the resource data, performing deformation processing on the segmented resource data to obtain deformed fields, and transmitting the deformed fields to corresponding distributed processing nodes for distributed storage;
recording and storing a log;
the client resource data comprises client attribute data and safety data, the safety performance of the client is calculated according to the safety data, and the client attribute data is stored in the distributed processing node;
using the formulaComputing the security of the client, wherein +.>For the security of the client +.>Whether the address of the client is an illegal address field of the cloud platform, if not, the address of the client is +.>1, if yes, then->Is 0; />The method comprises the steps that the association factor of the client and the cloud platform is given when the client registers on the cloud platform; />The method comprises the steps that i security data of a client comprises a hardware security level, a software security level and a network security level, wherein the value of i is 1 to n, and n is the total number of the security data; />Weighting the influence of the ith security factor on the security of the client;
in data storage, if the data to be stored is X, the X is divided into N fields, respectivelyThe storage mode is that each field is subjected to the following operation: for->Fields are formulated->Calculation of->Is->Storage data after field deformation, +.>Storing data for a source->Denoted as->A field-set allocation factor; the field is +_after morphing>And->All are sent to the corresponding distributed processing nodes for storage; recording in a storage log distributed processing nodes storing client resource data and corresponding allocation factors +.>。
2. A method of processing big data based on resource data according to claim 1, wherein the server stores data in each of the distributed processing nodes with a security level, and the data is stored according to the security of the data.
3. The method for processing resource data based on big data according to claim 1, wherein a plurality of security levels are set for the security of the data in the cloud platform, each level having a corresponding preset level range value; after the client security is calculated, determining the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes from the security levels, and distributing the resource data to the distributed processing nodes for storage.
4. A method of big data based resource data processing as claimed in claim 1, wherein the distributed processing nodes storing the client resource data and their corresponding allocation factors are recorded in a storage log.
5. A big data based resource data processing system, comprising: cloud platform, customer end and distributed processing node;
the cloud platform acquires client resource data;
the cloud platform extracts security data from the resource data, calculates security performance of a corresponding client, and selects a corresponding distributed processing node according to the security performance of the client;
the cloud platform performs field segmentation on the resource data, performs deformation processing on the segmented resource data to obtain deformed fields, and sends the deformed fields to corresponding distributed processing nodes for distributed storage;
the cloud platform records and stores the log;
the client resource data comprises client attribute data and safety data, the safety performance of the client is calculated according to the safety data, and the client attribute data is stored in the distributed processing node;
using the formulaComputing the security of the client, wherein +.>For the security of the client +.>Whether the address of the client is an illegal address field of the cloud platform, if not, the address of the client is +.>1, if yes, then->Is 0; />The method comprises the steps that the association factor of the client and the cloud platform is given when the client registers on the cloud platform; />The method comprises the steps that i security data of a client comprises a hardware security level, a software security level and a network security level, wherein the value of i is 1 to n, and n is the total number of the security data; />Weighting the influence of the ith security factor on the security of the client;
in data storage, if the data to be stored is X, the X is divided into N fields, respectivelyThe storage mode is that each field is subjected to the following operation: for->Fields are formulated->Calculation of->Is->Storage data after field deformation, +.>Storing data for a source->Denoted as->A field-set allocation factor; the field is +_after morphing>And->All are sent to the corresponding distributed processing nodes for storage; recording in a storage log distributed processing nodes storing client resource data and corresponding allocation factors +.>。
6. A big data based resource data processing system according to claim 5, wherein the server has a security level for the data stored in each of the distributed processing nodes, and wherein the data is stored according to the security of the data.
7. The big data-based resource data processing system of claim 5, wherein a plurality of security levels are set for the security of the data in the cloud platform, each level having a corresponding preset level range value; after the client security is calculated, determining the client level corresponding to the resource data and a plurality of corresponding distributed processing nodes from the security levels, and distributing the resource data to the distributed processing nodes for storage.
8. A big data based resource data processing system of claim 5, wherein the distributed processing nodes storing the client resource data and their corresponding allocation factors are recorded in a storage log.
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