CN116405334A - Block chain-based computing power network flow processing method, equipment and medium - Google Patents
Block chain-based computing power network flow processing method, equipment and medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/14—Charging, metering or billing arrangements for data wireline or wireless communications
- H04L12/1432—Metric aspects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/16—Arrangements for providing special services to substations
- H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
- H04L12/1854—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast with non-centralised forwarding system, e.g. chaincast
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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
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
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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
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- 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 disclosure provides a blockchain-based computing network traffic processing method, equipment and medium. In some embodiments of the present disclosure, a full blockchain network includes: a traffic handling blockchain, a micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: service identification node, traffic supervision node and charging node; the service identification node calls a traffic service identification intelligent contract to obtain the current traffic service type; the traffic monitoring node broadcasts the counted traffic use information to a traffic counting block chain network; the charging node determines the flow use cost according to the current flow service type and the flow use amount and broadcasts the flow use cost to the flow statistics block chain network; each node in the flow processing block chain micro-network performs consensus operation, and uploads flow use information to a target block and broadcasts the target block to the full block chain network; the information security and reliability of the network traffic data are improved.
Description
Technical Field
The disclosure relates to the technical field of blockchains, in particular to a blockchain-based computing power network flow processing method, device and medium.
Background
The data use frequency and the data use quantity in the aspect of traffic service show large-scale increasing trend, the mobile edge in the computing network calculates the sinking of the service position, and the continuous integration of resources such as distributed computing power, storage and the like, and the method also generates safer, transparent and scientific demands for dense, large-scale and decentralized traffic billing. The traffic service is provided by a network operator, establishes a connection between a user and a computing resource in the network, and provides a channel to enable stable transmission of data information. The safety and speed of the flow service directly affect the operation quality of the computing network, and the transparent specification of flow charging affects the efficiency and quality of the flow service.
Network traffic service identification is a precondition for realizing network supervision, and is also a basis for improving network service quality and realizing network security management. In the power computing network, as the power computing resources are distributed in different regions and are accessed through different network nodes, the power computing resources are easily suffered from network attack of malicious nodes in a service flow charging stage and service identification in the power computing task completion process, so that the information security and reliability of network flow data are lower.
Disclosure of Invention
The disclosure provides a blockchain-based computational power network traffic processing method, equipment and medium, which are used for at least solving the problem of low information security and reliability of the existing network traffic data.
The technical scheme of the present disclosure is as follows:
the embodiment of the disclosure provides a computational power network flow processing method based on a block chain, which is applied to a full block chain network, wherein the full block chain network comprises the following components: a traffic handling blockchain micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: the service identification node, the traffic supervision node and the charging node comprise:
the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type;
the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics blockchain network, wherein the flow usage information comprises flow usage fees;
the charging node determines the traffic usage cost according to the current traffic service type and the traffic usage amount, and broadcasts the traffic usage cost to the traffic statistics blockchain network;
And each node in the flow processing block chain micro-network performs consensus operation, uploads the flow use information to a target block through a flow statistics block chain network, and broadcasts the target block to the full block chain network.
The embodiment of the disclosure also provides an electronic device, including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the steps in the method described above.
The disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method.
The disclosed embodiments also provide a computer program product comprising a computer program/instruction which, when executed by a processor, implements the above-described method.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
in some embodiments of the present disclosure, a full blockchain network includes: a traffic handling blockchain micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: service identification node, traffic supervision node and charging node; the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type; the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics block chain network, wherein the flow usage information comprises flow usage fees; the charging node determines the flow use cost according to the current flow service type and the flow use amount and broadcasts the flow use cost to the flow statistics block chain network; each node in the flow processing block chain micro-network performs consensus operation, and uploads flow use information to a target block through the flow statistics block chain network and broadcasts the target block to the full block chain network; based on the block chain network, the identification of traffic service and the monitoring of traffic use in the process of completing the calculation task are realized, the data damage and loss caused by malicious node attack are reduced as much as possible, and the information security and reliability of network traffic data are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flow diagram of a blockchain-based algorithm flow processing method provided by an exemplary embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a full blockchain network according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a full blockchain network according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of the present disclosure.
It should be noted that, the user information related to the present disclosure includes, but is not limited to: user equipment information and user personal information; the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the user information in the present disclosure all conform to the regulations of the relevant laws and regulations and do not violate the well-known and popular public order.
In response to the technical problems described above, in some embodiments of the present disclosure, a full blockchain network includes: a traffic handling blockchain micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: service identification node, traffic supervision node and charging node; the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type; the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics block chain network, wherein the flow usage information comprises flow usage fees; the charging node determines the flow use cost according to the current flow service type and the flow use amount and broadcasts the flow use cost to the flow statistics block chain network; each node in the flow processing block chain micro-network performs consensus operation, and uploads flow use information to a target block through the flow statistics block chain network and broadcasts the target block to the full block chain network; based on the block chain network, the identification of traffic service and the monitoring of traffic use in the process of completing the calculation task are realized, the data damage and loss caused by malicious node attack are reduced as much as possible, and the information security and reliability of network traffic data are improved.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a flow chart of a block chain-based algorithm flow processing method according to an exemplary embodiment of the present disclosure. Wherein the full blockchain network includes: a traffic handling blockchain micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: service identification node, traffic supervision node and charging node. As shown in fig. 1, the method includes:
s101: the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type;
s102: the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics block chain network, wherein the flow usage information comprises flow usage fees;
s103: the charging node determines the flow use cost according to the current flow service type and the flow use amount and broadcasts the flow use cost to the flow statistics block chain network;
s104: and each node in the flow processing block chain micro-network performs consensus operation, and uploads flow use information to the target block through the flow statistics block chain network and broadcasts the target block to the full block chain network.
In this embodiment, the node is an active electronic device connected to a network, capable of sending, receiving or forwarding information over a communication channel, and having one or more physical hard disks thereon. The node may be a workstation, a server, etc., but is not limited thereto. A node in an embodiment of the present disclosure may include a plurality of physical hard disks. Physical hard disks include, but are not limited to: solid state drives, mechanical drives, hybrid drives, and the like.
Fig. 2 is a schematic structural diagram of a full blockchain network according to an embodiment of the present disclosure. As shown in fig. 2, the full blockchain network of the embodiments of the present disclosure includes: a traffic handling blockchain micro-network and a traffic statistics blockchain network. The traffic handling blockchain micro-network and the traffic statistics blockchain network need to be built prior to use.
In some embodiments of the present disclosure, the traffic handling blockchain network is built at the computational network orchestration management layer. One way this can be achieved is that the traffic handling blockchain network takes the form of a federated chain and each resource-matched computational task corresponds to a traffic handling blockchain micro-network. After the calculation task is finished, the flow processing blockchain micro-network forms a flow charging detail block, the flow charging detail block is recorded and uploaded through the flow counting blockchain network, and the flow processing blockchain micro-network corresponding to the successful calculation task is received at the gateway node to perform deleting and destroying operations. The nodes in the flow processing blockchain micro-network comprise four types, namely: traffic policing nodes, charging nodes, traffic identifying nodes, and participant nodes. The traffic monitoring node is managed by adding, deleting, updating and the like by the mobile edge computing service platform, and mainly completes monitoring, sorting and blocking of traffic use conditions based on mobile edge computing and basic-level equipment. The charging nodes are also managed by the mobile edge computing service platform, and corresponding cost statistics is carried out according to different types of charging events or charging strategies. The service identification node is deployed by an arrangement management layer in the computing network and is mainly responsible for the identification of traffic service types. The participant nodes comprise two nodes of a flow demand party and a flow provider party, and are mainly responsible for supervising and backing up the whole flow processing process.
In some embodiments of the present disclosure, the traffic statistics blockchain network is built at the computational network orchestration management layer. One way that this can be achieved is that the traffic statistics blockchain network contains two classes of nodes: a traffic policing node and a gateway node. The flow supervision nodes are consistent with the same-name nodes in the flow processing block chain network, are the collection of the nodes in each flow processing block chain network, are managed by the mobile edge computing service platform, initially complete the information broadcasting of the flow use condition and charging details of each calculation task, and ensure the safety and reliability of the process. The gateway node is managed by a network forwarding layer in the computing network and is responsible for adding corresponding edge gateway infrastructure nodes to realize the safe recording and transmission of the flow charging result.
In some embodiments of the present disclosure, a service identification node invokes a traffic service identification intelligent contract to perform current service identification to obtain a current traffic service type. Specifically, after the calculation task successfully matching the resource in the calculation network starts calculation, a fixed period is set
The service identification node in the flow processing block chain network calls the intelligent contract of flow service identification to perform +.>
Service identification in the time slot, obtaining the traffic service type +. >
。
In some embodiments of the present disclosure, a billing node determines a traffic usage fee based on a current traffic type and traffic usage amount and broadcasts the traffic usage fee to a traffic statistics blockchain network. One way this can be achieved is per unit cycle
(/>
) In, the charging node is based on->
Calculate accumulation to current period +.>
Flow use fee of->
：
wherein ,
for a traffic type cost mapping function, +.>
Can be adjusted by the operator according to different situations.
And after the charging node finishes the flow use cost statistics every period, broadcasting the flow charging detail in the flow statistics block chain network.
In some embodiments of the present disclosure, a traffic monitoring node collects traffic usage of a current period of time under a current computing task, counts traffic usage information of the current computing task after the current computing task is completed, and broadcasts the traffic usage information to a traffic statistics blockchain network. The flow monitoring node monitors the flow use condition under the current calculation task and collects the flow use amount in the current period
. The traffic monitoring node counts traffic usage information, including at least one of: the parameters of the segmented traffic service type, the segmented traffic use duration, the traffic total use amount, the overall cost, the task time consumption and the like. The traffic monitoring node packages and broadcasts the information to the traffic statistics blockchain network.
In some embodiments of the present disclosure, determining a traffic usage fee according to a current traffic type and a traffic usage amount, and after broadcasting the traffic usage fee to a traffic statistics blockchain network, determining whether the traffic usage fee is greater than an expected fee; at a flow rate usage rate greater than the expected rateIf the current calculation task is used, stopping executing the current calculation task; and if the flow using cost is less than or equal to the expected cost, continuing to execute the current computing task, and sending a resource allocation request to the computing network so as to enable the computing network to issue new computing resources. It should be noted that, if the computing task selects the offline processing mode, the charging node may complete the last period of the computing task
And (3) according to the information broadcast by the traffic monitoring node, accounting the traffic use cost.
In some embodiments of the present disclosure, after receiving the traffic usage information, the participant node performs a verification operation on the traffic usage information; in the case that the flow rate usage information passes verification, the flow rate usage information is stored in its own recording pool.
In some embodiments of the present disclosure, nodes in a traffic handling blockchain micro network perform consensus operations, upload traffic usage information into a target block through a traffic statistics blockchain network, and broadcast the target block to a full blockchain network. All nodes in the flow processing block chain network start a consensus flow, adopt PBFT (Practical Byzantine Fault Tolerance, practical Bayesian fault-tolerant algorithm) to carry out consensus, store flow use conditions and cost details in the process of completing the calculation task into blocks, and finally broadcast the blocks to the full block chain network.
After broadcasting the target block to the full blockchain network, each node in the traffic processing blockchain micro network receives the target block and performs verification operation on the target block; under the condition that the target block passes verification, adding the target block to the tail of the self block chain; in the event that the target block verification fails, the target block is discarded. If the participant node at this time is in an offline state and cannot receive the synchronization block, the participant node can subsequently recover the online state and apply for the calculation network, and perform information synchronization again according to the block under the same calculation task in the traffic statistics block chain network and the corresponding timestamp.
At the bookIn an embodiment, the interval is fixed
(/>
) The traffic monitoring node broadcasts newly-added blocks in the time period, namely newly-added traffic service condition information in the time period in the traffic statistics block chain network. The traffic statistics blockchain network starts a consensus mechanism, stores the information passing through the consensus into a new block, and then broadcasts the new block to the whole network.
In some embodiments of the present disclosure, the gateway node receives the target block and performs a verification operation on the target block; under the condition that the target block passes verification, adding the target block into a self block chain; sending a target block reception success notification to the traffic processing blockchain micro-network; and after receiving the successful notification of the target block, the flow processing block chain micro-network performs deleting operation on the current computing task and the flow processing block chain micro-network.
The random forest model needs to be built before it can be used. The construction process of the random forest model is as follows:
step one: before primary identification, a certain number of traffic service type decision trees are established and trained to form a random forest.
Step two: periodicity from multiple traffic types
Inner->
Constructing an original data set by the data packets;
step three: sampling in the original data set by adopting a put-back strategy to generate a sub-data set corresponding to the data volume of the original data set; wherein the internal elements of the sub-data set can be repeated, and the number of the elements in the sub-data set is
（/>
) The method comprises the steps of carrying out a first treatment on the surface of the Traffic types are classified as +.>
Seed of->
The method comprises the steps of carrying out a first treatment on the surface of the Each item of data in the data packet comprises each item of traffic
A plurality of features; wherein the traffic characteristics include at least one of: the average value, variance and the maximum value of the data packet size, the average value and variance of the data packet arrival time interval, the ratio of the uplink byte number to the downlink byte number, the average value and variance of the data packet arrival time interval, the number of downstream IP contained, the number of downstream substream fragments, the overall packet rate and the downstream byte rate;
step four: each decision tree randomly extracts from the characteristics of each flow service 
Features of
Each decision tree calculates entropy according to the sub-data set
, wherein ,/>
，/>
The probability of the result after each element decision in the target sub-data set is the probability value of each flow service type after decision;
step five: each decision tree is extracted according to the extracted
Are characterized by using ∈>
Each of the features being taken asThe node characteristics of the first branch are sequentially calculated and used (th +.>
Individual characteristics) entropy value of the decided data set +.>
，
wherein ,
for the +.>
The individual element is at->
Probability of post-decision outcome under individual attributes, +.>
Is->
Attribute number of individual features->
The weight of each decision result is occupied;
step six: calculating information gain for each feature
, wherein ,/>
Taking->
Maximum value of (m)
The corresponding feature is taken as the branch of the decision treeIs a node of (a);
step seven: repeatedly selecting the characteristics as branch nodes of the decision tree, and if the attribute selected by the next node is the attribute used when the parent node is split, obtaining the classified decision tree;
step eight: the decision tree after classification is not subjected to post pruning treatment, and the overall loss after a certain node is removed is calculated
, wherein ,/>
；
wherein ,
for the entropy value of the node, +. >
For the balance coefficient->
The number of leaf nodes contained in the nodes;
respectively carrying out loss calculation of pruning and non-pruning on target branch nodes in the classified decision tree to obtain a first loss result
And second loss result->
The method comprises the steps of carrying out a first treatment on the surface of the Pruning the target branch node under the condition that the first loss result is smaller than or equal to the second loss result, and reserving the target branch node under the condition that the first loss result is larger than the second loss result, wherein the target branch node is any one of decision trees;
step nine: repeating the creating process of the decision tree to obtain a random forest model, wherein the number of decision trees contained in the random forest model is larger than that of the random forest model
。
In some embodiments of the present disclosure, a service identification node invokes a traffic service identification intelligent contract to perform current service identification to obtain a current traffic service type, and one implementation manner is that the service identification node inputs traffic usage information of the current traffic into a random forest model which is already trained, so as to obtain a plurality of traffic service type classification results; and selecting the current traffic service type from the multiple traffic service type classification results by using a voting algorithm. The service identification node calculates service platform acquisition according to the mobile edge 
The traffic in the random forest uses the data information, the traffic service in the period is judged through the trained random forest, and the judgment output result of all decision trees in the random forest on the traffic service type in the period is obtained
. Making a final decision by using a voting method:
if it is
If the identification fails, the identification step needs to be executed again. If->
The final result of identifying the traffic type is +.>
。
In the embodiment, the large-scale scattered flow use condition in the computing power network is managed in a alliance chain mode, multiparty nodes are introduced to monitor together, the fault tolerance of the system is improved, transparent monitoring of the whole flow use process and information notification backup of related parties are realized by means of the self attribute of the block chain distributed account book, the safety and transparency of the whole system are enhanced, meanwhile, the resource waste of synchronous information is avoided, and the working efficiency is improved. The random forest algorithm is adopted to complete the identification work of the traffic service type, the traffic service type is used for charging the traffic service condition, and meanwhile, the charging node broadcasts the cost detail regularly, so that a user can know and check the cost in time, the occurrence of an event of charging error is avoided, the charging accuracy is improved, and the benefits of both operators and users are ensured. Two block chain networks of flow processing and flow statistics are designed, two functions of flow service identification charging and flow information reporting are divided, reliability of information transmission is ensured, and the number of nodes and resources involved in failure under a small probability is reduced. And generating a personalized flow processing block chain micro-network aiming at each calculation task, deleting and destroying the network when the task is finished, and reducing the storage pressure of each node.
Fig. 3 is a schematic structural diagram of a full blockchain network 30 according to an exemplary embodiment of the present application. As shown in fig. 3, the full blockchain network 30 includes: a traffic handling blockchain micro network 31 and a traffic statistics blockchain network 32. The traffic handling blockchain micro network 31 includes: a service identification node 311, a traffic policing node 312, a charging node 313 and a participant node 314. The traffic statistics blockchain network 32 includes: a gateway node 321 and a traffic policing node 322.
The service identification node 311 invokes the traffic service identification intelligent contract to identify the current service and obtain the current traffic service type;
the flow monitoring node 312 collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics blockchain network, wherein the flow usage information comprises flow usage fees;
the charging node 313 determines the traffic usage fee according to the current traffic type and traffic usage amount, and broadcasts the traffic usage fee to the traffic statistics blockchain network;
the nodes of the traffic handling blockchain micro network 31 perform consensus operations to upload traffic usage information into target blocks via the traffic statistics blockchain network and broadcast the target blocks to the full blockchain network 30.
Optionally, after the charging node 313 determines the traffic usage fee according to the current traffic type and the traffic usage amount and broadcasts the traffic usage fee to the traffic statistics blockchain network, the method further includes:
judging whether the flow using cost is greater than the expected cost;
stopping executing the current calculation task under the condition that the flow use cost is greater than the expected cost;
and if the flow using cost is less than or equal to the expected cost, continuing to execute the current computing task, and sending a resource allocation request to the computing network so as to enable the computing network to issue new computing resources.
Optionally, after broadcasting the traffic usage information to the traffic statistics blockchain network, the traffic supervising node 312 may be further configured to:
after receiving the traffic usage information, the participant node 314 performs a verification operation on the traffic usage information;
in the case that the flow rate usage information passes verification, the flow rate usage information is stored in its own recording pool.
Optionally, after broadcasting the target block to the full blockchain network, it is further operable to:
each node in the flow processing block chain micro-network receives a target block and performs verification operation on the target block;
under the condition that the target block passes verification, adding the target block to the tail of the self block chain;
In the event that the target block verification fails, the target block is discarded.
Optionally, after broadcasting the target block to the full blockchain network, it is further operable to:
the gateway node 321 receives the target block and performs verification operation on the target block;
under the condition that the target block passes verification, adding the target block into a self block chain;
sending a target block reception success notification to the traffic processing blockchain micro-network;
and after receiving the successful notification of the target block, the flow processing block chain micro-network performs deleting operation on the current computing task and the flow processing block chain micro-network.
Optionally, when the service identification node 311 invokes the traffic service identification intelligent contract to perform current service identification, the service identification node is configured to:
the service identification node inputs the flow use information of the current flow into a trained random forest model to obtain a plurality of flow service type classification results;
and selecting the current traffic service type from the multiple traffic service type classification results by using a voting algorithm.
Optionally, before using the random forest model, the service identification node 311 may be further configured to:
Periodicity from multiple traffic types
Inner->
Constructing an original data set by the data packets;
sampling in the original data set by adopting a put-back strategy to generate a sub-data set corresponding to the data volume of the original data set; wherein the internal elements of the sub-data set can be repeated, and the number of the elements in the sub-data set is
（/>
) The method comprises the steps of carrying out a first treatment on the surface of the Traffic types are classified as +.>
Seed of->
The method comprises the steps of carrying out a first treatment on the surface of the Each item of data in the data package comprises +.>
A plurality of features; wherein the traffic characteristics include at least one of: the average value, variance and the maximum value of the data packet size, the average value and variance of the data packet arrival time interval, the ratio of the uplink byte number to the downlink byte number, the average value and variance of the data packet arrival time interval, the number of downstream IP contained, the number of downstream substream fragments, the overall packet rate and the downstream byte rate;
each decision tree randomly extracts from the characteristics of each flow service
Features of
Each decision tree calculates entropy according to the sub-data set
, wherein ,/>
，/>
The probability of the result after each element decision in the target sub-data set is the probability value of each flow service type after decision;
each decision tree is extracted according to the extracted
Are characterized by using ∈ >
Each of the features is used as a node feature of the first branch, and the features are sequentially calculated and used (the +.>
Individual characteristics) entropy value of the decided data set +.>
，
wherein ,
for the +.>
The individual element is at->
Probability of post-decision outcome under individual attributes, +.>
Is->
Attribute number of individual features->
The weight of each decision result is occupied;
calculating information gain for each feature
, wherein ,/>
Taking->
Maximum value->
The corresponding characteristic is used as the node of the branch of the decision tree;
repeatedly selecting the characteristics as branch nodes of the decision tree, and if the attribute selected by the next node is the attribute used when the parent node is split, obtaining the classified decision tree;
the decision tree after classification is not subjected to post pruning treatment, and the overall loss after a certain node is removed is calculated
, wherein ,/>
；
wherein ,
for the entropy value of the node, +.>
For the balance coefficient->
The number of leaf nodes contained in the nodes;
respectively carrying out loss calculation of pruning and non-pruning on target branch nodes in the classified decision tree to obtain a first loss result
And second loss result->
The method comprises the steps of carrying out a first treatment on the surface of the Pruning the target branch node under the condition that the first loss result is smaller than or equal to the second loss result, and reserving the target branch node under the condition that the first loss result is larger than the second loss result, wherein the target branch node is any one of decision trees;
Repeating the creating process of the decision tree to obtain a random forest model, wherein the random forest model contains the number of decision trees
Should be greater than +.>
。
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 4 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application. As shown in fig. 4, the electronic device includes: a memory 41 and a processor 42. In addition, the electronic device further comprises a power supply component 43 and a communication component 44.
The memory 41 is used for storing a computer program and may be configured to store other various data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on an electronic device.
The memory 41 may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
A communication component 44 for data transmission with other devices.
A processor 42, executable computer instructions stored in memory 41, for: the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type;
the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics block chain network, wherein the flow usage information comprises flow usage fees;
the charging node determines the flow use cost according to the current flow service type and the flow use amount and broadcasts the flow use cost to the flow statistics block chain network;
and each node in the flow processing block chain micro-network performs consensus operation, and uploads flow use information to the target block through the flow statistics block chain network and broadcasts the target block to the full block chain network.
Optionally, the charging node, after determining the traffic usage fee according to the current traffic type and the traffic usage amount and broadcasting the traffic usage fee to the traffic statistics blockchain network, the processor 42 is further configured to:
Judging whether the flow using cost is greater than the expected cost;
stopping executing the current calculation task under the condition that the flow use cost is greater than the expected cost;
and if the flow using cost is less than or equal to the expected cost, continuing to execute the current computing task, and sending a resource allocation request to the computing network so as to enable the computing network to issue new computing resources.
Optionally, the traffic handling blockchain micro-network further comprises: a participant node; after the traffic monitoring node broadcasts traffic usage information to the traffic statistics blockchain network, the processor 42 is further operable to:
after receiving the flow use information, the participant node performs verification operation on the flow use information;
in the case that the flow rate usage information passes verification, the flow rate usage information is stored in its own recording pool.
Optionally, after broadcasting the target block to the full blockchain network, the processor 42 is further operable to:
each node in the flow processing block chain micro-network receives a target block and performs verification operation on the target block;
under the condition that the target block passes verification, adding the target block to the tail of the self block chain;
in the event that the target block verification fails, the target block is discarded.
Optionally, the traffic statistics blockchain network includes: a gateway node; after broadcasting the target block to the full blockchain network, the processor 42 may also be configured to:
the gateway node receives the target block and performs verification operation on the target block;
under the condition that the target block passes verification, adding the target block into a self block chain; and
sending a target block receiving success notice to a flow processing block chain micro-network;
and after receiving the successful notification of the target block, the flow processing block chain micro-network performs deleting operation on the current computing task and the flow processing block chain micro-network.
Optionally, the service identifying node invokes the traffic service identifying intelligent contract to identify the current service, and when the current traffic service type is obtained, the processor 42 is configured to:
the service identification node inputs the flow use information of the current flow into a trained random forest model to obtain a plurality of flow service type classification results;
and selecting the current traffic service type from the multiple traffic service type classification results by using a voting algorithm.
Optionally, before using the random forest model, the processor 42 may be further configured to:
periodicity from multiple traffic types 
Inner->
Constructing an original data set by the data packets;
sampling in the original data set by adopting a put-back strategy to generate a sub-data set corresponding to the data volume of the original data set; wherein the internal elements of the sub-data set can be repeated, and the number of the elements in the sub-data set is
（/>
) The method comprises the steps of carrying out a first treatment on the surface of the Traffic types are classified as +.>
Seed of->
The method comprises the steps of carrying out a first treatment on the surface of the Each item of data in the data package comprises +.>
A plurality of features; wherein the traffic characteristics include at least one of: mean, variance, and maximum of packet sizes, mean and variance of packet arrival time intervals, ratio of number of upstream and downstream bytes, and packet arrival time intervalsMean and variance, number of downstream IP contained, number of downstream substream fragments, overall packet rate, and downstream byte rate;
each decision tree randomly extracts from the characteristics of each flow service
Features of
Each decision tree calculates entropy according to the sub-data set
, wherein ,/>
，/>
The probability of the result after each element decision in the target sub-data set is the probability value of each flow service type after decision;
each decision tree is extracted according to the extracted
Are characterized by using ∈>
Each of the features is used as a node feature of the first branch, and the features are sequentially calculated and used (the +. >
Individual characteristics) entropy value of the decided data set +.>
，
wherein ,
for the +.>
The individual element is at->
Probability of post-decision outcome under individual attributes, +.>
Is->
Attribute number of individual features->
The weight of each decision result is occupied;
calculating information gain for each feature
, wherein ,/>
Taking->
Maximum value->
The corresponding characteristic is used as the node of the branch of the decision tree;
repeatedly selecting the characteristics as branch nodes of the decision tree, and if the attribute selected by the next node is the attribute used when the parent node is split, obtaining the classified decision tree;
the decision tree after classification is not subjected to post pruning treatment, and the overall loss after a certain node is removed is calculated
, wherein ,/>
；/>
wherein ,
for the entropy value of the node, +.>
For the balance coefficient->
The number of leaf nodes contained in the nodes;
respectively carrying out loss calculation of pruning and non-pruning on target branch nodes in the classified decision tree to obtain a first loss result
And second loss result->
The method comprises the steps of carrying out a first treatment on the surface of the Pruning the target branch node under the condition that the first loss result is smaller than or equal to the second loss result, and reserving the target branch node under the condition that the first loss result is larger than the second loss result, wherein the target branch node is any one of decision trees;
Repeating the creating process of the decision tree to obtain a random forest model, wherein the random forest model contains the number of decision trees
Should be greater than +.>
。
Accordingly, embodiments of the present application also provide a computer-readable storage medium storing a computer program. The computer-readable storage medium stores a computer program that, when executed by one or more processors, causes the one or more processors to perform the steps in the method embodiment of fig. 1.
Accordingly, embodiments of the present application also provide a computer program product comprising a computer program/instructions for executing the steps of the method embodiment of fig. 1 by a processor.
The communication assembly of fig. 4 is configured to facilitate wired or wireless communication between the device in which the communication assembly is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a mobile communication network of WiFi,2G, 3G, 4G/LTE, 5G, etc., or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
The power supply assembly shown in fig. 4 provides power for various components of the device in which the power supply assembly is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.
The electronic device further comprises a display screen and an audio component.
The display screen includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation.
An audio component, which may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a speech recognition mode. The received audio signal may be further stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should be noted that in this document, relational terms such as "first" and "second" and the like are 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. Moreover, 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely a specific embodiment of the application to enable one skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A computational power network flow processing method based on a block chain is applied to a full block chain network, wherein the full block chain network comprises the following components: a traffic handling blockchain micro-network and a traffic statistics blockchain network, the traffic handling blockchain micro-network comprising: service identification node, traffic supervision node and charging node, which is characterized in that:
the service identification node calls a traffic service identification intelligent contract to identify the current service and obtain the current traffic service type;
the flow monitoring node collects the flow usage of the current time period under the current calculation task, counts the flow usage information of the current calculation task after the current calculation task is completed, and broadcasts the flow usage information to the flow statistics blockchain network, wherein the flow usage information comprises flow usage fees;
The charging node determines the traffic usage cost according to the current traffic service type and the traffic usage amount, and broadcasts the traffic usage cost to the traffic statistics blockchain network;
and each node in the flow processing block chain micro-network performs consensus operation, uploads the flow use information to a target block through a flow statistics block chain network, and broadcasts the target block to the full block chain network.
2. The method of claim 1, wherein the charging node, after determining the traffic usage charge based on the current traffic type and the traffic usage amount and broadcasting the traffic usage charge to the traffic statistics blockchain network, further comprises:
judging whether the flow using cost is greater than an expected cost;
stopping executing the current calculation task if the flow use fee is greater than the expected fee;
and if the flow using cost is less than or equal to the expected cost, continuing to execute the current computing power task, and sending a resource allocation request to a computing power network so as to enable the computing power network to issue new computing power resources.
3. The method of claim 1, wherein the traffic handling blockchain micro network further comprises: a participant node; after the traffic monitoring node broadcasts the traffic usage information to the traffic statistics blockchain network, the method further comprises:
after receiving the flow use information, the participant node performs verification operation on the flow use information;
and under the condition that the flow rate use information passes verification, storing the flow rate use information into a recording pool of the flow rate use information.
4. The method of claim 1, wherein after said broadcasting the target block to the full blockchain network, the method further comprises:
each node in the flow processing block chain micro-network receives the target block and performs verification operation on the target block;
if the target block passes verification, adding the target block to the end of a self block chain;
the target block is discarded if the target block verification fails.
5. The method of claim 1, wherein the traffic statistics blockchain network comprises: a gateway node; after the broadcasting the target block to the full blockchain network, the method further includes:
The gateway node receives the target block and performs verification operation on the target block;
under the condition that the target block passes verification, adding the target block into a self block chain;
sending a target block reception success notification to the traffic processing blockchain micro-network;
and after receiving the successful notification of the target block, the flow processing blockchain micro-network performs deleting operation on the current computing task and the flow processing blockchain micro-network.
6. The method according to claim 1, wherein the service identification node invokes a traffic service identification intelligent contract to perform current service identification to obtain a current traffic service type, and comprises:
the service identification node inputs the flow use information of the current flow into a trained random forest model to obtain a plurality of flow service type classification results;
and selecting the current traffic service type from a plurality of traffic service type classification results by using a voting algorithm.
7. The method of claim 6, wherein prior to using the random forest model, the method further comprises:
Periodicity from multiple traffic types
Inner->
Construction of original data from individual data packetsA collection;
sampling in the original data set by adopting a replacement strategy to generate a sub-data set corresponding to the data volume of the original data set; wherein the internal elements of the sub-data set can be repeated, and the number of the elements in the sub-data set is
（
) The method comprises the steps of carrying out a first treatment on the surface of the Traffic types are classified as +.>
Seed of->
The method comprises the steps of carrying out a first treatment on the surface of the Each item of data in the data packet comprises +.>
A plurality of features; wherein the traffic characteristics include at least one of: the average value, variance and the maximum value of the data packet size, the average value and variance of the data packet arrival time interval, the ratio of the uplink byte number to the downlink byte number, the average value and variance of the data packet arrival time interval, the number of downstream IP contained, the number of downstream substream fragments, the overall packet rate and the downstream byte rate;
each decision tree randomly extracts from the characteristics of each flow service randomly
Features of
Each decision tree calculates entropy values according to the sub-data sets
, wherein ,/>
，/>
The probability of the result after each element decision in the target sub-data set is the probability value of each flow service type after decision;
Each of the decision trees is based on the extracted
Are characterized by using ∈>
Each of the features is used as a node feature of the first branch, and the features are sequentially calculated and used (the +.>
Individual characteristics) entropy value of the decided data set +.>
，
wherein ,
for the +.>
The individual element is at->
Probability of post-decision outcome under individual attributes, +.>
Is->
Attribute number of individual features->
The weight of each decision result is occupied;
calculating information gain for each feature
, wherein ,/>
Taking->
Maximum value->
The corresponding characteristic is used as the node of the branch of the decision tree;
repeatedly selecting the characteristics as branch nodes of the decision tree, and if the attribute selected by the next node is the attribute used when the parent node is split, obtaining the classified decision tree;
the decision tree subjected to classification is not subjected to post pruning treatment, and the overall loss after a certain node is removed is calculated
, wherein ,/>
；
wherein ,
for the entropy value of the node, +.>
For the balance coefficient->
The number of leaf nodes contained in the nodes;
pruning is respectively carried out on target branch nodes in the decision tree with the classification completedAnd calculating the loss without pruning to obtain a first loss result
And second loss result- >
The method comprises the steps of carrying out a first treatment on the surface of the Pruning the target branch node when the first loss result is smaller than or equal to the second loss result, and reserving the target branch node when the first loss result is larger than the second loss result, wherein the target branch node is any one of the decision trees;
repeating the creation process of the decision tree to obtain a random forest model, wherein the random forest model contains the number of decision trees
Should be greater than +.>
。
8. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the steps in the method of any of claims 1-7.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1-7.
10. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the method of any of claims 1-7.
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