CN109561158A - A kind of method and system of distributed intelligence network hydrodynamics - Google Patents
A kind of method and system of distributed intelligence network hydrodynamics Download PDFInfo
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Abstract
The invention discloses a kind of method and system of distributed intelligence network hydrodynamics, including S101, acquisition network state information data, extract and identify its feature；If S102, identified successfully, optimal solution is transferred, distributed network computing is called according to scheme, to solve the problems, such as this network state；If S103, recognition failures, then it is assumed that this network state is new network state, then solves the problems, such as network state using similar S102 method is opposite；S014, S101~S103 is repeated, the data splitting of i group network state information and its solution, composition data collection 1 can be obtained；S105, then pass through confrontation e-learning data set 1, obtain bigger data set 3；S106, data set 3 is further learnt using intensified learning method, obtains the optimal network state information of multiple groups and its solution combination；S107, every group of obtained combination is packaged respectively using block chain technology, is then passed in local expert system library.
Description
Technical field
The present invention relates to network performance technical fields, specifically for, the present invention relates to a kind of distributed intelligence networks
The method and system of hydrodynamics.
Background technique
As science and technology develops rapidly, standard follows these three levels and is developed for internet at present: 1. information interconnected networks
(internet PC, mobile Internet), this stage solve the problems, such as information asymmetry, and information is no longer separated, those with spy
Different channel obtains information and the person who resells at inflated prices to seek profit eliminates；Object internet 2. (Internet of Things, artificial intelligence), this stage solution
The problem of object divergence of having determined, object become energy and human interaction by stationary；Product type and design are become by imposing uniformity without examining individual cases
At customization, personalization；3. being worth internet (block chain), this stage is also referred to as being worth the stage of internet, this stage
Solve the problems, such as that value is not reciprocity, the method for salary distribution is no longer rely on position, annual pay, bonus etc., and the value that everyone creates can
It is precisely recorded and is fulfilled at any time.Value internet is but currently to go multi-tiling chain technology using block chain technology as foundation stone
Value delivery efficiency it is relatively low some, may be enough for current task, but with the increase of information content, value
There is still a need for further promoted for the speed of internet.
Secondly, industry internet is the concept amplified out from consumption internet in numerous internet branches, refer to biography
System industry borrows power big data, cloud computing, intelligent terminal and network advantage, promotes internal efficiency and external service ability, is to pass
System industry realizes one of the important path of transition and upgrade by " internet+".The rise of industry internet, it is meant that manufacture, agriculture
Many traditional fields such as industry, the energy, logistics, traffic, education will all be interconnected net in succession and change and reconstruct, and pass through internet
The efficiency of inter-trade collaboration is improved, realizes great-leap-forward development.But current industry internet cooperative mode is that business cooperates with, and is deposited
In the relatively low problem of safety and efficiency, if the commercial activity that reply is following, needs more intelligent elastic collaborative party
Formula.Wherein industry internet is an important content in industry internet, and industry internet is conceived to transaction issues, and industry is mutually
Networking is conceived to manufacturing issue, but by internet to interact still efficiency very low for many contents in industry internet, such as
Fruit can improve the efficiency of industry internet, then mean the industrial revolution of a new round.
In conclusion problem of the existing technology is: value internet needs to improve network transmission efficiency；Industry interconnection
Net needs to upgrade cooperative mode, is cooperateed with by business to intelligent coordinated；These problems can be attributed to network performance low efficiency, intelligence substantially
The low problem of energyization, so strengthening needing to become those skilled in the art's technical problem urgently to be solved and grinding for network performance
The emphasis studied carefully.
Summary of the invention
The problems such as to solve low efficiency present in existing network performance, intelligent low and weak robustness, the present invention provides
A kind of method and system of distributed intelligence network hydrodynamics.
Implementation method of the invention: S101. acquires network state information, is denoted as network state 1.0, then extracts and identify
The feature of network state information；S102. if identified successfully, corresponding optimal solution in local expert system is transferred,
It is denoted as solution 1.0, distributed network computing is then called according to scheme, to solve the problems in this network state；This
When obtain one group of network state and corresponding solution (1.0,1.0)；If S103. identifying unsuccessful, then it is assumed that this network
State is new network state, is denoted as network state 1.1, then transfers corresponding suboptimum solution party in local expert system
Case is denoted as solution 1.1, then calls distributed network computing according to scheme, so that opposite solve in this new network state
The problem of；One group of network state and corresponding solution (1.1,1.1) are obtained at this time；S104. S101~S103 is recycled, can be obtained
Multiple groups network state and its solution combination, are denoted as (1.2,1.2) ... (1.i, 1.i), these combinations form data set 1；
S105. using the method for confrontation network, it is based on data set 1, can learn to obtain more networks state and its solution combination, note
For (1.i+1,1.i+1) ..., (1.n, 1.n), these combinations form data set 3；S106. it utilizes and then utilizes intensified learning
Method is based on data set 3, can learn to obtain optimal network state and its solution combination；S107. finally, utilizing block chain
The optimal network state that study obtains and its solution are packaged by technology, are passed in distributed local expert system.
Step S101: extracting and identifies collected network state information treatment process:
Acquire network state information data, comprising: the Ip of the type Type of current network device, current network device
The information such as open end slogan PortNum, the currently used service Service of location, transmission and receiving；
Remember that current collected network state information is 1.0；
Then the feature of network state information is extracted using clustering algorithm and Vector Quantization algorithm；
The feature that S101 is extracted is compared with the feature in local expert system, obtains state characteristic similarity
Probability sorting is as a result, filter out ranking results not less than α (such as α=0.67) and no more than the ranking results of α；
Step S102 calls solution and distributed network computing, treatment process if feature identifies successfully:
The feature that maximum comparability probability is chosen according to step S101, as identification Success Flag, i.e., collected network
Most like feature in status information 1.0 and local expert system, and transfer the corresponding solution of this most like feature；
Calculation power support is provided to solve this network state according to 1.0 calling distributed network computings in solution, is supported
The range for calculating power be (default that is arranged in solution 1.0 calculates force value, solve to calculate needed for this network state power supports 1.5~
2.0 times), so that the problems in network state 1.0 is solved, wherein calculating power is by the distributed network computing based on block chain technology
In node provide；
And obtain a combination and played state 1.0 and its solution 1.0, it is denoted as (1.0,1.0)；
Step S103 is handled if feature recognition failures according to new network state method, treatment process:
The ranking results that similar features probability is not more than α are chosen, as the mark of recognition failures, then by current network state
Information is considered new network state information, is denoted as 1.1；
Then it selects no more than the maximum value in α ranking results, as identification feature as a result, then transferring local expert
Solution corresponding with this result, is denoted as 1.1 in system；
Calculation power support is provided to solve this network state according to distributed network computing is called in solution 1.1, is supported
The range for calculating power is that (default being arranged in solution calculates force value, solves to calculate power is supported 1.5~2.0 needed for this network state
Times), so that opposite solve the problems in network state 1.1；
And obtain another and combine new network state feature 1.1 and its solution 1.1, it is denoted as (1.1,1.1)；
Step S104: more networks state and solution combination are obtained, treatment process:
Circulation step S101~S103 obtains i group network state and its solution party wherein the frequency of acquisition network is 60HZ
Case combination, is denoted as (1.2,1.2), (1.3,1.3) ..., (1.i, 1.i)；
Obtained multiple groups group data are closed and are saved into the data set that can be used to learn, data set 1 is denoted as；
Step S105: obtaining more networks state using confrontation network (GAN) learning data set 1 and solution combine, right
Anti- network is by generation network G and differentiates that network D is formed, and the target for generating network G is to generate to generate true data as far as possible and go to take advantage of
It deceives and differentiates network D, and differentiate that the target of network D is just to try to the data that generation network G generates to be distinguished from truthful data,
A large amount of data can be generated in this process, treatment process:
First by data set 1 that S104 is obtained in local expert system network state and its solution combine combination
Get up, as the training data for generating network G in confrontation network；
By generating study of the network G to above-mentioned data, non-genuine more networks status information and its solution are generated
Combined data, as data set 2；
Then data set 2 and the data in local expert system are compared by fighting the differentiation network D of network, i.e.,
Comparison (data set 2, the data in local expert system), and threshold value mark carried out to comparing result, threshold value be set as β (such as β=
0.67)；
The data that above-mentioned threshold value is less than β are deleted, and remaining data set, data set 1 and local expert system after deleting
In data merge into data set 3；
Data set 3 includes the n group combination of network state information and its solution, is denoted as (1.0,1.0), (1.1,
1.1),...,(1.i,1.i),(1.i+1,1.i+1),...,(1.n,1.n)；
Step S106 obtains network state and its solution using the method learning data set 3 of enhancing study (RL)
Optimum combination, treatment process:
Need to carry out the node of intensified learning as intelligent body agent in established distributed local expert system；
By each network state information and its solution in data set 3, such as (1.1,1.1) are as enhancing study
Input a；
Intelligent body a can be input to evaluation environment Env in, evaluation environment can provide input a after a prize outcome r with
And the state s that evaluation environment is current, wherein evaluation environment is local expert system library, prize outcome r is that local expert system will
It is that current learning outcome and the local preset Comparative result in expert system library come out as a result, belong to similarity probabilities value, and state s
It is the feedback whether local expert system library needs to learn again for prize outcome r；Similitude can use existing similar
Property comparison algorithm realize, state s and corresponding relationship between whether needing to learn again can rule of thumb or demand is set.
Then intelligent body provides next input a according to obtained prize outcome r and current state s；
Above-mentioned three small step is recycled, finally the obtained prize outcome r of each input a is ranked up, preceding 75% is chosen and makees
To be optimal as a result, obtaining the optimal network state of multiple groups and solution combination；
Step S107: the step S106 combination obtained is packaged using block chain technology, treatment process:
The step S106 optimal network state obtained and its solution combination are subjected to encryption envelope using block chain technology
Dress, using symmetric encipherment algorithm；
Then distribute hash value to all nodes, distribute expert system library file fragment to orientation node；
Further, the network information data of acquisition described in step S101 are as follows:
Network state information includes various network performance informations in scheme disclosed by the invention, is not only limited to distribution speed
(TPS), anti-congestion (DDoS), network acceleration (CDN) etc.；
Further, local expert system structure described in step S102 are as follows:
Initial local expert system, by open personnel's input, including each network state and its solution, originally
Ground expert system is made of several sub- expert systems, each is from expert system by several network states and its solution party
Case is constituted, and structure chart is as shown in Figure 2.
Technical solution provided by the invention has the beneficial effect that
As shown in figure 3, distributed memory system is made of multiple child nodes in scheme disclosed by the invention, each node
A expert system can be stored, can be called in expert system when any one node has network state in this way
Solution, and each node can share to any one of distributed memory system node after study, have height
Sharing and high security；
Distributed network computing system is also to be made of multiple child nodes in scheme disclosed by the invention, and any one in system
A node required calculation power when being calculated, can be supported by other nodes, and it is distributed to reach high-speed block chain
Database retrieval polymerizable functional has higher load, robustness and scalability.
The expert system proposed in scheme disclosed by the invention has higher with the continuous expert system for learning, strengthening
Intelligence, scalability and robustness.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, below by embodiment or the prior art
Attached drawing needed in description is briefly described, it should be apparent that, the accompanying drawings in the following description is only the application's
Some embodiments without creative efforts, can be with root for the common invoice technical staff of this field
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the overall flow figure of proposition method of the present invention；
Fig. 2 is the composite structural diagram of expert system；
Fig. 3 is the effect picture that the scheme learnt is shared to other nodes by child node by distributed network computing；
Specific embodiment
In order to which technical solution of the present invention is more clearly understood, with reference to embodiments, the Ip standby to summary of the invention
The open port of location, transmission and receiving is explained in more detail, but the protection scope invented is not limited to following examples, this theory
All features disclosed in bright book or disclosed all methods or in the process the step of, in addition to mutually exclusive feature and/or step
Other than rapid, it can combine in any way.
Principle is described further with reference to the accompanying drawing.
As shown in Figure 1, a kind of method and system of distributed intelligence network hydrodynamics provided in an embodiment of the present invention, build
It is vertical the following steps are included:
Two parts are mainly segmented into, first part is to establish initial local expert system；Second part is study, strengthens
Local expert system；
The initial local expert system of first part, comprising the following steps:
Developer acquires network state information data by network packet capturing mode；
The feature of network state information is extracted by clustering algorithm and Vector Quantization algorithm；
Using the network state information feature extracted as sample, network state information feature database is established；
It is same to use the similar above method, it establishes opposite with every network state information in network state information feature database
The solution answered；
The network state information feature for extracting network state information feature database one by one and its solution in solution library
Scheme forms multiple groups combination；
By heterogeneous networks status information classification, different sub- expert system libraries, same sub- expert system library Zhong Bao are formed
Containing multiple groups generic network state information feature and corresponding solution, sub- expert system library structure is with reference to shown in Fig. 2；
Different sub- expert system libraries forms local expert system library, and local expert system structure is with reference to shown in Fig. 2；
Meanwhile every group of network state information and its solution are encapsulated using block chain technology；
Then local expert system is stored to each node under distributed memory system using distributed network computing
In, each node repeats to deposit three parts；
Each node storage is the hash file in local expert system library, the fragment file in local expert system library, sheet
The redundant file in ground expert system library；
So far, completion has been established in an initial local expert system, and is split into three kinds of files and is respectively stored in distribution
In each node under formula storage system, and three parts of each node repeated access, this ensure that file security, and adjust
When calling local expert system library with other nodes, its hash file only need to be called, lightweight and height when ensure that calling
Effect property；
Local expert system is strengthened in second part study, the specific steps are as follows:
Step S101: extracting and identifies collected network state information treatment process:
Acquire network state information data, comprising: the Ip of the type Type of current network device, current network device
The information such as open end slogan PortNum, the currently used service Service of location, transmission and receiving；
Remember that current collected network state information is 1.0；
Then the feature of network state information is extracted using clustering algorithm and Vector Quantization algorithm；
The feature that S101 is extracted is compared with the feature in local expert system, obtains state characteristic similarity
Probability sorting is as a result, filter out ranking results not less than α (such as α=0.67) and no more than the ranking results of α；
Step S102 calls solution and distributed network computing, treatment process if feature identifies successfully:
The feature that maximum comparability probability is chosen according to step S101, as identification Success Flag, i.e., collected network
Most like feature in status information 1.0 and local expert system, and transfer the corresponding solution of this most like feature；
Calculation power support is provided to solve this network state according to 1.0 calling distributed network computings in solution, is supported
The range for calculating power be (default that is arranged in solution 1.0 calculates force value, solve to calculate needed for this network state power supports 1.5~
2.0 times), to solve the problems in network state 1.0；
And obtain a combination and played state 1.0 and its solution 1.0, it is denoted as (1.0,1.0)；
Step S103 is handled if feature recognition failures according to new network state method, treatment process:
The ranking results that similar features probability is not more than α are chosen, as the mark of recognition failures, then by current network state
Information is considered new network state information, is denoted as 1.1；
Then it selects no more than the maximum value in α ranking results, as identification feature as a result, then transferring local expert
Solution corresponding with this result, is denoted as 1.1 in system；
Calculation power support is provided to solve this network state according to distributed network computing is called in solution 1.1, is supported
The range for calculating power is that (default being arranged in solution calculates force value, solves to calculate power is supported 1.5~2.0 needed for this network state
Times), so that opposite solve the problems in network state 1.1；
And obtain another and combine new network state feature 1.1 and its solution 1.1, it is denoted as (1.1)；
Step S104: more networks state and solution combination are obtained, treatment process:
Circulation step S101~S103 obtains i group network state and its solution party wherein the frequency of acquisition network is 60HZ
Case combination, is denoted as (1.2,1.2), (1.3,1.3) ..., (1.i, 1.i)；
Obtained multiple groups group data are closed and are saved into the data set that can be used to learn, data set 1 is denoted as；
Step S105: obtaining more networks state using confrontation network (GAN) learning data set 1 and solution combine, right
Anti- network is by generation network G and differentiates that network D is formed, and the target for generating network G is to generate to generate true data as far as possible and go to take advantage of
It deceives and differentiates network D, and differentiate that the target of network D is just to try to the data that generation network G generates to be distinguished from truthful data,
A large amount of data can be generated in this process, treatment process:
First by data set 1 that S104 is obtained in local expert system network state and its solution combine combination
Get up, as the training data for generating network G in confrontation network；
By generating study of the network G to above-mentioned data, non-genuine more networks status information and its solution are generated
Combined data, as data set 2；
Then data set 2 and the data in local expert system are compared by fighting the differentiation network D of network, i.e.,
Comparison (data set 2, the data in local expert system), and threshold value mark carried out to comparing result, threshold value be set as β (such as β=
0.67)；
The data that above-mentioned threshold value is less than β are deleted, and remaining data set, data set 1 and local expert system after deleting
In data merge into data set 3；
Data set 3 includes the n group combination of network state information and its solution, is denoted as (1.0,1.0), (1.1,
1.1),...,(1.i,1.i),(1.i+1,1.i+1),...,(1.n,1.n)；
Step S106 obtains network state and its solution using the method learning data set 3 of enhancing study (RL)
Optimum combination, treatment process:
Using the system of the middle proposition of the invention that proposes a plan as intelligent body agent；
By each network state information and its solution in data set 3, such as (1.1,1.1) are as enhancing study
Input a；
A can be input in evaluation environment Env by intelligent body, and evaluation environment Env here is local expert system, evaluationization
Border can provide a prize outcome r after inputting a and evaluate the current state s of environment；
Then intelligent body provides next input a according to obtained prize outcome r and current state s；
Above-mentioned three small step is recycled, finally the obtained prize outcome r of each input a is ranked up, preceding 75% is chosen and makees
To be optimal as a result, obtaining the optimal network state of multiple groups and solution combination；
Step S107: the step S106 combination obtained is packaged using block chain technology, treatment process:
The step S106 optimal network state obtained and its solution combination are subjected to encryption envelope using block chain technology
Dress, using symmetric encipherment algorithm；
Then distribute hash value to all nodes, distribute expert system library file fragment to orientation node；
As shown in figure 3, distributed memory system is made of multiple child nodes in scheme disclosed by the invention, each node
A expert system can be stored, can be called in expert system when any one node has network state in this way
Solution, and each node can share to any one of distributed memory system node after study, have height
Sharing and high security；
It further illustrates, in Fig. 3,
Distributed network computing system is also to be made of multiple child nodes in scheme disclosed by the invention, and any one in system
A node required calculation power when being calculated, can be supported by other nodes, and it is distributed to reach high-speed block chain
Database retrieval polymerizable functional has higher load, robustness and scalability.
The expert system proposed in scheme disclosed by the invention has higher with the continuous expert system for learning, strengthening
Intelligence, scalability and robustness.
Claims (9)
1. a kind of method of distributed intelligence network hydrodynamics, which is characterized in that including S101, acquisition network state information number
According to extracting and identify its feature；If S102, identified successfully, optimal solution is transferred from expert system, according to scheme
Distributed network computing is called, solves the problems, such as this network state, obtains one group of network state and solution combination；S103, such as
Fruit recognition failures, then it is assumed that this network state is new network state, then solves network state using similar S102 method is opposite
Problem obtains one group of new network state and solution combination；S014, S101~S103 is repeated, i group network state letter can be obtained
The data splitting of breath and its solution, is denoted as composition data collection 1；S105, then pass through confrontation e-learning data set 1, obtain
To bigger data set 3；S106, data set 3 is further learnt using intensified learning method, it is optimal obtains multiple groups
Network state information and its solution combination；S107, every group of obtained combination is packaged respectively using block chain technology,
It is transmitted in local expert system library, obtains distributed local expert system.
2. the method for distributed intelligence network hydrodynamics according to claim 1, which is characterized in that the step
S101 is extracted and is identified collected network state information feature, and specific building process is as follows:
(1) network state information data are acquired, comprising: the type Type of current network device, current network device the address Ip,
The information such as the open end slogan PortNum, the currently used service Service that send and receive；
(2) remember that current collected network state information is 1.0；
(3) feature of network state information is then extracted using clustering algorithm and Vector Quantization algorithm；
(4) feature that extraction obtains in (3) and the feature in local expert system are compared, obtains state characteristic similarity
Probability sorting as a result, filter out the ranking results not less than α and the ranking results no more than α respectively.
3. the method for distributed intelligence network hydrodynamics according to claim 2, which is characterized in that the step
S102 calls solution and distributed network computing if feature identifies successfully, and specific building process is as follows:
(1) ranking results that similar features probability is not less than α are chosen, are successfully indicated as identification, and choose maximum value conduct
Identification feature as a result, then transferring solution corresponding with this maximum value tag in local expert system；
(2) corresponding solution for remembering most like feature is 1.0；
(3) distributed network computing is called according to solution 1.0 to solve this network state and providing calculation power support, supports to calculate power
Range be that the default being arranged in solution 1.0 calculates 1.5~2.0 times of force value, to solve asking in network state 1.0
Topic；
(4) and obtain a combination: network state 1.0 and its solution 1.0 are denoted as (1.0,1.0).
4. the method for distributed intelligence network hydrodynamics according to claim 2, which is characterized in that the step
S103 is handled if feature recognition failures according to new network state method, and specific building process is as follows:
(1) ranking results that similar features probability is not more than α are chosen, as the mark of recognition failures, then by current network state
Information is considered new network state information, is denoted as 1.1；
(2) it then selects no more than the maximum value in α ranking results, as identification feature as a result, then transferring local expert
Solution corresponding with this result, is denoted as 1.1 in system；
(3) calculation power support is provided to solve this network state according to distributed network computing is called in solution 1.1, supports to calculate
The range of power is that the default being arranged in solution 1.1 calculates 1.5~2.0 times of force value, so that opposite solve in network state 1.1
The problem of；
(4) and another new network state feature 1.1 of combination and its solution 1.1 are obtained, is denoted as (1.1,1.1).
5. the method for distributed intelligence network hydrodynamics according to claim 1, which is characterized in that the step
S104 obtains more networks state and solution combination, specific building process is as follows:
(1) circulation step S101~S103 obtains i group network state and its solution party wherein the frequency of acquisition network is 60HZ
Case combination, is denoted as (1.2,1.2), (1.3,1.3) ..., (1.i, 1.i)；
(2) obtained multiple groups group data are closed and is saved into the data set that can be used to learn, be denoted as data set 1.
6. the method for distributed intelligence network hydrodynamics according to claim 1, which is characterized in that the step
S105 obtains more networks state using confrontation network (GAN) learning data set 1 and solution combines, and fights network by generating
Network G and differentiation network D composition, the target for generating network G is to try to generate true data and go to cheat to differentiate network D, and sentences
The target of other network D is just to try to the data that generation network G generates to be distinguished from truthful data, can produce in this process
Raw a large amount of data, specific building process are as follows:
(1) first by data set 1 that S104 is obtained in local expert system network state and its solution combine combination
Get up, as the training data for generating network G in confrontation network；
(2) by generating study of the network G to above-mentioned data, non-genuine more networks status information and its solution group are generated
The data of conjunction, as data set 2；
(3) then data set 2 and the data in local expert system are compared by fighting the differentiation network D of network, i.e.,
Comparison (data set 2, the data in local expert system), and threshold value mark is carried out to comparing result, threshold value is set as β；
(4) above-mentioned data less than threshold value beta are deleted, and after deleting in remaining data set, data set 1 and local expert system
Data merge into data set 3；
(5) data set 3 includes the n group combination of network state information and its solution, is denoted as (1.0,1.0), (1.1,
1.1),...,(1.i,1.i),(1.i+1,1.i+1),...,(1.n,1.n)。
7. the method for distributed intelligence network hydrodynamics according to claim 1, which is characterized in that the step
S106 obtains the optimum combination of network state and its solution using the method learning data set 3 of enhancing study (RL), specifically
Building process is as follows:
(1) need to carry out the node of intensified learning as intelligent body agent in the local expert system of the distribution constructed；
(2) using in data set 3 each network state information and its solution as enhancing study input a；
(3) a prize outcome r after inputting a is provided by distributed local expert system and evaluates the current state s of environment,
Wherein evaluation environment is currently in the local expert system library for carrying out intensified learning, and prize outcome r is that local expert system will be worked as
The similarity probabilities value that the preset Comparative result of preceding learning outcome and local expert system library obtains, and state s is local expert
The feedback whether system library needs to learn again for prize outcome r；
(4) then intelligent body according to obtained prize outcome r and current state s provides next input a；
(5) circulation step (2)~(4) are finally ranked up the obtained prize outcome r of each input a, choose preceding 75% and make
To be optimal as a result, obtaining the optimal network state of multiple groups and solution combination.
8. the method for distributed intelligence network hydrodynamics according to claim 1, which is characterized in that the step
The step S106 combination obtained is packaged by S107 using block chain technology, and specific building process is as follows:
(1) the step S106 optimal network state obtained and its solution combination are subjected to encryption envelope using block chain technology
Dress, using symmetric encipherment algorithm；
(2) then distribute hash value to all nodes, distribute expert system library file fragment to orientation node.
9. a kind of system of distributed intelligence network hydrodynamics, it is characterised in that: described in any item using claim 1-8
Method is established, and in establishment process, expert system described in step S102 is initial local expert system, foundation include with
Lower step:
1) developer acquires network state information data by network packet capturing mode；
2) feature of network state information is extracted by clustering algorithm and Vector Quantization algorithm；
3) using the network state information feature extracted as sample, network state information feature database is established；
4) solution corresponding with every network state information in network state information feature database is established；
5) the network state information feature for extracting network state information feature database one by one and its solution party in solution library
Case forms multiple groups combination；
6) heterogeneous networks status information classification is pressed, different sub- expert system libraries is formed, includes in same sub- expert system library
Multiple groups generic network state information feature and corresponding solution, different sub- expert system libraries form local expert system
System library；
7) every group of network state information and its solution are encapsulated using block chain technology；Then utilize distributed network computing will
Into each node under distributed memory system, each node repeats to deposit three parts for local expert system storage；Each node is deposited
Storage be the hash file in local expert system library, the fragment file in local expert system library, local expert system library redundancy
File；So far, completion has been established in an initial local expert system.
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| CN107193490A (en) * | 2017-05-16 | 2017-09-22 | 北京中星仝创科技有限公司 | A kind of distributed data-storage system and method based on block chain |
| CN108959310A (en) * | 2017-05-23 | 2018-12-07 | 易链科技（深圳）有限公司 | Data processing method, device and computer readable storage medium based on block chain |
| CN109561100A (en) * | 2018-12-24 | 2019-04-02 | 浙江天脉领域科技有限公司 | Method and system based on the distributed duplexing energized network attacking and defending with artificial intelligence |
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| CN107193490A (en) * | 2017-05-16 | 2017-09-22 | 北京中星仝创科技有限公司 | A kind of distributed data-storage system and method based on block chain |
| CN108959310A (en) * | 2017-05-23 | 2018-12-07 | 易链科技（深圳）有限公司 | Data processing method, device and computer readable storage medium based on block chain |
| CN109561100A (en) * | 2018-12-24 | 2019-04-02 | 浙江天脉领域科技有限公司 | Method and system based on the distributed duplexing energized network attacking and defending with artificial intelligence |
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