CN115085941B - Computer data information processing method based on block chain network - Google Patents

Computer data information processing method based on block chain network Download PDF

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CN115085941B
CN115085941B CN202210880016.0A CN202210880016A CN115085941B CN 115085941 B CN115085941 B CN 115085941B CN 202210880016 A CN202210880016 A CN 202210880016A CN 115085941 B CN115085941 B CN 115085941B
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侯胜旭
覃家良
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Shenzhen Mack Storage Technology Co ltd
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Abstract

The invention discloses a computer data information processing method based on a block chain network, which comprises the following steps: (S1) sending out network data information, outputting the network data information and transmitting the network data information to the next application device; (S2) in the Internet data communication system, controlling data transmission through a network data interactive communication interface; (S3) connecting the node equipment through block chain link points connected in a chain manner, and communicating with each other through a block chain network; (S4) receiving data information sent by a network data sending terminal through the block chain network node to realize the calculation of the network data information; and (S5) outputting the calculated network data information to meet the user requirements. The invention can realize the decentralization, traceability and non-tampering of network data information by applying the blockchain network, and realizes the encryption and processing capabilities of the network data information through an encryption algorithm and an improved C4.5 decision tree algorithm model.

Description

Computer data information processing method based on block chain network
Technical Field
The present invention relates to the field of internet technologies, and more particularly, to a method for processing computer data information based on a blockchain network.
Background
With the continuous development of internet information technology, computers are widely used in different fields. Under the promotion of the development of digitalization, informatization and networking, especially the development of information globalization, the construction and wide application of a network information platform is promoted, and network data information is developed explosively, so that the current network data information has the problems of large data volume of a characteristic book, low processing efficiency, unsafe data in the interaction process, easy data leakage, data falsification and the like, and the safety performance is low in the interaction process of the network data information and the data information after the network data information is processed.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a computer data information processing method based on a block chain network, which realizes the interaction of data information by applying the block chain network, improves the data information interaction capability and realizes the data information interaction by applying the block chain network
In order to achieve the technical effects, the invention adopts the following technical scheme:
a computer data information processing method based on a block chain network comprises the following steps:
(S1) sending out network data information, and outputting and transmitting the network data information to the next application device;
in the step, when the network data information is sent out, the network data information is marked with data information compatible with a block chain network, and the tracking in the network data information transmission process is realized by marking a timestamp or a network data information identifier on the data information;
(S2) in the Internet data communication system, controlling data transmission through a network data interactive communication interface;
in the step, network data information transmission is realized by controlling the network data information nodes during data transmission control, and network data information node information interaction is synchronously realized during data transmission control;
(S3) connecting the node devices through the block chain nodes connected in a chain manner, and communicating with each other through a block chain network to enable the sent network data information to be subjected to information interaction and transmission through the block chain network;
in the step, the encryption of the data information of the block link point is realized through a symmetric encryption algorithm; the decryption of the data information of the block chain link point is realized by a symmetrical decryption method, and further the primary encryption of the data information of the block chain link point is realized;
encrypting data information through a public key of a receiver, then generating a data ciphertext, when receiving data, firstly calculating an abstract of transmitted data by using a hash function in a block chain, and then digitally signing the abstract through a private key; the data sender simultaneously sends a data ciphertext and a signature, and the receiver decrypts the data through a public key so as to realize encryption and decryption calculation of internet data information;
in the step, the second-level encryption of the data information of the block link point is realized through an elliptic curve equation;
(S4) receiving data information sent by a network data sending terminal through the block chain network node to realize the calculation of the network data information;
in the step, the processing of the network data information is realized through an improved C4.5 decision tree algorithm model, wherein the improved C4.5 decision tree algorithm model comprises a drosophila optimization algorithm model, so that the classification capability of the network data information is improved;
and (S5) outputting the calculated network data information to meet the user requirements.
As a further technical solution of the present invention; the improved C4.5 decision tree algorithm model is a classification algorithm model fused with a drosophila optimization algorithm model.
As a further technical solution of the present invention; the improved C4.5 decision tree algorithm model is carried out by the following steps:
(1) Extracting data information from network data nodes, and collecting the network data information
Figure 576964DEST_PATH_IMAGE001
Partitioning network data information into
Figure 222840DEST_PATH_IMAGE002
A classification module, then
Figure 380152DEST_PATH_IMAGE003
Is provided with
Figure 298429DEST_PATH_IMAGE004
An attribute tag, defining
Figure 199389DEST_PATH_IMAGE005
For each set of the attribute tags, a set of attributes,
Figure 135990DEST_PATH_IMAGE006
(ii) a Suppose that
Figure 464203DEST_PATH_IMAGE007
Is of the class
Figure 869777DEST_PATH_IMAGE008
The expected values required for a fixed sample classification for the network data samples in (1) are:
Figure 325160DEST_PATH_IMAGE009
（1）
(2) Constructing a drosophila optimization algorithm model, realizing global optimization retrieval to improve the network data classification retrieval capability,
(3) And (3) carrying out data classification on the network data information set S through the network data nodes retrieved by the drosophila optimization algorithm model, and recording classified subset information as:
Figure 132579DEST_PATH_IMAGE010
（2）
(4) Calculating information gain for the retrieved network data node data information, subtracting the currently received data information from the input data node data information, and then:
Figure 897273DEST_PATH_IMAGE011
（3）
(5) The information gain ratio of the attribute a is calculated by calculating the information gain for the retrieved network data node data information, and is referred to as:
Figure 773831DEST_PATH_IMAGE012
（4）
the data information of the network data node is classified as:
Figure 282172DEST_PATH_IMAGE013
（5）
through continuous data calculation, will
Figure 678519DEST_PATH_IMAGE014
And if the data set is independently represented, outputting the data information classification attribute of the network data node.
As a further technical solution of the present invention; the construction method of the drosophila optimization algorithm model comprises the following steps:
(A) Randomly initializing the fruit fly group position, namely the position of network node data information in the Internet; the positions are defined as follows:
Figure 364846DEST_PATH_IMAGE015
； （6）
(B) Giving the fruit fly individual a random direction and distance for searching network node data information by using smell;
Figure 276170DEST_PATH_IMAGE016
（7）
Figure 571891DEST_PATH_IMAGE017
（8）
(C) Since the position of the network node data information can not be obtained, the distance between the network node data information source point and the network node data information source point is estimated firstly
Figure 88323DEST_PATH_IMAGE018
Then, a taste concentration determination value is calculated
Figure 194820DEST_PATH_IMAGE019
The value is the inverse of the distance,
Figure 547435DEST_PATH_IMAGE020
；（9）
Figure 397579DEST_PATH_IMAGE021
；（10）
(D) Taste concentration determination value
Figure 768517DEST_PATH_IMAGE022
Substituting into taste concentration determination function to obtain taste concentration of individual fruit fly position
Figure 295183DEST_PATH_IMAGE023
；
Figure 118782DEST_PATH_IMAGE024
；（11）
(E) Finding out the fruit flies with the highest taste concentration in the fruit fly population;
Figure 772617DEST_PATH_IMAGE025
（12）
(F) The optimal taste concentration value and the x, y coordinates are retained, and the fruit fly colony flies to the position of the optimal taste concentration value by using vision.
Figure 14374DEST_PATH_IMAGE026
（13）
Figure 993831DEST_PATH_IMAGE027
（14）
Figure 570306DEST_PATH_IMAGE028
（15）
Figure 11521DEST_PATH_IMAGE029
（16）
(G) And (5) entering iterative optimization, repeatedly executing the steps B-E, judging whether the taste concentration is superior to the previous taste concentration, and if so, executing the step F.
As a further technical solution of the present invention; the drosophila optimization algorithm model realizes network data information definition recognition by constructing a logical chaotic mapping theoretical model, wherein the logical chaotic mapping theoretical model is as follows:
Figure 91472DEST_PATH_IMAGE030
（17）
n represents the iteration times of the drosophila model for network data node information retrieval in the continuous calculation process, and u is a chaotic control network data node information parameter;
network data node information chaos variable C xi The formula of (1) is:
Figure 976251DEST_PATH_IMAGE031
（18）
wherein x (n) i Is the ith chaotic variable C after the chaotic value is changed in the network data node information retrieval process xi (ii) a When C is present xi ∈[0，1]And C xi When the state belongs to {0.34,0.52 and 0.79}, the improved C4.5 decision tree algorithm model is in a chaotic state; then the iterative calculation is needed again for stimulation;
in the formula (18), x i ∈[a i ，b i ]Available C xi The following changes are made by expressions (17) and (18):
Figure 525176DEST_PATH_IMAGE032
（19）
Figure 520813DEST_PATH_IMAGE033
（20）
wherein x i And the value of the ith network data node information chaotic variable is represented, and the variable is converted into a conventional variable after chaotic mapping transformation.
A computer data information processing system based on a blockchain network, comprising:
a network data transmitting terminal; the system comprises a network data sending terminal, a network data sending terminal and a network data sending terminal, wherein the network data sending terminal is used for sending network data information and outputting and transmitting the network data information to next application equipment;
a communication controller; in the internet data communication system, between data circuit and host computer, the communication interface equipment used for controlling data transmission;
a block chain network module; the node equipment is connected through block chain link points connected in a chain manner, and are connected and communicated with each other through a block chain network, the block chain network module is sequentially provided with a data layer, a network layer, a consensus layer, an excitation layer and an intelligent contract layer from bottom to top, and the block chain network module is a master-slave block chain structure model; and
a data application terminal; the data application terminal is provided with an improved C4.5 decision tree model, and the improved C4.5 decision tree model is provided with a block chain network node;
the output end of the network data sending terminal is connected with the input end of the communication controller, the output end of the communication controller is connected with the input end of the block chain network module, and the output end of the block chain network module is connected with the input end of the data application terminal.
Positive and advantageous effects
According to the invention, the node devices are connected by adopting the block chain nodes connected in a chain manner, so that the convenience in the network data interaction process is improved, the network data information safety capability is improved by applying the block chain network, the decentralized, traceable and non-falsification capabilities of the network data information can be realized, the safety capability in the network data interaction process is improved by an encryption algorithm, the processing capability of the network data information is realized by an improved C4.5 decision tree algorithm model, and the network data information application capability is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive labor, wherein:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the C4.5 algorithm of the present invention;
FIG. 3 is a schematic flow chart of a fruit fly algorithm model according to the present invention;
FIG. 4 is a schematic diagram of data information interaction in a blockchain network according to the present invention;
FIG. 5 is a diagram of an elliptical algorithm function according to the present invention;
FIG. 6 is a schematic diagram of an iterative calculation of a drosophila optimization algorithm model in the present invention;
FIG. 7 is a schematic diagram of the system of the present invention;
FIG. 8 is a block chain architecture of the present invention;
FIG. 9 is a block chain node architecture diagram of the present invention;
FIG. 10 is a diagram illustrating a master-slave blockchain architecture according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.
As shown in fig. 1, a method for processing computer data information based on a blockchain network includes the following steps:
(S1) sending out network data information, and outputting and transmitting the network data information to the next application device;
in the step, when the network data information is sent out, the network data information is marked with data information compatible with a block chain network, and the tracking in the network data information transmission process is realized by marking a timestamp or a network data information identifier on the data information;
(S2) in the Internet data communication system, controlling data transmission through a network data interactive communication interface;
in the step, network data information transmission is realized by controlling the network data information nodes during data transmission control, and network data information node information interaction is synchronously realized during data transmission control;
(S3) connecting the node devices through the block chain nodes connected in a chain manner, and communicating with each other through a block chain network to enable the sent network data information to be subjected to information interaction and transmission through the block chain network;
in the step, the encryption of the data information of the block link point is realized through a symmetric encryption algorithm; the decryption of the data information of the block chain link point is realized by a symmetrical decryption method, and further the primary encryption of the data information of the block chain link point is realized;
encrypting data information through a public key of a receiver, then generating a data ciphertext, when receiving data, firstly calculating the abstract of the transmitted data by using a hash function in a block chain, and then digitally signing the abstract through a private key; the data sender simultaneously sends a data ciphertext and a signature, and the receiver decrypts the data through a public key so as to realize encryption and decryption calculation of internet data information;
in the step, the second-level encryption of the data information of the block chain link point is realized through an elliptic curve equation;
(S4) receiving data information sent by a network data sending terminal through the block chain network node to realize the calculation of the network data information;
in the step, the processing of the network data information is realized through an improved C4.5 decision tree algorithm model, wherein the improved C4.5 decision tree algorithm model comprises a drosophila optimization algorithm model, so that the classification capability of the network data information is improved;
and (S5) outputting the calculated network data information to meet the user requirements.
According to the invention, the node devices are connected by adopting the block chain nodes connected in a chain manner, so that the convenience in the network data interaction process is improved, the network data information safety capability is improved by applying the block chain network, the decentralized, traceable and non-falsification capabilities of the network data information can be realized, the safety capability in the network data interaction process is improved by an encryption algorithm, the processing capability of the network data information is realized by an improved C4.5 decision tree algorithm model, and the network data information application capability is improved.
In the above steps, as shown in fig. 3, the method for implementing the secondary encryption of the network data information by the elliptic curve equation is as follows:
an elliptic curve function is constructed, and a function equation model is constructed as follows:
Figure 501277DEST_PATH_IMAGE034
（1）
in formula (1), there are 3 focal points of the non-vertical line and the curve, and on the non-vertical line, the tangent line intersects the curve at other points, and it is assumed that there are 2 points Q and P, where Q and P intersect
Figure 556957DEST_PATH_IMAGE035
Then, there are:
R=Q+P （2）
wherein R is represented by the formula
Figure 842445DEST_PATH_IMAGE036
Symmetrical about the X axis. When the point Q is coincident with the point P, assuming that the coincident point is D, the straight line is tangent to the curve,
Figure 126927DEST_PATH_IMAGE037
indicated as intersection points. Then there is the formula:
Figure 181471DEST_PATH_IMAGE038
（3）
at this time, R is equal to
Figure 408053DEST_PATH_IMAGE039
Still symmetric about the X axis, then:
Q=aP （4）
since there are 3 intersections, a =3, the formula can be transformed into:
Q=3P （5）
when performing encryption calculation in a block chain, assuming that a modulus is p, a base point is G, and an ordinal number is n, then:
public key = private key G; (6)
The Q point and the P point represent two different points on the curve, which can be calculated by the following formula:
Figure 430104DEST_PATH_IMAGE040
（7）
Figure 298703DEST_PATH_IMAGE041
（8）
Figure 224065DEST_PATH_IMAGE042
（9）
the encryption of the public key is completed through the algorithm, and when the private key encryption is completed, the data z is encrypted by adopting the private key dA, and the method is adopted: selecting data k, let:
Figure 355969DEST_PATH_IMAGE043
then, the following formula is used for calculation:
Figure 616049DEST_PATH_IMAGE044
（10）
then, calculating:
Figure 740869DEST_PATH_IMAGE045
（11）
when it occurs
Figure 770005DEST_PATH_IMAGE046
If the segment is 0, then the selection is re-made. Then the following formula is used for calculation:
Figure 72810DEST_PATH_IMAGE047
（12）
if s =0 is obtained after the final calculation, the calculation is re-performed. A data signature (r, s) is then generated, implementing the private keying. The safety of human resource information is ensured.
In the above embodiment, the improved C4.5 decision tree algorithm model is a classification algorithm model fused with a drosophila optimization algorithm model.
As shown in fig. 2, in the above embodiment, the improved C4.5 decision tree algorithm model is performed by the following steps:
(1) Extracting data information from network data nodes, the network data information set being recorded as
Figure 836498DEST_PATH_IMAGE048
Partitioning network data information into
Figure 250162DEST_PATH_IMAGE049
A classification module, then
Figure 399383DEST_PATH_IMAGE050
Is provided with
Figure 919095DEST_PATH_IMAGE051
An attribute tag, define
Figure 153767DEST_PATH_IMAGE052
For each of the set of attribute tags,
Figure 121855DEST_PATH_IMAGE053
(ii) a Suppose that
Figure 125583DEST_PATH_IMAGE054
Is a
Figure 770191DEST_PATH_IMAGE055
The expected values required for a fixed sample classification for the network data samples in (1) are:
Figure 226580DEST_PATH_IMAGE056
（13）
(2) Constructing a drosophila optimization algorithm model, realizing global optimization retrieval to improve the classified retrieval capability of network data,
as shown in fig. 4, the drosophila optimization algorithm (FOA) can implement a global optimization search, mainly based on the search of network data information types: the source of the network data information is sensed by retrieving the network nodes or the network data information, the block chain network nodes and the like, and useful information is searched for the position of the data information of the relevant network nodes through sensitive vision, so that the capability and the working efficiency of the data nodes in classification are improved.
(3) And (3) carrying out data classification on the network data information set S through the network data nodes retrieved by the drosophila optimization algorithm model, and recording classified subset information as:
Figure 496893DEST_PATH_IMAGE057
（14）
(4) Calculating information gain for the retrieved network data node data information, subtracting the currently received data information from the input data node data information, and then:
Figure 151865DEST_PATH_IMAGE058
（15）
(5) The information gain ratio of the attribute a is calculated by calculating the information gain for the retrieved network data node data information, and is referred to as:
Figure 983686DEST_PATH_IMAGE059
（16）
the data information of the network data node is classified as:
Figure 927371DEST_PATH_IMAGE060
（17）
through continuous data calculation, will
Figure 486529DEST_PATH_IMAGE061
And if the data set is independently represented, outputting the data information classification attribute of the network data node.
As shown in fig. 5-6, in a specific embodiment, the generated decision tree can be pruned by using a C4.5 algorithm, so as to continuously refine the decision tree model and provide support for subsequent data classification.
In the above embodiment, as shown in fig. 3, the construction method of the drosophila optimization algorithm model is as follows:
(A) Randomly initializing the fruit fly group position, namely the position of network node data information in the Internet; the positions are defined as follows:
Figure 448537DEST_PATH_IMAGE062
； （18）
(B) Giving the fruit fly individual a random direction and distance for searching network node data information by using smell;
Figure 700527DEST_PATH_IMAGE063
（19）
Figure 131508DEST_PATH_IMAGE064
（20）
(C) Because the position of the network node data information can not be known, the distance between the network node data information source point and the network node data information source point is estimated firstly
Figure 245089DEST_PATH_IMAGE065
Then, the taste concentration judgment value is calculated
Figure 77916DEST_PATH_IMAGE066
The value is the reciprocal of the distance,
Figure 235228DEST_PATH_IMAGE067
；（21）
Figure 668352DEST_PATH_IMAGE068
；（22）
(D) Taste concentration determination value
Figure 303733DEST_PATH_IMAGE069
Substituting into taste concentration determination function to obtain taste concentration of individual fruit fly position
Figure 256645DEST_PATH_IMAGE070
；
Figure 335591DEST_PATH_IMAGE071
；（23）
(E) Finding out the fruit flies with the highest taste concentration in the fruit fly population;
Figure 741164DEST_PATH_IMAGE072
（24）
(F) The optimal taste concentration value and the x, y coordinates are retained, and the fruit fly colony flies to the position of the optimal taste concentration value by using vision.
Figure 491820DEST_PATH_IMAGE073
（25）
Figure 299239DEST_PATH_IMAGE074
（26）
Figure 798354DEST_PATH_IMAGE075
（27）
Figure 441956DEST_PATH_IMAGE076
（28）
(G) And (4) entering iterative optimization, repeatedly executing the steps B-E, judging whether the taste concentration is superior to the previous taste concentration, and executing the step F if the taste concentration is superior to the previous taste concentration.
In the method, in order to improve the retrieval capability of the network nodes, a logical chaotic mapping theoretical model is constructed:
Figure 419139DEST_PATH_IMAGE077
（29）
n represents the iteration times of the drosophila model for network data node information retrieval in the continuous calculation process, and u is a chaotic control network data node information parameter;
for some values of the network data node information parameter u (where u =4 is 1), the network data node information search system exhibits chaotic behavior.
Network data node information chaos variable C xi The formula of (1) is:
Figure 346644DEST_PATH_IMAGE078
（30）
wherein x (n) i Is the ith chaotic variable C after the chaotic value is changed in the network data node information retrieval process xi (ii) a When C is present xi ∈[0，1]And C xi When the state belongs to {0.34,0.52 and 0.79}, the improved C4.5 decision tree algorithm model is in a chaotic state; then it is necessary toPerforming iterative calculation again for stimulation;
in the formula (30), x i ∈[a i ，b i ]Available C xi The following modifications are made by expressions (4) and (5):
Figure 16660DEST_PATH_IMAGE079
（31）
Figure 646093DEST_PATH_IMAGE080
（32）
wherein x is i The value of the information chaotic variable of the ith network data node is expressed, and the variable is converted into a conventional variable after chaotic mapping transformation;
in conclusion, the linear decreasing step is very effective for improving the accuracy of the FOA, and meanwhile, the logic chaotic mapping can weaken the sensitivity of the initial network data node information retrieval condition of the optimization solution and improve the stability of the FOA data.
In a particular embodiment, the iteration steps are fixed in the processing of the FOA. That is, the drosophila search the engineering management project data information through fixed steps in the iterative optimization process, and by performing iterative calculations, the drosophila population is closer to the engineering management project data information, i.e., when the iterative steps are fixed, it is difficult to find the location of the engineering management project data information. The project management project data information is processed in the last few iterations. Perhaps some individual fruit flies are further and further away from the project management project data information. The Fruit Fly Optimization Algorithm is based on cooperation and competition behaviors of Fruit Fly populations in foraging processes, and the Fruit Fly Optimization Algorithm (FOA) is a new method for deducing and seeking global Optimization based on the Fruit Fly foraging behaviors.
The Fruit Fly Optimization Algorithm (FOA) is a new method for deriving global Optimization based on the foraging behavior of Fruit flies. The fruit fly is superior to other species in sense perception, particularly in smell sense and vision, the smell organ of the fruit fly can well collect various smells floating in the air, even can smell food sources beyond 40 kilometers, and then the fruit fly can also find the position where the food and a partner gather by using sharp vision after flying close to the position of the food and fly to the direction.
As shown in fig. 7-10, in the above steps, in the data interaction process, the adopted blockchain information management platform is a hyper-hedger Fabric-based modular blockchain solution support platform, the blockchain information management platform includes a blockchain network, a blockchain node and a node server, when data is shared, an RS485 communication module or an RS232 communication module is adopted for data transmission, which needs to be set according to the situation of a user node, and the wireless communication module adopted for data transmission includes a TCP/IP network system, a ZigBee wireless network, a GPRS communication module, a CDMA wireless communication module, a cloud communication module or a bluetooth communication module; the various communication modules are within the range of the blockchain network. The block chain communication efficiency can be improved.
Because the data transmission process is easily interfered by the outside, the data encryption unit is arranged in the data interaction process, the data encryption unit can also comprise a DES encryption unit, a 3DES encryption unit, a Blowfish encryption unit, a Twofish encryption unit, an IDEA encryption unit, an RC6 encryption unit, a character string encryption unit or a CAST5 encryption unit and the like, and the confidentiality degree in the bid and bid data interaction process is improved through data encryption. In the interaction process, besides the node equipment, a plurality of communication protocols interacting with the data chain nodes are also arranged, and the communication protocols at least comprise TCP/IP, UDP, IPSec, MODBUS/TCP, OPC, proprietary protocols, PROFIBUS-DP, MPI, PPI, S7, FX series programming port and serial port protocols, Q series serial port 4C protocols, ethernet 3E protocols, CC-LINK, A series or ohm dragon HostLink protocols [14-15], so that the communication requirements of different monitoring equipment interfaces or communication equipment are met, and the data interaction capacity is improved.
A computer data information processing system based on a blockchain network, comprising:
a network data transmitting terminal; the system comprises a network data sending terminal, a network data sending terminal and a network data sending terminal, wherein the network data sending terminal is used for sending network data information and outputting and transmitting the network data information to next application equipment;
a communication controller; in the internet data communication system, between data circuit and host computer, the communication interface equipment used for controlling data transmission;
a block chain network module; the node equipment is connected through block chain link points which are connected in a chain manner, and are connected and communicated with each other through a block chain network, the block chain network module is sequentially provided with a data layer, a network layer, a consensus layer, an excitation layer and an intelligent contract layer from bottom to top, and the block chain network module is a master-slave block chain structure model; and
a data application terminal; the data application terminal is provided with an improved C4.5 decision tree model, and the improved C4.5 decision tree model is provided with a blockchain network node;
the output end of the network data sending terminal is connected with the input end of the communication controller, the output end of the communication controller is connected with the input end of the block chain network module, and the output end of the block chain network module is connected with the input end of the data application terminal.
In particular embodiments, the communication controller manages data input and output to a host or computer network. It can be a complex foreground mainframe computer interface or simple devices such as multiplexers, bridges and routers. These devices convert the parallel data of the computer into serial data for transmission over the communication line and perform all necessary control functions, error detection and synchronization. Modern devices also perform data compression, routing, security functions, and collect management information. The main functions of the communication controller are: (1) providing an electrical interface to the data circuit and to the host; (2) assembling the serial bit stream on the data circuit into characters according to the serial-parallel conversion principle, or inversely disassembling the characters into the serial bit stream; (3) the conversion of the data transmission rate on the circuit and the host transmission rate; (4) transforming the transmission code and the internal code of the host; (5) performing a transmission control procedure, such as a data communication basic type control procedure, an advanced data link control procedure, and the like; (6) detection and correction of transmission errors, such as vertical horizontal parity check, cyclic code check, etc.
In the above embodiments, the blockchain is referred to as a multi-party co-maintained, decentralized, traceable, and non-falsifiable distributed database, and is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, and an encryption algorithm. Request data in a certain period can be packed into a data block (block) through a cryptographic technology, and the data block is connected into a chain structure for storage according to time sequence by using a Hash fingerprint. A data block is typically composed of a header and a body. The block header usually stores data such as the version number of the system, the hash value of the previous block, the merkle root, and the timestamp, and the block body contains detailed request data. Taking the bitcoin as an example, the block head of the bitcoin also stores data such as mining random numbers and the like in addition to the above information, and the block body stores specific transaction data.
As shown in fig. 9, the blockchain platform is a hyper hedger Fabric-based modular blockchain solution support platform. The Fabric platform is an alliance chain structure, supports an intelligent contract technology, does not depend on tokens when the system operates, can support about hundred transactions per second of throughput, and basically meets the requirement of performing internet data information cross-organization transactions between alliance organizations. In addition, fabric adopts a modular architecture, wherein a consensus algorithm and the like can be used as a pluggable module for internet users to choose. Meanwhile, the method can enable the Internet user to redesign and develop the specific module according to the self requirement, so that the Fabric is selected as a block chain foundation platform of the Internet data information transaction system. The Fabric mainly comprises member service modules (Membership Services), block chain service modules (Blockchain Services) and chain code service modules (Chaincode Services). The member service module mainly provides functions of member registration, identity management, transaction review and the like, and performs mechanism registration authentication and transaction authentication through a registration certificate authority (ECA) and a transaction authentication center (TCA). The block chain service module is mainly responsible for point-to-point communication between nodes, consensus, and the storage of account book data. The chain code service module provides intelligent contract service, provides a safe contract running environment and the like. Meanwhile, the platform realizes asynchronous communication through an Event Stream (Event Stream) between all the components.
As shown in fig. 10, the block link points are connected in a chain manner, that is, the block link points are connected and communicated with each other through a block chain network, and the nodes are connected in a chain manner, so that information interaction between different nodes can be realized. When data sharing is carried out, the alternation and sharing of the internet data are realized through different data nodes.
The data layer utilizes a Merkle tree for data storage, and is structurally connected in a chain manner through blocks. Because the digital signature, the hash function and the asymmetric encryption technology are utilized in the data structure, the security performance is higher in data interaction. The network layer is mainly formed by interweaving a plurality of network nodes, and data communication and connection are realized by using a point-to-point technology through different network nodes, so that the technical weakness that a central server is used in the traditional technology is eliminated, and node equipment in the network can be intercommunicated and interconnected. In a consensus layer in the network, the existence of a consensus mechanism can carry out consistency interaction on data set in the block chain network, and the block chain network has better data consensus capability and data anti-attack capability. The excitation layer can provide excitation measures in the block chain, so that the action of the network node is sufficiently excited in the block chain, and the security verification can be carried out. In the intelligent contract layer, various program algorithms are fully utilized to execute and calculate the relationship between various data in the block chain network.
The master-slave blockchain structure model is provided with blockchain network nodes connected in a closed loop and blockchain devices connected with the blockchain network nodes.
The master-slave block chain structure model can summarize the relation of each industry of the internet service, match the service mode with the industry, simplify the service process, improve the service quality, enable the internet user to obtain the applied service process in the internet service system, enable the internet user to enjoy the flow service system in the service system, make the service mode more systematic and have better experience, and the master-slave block chain structure model is as shown in fig. 3
The master-slave block chain structure model mainly forms a service special matching mode, and a circulation system is established through a block chain network to complete service circulation of internet users, internet user demand terminals, electricity selling companies and a power grid. The method comprises the following steps that an internet user side carries out demand butt joint aiming at a microgrid, industry, residents and business respectively, and internet user demand data are transferred to a block chain internet user network; the method comprises the steps that an internet user demand end mainly analyzes the whole internet, network data types, network data IDs, network data safety data information and network data application information in the whole internet are integrated into a block chain network in the network transmission process through internet user demand data, and therefore equipment maintenance, distributed network data analysis, energy-saving design, data transaction, service agency, energy efficiency detection and other data information services are achieved, and network application special application is conducted on internet users who put forward demands; the main service modes are equipment maintenance, energy efficiency detection, service agency, data transaction, energy-saving design and distributed matching mode. The service mode of the master-slave block chain has an important effect on improving the service evaluation of the whole internet, so that a service system can be specially applied according to the requirements of internet users, the matching time is shortened to the maximum degree, meanwhile, a circulating service system has an important effect on enhancing the internet application, the served internet is stored to the maximum degree in the process, the requirements of the internet users are met, and the circulating application of the internet is guaranteed.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the above-described methods to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.

Claims (3)

1. A computer data information processing method based on a block chain network is characterized in that: the method comprises the following steps:
(S1) sending out network data information, and outputting and transmitting the network data information to the next application device;
in the step, when the network data information is sent out, the network data information is marked with data information compatible with a block chain network, and the tracking in the network data information transmission process is realized by marking a timestamp or a network data information identifier on the data information;
(S2) in the Internet data communication system, controlling data transmission through a network data interactive communication interface;
in the step, network data information transmission is realized by controlling the network data information nodes during data transmission control, and network data information node information interaction is synchronously realized in the data transmission control process;
(S3) connecting the node devices through the block chain nodes connected in a chain manner, and communicating with each other through a block chain network to enable the sent network data information to carry out information interaction and transmission through the block chain network;
in the step, the encryption of the data information of the block link point is realized through a symmetric encryption algorithm; the decryption of the data information of the block chain link point is realized by a symmetrical decryption method, and further the primary encryption of the data information of the block chain link point is realized;
encrypting data information through a public key of a receiver, then generating a data ciphertext, when receiving data, firstly calculating an abstract of transmitted data by using a hash function in a block chain, and then digitally signing the abstract through a private key; the data sender simultaneously sends a data ciphertext and a signature, and the receiver decrypts the data through a public key so as to realize encryption and decryption calculation of internet data information;
in the step, the second-level encryption of the data information of the block chain link point is realized through an elliptic curve equation;
(S4) receiving data information sent by a network data sending terminal through the block chain network node to realize the calculation of the network data information;
in the step, the processing of the network data information is realized through an improved C4.5 decision tree algorithm model, wherein the improved C4.5 decision tree algorithm model comprises a drosophila optimization algorithm model, so that the classification capability of the network data information is improved;
(S5) outputting the calculated network data information to meet the user requirements;
the construction method of the drosophila optimization algorithm model comprises the following steps:
(A) Randomly initializing the fruit fly group position, namely the position of network node data information in the Internet; the positions are defined as follows:
Figure 93089DEST_PATH_IMAGE001
； （6）
(B) Giving the fruit fly individual a random direction and distance for searching network node data information by using smell;
Figure 823147DEST_PATH_IMAGE002
（7）
Figure 39496DEST_PATH_IMAGE003
（8）
(C) Because the position of the network node data information can not be known, the distance between the network node data information source point and the network node data information source point is estimated firstly
Figure 564018DEST_PATH_IMAGE004
Then, the taste concentration judgment value is calculated
Figure 115085DEST_PATH_IMAGE005
The value is the inverse of the distance,
Figure 898103DEST_PATH_IMAGE006
；（9）
Figure 952646DEST_PATH_IMAGE007
；（10）
(D) Taste concentration determination value
Figure 710387DEST_PATH_IMAGE008
Substituting into taste concentration determination function to obtain taste concentration of individual fruit fly position
Figure 499482DEST_PATH_IMAGE009
；
Figure 774606DEST_PATH_IMAGE010
；（11）
(E) Finding out the fruit flies with the highest taste concentration in the fruit fly population;
Figure 949235DEST_PATH_IMAGE011
（12）
(F) Keeping the optimal taste concentration value and the x, y coordinates, and flying the fruit fly colony to the optimal taste concentration value by using vision;
Figure 81139DEST_PATH_IMAGE012
（13）
Figure 879504DEST_PATH_IMAGE013
（14）
Figure 489476DEST_PATH_IMAGE014
（15）
Figure 784192DEST_PATH_IMAGE015
（16）
(G) And (5) entering iterative optimization, repeatedly executing the steps B-E, judging whether the taste concentration is superior to the previous taste concentration, and if so, executing the step F.
2. The method for processing computer data information based on blockchain network according to claim 1, wherein: the improved C4.5 decision tree algorithm model is carried out by the following steps:
(1) Extracting data information from network data nodes, the network data information set being recorded as
Figure 837729DEST_PATH_IMAGE016
Dividing network data information into
Figure 585105DEST_PATH_IMAGE017
A classification module, then
Figure 264349DEST_PATH_IMAGE018
Is provided with
Figure 397258DEST_PATH_IMAGE019
An attribute tag, defining
Figure 136544DEST_PATH_IMAGE020
For each set of the attribute tags, a set of attributes,
Figure 840058DEST_PATH_IMAGE021
(ii) a Suppose that
Figure 808145DEST_PATH_IMAGE022
Is of the class
Figure 811873DEST_PATH_IMAGE023
The expected values required for a fixed sample classification for the network data samples in (1) are:
Figure 722061DEST_PATH_IMAGE024
（1）
(2) Constructing a drosophila optimization algorithm model, realizing global optimization retrieval to improve the classified retrieval capability of network data,
(3) And (3) carrying out data classification on the network data information set S through the network data nodes retrieved by the drosophila optimization algorithm model, and recording classified subset information as:
Figure 178450DEST_PATH_IMAGE025
（2）
(4) Calculating information gain for the retrieved network data node data information, and subtracting the currently received data information from the input data node data information, then:
Figure 448763DEST_PATH_IMAGE026
（3）
(5) The information gain ratio of the attribute a is calculated by calculating the information gain for the retrieved network data node data information, and is referred to as:
Figure 510260DEST_PATH_IMAGE027
（4）
the data information of the network data node is classified as:
Figure 325769DEST_PATH_IMAGE028
（5）
through a constantData calculation of
Figure 535034DEST_PATH_IMAGE029
And if the data set is independently represented, outputting the data information classification attribute of the network data node.
3. The method for processing computer data information based on blockchain network according to claim 1, wherein: the drosophila optimization algorithm model realizes network data information definition recognition by constructing a logical chaotic mapping theoretical model, wherein the logical chaotic mapping theoretical model is as follows:
Figure 844923DEST_PATH_IMAGE030
（17）
n represents the iteration times of the drosophila model for network data node information retrieval in the continuous calculation process, and u is a chaotic control network data node information parameter;
network data node information chaos variable C xi The formula of (1) is:
Figure 823244DEST_PATH_IMAGE031
（18）
wherein C is x (n) i Is the ith chaotic variable C after the chaotic value is changed in the network data node information retrieval process xi (ii) a When C is present xi ∈[0，1]And C xi When the element belongs to {0.34,0.52 and 0.79}, the improved C4.5 decision tree algorithm model is in a chaotic state; then iterative computation is needed again for stimulation;
in the formula (18), x i ∈[a i ，b i ]Available C xi The following modifications are made by expressions (17) and (18):
Figure 809654DEST_PATH_IMAGE032
（19）
Figure 489903DEST_PATH_IMAGE033
（20）
wherein x i And the value of the ith network data node information chaotic variable is represented, and the variable is converted into a conventional variable after chaotic mapping transformation.
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