CN111683366A

CN111683366A - Communication data processing method based on artificial intelligence and block chain and big data platform

Info

Publication number: CN111683366A
Application number: CN202010507643.0A
Authority: CN
Inventors: 宗陈星
Original assignee: Individual
Current assignee: Shanghai Star Earth Communication Engineering Research Institute
Priority date: 2020-06-05
Filing date: 2020-06-05
Publication date: 2020-09-18
Anticipated expiration: 2040-06-05
Also published as: CN112566110A; CN112566109A; CN111683366B

Abstract

The embodiment of the disclosure provides a communication data processing method and a big data platform based on artificial intelligence and block chains, wherein encryption enabling field distribution is determined according to encrypted data services of encrypted data segment types, then an encryption negotiation data packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution are obtained, then first network element ciphertext characteristics of each network element entity and second network element ciphertext characteristics of each network element entity are extracted and combined to obtain target network element ciphertext characteristics, a block chain storage unit of each network element entity corresponding to the encrypted data segment types is determined, and therefore generation of encryption verification key information of each network element entity is carried out. Therefore, the safety of the subsequent encryption verification key information can be improved for the encrypted data service corresponding to the encrypted data segment type, so that the subsequent 5G network communication equipment can conveniently carry out encryption verification of block chain storage distribution with higher safety level based on the encryption verification key information in the communication process, and further, the communication intrusion is avoided.

Description

Communication data processing method based on artificial intelligence and block chain and big data platform

Technical Field

The disclosure relates to the technical field of artificial intelligence and communication encryption, in particular to a communication data processing method and a big data platform based on artificial intelligence and a block chain.

Background

The transmission of communication data by using the high-speed 5G technology is an important application scene of a new generation of mobile internet technology, and with the rapid development of artificial intelligence and block chain technology, communication encryption can be performed on the communication transmission process by combining the artificial intelligence and the block chain technology, so that the safety of high-speed data transmission is ensured in turn.

The block chain technology integrates several key technologies of distributed storage, modern cryptography, a point-to-point network, a consensus mechanism and an intelligent contract, exchanges, stores and processes data, and is a new technology with high safety and efficiency and shared intelligence. At present, in a communication encryption starting process, an encrypted data segment class of a certain communication encryption is usually started, and how to improve the security of subsequent encryption verification key information for encrypted data services corresponding to the encrypted data segment class is convenient for subsequent 5G network communication devices to perform encryption verification of block chain storage distribution with higher security level based on the encryption verification key information in a communication process, so as to avoid communication intrusion.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide a communication data processing method and a big data platform based on artificial intelligence and a block chain, which can improve the security of subsequent encryption and verification key information for encrypted data services corresponding to encrypted data segment categories, so that subsequent 5G network communication devices perform encryption and verification distributed in block chain storage at a higher security level based on the encryption and verification key information during communication, thereby avoiding communication intrusion.

In a first aspect, the present disclosure provides a communication data processing method based on artificial intelligence and a blockchain, which is applied to a big data platform communicatively connected to a plurality of 5G network communication devices, and the method includes:

acquiring the type of an encrypted data segment for starting communication encryption of the 5G network communication equipment, determining encryption enabling field distribution according to encrypted data service of the type of the encrypted data segment, and acquiring an encryption negotiation data packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution;

respectively inputting the encrypted negotiation data packet set and the negotiation field switching information into a configured data encryption model, extracting first network element ciphertext characteristics of each network element entity through a first encryption node of the data encryption model, and extracting second network element ciphertext characteristics of each network element entity through a second encryption node of the data encryption model;

merging the first network element ciphertext feature and the second network element ciphertext feature through a merging node of the data encryption model to obtain a target network element ciphertext feature;

and determining a blockchain storage unit of each network element entity corresponding to the encrypted data segment type according to the target network element ciphertext characteristic, respectively generating encryption verification key information of each corresponding network element entity according to the blockchain storage unit, and sending the encryption verification key information to corresponding 5G network communication equipment.

In a possible implementation manner of the first aspect, the negotiation field switching information includes a negotiation communication position, a switching manner, and a negotiation object;

the step of extracting the first network element cryptograph characteristics of each network element entity through the first encryption node of the data encryption model and extracting the second network element cryptograph characteristics of each network element entity through the second encryption node of the data encryption model comprises the following steps:

inputting the encryption negotiation data packet set to a first encryption node, and performing feature extraction on the encryption negotiation data packets in the encryption negotiation data packet set to obtain corresponding encryption negotiation data packet features;

performing characteristic offset processing on the encrypted negotiation data packet characteristics by using the first encryption node and communication encryption deviation parameters corresponding to the encrypted data service to obtain encrypted negotiation data packet characteristics after characteristic offset processing;

extracting first network element ciphertext characteristics of each network element entity according to the encrypted negotiation data packet characteristics after the characteristic offset processing; and

inputting the switching information of the negotiation field into a second encryption node, and performing feature extraction on the switching information of the negotiation field to obtain a negotiation communication position feature, a negotiation object feature and a switching mode feature;

performing characteristic offset processing on the negotiation communication position characteristic, the negotiation object characteristic and the switching mode characteristic by using the second encryption node and the communication encryption deflection parameter corresponding to the encrypted data service to obtain a negotiation field switching information array;

acquiring the encrypted negotiation data packet characteristics corresponding to the encrypted negotiation data packet set, inputting the encrypted negotiation data packet characteristics to the negotiation field switching information array for characteristic combination to obtain a combined target characteristic sequence, and extracting second network element ciphertext characteristics of each network element entity according to the target characteristic sequence.

In a possible implementation manner of the first aspect, the step of combining the first network element ciphertext feature and the second network element ciphertext feature by the combining node of the data encryption model to obtain a target network element ciphertext feature includes:

and merging the feature positions corresponding to the first network element ciphertext feature and the second network element ciphertext feature respectively to obtain a target network element ciphertext feature through the merging node of the data encryption model.

In a possible implementation manner of the first aspect, the step of determining, according to the target network element ciphertext feature, a blockchain storage unit of each network element entity corresponding to the encrypted data segment category includes:

acquiring ciphertext protocol configuration content corresponding to a ciphertext protocol unit encrypted by communication associated with the encrypted data segment type from the target network element ciphertext characteristic, wherein the ciphertext protocol configuration content is obtained by performing protocol analysis on ciphertext protocol session content in the target network element ciphertext characteristic by adopting a protocol generation form matched with the encrypted data segment associated type of the corresponding ciphertext protocol unit;

according to the data block loading mode respectively matched with each protocol generation form, data block loading is carried out on the ciphertext protocol configuration content sent by each corresponding ciphertext protocol unit to obtain corresponding ciphertext protocol session content;

respectively carrying out session frequency analysis on session contents of the ciphertext protocols, and determining session frequency parameters corresponding to the ciphertext protocol units, wherein the session frequency parameters are used for reflecting the frequency degree of communication encryption of the ciphertext protocol units related to the encrypted data segment type;

screening the highest frequency degree of the session from the session frequency parameters corresponding to the ciphertext protocol units, and determining the session frequency comparison degrees corresponding to the ciphertext protocol units according to the comparison values between the session frequency parameters corresponding to the ciphertext protocol units and the highest frequency degree of the session; wherein, the session frequency comparison degree corresponding to the ciphertext protocol unit is positively correlated with the corresponding contrast value;

and carrying out session identification on the cryptograph protocol session content of the cryptograph protocol unit with the session frequency comparison degree larger than the set session frequency comparison degree, and obtaining the block chain storage unit of each network element entity corresponding to the encrypted data segment type according to the session encryption identifier in the session identification result, wherein each session encryption identifier and each block chain storage unit are in one-to-one correspondence.

In a possible implementation manner of the first aspect, the step of performing session frequency analysis on each ciphertext protocol session content and determining a session frequency parameter corresponding to each ciphertext protocol unit includes:

dividing each cryptograph protocol session content into more than one unit session set of session service, carrying out session frequency detection on each unit session set, determining the number of session nodes with encryption application frequency times larger than set frequency times in the included unit session set for each cryptograph protocol session content, determining the proportion of the session nodes for each cryptograph protocol session content according to the number of the session nodes in the cryptograph protocol session content and the total number of the unit session sets included in the cryptograph protocol session content, and determining the session frequency parameters corresponding to each cryptograph protocol unit according to the proportion of the session nodes; or

Dividing each cryptograph protocol session content into more than one unit session set of session services, performing session frequency detection on each unit session set, determining session nodes with encryption application frequency more than set frequency in the unit session set, determining communication encryption continuous quantity corresponding to each session node, and determining session frequency parameters corresponding to each cryptograph protocol unit according to the quantity of effective session nodes with the communication encryption continuous quantity more than or equal to an energy threshold in the session nodes included in each cryptograph protocol session content; or

Dividing each cryptograph protocol session content into more than one unit session set of session service, calculating the frequency weight of the directed weighted graph corresponding to each unit session set, fusing the frequency weight of the directed weighted graph corresponding to each unit session set included in the cryptograph protocol session content for each cryptograph protocol session content to obtain a frequency weight sequence corresponding to the cryptograph protocol session content, and taking the frequency weight sequence corresponding to each cryptograph protocol session content as the session frequency parameter corresponding to each cryptograph protocol unit.

In a possible implementation manner of the first aspect, the dividing each ciphertext protocol session content into more than one unit session set of the session service, and calculating a frequency weight of a directed weighted graph corresponding to each unit session set respectively includes:

for the ciphertext protocol session content corresponding to each communication encryption member, dividing the corresponding ciphertext protocol session content into more than one unit session set of session service in directed space corresponding to the directed weighted graph;

generating a frequency distribution comparison map corresponding to the calculation result of the graph node of each unit session set in the directed weighted graph, and determining more than one frequency connection map included in the frequency distribution comparison map corresponding to each unit session set;

for each frequency connected graph in each unit conversation set, determining a frequency connected graph distribution comparison map corresponding to the frequency connected graph respectively based on image values of frequency image points included in the frequency connected graph;

determining a preset number of associated frequency connected graphs associated with the current frequency connected graph in the current unit session set for the current frequency connected graph in the current unit session set currently processed in each unit session set, combining the associated frequency connected graphs and the current frequency connected graph to form a frequency connected graph set, and performing fusion processing on frequency connected graph distribution comparison graphs of each frequency connected graph in the frequency connected graph set according to weights corresponding to the frequency connected graph set to obtain an authorization block comparison graph corresponding to the current frequency connected graph in the current unit session set;

performing fusion processing on an authorization block comparison map of an associated frequency connected graph corresponding to the same frequency connected graph sequence number in a previous set of a current unit session set and an authorization block comparison map of a current frequency connected graph in the current unit session set to obtain a frequency relation distribution comparison map corresponding to the current frequency connected graph in the current unit session set;

screening out a minimum image value from frequency relation distribution comparison maps corresponding to frequency connected graphs corresponding to the same frequency connected graph serial number in different unit session sets as an image comparison value corresponding to each frequency connected graph of the corresponding frequency connected graph serial number, and regarding a current frequency connected graph in a current unit session set currently processed in each unit session set, taking a quotient of the frequency relation distribution comparison map of the current frequency connected graph and the image comparison value as an image confidence coefficient ratio corresponding to the current frequency connected graph in the current unit session set;

when the image confidence coefficient ratio is larger than a preset threshold value, taking a first preset numerical value as a session frequency reference value corresponding to a current frequency connected graph in the current unit session set;

when the image confidence coefficient ratio is smaller than or equal to the preset threshold, taking a second preset numerical value as a session frequency reference value corresponding to the current frequency connected graph in the current unit session set; the second preset value is smaller than the first preset value;

acquiring a conversation frequency intensive value of a relevant frequency connected graph corresponding to the same frequency connected graph sequence number as the current frequency connected graph in a relevant unit conversation set before the current unit conversation set, and fusing the conversation frequency intensive value corresponding to the relevant frequency connected graph and a conversation frequency reference value corresponding to the current frequency connected graph to obtain a conversation frequency intensive value corresponding to the current frequency connected graph in the current unit conversation set;

taking the difference value between the first preset dense value and the conversation frequency dense value as a reference dense value corresponding to the corresponding frequency connected graph;

for the current frequency connected graph in the current unit session set currently processed in each unit session set, obtaining the session dense estimation value corresponding to the associated frequency connected graph with the same frequency connected graph sequence number as the current frequency connected graph in the associated unit session set of the current unit session set, and a first product of the conversation dense estimation value corresponding to the associated frequency connected graph and the conversation frequency dense value corresponding to the current frequency connected graph in the current unit conversation set is obtained, performing summation operation with a second product of a frequency connected graph distribution comparison graph corresponding to a current frequency connected graph in the current unit session set and a reference dense value to obtain a session dense estimation value corresponding to the current frequency connected graph in the current unit session set, and determining a frequency connected graph description value corresponding to each frequency connected graph based on the frequency connected graph distribution comparison graph and the session dense estimation value;

and calculating the frequency weight of the directed weighted graph corresponding to each unit session set according to the frequency connected graph description value corresponding to the frequency connected graph included in each unit session set.

In a possible implementation manner of the first aspect, the step of generating encryption verification key information of each corresponding network element entity according to the blockchain storage unit includes:

acquiring session access encryption information corresponding to the block chain storage unit during data encryption storage, wherein the session access encryption information comprises at least one session access encryption node;

calculating a quantum encryption key corresponding to the session access encryption information, wherein the quantum encryption key represents a key character of the session access encryption information relative to each quantum encryption category in a simulated encryption process;

if the key length value of the quantum encryption key is greater than or equal to a set confidence threshold, calculating a quantum encryption key set of the session access encryption information in a formal encryption process, wherein the quantum encryption key set comprises at least one of a target total quantum encryption key and a target unit quantum encryption key, the target total quantum encryption key represents a key character of the session access encryption information relative to each quantum encryption category, and the target unit quantum encryption key represents a key character of a session access encryption node corresponding to the most front unit quantum encryption key in the session access encryption information relative to each quantum encryption category;

and generating corresponding encryption verification key information of each network element entity according to the quantum encryption key set.

In a possible implementation manner of the first aspect, the step of calculating a quantum encryption key corresponding to the session access encryption information includes:

extracting a first communication encryption relation unit session set corresponding to the session access encryption information, wherein the first communication encryption relation unit session set comprises at least one first communication encryption relation authorization node certificate, and each first communication encryption relation authorization node certificate corresponds to one session access encryption node;

extracting a first relation authorization node certificate set corresponding to the first communication encryption relation unit session set, wherein the first relation authorization node certificate set comprises at least one first relation authorization node certificate, and each first relation authorization node certificate corresponds to one first communication encryption relation authorization node certificate;

generating a second communication encryption relation unit session set according to the first relation authorization node certificate set and the first communication encryption relation unit session set, wherein the second communication encryption relation unit session set comprises at least one second communication encryption relation authorization node certificate, and each second communication encryption relation authorization node certificate corresponds to one session access encryption node;

extracting a third communication encryption relation unit session set corresponding to the second communication encryption relation unit session set, wherein the third communication encryption relation unit session set comprises at least one third communication encryption relation authorization node certificate, and each third communication encryption relation authorization node certificate corresponds to one second communication encryption relation authorization node certificate;

extracting a first feature unit session set corresponding to the third communication encryption relation unit session set, wherein the first feature unit session set comprises at least one first feature vector, and each first feature vector corresponds to a third communication encryption relation authorization node certificate;

performing feature combination on the first feature unit session set to obtain a second feature vector;

and calculating a quantum encryption key corresponding to the second feature vector, wherein the quantum encryption key represents a key character of the session access encryption information relative to each quantum encryption category in the simulated encryption process.

In one possible implementation manner of the first aspect, the quantum encryption key set includes the target unit quantum encryption key; the step of calculating the quantum encryption key set of the session access encryption information in the formal encryption process comprises the following steps: calculating a target unit quantum encryption key of the session access encryption information in the formal encryption process, wherein the target unit quantum encryption key is the most advanced key in a unit quantum encryption key set, the unit quantum encryption key set comprises at least one unit quantum encryption key, and each unit quantum encryption key corresponds to one session access encryption node; the step of generating encryption verification key information of each corresponding network element entity according to the quantum encryption key set includes: if the key length value of the target unit quantum encryption key is greater than or equal to a second length, determining that the session access encryption information belongs to first-class encryption verification key information; if the key degree value of the target unit quantum encryption key is smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information; or

The quantum encryption key set comprises the target total quantum encryption key; the calculating of the quantum encryption key set of the session access encryption information in the formal encryption process includes: acquiring the target total quantum encryption key of the session access encryption information in the formal encryption process; the step of generating encryption verification key information of each corresponding network element entity according to the quantum encryption key set includes: if the key length value of the target total quantum encryption key is greater than or equal to a second length, determining that the session access encryption information belongs to first-class encryption verification key information; if the key length value of the target total quantum encryption key is smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information; or

The quantum encryption key set comprises the target unit quantum encryption key and the target total quantum encryption key; the step of calculating the quantum encryption key set of the session access encryption information in the formal encryption process comprises the following steps: acquiring the target unit quantum encryption key and the target total quantum encryption key of the session access encryption information in the formal encryption process, wherein the target unit quantum encryption key is the most front key in a unit quantum encryption key set, the unit quantum encryption key set comprises at least one unit quantum encryption key, and each unit quantum encryption key corresponds to one session access encryption node; the step of generating encryption verification key information of each corresponding network element entity according to the quantum encryption key set includes: if at least one of the target unit quantum encryption key and the target total quantum encryption key is greater than or equal to a second length, determining that the session access encryption information belongs to first-class encryption verification key information; if the key length values of the target unit quantum encryption key and the target total quantum encryption key are both smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information;

when the session access encryption information belongs to the first type encryption authentication key information, the session encryption category corresponding to the session access encryption information is the session encryption category associated with the session access encryption information, the session access encryption information belongs to the second type encryption authentication key information, and the session encryption category corresponding to the session access encryption information is the session encryption category associated with the session access encryption information and the other session encryption categories associated with the session encryption category.

In a possible implementation manner of the first aspect, the data encryption model is configured by:

acquiring association encryption negotiation data packet sets and association negotiation field switching information of a plurality of communication access processes, and generating configuration data by using the association encryption negotiation data packet sets and the association negotiation field switching information;

acquiring encrypted data services of a plurality of users, generating configuration labels by using the encrypted data services, extracting encrypted negotiation data packet characteristics of the associated encrypted negotiation data packet set, and extracting a negotiation field switching information array of the associated negotiation field switching information;

inputting the encrypted negotiation data packet characteristics and the negotiation field switching information array into a preset artificial intelligence network to obtain a configuration result;

and adjusting parameters of the artificial intelligence network and continuing configuration based on the difference between the configuration result and the configuration label until configuration conditions are met, and obtaining the data encryption model.

In a second aspect, an embodiment of the present disclosure further provides a communication data processing apparatus based on artificial intelligence and a blockchain, which is applied to a big data platform communicatively connected to a plurality of 5G network communication devices, where the apparatus includes:

an obtaining module, configured to obtain a type of an encrypted data segment for starting communication encryption of the 5G network communication device, determine encryption enabling field distribution according to an encrypted data service of the encrypted data segment type, and obtain an encryption negotiation packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution;

the extraction module is used for respectively inputting the encrypted negotiation data packet set and the negotiation field switching information into a configured data encryption model, extracting first network element ciphertext characteristics of each network element entity through a first encryption node of the data encryption model, and extracting second network element ciphertext characteristics of each network element entity through a second encryption node of the data encryption model;

the merging module is used for merging the first network element ciphertext feature and the second network element ciphertext feature through a merging node of the data encryption model to obtain a target network element ciphertext feature;

and the generating module is used for determining the blockchain storage unit of each network element entity corresponding to the encrypted data segment type according to the target network element ciphertext feature, respectively generating the encryption verification key information of each corresponding network element entity according to the blockchain storage unit, and sending the encryption verification key information to the corresponding 5G network communication equipment.

In a third aspect, an embodiment of the present disclosure further provides a communication data processing system based on artificial intelligence and a blockchain, where the communication data processing system based on artificial intelligence and a blockchain includes a big data platform and a plurality of 5G network communication devices communicatively connected to the big data platform;

In a fourth aspect, an embodiment of the present disclosure further provides a big data platform, where the big data platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one 5G network communication device, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the artificial intelligence and blockchain based communication data processing method in any one of the first aspect or the possible designs of the first aspect.

In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform an artificial intelligence and blockchain based communication data processing method in the first aspect or any one of the possible designs of the first aspect.

Based on any one of the above aspects, the present disclosure determines encryption enabling field distribution according to encrypted data services of encrypted data segment categories, then obtains an encryption negotiation data packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution, then extracts a first network element ciphertext feature of each network element entity and a second network element ciphertext feature of each network element entity, and after combining the first network element ciphertext feature and the second network element ciphertext feature to obtain a target network element ciphertext feature, determines a block chain storage unit of each network element entity corresponding to the encrypted data segment categories, thereby generating encryption verification key information of each network element entity. Therefore, the safety of the subsequent encryption verification key information can be improved for the encrypted data service corresponding to the encrypted data segment type, so that the subsequent 5G network communication equipment can conveniently carry out encryption verification of block chain storage distribution with higher safety level based on the encryption verification key information in the communication process, and further, the communication intrusion is avoided.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of a communication data processing system based on artificial intelligence and a block chain according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a communication data processing method based on artificial intelligence and a blockchain according to an embodiment of the present disclosure;

fig. 3 is a schematic functional block diagram of a communication data processing apparatus based on artificial intelligence and a block chain according to an embodiment of the present disclosure;

fig. 4 is a schematic block diagram of a structure of a big data platform for implementing the artificial intelligence and blockchain-based communication data processing method according to the embodiment of the present disclosure.

Detailed Description

The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.

FIG. 1 is a communication encryption diagram of a communication data processing system 10 based on artificial intelligence and blockchains according to an embodiment of the present disclosure. The artificial intelligence and blockchain based communication data processing system 10 may include a big data platform 100 and a 5G network communication device 200 communicatively coupled to the big data platform 100. The artificial intelligence and blockchain based communication data processing system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the artificial intelligence and blockchain based communication data processing system 10 may also include only some of the components shown in fig. 1 or may also include other components.

In this embodiment, the 5G network communication device 200 may comprise a mobile device, a tablet computer, a laptop computer, or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.

In this embodiment, the big data platform 100 and the 5G network communication device 200 in the artificial intelligence and blockchain based communication data processing system 10 may cooperatively perform the artificial intelligence and blockchain based communication data processing method described in the following method embodiment, and for the specific steps of the big data platform 100 and the 5G network communication device 200, reference may be made to the following detailed description of the method embodiment.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flowchart of a communication data processing method based on artificial intelligence and a block chain according to an embodiment of the present disclosure, and the communication data processing method based on artificial intelligence and a block chain according to this embodiment may be executed by the big data platform 100 shown in fig. 1, which is described in detail below.

Step S110, obtaining the encrypted data segment type of the 5G network communication device 200 starting communication encryption, determining encryption enabling field distribution according to the encrypted data service of the encrypted data segment type, and obtaining the encryption negotiation packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution.

Step S120, the encryption negotiation data packet set and the negotiation field switching information are respectively input into the configured data encryption model, the first network element ciphertext characteristics of each network element entity are extracted through the first encryption node of the data encryption model, and the second network element ciphertext characteristics of each network element entity are extracted through the second encryption node of the data encryption model.

And S130, merging the first network element ciphertext feature and the second network element ciphertext feature through the merging node of the data encryption model to obtain a target network element ciphertext feature.

Step S140, determining the blockchain storage unit of the encrypted data segment category corresponding to each network element entity according to the target network element ciphertext feature, generating the encryption verification key information of each corresponding network element entity according to the blockchain storage unit, and sending the encryption verification key information to the corresponding 5G network communication device 200.

In this embodiment, the encrypted data segment category may be any data information configured to be encrypted, such as but not limited to text information, video information, audio information, picture information, and the like.

In this embodiment, the encrypted data traffic may refer to a data traffic type generated when communication encryption is started, for example, may refer to a data traffic type in a certain communication encryption area of a certain encrypted data segment category, or may refer to a data traffic type of a certain communication encryption time node of a certain encrypted data segment category.

In this embodiment, the distribution of the encryption enabling field may be specifically determined according to the node where the encrypted data service is located, for example, the node where the encrypted data service is located is a communication encryption area B of the encrypted data segment class a in the encryption process, and then the distribution of the encryption enabling field is the field distribution corresponding to the communication encryption area B.

In this embodiment, the encryption negotiation packet set may be used to represent a specifically generated encryption negotiation packet (e.g., an interaction behavior, a test behavior, etc.), and the negotiation field switching information may be used to represent a forward-backward conversion process of a type of the specifically generated encryption negotiation packet, for example, information in switching from the interaction behavior to the test behavior.

In this embodiment, the blockchain storage unit may be used to represent a blockchain storage unit formed by blockchain link points formed by communication data nodes corresponding to each network element entity, for example, a communication data node C in an encryption process for a certain encrypted data segment class a, or a communication data node D which is temporarily mentioned next time and initiated, and the like, which is not specifically limited herein.

Based on the above steps, this embodiment determines encryption enabling field distribution according to an encrypted data service of an encrypted data segment category, then obtains an encryption negotiation data packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution, then extracts a first network element ciphertext feature of each network element entity and a second network element ciphertext feature of each network element entity, and after combining the first network element ciphertext feature and the second network element ciphertext feature to obtain a target network element ciphertext feature, determines a block chain storage unit of each network element entity corresponding to the encrypted data segment category, thereby generating encryption verification key information of each network element entity. In this way, the security of the subsequent encryption verification key information can be improved for the encrypted data service corresponding to the encrypted data segment category, so that the subsequent 5G network communication device 200 performs encryption verification of block chain storage distribution with higher security level based on the encryption verification key information in the communication process, thereby avoiding communication intrusion.

In a possible implementation manner, for step S110, the negotiating field switching information may specifically include negotiating a communication location, a switching manner, and a negotiation object.

The negotiation communication position may refer to a time node or an area node when the negotiation field is switched, the switching manner may refer to a negotiation field before the negotiation field is switched and a negotiation field after the negotiation field is switched, and the negotiation object may refer to a position where the communication node is located when the negotiation field is switched.

On this basis, step S120 may be specifically implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S121, inputting the encryption negotiation data packet set to the first encryption node, and performing feature extraction on the encryption negotiation data packet in the encryption negotiation data packet set to obtain the corresponding encryption negotiation data packet feature.

And a substep S122, performing characteristic offset processing on the encrypted negotiation data packet characteristic by using the first encryption node and the communication encryption deviation parameter corresponding to the encrypted data service to obtain the encrypted negotiation data packet characteristic after the characteristic offset processing.

And a substep S123 of extracting the first network element cryptograph characteristics of each network element entity according to the encrypted negotiation data packet characteristics after the characteristic offset processing.

And a substep S124 of inputting the switching information of the negotiation field into the second encryption node, and performing feature extraction on the switching information of the negotiation field to obtain the feature of the negotiation communication position, the feature of the negotiation object and the feature of the switching mode.

And a substep S125, performing characteristic offset processing on the negotiation communication position characteristic, the negotiation object characteristic and the switching mode characteristic by using the communication encryption bias parameter corresponding to the second encryption node and the encrypted data service to obtain a negotiation field switching information array.

And a substep S126, obtaining the encrypted negotiation data packet characteristics corresponding to the encrypted negotiation data packet set, inputting the encrypted negotiation data packet characteristics into the negotiation field switching information array for characteristic combination to obtain a combined target characteristic sequence, and extracting the second network element ciphertext characteristics of each network element entity according to the target characteristic sequence.

In a possible implementation manner, in step S130, in order to improve the merging efficiency, the embodiment may merge the feature positions of the first network element ciphertext feature and the second network element ciphertext feature, which respectively correspond to one another, by using the merging node of the data encryption model, to obtain the target network element ciphertext feature.

In a possible implementation manner, for step S140, in order to accurately determine the blockchain storage unit corresponding to the encrypted data segment class for each network element entity, the following exemplary sub-steps may be implemented specifically, and are described in detail below.

And a substep S141 of obtaining ciphertext protocol configuration content corresponding to the ciphertext protocol unit encrypted by the communication of the associated encrypted data segment type from the target network element ciphertext characteristic, wherein the ciphertext protocol configuration content is obtained by performing protocol analysis on the ciphertext protocol session content in the target network element ciphertext characteristic by adopting a protocol generation form matched with the associated type of the encrypted data segment of the corresponding ciphertext protocol unit.

And a substep S142, loading data blocks to the cryptograph protocol configuration contents sent by the corresponding cryptograph protocol units according to the data block loading mode respectively matched with each protocol generation form, so as to obtain corresponding cryptograph protocol session contents.

And a substep S143, performing session frequency analysis on the session content of each cryptograph protocol respectively, and determining a session frequency parameter corresponding to each cryptograph protocol unit. The session frequency parameter is used for reflecting the frequency degree of communication encryption of the ciphertext protocol unit of the associated encrypted data segment type.

And a substep S144, selecting the highest frequency degree of the session from the session frequency parameters corresponding to each ciphertext protocol unit, and determining the session frequency comparison degree corresponding to each ciphertext protocol unit according to the comparison value between the session frequency parameter corresponding to each ciphertext protocol unit and the highest frequency degree of the session. And the session frequency comparison degree corresponding to the ciphertext protocol unit is positively correlated with the corresponding comparison value.

And a substep S145, performing session identification on the cryptograph protocol session content of the cryptograph protocol unit with the session frequency comparison degree greater than the set session frequency comparison degree, and obtaining a block chain storage unit of each network element entity corresponding to the encrypted data segment type according to the session encryption identifier in the session identification result, wherein each session encryption identifier and each block chain storage unit are in one-to-one correspondence.

Exemplarily, the substep S143 can be specifically realized by the following embodiment (1), embodiment (2) or embodiment (3).

(1) Dividing each cryptograph protocol session content into more than one unit session set of session service, carrying out session frequency detection on each unit session set, determining the number of session nodes with the encryption application frequency number being more than the set frequency number in the unit session set for each cryptograph protocol session content, determining the proportion of the session nodes for each cryptograph protocol session content according to the number of the session nodes in the cryptograph protocol session content and the total number of the unit session sets included in the cryptograph protocol session content, and determining the session frequency parameters corresponding to each cryptograph protocol unit according to the proportion of the session nodes.

(2) Dividing each cryptograph protocol session content into more than one unit session set of session service, carrying out session frequency detection on each unit session set, determining session nodes with encryption application frequency times larger than set frequency times in the unit session sets, determining communication encryption continuous quantity corresponding to each session node, and determining session frequency parameters corresponding to each cryptograph protocol unit according to the quantity of effective session nodes with the communication encryption continuous quantity larger than or equal to an energy threshold value in the session nodes included in each cryptograph protocol session content.

(3) Dividing each cryptograph protocol session content into more than one unit session set of session service, calculating the frequency weight of the directed weighted graph corresponding to each unit session set, fusing the frequency weight of the directed weighted graph corresponding to each unit session set included in the cryptograph protocol session content for each cryptograph protocol session content to obtain a frequency weight sequence corresponding to the cryptograph protocol session content, and taking the frequency weight sequence corresponding to each cryptograph protocol session content as the session frequency parameter corresponding to each cryptograph protocol unit.

For example, for each respective ciphertext protocol session content of each communication encryption member, the respective ciphertext protocol session content may be divided into a set of unit sessions of more than one session service that are in a directed space corresponding to a directed weighted graph. On the basis, a frequency distribution map corresponding to the calculation result of the graph node of each unit session set in the directed weighted graph can be generated, and more than one frequency connected graph included in the frequency distribution map corresponding to each unit session set is determined.

Therefore, for each frequency connected graph in each unit conversation set, the frequency connected graph distribution comparison map corresponding to the frequency connected graph is determined based on the image value of the frequency image point included in the frequency connected graph. Then, for the current frequency connected graph in the current unit conversation set currently processed in each unit conversation set, determining a preset number of associated frequency connected graphs associated with the current frequency connected graph in the current unit conversation set, combining the associated frequency connected graphs and the current frequency connected graph to form a frequency connected graph set, and fusing the frequency connected graph distribution comparison graphs of each frequency connected graph in the frequency connected graph set according to the weight corresponding to the frequency connected graph set to obtain an authorization block comparison graph corresponding to the current frequency connected graph in the current unit conversation set.

On this basis, the authorization block comparison map of the associated frequency connected graph corresponding to the same frequency connected graph sequence number in the previous set of the current unit session set and the authorization block comparison map of the current frequency connected graph in the current unit session set can be fused to obtain the frequency relation distribution comparison map corresponding to the current frequency connected graph in the current unit session set. Then, screening out a minimum image value from frequency relation distribution comparison maps corresponding to frequency connected graphs corresponding to the same frequency connected graph serial number in different unit session sets as an image comparison value corresponding to each frequency connected graph of the corresponding frequency connected graph serial number, and regarding a current frequency connected graph in a current unit session set currently processed in each unit session set, taking a quotient of the frequency relation distribution comparison map of the current frequency connected graph and the image comparison value as an image confidence coefficient ratio corresponding to the current frequency connected graph in the current unit session set.

In this way, when the image confidence ratio is greater than the preset threshold, the first preset value may be used as the session frequency reference value corresponding to the current frequency connectivity map in the current unit session set. For another example, when the image confidence ratio is smaller than or equal to the preset threshold, the second preset value may be used as the session frequency reference value corresponding to the current frequency connectivity map in the current unit session set. It will be appreciated that the second predetermined value should be less than the first predetermined value.

Then, in the association unit session set before the current unit session set, the session frequency dense value of the association frequency connected graph corresponding to the same frequency connected graph sequence number as the current frequency connected graph may be obtained, and the session frequency dense value corresponding to the association frequency connected graph and the session frequency reference value corresponding to the current frequency connected graph are subjected to fusion processing to obtain the session frequency dense value corresponding to the current frequency connected graph in the current unit session set, so that the difference value between the first preset dense value and the session frequency dense value may be used as the reference dense value corresponding to the corresponding frequency connected graph.

Then, for the current frequency connected graph in the current unit conversation set currently processed in each unit conversation set, obtaining the conversation intensive estimation value corresponding to the associated frequency connected graph with the same frequency connected graph sequence number as the current frequency connected graph in the associated unit conversation set of the current unit conversation set, and a first product of the conversation dense estimation value corresponding to the associated frequency connected graph and the conversation frequency dense value corresponding to the current frequency connected graph in the current unit conversation set, and performing summation operation with a second product of the frequency connected graph distribution comparison graph corresponding to the current frequency connected graph in the current unit session set and the reference dense value to obtain a session dense estimation value corresponding to the current frequency connected graph in the current unit session set, and determining a frequency connected graph description value corresponding to each frequency connected graph based on the frequency connected graph distribution comparison graph and the session dense estimation value. In this way, the frequency weights of the directed weighted graphs corresponding to the unit session sets can be calculated according to the frequency connected graph description values corresponding to the frequency connected graphs included in the unit session sets.

Based on the design, the frequency weight of the directed weighted graph corresponding to each unit session set can be calculated by effectively combining the frequency relation, so that the subsequent determination of the blockchain storage unit of each network element entity corresponding to the encrypted data segment type is facilitated.

In a possible implementation manner, still referring to step S140, in the process of generating the encryption verification key information of each corresponding network element entity according to the blockchain storage unit, the following sub-steps may be specifically further implemented, which are described in detail below.

And a substep S146, obtaining session access encryption information corresponding to the blockchain storage unit during data encryption storage, wherein the session access encryption information includes at least one session access encryption node.

In sub-step S147, a quantum encryption key corresponding to the session access encryption information is calculated, where the quantum encryption key represents a key character of the session access encryption information with respect to each quantum encryption category in the simulated encryption process.

And a substep S148, if the key length value of the quantum encryption key is greater than or equal to the set confidence threshold, calculating a quantum encryption key set of the session access encryption information in the formal encryption process, wherein the quantum encryption key set includes at least one of a target total quantum encryption key and a target unit quantum encryption key, the target total quantum encryption key represents a key character of the session access encryption information relative to each quantum encryption category, and the target unit quantum encryption key represents a key character of a session access encryption node corresponding to the most preceding unit quantum encryption key in the session access encryption information relative to each quantum encryption category.

And a substep S149, determining a session encryption category corresponding to the session access encryption information according to the quantum encryption key set, and generating encryption verification key information of each corresponding network element entity according to the session encryption category.

Exemplarily, in the sub-step S147, the following embodiments may be exemplarily implemented.

(1) And extracting a first communication encryption relation unit session set corresponding to the session access encryption information, wherein the first communication encryption relation unit session set comprises at least one first communication encryption relation authorization node certificate, and each first communication encryption relation authorization node certificate corresponds to one session access encryption node.

(2) And extracting a first relation authorization node certificate set corresponding to the first communication encryption relation unit session set, wherein the first relation authorization node certificate set comprises at least one first relation authorization node certificate, and each first relation authorization node certificate corresponds to one first communication encryption relation authorization node certificate.

(3) And generating a second communication encryption relation unit session set according to the first relation authorization node certificate set and the first communication encryption relation unit session set, wherein the second communication encryption relation unit session set comprises at least one second communication encryption relation authorization node certificate, and each second communication encryption relation authorization node certificate corresponds to one session access encryption node.

(4) And extracting a third communication encryption relation unit session set corresponding to the second communication encryption relation unit session set, wherein the third communication encryption relation unit session set comprises at least one third communication encryption relation authorization node certificate, and each third communication encryption relation authorization node certificate corresponds to one second communication encryption relation authorization node certificate.

(5) And extracting a first characteristic unit session set corresponding to the third communication encryption relation unit session set, wherein the first characteristic unit session set comprises at least one first characteristic vector, and each first characteristic vector corresponds to one third communication encryption relation authorization node certificate.

(6) And combining the characteristics of the first characteristic unit conversation set to obtain a second characteristic vector.

(7) And calculating a quantum encryption key corresponding to the second feature vector, wherein the quantum encryption key represents a key character of the access encryption information relative to each quantum encryption category in the simulation encryption process session.

In a possible implementation manner, when the quantum encryption key set includes the target unit quantum encryption key, the target unit quantum encryption key of the session access encryption information in the formal encryption process may be calculated. The target unit quantum encryption key is the most advanced key in the unit quantum encryption key set, the unit quantum encryption key set comprises at least one unit quantum encryption key, and each unit quantum encryption key corresponds to one session access encryption node. Therefore, if the key length value of the target unit quantum encryption key is larger than or equal to the second length, the session access encryption information is determined to belong to the first type of encryption verification key information. And if the key length value of the target unit quantum encryption key is smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information.

For another example, when the quantum encryption key set includes the target total quantum encryption key, the target total quantum encryption key of the session access encryption information in the formal encryption process may be obtained. Therefore, if the key length value of the target total sub-encryption key is greater than or equal to the second length, the session access encryption information is determined to belong to the first type encryption verification key information. And if the key length value of the target total sub-encryption key is smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information.

For another example, when the quantum encryption key set includes a target unit quantum encryption key and a target total quantum encryption key, a target unit quantum encryption key and a target total quantum encryption key of the session access encryption information in the formal encryption process may be obtained, where the target unit quantum encryption key is a top key in the unit quantum encryption key set, the unit quantum encryption key set includes at least one unit quantum encryption key, and each unit quantum encryption key corresponds to one session access encryption node. Therefore, if at least one of the key length values in the target unit quantum encryption key and the target total quantum encryption key is greater than or equal to the second length, the session access encryption information is determined to belong to the first type of encryption verification key information. And if the key length values of the target unit quantum encryption key and the target total quantum encryption key are both smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information.

When the session access encryption information belongs to the first type of encryption and verification key information, the session encryption category corresponding to the session access encryption information is the session encryption category associated with the session access encryption information, the session access encryption information belongs to the second type of encryption and verification key information, and the session encryption category corresponding to the session access encryption information is the session encryption category associated with the session access encryption information and other session encryption categories associated with the session encryption category.

In a possible implementation manner, the data encryption model may be configured by:

(1) acquiring association encryption negotiation data packet sets and association negotiation field switching information of a plurality of communication access processes, and generating configuration data by using the association encryption negotiation data packet sets and the association negotiation field switching information.

(2) Acquiring encrypted data services of a plurality of users, generating configuration labels by using the encrypted data services, extracting encrypted negotiation data packet characteristics of an associated encrypted negotiation data packet set, and extracting a negotiation field switching information array of associated negotiation field switching information.

(3) And inputting the encrypted negotiation data packet characteristics and the negotiation field switching information array into a preset artificial intelligence network to obtain a configuration result.

(4) And adjusting parameters of the artificial intelligent network and continuing configuration based on the difference between the configuration result and the configuration label until the configuration condition is met, and finishing the configuration to obtain the data encryption model.

Fig. 3 is a schematic diagram of functional modules of a communication data processing apparatus 300 based on artificial intelligence and a block chain according to an embodiment of the present disclosure, and this embodiment may divide the functional modules of the communication data processing apparatus 300 based on artificial intelligence and a block chain according to a method embodiment executed by the big data platform 100, that is, the following functional modules corresponding to the communication data processing apparatus 300 based on artificial intelligence and a block chain may be used to execute various method embodiments executed by the big data platform 100. The artificial intelligence and blockchain based communication data processing apparatus 300 may include an obtaining module 310, an extracting module 320, a merging module 330, and a generating module 340, and the functions of the functional modules of the artificial intelligence and blockchain based communication data processing apparatus 300 are described in detail below.

An obtaining module 310, configured to obtain the encrypted data segment type of the 5G network communication device 200 for starting communication encryption, determine encryption enabling field distribution according to the encrypted data service of the encrypted data segment type, and obtain an encryption negotiation packet set and negotiation field switching information of each network element entity corresponding to the encryption enabling field distribution. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

An extracting module 320, configured to input the negotiation packet set and the negotiation field switching information to a configured data encryption model, extract a first network element ciphertext feature of each network element entity through a first encryption node of the data encryption model, and extract a second network element ciphertext feature of each network element entity through a second encryption node of the data encryption model. The extracting module 320 may be configured to perform the step S120, and the detailed implementation of the extracting module 320 may refer to the detailed description of the step S120.

And a merging module 330, configured to merge the first network element ciphertext feature and the second network element ciphertext feature through a merging node of the data encryption model to obtain a target network element ciphertext feature. The merging module 330 may be configured to perform the step S130, and the detailed implementation of the merging module 330 may refer to the detailed description of the step S130.

The generating module 340 is configured to determine, according to the target network element ciphertext feature, a blockchain storage unit of each network element entity corresponding to the encrypted data segment type, generate, according to the blockchain storage unit, encryption verification key information of each corresponding network element entity, and send the encryption verification key information to the corresponding 5G network communication device 200. The generating module 340 may be configured to execute the step S140, and the detailed implementation of the generating module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 shows a hardware structure diagram of a big data platform 100 for implementing the above control device according to an embodiment of the present disclosure, and as shown in fig. 4, the big data platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the extracting module 320, the merging module 330, and the generating module 340 included in the artificial intelligence and blockchain based communication data processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the artificial intelligence and blockchain based communication data processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned 5G network communication device 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the big data platform 100, and implementation principles and technical effects thereof are similar, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the disclosure also provides a readable storage medium, in which computer execution instructions are stored, and when a processor executes the computer execution instructions, the method for processing communication data based on artificial intelligence and block chains is implemented.

The readable storage medium described above 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. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims

1. A communication data processing method based on artificial intelligence and a block chain is applied to a big data platform in communication connection with a plurality of 5G network communication devices, and the method comprises the following steps:

respectively inputting the encrypted negotiation data packet set and the negotiation field switching information into a configured data encryption model, extracting first network element ciphertext characteristics of each network element entity through a first encryption node of the data encryption model, and extracting second network element ciphertext characteristics of each network element entity through a second encryption node of the data encryption model, wherein the data encryption model is obtained by training based on artificial intelligence training samples;

2. The method according to claim 1, wherein the negotiation field switching information includes negotiation communication position, switching manner, negotiation object;

3. The method for processing communication data based on artificial intelligence and a block chain according to claim 1, wherein the step of combining the first network element ciphertext feature and the second network element ciphertext feature by the combining node of the data encryption model to obtain a target network element ciphertext feature comprises:

4. The method according to claim 1, wherein the step of determining the blockchain storage unit of each network element entity corresponding to the encrypted data segment type according to the target network element ciphertext feature comprises:

respectively carrying out session frequency analysis on session contents of the ciphertext protocols, and determining session frequency parameters corresponding to the ciphertext protocol units, wherein the session frequency parameters are used for reflecting the session frequency degree of the ciphertext protocol units related to the encrypted data segment type;

5. The method of claim 4, wherein the step of performing session frequency analysis on session contents of the ciphertext protocols and determining the session frequency parameter corresponding to each ciphertext protocol unit comprises:

Dividing each cryptograph protocol session content into more than one unit session set of session services, performing session frequency detection on each unit session set, determining session nodes with encryption application frequency times larger than set frequency times in the unit session sets, determining session application persistence quantity corresponding to each session node, and determining session frequency parameters corresponding to each cryptograph protocol unit according to the number of effective session nodes with the session application persistence quantity larger than or equal to an energy threshold value in the session nodes included in each cryptograph protocol session content; or

6. The method according to claim 4, wherein the step of dividing each ciphertext protocol session content into more than one unit session set of session service, and calculating the frequency weight of the directed weighted graph corresponding to each unit session set comprises:

7. The method for processing communication data according to claim 1, wherein the step of generating encryption and verification key information of each corresponding network element entity according to the blockchain storage unit comprises:

calculating a quantum encryption key set of the session access encryption information in a formal encryption process by using a quantum encryption key corresponding to the session access encryption information, wherein the quantum encryption key set comprises at least one of a target total quantum encryption key and a target unit quantum encryption key, the target total quantum encryption key represents a key character of the session access encryption information relative to each quantum encryption category, and the target unit quantum encryption key represents a key character of a session access encryption node corresponding to an earliest unit quantum encryption key in the session access encryption information relative to each quantum encryption category;

8. The artificial intelligence and blockchain based communication data processing method according to claim 7, wherein:

the quantum encryption key set comprises the target unit quantum encryption key; the step of calculating the quantum encryption key set of the session access encryption information in the formal encryption process comprises the following steps: calculating a target unit quantum encryption key of the session access encryption information in the formal encryption process, wherein the target unit quantum encryption key is the most advanced key in a unit quantum encryption key set, the unit quantum encryption key set comprises at least one unit quantum encryption key, and each unit quantum encryption key corresponds to one session access encryption node; the step of generating encryption verification key information of each corresponding network element entity according to the quantum encryption key set includes: if the key length value of the target unit quantum encryption key is greater than or equal to a second length, determining that the session access encryption information belongs to first-class encryption verification key information; if the key degree value of the target unit quantum encryption key is smaller than the second length, determining that the session access encryption information belongs to second type encryption verification key information; or

9. The artificial intelligence and blockchain based communication data processing method according to any one of claims 1 to 8, wherein the data encryption model is configured by:

10. A big data platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one 5G network communication device, the machine-readable storage medium is configured to store a program, instructions, or code, and the processor is configured to execute the program, instructions, or code in the machine-readable storage medium to perform the artificial intelligence and blockchain based communication data processing method according to any one of claims 1 to 9.