CN112488712A

CN112488712A - Safety identification method and safety identification system based on block chain big data

Info

Publication number: CN112488712A
Application number: CN202011477761.8A
Authority: CN
Inventors: 杨刘琴
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-06-24
Filing date: 2020-06-24
Publication date: 2021-03-12
Also published as: CN111652615A; CN111652615B; CN112488713A

Abstract

The embodiment of the application provides a safety identification method and a safety identification system based on block chain big data, the payment safety characteristics of a distributed account book can be well learned through the payment response big data information of the distributed account book in each account book distribution interval which is transacted through intelligent learning and the preset consensus rule label corresponding to each payment response object, so that safety protection can be conveniently carried out subsequently by providing a safety protection updating script which accords with the payment safety characteristics in the intelligent payment process, and therefore safety in the subsequent block chain payment process is improved through safety identification and comparison of consensus prediction rules.

Description

Safety identification method and safety identification system based on block chain big data

Technical Field

The application relates to the technical field of block chains and secure payment, in particular to a secure identification method and a secure identification system based on block chain big data.

Background

With the rapid development of mobile payment technology, the use of blockchain payment in the internet is more and more common, the blockchain is simply a decentralized distributed account book, and a scheme is provided for solving the multi-party trust problem essentially through a public and encrypted non-falsification technical means. In view of this, how to adaptively improve the security in the blockchain payment process is a technical problem that needs to be solved by those skilled in the art.

Disclosure of Invention

In view of this, an object of the present application is to provide a block chain big data-based security identification method and a security identification system, which can well learn the payment security characteristics of a distributed ledger by intelligently learning the payment response big data information of the distributed ledger within each ledger distribution interval that has completed a transaction and a preset consensus rule tag corresponding to each payment response object, so as to subsequently provide a security protection update script meeting the payment security characteristics for a user for automatic control, thereby improving the security in the subsequent block chain payment process by security identification and comparison of consensus prediction rules.

In a first aspect, the present application provides a safety identification method based on blockchain big data, which is applied to an artificial intelligence cloud service platform, where the artificial intelligence cloud service platform is in communication connection with blockchain verification service systems of multiple different distributed accounts, each blockchain verification service system includes a blockchain request response component and a payment encryption component in communication connection with the blockchain request response component, and the method includes:

acquiring payment response big data information of a distributed account book in each account book distribution interval in which transaction is completed, wherein the payment response big data information is obtained after encryption completion of a block chain request response component through a payment encryption component in a block chain verification service system of the distributed account book, the payment response big data information comprises payment response objects and payment account book information sets corresponding to the payment response objects, the payment response objects are used for representing verification objects generated in each time in a consensus payment verification process, and the payment account book information sets are used for recording consensus payment verification data under the corresponding payment response objects;

configuring to obtain a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed account book in each account book distribution interval which finishes the transaction and a preset consensus rule label corresponding to each payment response object;

according to the safety identification artificial intelligence model, carrying out safety identification on payment response data information of the distributed ledger under each payment response object in a preset time period to obtain a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period;

and generating at least one safety protection updating script and a protection execution node sequence corresponding to each safety protection updating script according to a comparison relation between a consensus prediction rule of the payment book information set corresponding to each payment response object and a preset consensus rule tag, wherein the protection execution node sequence comprises at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item.

In a possible implementation manner of the first aspect, the step of configuring, according to payment response big data information of the distributed ledger within each ledger distribution interval in which a transaction is completed and a preset consensus rule tag corresponding to each payment response object, to obtain a corresponding safety recognition artificial intelligence model includes:

extracting transaction protocol characteristic information of a payment book information set corresponding to each payment response object;

inputting the transaction protocol characteristic information into a model to be generated by taking the transaction protocol characteristic information as an input characteristic of the model to be generated, and analyzing a learnable characteristic of the transaction protocol characteristic information in a transaction protocol category through the model to be generated, wherein the learnable characteristic comprises a learnable characteristic section set;

segmenting the learnable feature segment set according to preset marks to obtain a plurality of learning segmentation features;

determining a plurality of first updating command contents according to the feature vectors corresponding to the learnable features, wherein the plurality of first updating command contents are respectively the updating command contents for learning control of the plurality of learning segmentation features in the model to be generated, the model to be generated is used for learning the learning segmentation characteristics after the segmentation processing is carried out on the multiple learnable characteristic segment sets, and the updating command content of each learning and dividing characteristic after the dividing processing mapped in the model to be generated, the plurality of learnable feature segment sets are learnable feature segment sets included in a plurality of learnable features acquired within the transaction protocol category, the first updating command content is obtained according to the characteristic parameter type represented by the characteristic vector and preset updating command contents corresponding to different characteristic parameter types;

sequencing the plurality of first updating command contents according to the sequence of each first updating command content in the plurality of first updating command contents from high convergence to low convergence to obtain an updating command content sequence;

determining updating command content mapped in the model to be generated by a learning segmentation feature in the plurality of learning segmentation features based on a preset similarity ratio threshold and the updating command content sequence, wherein the preset similarity ratio threshold is used for indicating the proportion of the learnable feature segment set and similar parts of the learnable feature segment set acquired in the transaction protocol category in the learnable feature segment set;

when the content of an update command mapped by the learning segmentation features in the model to be generated is matched with preset content of the update command, determining that the learnable features are target learnable features, and when the learnable features are determined to be the target learnable features, controlling the model to be generated to learn the learning segmentation features after segmentation processing is carried out on a plurality of learnable feature segment sets obtained in the transaction protocol category according to the first content of the update command for each first content of the plurality of first update commands, and generating corresponding prediction consensus rules after the learning control;

and updating the updating command content of the model to be generated according to the prediction consensus rule of each payment response object and the preset consensus rule label corresponding to each payment response object.

In a possible implementation manner of the first aspect, the step of extracting transaction agreement feature information of the payment ledger information set corresponding to each payment response object includes:

determining a rule signature vector associated with a consensus rule tag corresponding to the payment response object in consensus payment verification data for each data item of the payment ledger information set;

for the unit rule information of each signature verification unit on the rule signature vector in each piece of consensus payment verification data, determining the rule signature vector coverage of each piece of consensus payment verification data according to the unit rule information of each signature verification unit, and determining the confidence rule signature vector coverage of each piece of consensus payment verification data according to the rule signature vector coverage of each piece of consensus payment verification data, wherein the unit rule information of each piece of signature verification unit comprises at least one of the number, the arrangement number and the characteristic value of the signature verification unit;

and sequencing the consensus payment verification data according to the sequence of the confidence rule signature vector coverage from high to low, and selecting the consensus payment verification data with the characteristic quantity in the front sequence as the transaction protocol characteristic information of the payment book information set according to the preset characteristic quantity.

In a possible implementation manner of the first aspect, if the unit rule information of the signature verification unit includes the number of signature verification units, the step of determining the coverage of the regular signature vector of each consensus payment verification data according to the unit rule information of each signature verification unit for the unit rule information of each signature verification unit on the regular signature vector in each consensus payment verification data includes:

and for each piece of consensus payment verification data, determining the coverage of the first regular signature vector corresponding to each associated regular signature vector according to the sum of the number of signature verification units on each associated regular signature vector in the piece of consensus payment verification data, and determining the coverage of the regular signature vector of the consensus payment verification data according to the sum of the coverage of the first regular signature vector corresponding to each associated regular signature vector, wherein the larger the sum of the number is, the larger the coverage of the first regular signature vector is.

In a possible implementation manner of the first aspect, if the unit rule information of the signature verification unit includes an arrangement number of the signature verification unit, the step of determining, for the unit rule information of each signature verification unit on the regular signature vector in each piece of consensus payment verification data, the regular signature vector coverage of each piece of consensus payment verification data according to the unit rule information of each signature verification unit includes:

for each piece of consensus payment verification data, determining a maximum signature verification interval and a minimum signature verification interval determined by two adjacent signature verification units on each regular signature vector according to the arrangement number of the signature verification units on each regular signature vector in the consensus payment verification data, determining the coverage of a second regular signature vector corresponding to each regular signature vector according to whether the ratio of the maximum signature verification interval to the minimum signature verification interval on each regular signature vector is smaller than a preset threshold value, determining the regular signature vector coverage of the consensus payment verification data according to the sum of the second regular signature vector coverage corresponding to each regular signature vector, when the ratio is smaller than a preset threshold value, the coverage of the corresponding second regular signature vector is larger than that of the corresponding second regular signature vector when the ratio is larger than the set threshold value;

for each regular signature vector in each piece of consensus payment verification data, determining an average arrangement number point of the signature verification units on the regular signature vector according to the arrangement number of the signature verification units on the regular signature vector;

determining a site forming sequence corresponding to each associated regular signature vector according to the relation of average arrangement number points on each associated regular signature vector, determining third regular signature vector coverage corresponding to each associated regular signature vector according to the sequence association degree of the site forming sequence and the sequence of time corresponding to the data of the consensus payment verification data, and determining the regular signature vector coverage of the consensus payment verification data according to the sum of the third regular signature vector coverage corresponding to each associated regular signature vector, wherein the larger the sequence association degree is, the larger the third regular signature vector coverage is, and the sequence of time corresponding to the data of the consensus payment verification data is a sequence formed by the consensus payment verification data along a forward time axis;

for each regular signature vector in each piece of consensus payment verification data, determining an average arrangement number point of the signature verification units on the regular signature vector according to the arrangement numbers of the signature verification units on the regular signature vector, determining a middle arrangement number point of the average arrangement number points on any two regular signature vectors in every three adjacent regular signature vectors, and simultaneously determining the matching degree of the average arrangement number point on the rest regular signature vector and the middle arrangement number point;

determining the contact degree of every two adjacent three regular signature vectors according to the matching degree, wherein the contact degree is higher when the matching degree is higher, or determining the middle array number point of the average array number point on two adjacent regular signature vectors in every two adjacent three regular signature vectors, and determining the contact degree of every two adjacent three regular signature vectors according to the sequence association degree of the two middle array number points to determine the fourth regular signature vector coverage degree corresponding to every two adjacent three regular signature vectors, wherein the contact degree is higher when the sequence association degree is higher;

and determining the regular signature vector coverage of the consensus payment verification data according to the sum of the fourth regular signature vector coverage corresponding to every three adjacent regular signature vectors, wherein the higher the coincidence degree is, the larger the fourth regular signature vector coverage is.

In a possible implementation manner of the first aspect, if the unit rule information of the signature verification unit includes a feature value of the signature verification unit, the step of determining, for the unit rule information of each signature verification unit on the rule signature vector in each piece of consensus payment verification data, the rule signature vector coverage of each piece of consensus payment verification data according to the unit rule information of each signature verification unit includes:

for each piece of consensus payment verification data, determining the feature value change feature of a first signature verification unit and a last signature verification unit on each regular signature vector in each regular signature vector according to the feature value of the signature verification unit on each regular signature vector in the piece of consensus payment verification data, determining the coverage of a fifth regular signature vector corresponding to each regular signature vector according to whether the feature value change feature meets a preset feature change rule, and determining the coverage of the regular signature vector of the piece of consensus payment verification data according to the sum of the coverage of the fifth regular signature vector corresponding to each regular signature vector, wherein the coverage of the fifth regular signature vector corresponding to the piece of consensus payment verification data when the preset feature change rule is met is greater than the coverage of the fifth regular signature vector corresponding to the piece of consensus payment verification data when the preset feature change rule is not met;

for each piece of consensus payment verification data, determining a gradient value of a signature verification unit on each regular signature vector according to a characteristic value of the signature verification unit on each regular signature vector in the piece of consensus payment verification data, determining a sixth regular signature vector coverage corresponding to each regular signature vector according to an average value of absolute values of the gradient values of the signature verification units on each regular signature vector, and determining a regular signature vector coverage of the piece of consensus payment verification data according to a sum of the sixth regular signature vector coverage corresponding to each regular signature vector, wherein the larger the average value is, the larger the sixth regular signature vector coverage is.

In a possible implementation manner of the first aspect, the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to a comparison relationship between a consensus prediction rule of a payment ledger information set corresponding to each payment response object and a predetermined consensus rule tag includes:

performing simulation verification on the target consensus prediction rule and the payment response object corresponding to the target consensus prediction rule according to a preset consensus payment verification strategy, and respectively generating payment verification strategy result information of each consensus payment verification strategy;

and generating at least one security protection updating script and a protection execution node sequence corresponding to each security protection updating script according to the payment verification strategy result information of each consensus payment verification strategy.

In a possible implementation manner of the first aspect, the step of performing simulation verification on the target consensus prediction rule and the payment response object corresponding to the target consensus prediction rule according to a predetermined consensus payment verification policy, and generating payment verification policy result information of each consensus payment verification policy respectively includes:

acquiring a preset signature verification unit corresponding to each preset consensus payment verification strategy, forming a signature verification unit sequence of each preset consensus payment verification strategy, and selecting a target signature verification unit in the front sequence from the signature verification unit sequence according to a preset unit quantity threshold corresponding to each consensus payment verification strategy to obtain a target signature verification unit corresponding to each preset consensus payment verification strategy;

and matching the consensus prediction rule of the payment book information set corresponding to each payment response object with the target signature verification unit corresponding to each preset consensus payment verification strategy, and determining the consensus prediction rule matched with each preset consensus payment verification strategy according to the matching result so as to generate payment verification strategy result information of each consensus payment verification strategy.

In a possible implementation manner of the first aspect, the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to the payment verification policy result information of each consensus payment verification policy includes:

respectively acquiring preset protection execution node information matched with the consensus prediction rules aiming at each consensus prediction rule of the payment verification policy result information of each consensus payment verification policy, acquiring a target protection execution node set associated with the preset protection execution node information and the consensus payment verification policy, and determining the consensus payment verification policy as a security protection update script when the number of target protection execution nodes in the target protection execution node set is greater than a set number;

on the basis of determining the consensus payment verification strategy as a safety protection updating script, calculating the target protection execution node set to obtain protection verification information corresponding to the target protection execution node set, extracting protection features of each target protection execution node of the consensus prediction rule in the target protection execution node set, and obtaining protection feature vectors of each target protection execution node in the target protection execution node set;

determining target protection execution nodes with verification history frequency greater than a preset threshold value in protection verification information corresponding to the target protection execution node set as key target protection execution nodes;

calculating a first protection transaction parameter of the whole node sequence according to the protection feature vector of each target protection execution node in the target protection execution node set, and calculating a second protection transaction parameter of the key target protection execution node according to the protection feature vector of each target protection execution node in the key target protection execution node;

calculating preset weight coefficients corresponding to the first protection transaction parameter, the second protection transaction parameter, the first protection transaction parameter and the second protection transaction parameter respectively to obtain a feature coefficient of the key target protection execution node, calculating a calculation result of a protection feature vector and the feature coefficient of each target protection execution node in the target protection execution node set, and obtaining a first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to the calculation result;

calculating the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set and the protection verification information to obtain the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set;

or acquiring a first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to a calculation result of the protection feature vector and the feature coefficient of each target protection execution node in the target protection execution node set, and calculating the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to a preset difference range to acquire a second execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set, wherein a parameter difference between the second execution consensus algorithm reference degree and the first execution consensus algorithm reference degree is not in the difference range;

calculating the second execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set and the protection verification information to obtain the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set;

determining a target coefficient of each target protection execution node in the target protection execution node set according to the execution consensus algorithm reference degree and the protection verification information, and calculating a ratio of the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set to a preset constant, wherein the target coefficient is a value obtained by dividing the execution consensus algorithm reference degree by a characteristic vector value of the protection verification information;

calculating the product of the ratio of the execution consensus algorithm reference degree of each target protection execution node to a preset constant and a corresponding target coefficient, and obtaining the screening degree of each target protection execution node in the target protection execution node set;

and arranging the target protection execution nodes with the screening degrees larger than the set screening degree according to the screening degree of each target protection execution node, and determining the target protection execution nodes of the same command type as one protection consensus verification item so as to determine the protection execution node sequence corresponding to the safety protection updating script.

In a possible implementation manner of the first aspect, after the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to a comparison relationship between a consensus prediction rule of a payment ledger information set corresponding to each payment response object and a predetermined consensus rule tag, the method further includes:

and sending the at least one safety protection updating script and the protection execution node sequence corresponding to each safety protection updating script to a payment encryption component in the block chain verification service system of the distributed account book, so that the payment encryption component protects the payment safety verification process corresponding to the block chain request response component according to the safety protection updating script specified by the distributed account book and the protection execution node sequence corresponding to the safety protection updating script.

In a second aspect, an embodiment of the present application further provides a block chain big data based security identification apparatus, which is applied to an artificial intelligence cloud service platform, where the artificial intelligence cloud service platform is communicatively connected to a block chain verification service system of multiple different distributed accounts, the block chain verification service system includes a block chain request response component and a payment encryption component communicatively connected to the block chain request response component, and the apparatus includes:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring payment response big data information of a distributed account book in each account book distribution interval for which transaction is completed, the payment response big data information is obtained after encryption of a block chain request response component is completed through a payment encryption component in a block chain verification service system of the distributed account book, the payment response big data information comprises payment response objects and payment account book information sets corresponding to each payment response object, the payment response objects are used for representing verification objects generated each time in the consensus payment verification process, and the payment account book information sets are used for recording consensus payment verification data under corresponding payment response objects;

the learning control module is used for configuring and obtaining a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed account book in each account book distribution interval which finishes transaction and a preset consensus rule label corresponding to each payment response object;

the safety identification module is used for carrying out safety identification on payment response data information of the distributed ledger under each payment response object in a preset time period according to the safety identification artificial intelligence model to obtain a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period;

and the generating module is used for generating at least one safety protection updating script and a protection execution node sequence corresponding to each safety protection updating script according to a comparison relation between a consensus prediction rule of the payment book information set corresponding to each payment response object and a preset consensus rule label, wherein the protection execution node sequence comprises at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item.

In a third aspect, an embodiment of the present application further provides a block chain big data based security identification system, where the block chain big data based security identification system includes an artificial intelligence cloud service platform and a block chain verification service system of multiple different distributed ledgers communicatively connected to the artificial intelligence cloud service platform, and the block chain verification service system includes a block chain request response component and a payment encryption component communicatively connected to the block chain request response component;

the payment encryption component encrypts the blockchain request response component to obtain payment response big data information of the distributed account book in each account book distribution interval after transaction is completed;

the artificial intelligence cloud service platform is used for acquiring payment response big data information of a distributed ledger in each ledger distribution interval in which transaction is completed, wherein the payment response big data information comprises payment response objects and payment ledger information sets corresponding to the payment response objects, the payment response objects are used for representing verification objects generated each time in the consensus payment verification process, and the payment ledger information sets are used for recording consensus payment verification data under the corresponding payment response objects;

the artificial intelligence cloud service platform is used for configuring and obtaining a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed account book in each account book distribution interval which is subjected to transaction and a preset consensus rule label corresponding to each payment response object;

the artificial intelligence cloud service platform is used for carrying out safety identification on payment response data information of the distributed ledger under each payment response object in a preset time period according to the safety identification artificial intelligence model to obtain a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period;

the artificial intelligence cloud service platform is used for generating at least one safety protection updating script and a protection execution node sequence corresponding to each safety protection updating script according to a comparison relation between a consensus prediction rule of a payment book information set corresponding to each payment response object and a preset consensus rule tag, wherein the protection execution node sequence comprises at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item.

In a fourth aspect, an embodiment of the present application further provides an artificial intelligence cloud service platform, where the artificial intelligence cloud service 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 used for being in communication connection with at least one security identification system based on blockchain big data, the machine-readable storage medium is used for storing a program, a command, or a code, and the processor is used for executing the program, the command, or the code in the machine-readable storage medium to execute the method for security identification based on blockchain big data in any one of the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where a command is stored in the computer-readable storage medium, and when the command is detected on a computer, the computer is enabled to execute the method for secure identification based on blockchain big data in the first aspect or any one of the possible implementation manners of the first aspect.

According to any one of the aspects, the payment safety characteristics of the distributed account book can be well learned through the payment response big data information of the distributed account book in each account book distribution interval which finishes transaction and the preset consensus rule label corresponding to each payment response object, so that safety protection can be conveniently performed subsequently by providing a safety protection updating script which accords with the payment safety characteristics in the intelligent payment process, and therefore safety in the subsequent block chain payment process is improved through safety identification and comparison of consensus prediction rules.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of a block chain big data based security identification system according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a block chain big data based security identification method according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of a block chain big data based security identification apparatus according to an embodiment of the present application;

fig. 4 is a block diagram schematically illustrating a structure of an artificial intelligence cloud service platform for implementing the above block chain big data-based security identification method according to the embodiment of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is an interaction diagram of a block chain big data-based secure identification system 10 according to an embodiment of the present invention. The safety identification system 10 based on the blockchain big data can comprise an artificial intelligence cloud service platform 100 and a blockchain verification service system 200 which is in communication connection with the artificial intelligence cloud service platform 100. The blockchain big data based security identification system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the blockchain big data based security identification system 10 may also include only a part of the components shown in fig. 1 or may also include other components.

In this embodiment, the artificial intelligence cloud service platform 100 and the blockchain verification service system 200 in the security identification system 10 based on blockchain big data may cooperatively execute the security identification method based on blockchain big data described in the following method embodiment, and the detailed description of the following method embodiment may be referred to in the execution step portions of the artificial intelligence cloud service platform 100 and the blockchain verification service system 200.

In this embodiment, the blockchain verification service system 200 may specifically include a blockchain request response component and a payment encryption component communicatively connected to the blockchain request response component, where the blockchain request response component may be configured to record payment response big data information in a payment verification process, and the payment encryption component may be configured to encrypt related security information in the payment response process of the blockchain request response component and protect the payment security verification process, and this embodiment is not limited in this embodiment.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of a security identification method based on blockchain big data according to an embodiment of the present invention, where the security identification method based on blockchain big data according to the embodiment may be executed by the artificial intelligence cloud service platform 100 shown in fig. 1, and the security identification method based on blockchain big data is described in detail below.

Step S110, obtaining payment response big data information of the distributed ledger in each ledger distribution interval of the completed transaction.

And step S120, configuring and obtaining a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed ledger in each ledger distribution interval which finishes the transaction and a preset consensus rule label corresponding to each payment response object.

Step S130, carrying out safety recognition on payment response data information of the distributed ledger under each payment response object in the preset time period according to the safety recognition artificial intelligence model, and obtaining a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period.

Step S140, generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to the consensus prediction rule of the payment ledger information set corresponding to each payment response object.

In this embodiment, the artificial intelligence cloud service platform 100 may provide the distributed ledger with payment response big data information of the distributed ledger in different ledger distribution intervals, and the distributed ledger may flexibly select payment response big data information of a part or all of the ledger distribution intervals to complete a transaction, so that the artificial intelligence cloud service platform 100 may obtain payment response big data information of the distributed ledger in each ledger distribution interval where a transaction is completed.

In this embodiment, the payment response big data information may be obtained by encrypting the blockchain request response component through a payment encryption component in the blockchain verification service system 200 of the distributed ledger. As a possible example, the payment response big data information may include payment response objects and payment ledger information sets corresponding to each payment response object, where the payment response objects are used to characterize verification objects (e.g., user biometric verification, user payment environment verification, and other behaviors) generated each time in the consensus payment verification process, and the payment ledger information sets may be used to record consensus payment verification data under corresponding payment response objects, for example, each payment response object usually lasts for a certain time, within this time period, the consensus payment verification data under the corresponding payment response object may be recorded with each node (e.g., one-time verification behavior) as one recording point, and the payment ledger information sets are obtained after aggregation.

In this embodiment, the preset consensus rule tag may be used to represent the type of consensus payment verification corresponding to each payment response object, for example, a workload certification mechanism, a rights and interests certification mechanism, a shares authorization certification mechanism, and the like, and the preset consensus rule tag corresponding to each payment response object of the distributed ledger may be set according to the historical use condition and uploaded to the artificial intelligent cloud service platform 100 for recording.

In this embodiment, the protection execution node sequence may include at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item, where the node sequences may form a control command for a subsequent payment verification process with a time axis as a direction and a unit time as a protection unit.

Based on the design, the payment response big data information of the distributed account book in each account book distribution interval which has completed the transaction and the preset consensus rule label corresponding to each payment response object can be learned well through the intelligent learning of the payment response big data information of the distributed account book, so that the payment safety characteristics of the distributed account book can be learned well, safety protection can be performed subsequently by providing a safety protection updating script which accords with the payment safety characteristics in the intelligent payment process, and therefore safety identification and comparison of consensus prediction rules are performed, and safety in the follow-up block chain payment process is improved.

In one possible implementation manner, regarding step S120, in order to improve the learning control effect and avoid the introduction of noise learning, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S121 of extracting transaction agreement characteristic information of the payment ledger information set corresponding to each payment response object.

And a substep S122, taking the transaction protocol characteristic information as an input characteristic of the model to be generated, inputting the transaction protocol characteristic information into the model to be generated, and analyzing the learnable characteristics of the transaction protocol characteristic information in the transaction protocol category through the model to be generated, wherein the learnable characteristics comprise a learnable characteristic section set.

On this basis, considering that the set of learnable feature segments is generally separated by some identifier, it may be performed that:

in the substep S123, the learnable feature segment set is segmented according to a preset mark (e.g., a semicolon, a pause mark, etc.) to obtain a plurality of learnable segmentation features, and a plurality of first update command contents are determined according to the feature vectors corresponding to the learnable features.

It should be noted that the plurality of first update command contents are update command contents for learning and controlling the plurality of learning segmented features in the model to be generated, the model to be generated is used for learning the learning segmented features after the segmentation processing is performed on the plurality of learnable feature segment sets, and update command contents mapped by each of the segmented learning segmented features in the model to be generated, and the plurality of learnable feature segment sets are learnable feature segment sets included in the plurality of learnable features acquired in the transaction protocol category. It should be noted that the content of the first update command is obtained according to the feature parameter type represented by the feature vector and the preset update command content corresponding to different feature parameter types.

And a substep S124, sorting the plurality of first update command contents according to the order of the convergence degree from high to low of each first update command content in the plurality of first update command contents, so as to obtain an update command content sequence.

And a substep S125, determining an update command content of the model to be generated mapped by the learning segmentation feature of the plurality of learning segmentation features based on a preset similarity ratio threshold and the update command content sequence.

It should be noted that the preset similarity ratio threshold is used to indicate the proportion of the learnable feature interval set and the similar part of the learnable feature interval set obtained in the transaction protocol category in the learnable feature interval set.

And a substep S126, when the updating command content mapped by the learning segmentation feature in the model to be generated matches the preset updating command content, determining that the learnable feature is the target learnable feature, when the learnable feature is determined to be the target learnable feature, controlling the learning segmentation feature after the segmentation processing is carried out on the plurality of learnable feature segment sets obtained by the model to be generated in the transaction protocol category according to the first updating command content for each first updating command content in the plurality of first updating command contents, and generating the corresponding prediction consensus rule after the learning control for each learning segmentation feature after the segmentation processing is carried out.

And a substep S127 of updating the updating command content of the model to be generated according to the prediction consensus rule of each payment response object and the preset consensus rule tag corresponding to each payment response object.

It should be noted that the number of update iterations may be set, and when the number of update iterations reaches the set number, it indicates that the learning control of the model to be generated is completed, and the safety recognition artificial intelligence model whose learning control is completed is output.

In a possible implementation manner, during the above sub-step S121, in order to enable the extracted transaction protocol feature information to effectively relate to the relevance of different data features so as to improve the subsequent learning control effect, the sub-step S121 may be implemented by the following exemplary sub-steps, which are described in detail below.

(1) In the consensus payment verification data of each data item of the payment ledger information set, a rule signature vector associated with a consensus rule tag corresponding to a payment response object is determined, then for unit rule information of each signature verification unit on the rule signature vector in each consensus payment verification data, the rule signature vector coverage of each consensus payment verification data is determined according to the unit rule information of each signature verification unit, and the confidence rule signature vector coverage of each consensus payment verification data is determined according to the rule signature vector coverage of each consensus payment verification data.

(2) And sequencing the consensus payment verification data according to the sequence of the confidence rule signature vector coverage from high to low, and selecting the consensus payment verification data with the characteristic quantity in the front sequence as the transaction protocol characteristic information of the payment book information set according to the preset characteristic quantity.

Wherein the unit rule information of the signature verification unit may include at least one of a number, an arrangement number, and a characteristic value of the signature verification unit. Next several possible examples will be given of the present embodiment to determine the regular signature vector coverage for each consensus payment verification data.

For example, if the unit rule information of the signature verification unit includes the number of the signature verification units, for each piece of consensus payment verification data, determining the coverage of the first regular signature vector corresponding to each associated regular signature vector according to the sum of the number of the signature verification units on each associated regular signature vector in the piece of consensus payment verification data, and determining the coverage of the first regular signature vector of the consensus payment verification data according to the sum of the coverage of the first regular signature vector corresponding to each associated regular signature vector, wherein the larger the sum of the numbers, the larger the coverage of the first regular signature vector.

For another example, if the unit rule information of the signature verification unit includes the arrangement number of the signature verification unit, for each piece of consensus payment verification data, the maximum signature verification interval and the minimum signature verification interval determined by two adjacent signature verification units on each regular signature vector in the piece of consensus payment verification data may be determined according to the arrangement number of the signature verification unit on each regular signature vector in the piece of consensus payment verification data, the second regular signature vector coverage degree corresponding to each regular signature vector is determined according to whether the ratio of the maximum signature verification interval to the minimum signature verification interval on each regular signature vector is smaller than a preset threshold, and the regular signature vector coverage degree of the piece of consensus payment verification data is determined according to the sum of the second regular signature vector coverage degrees corresponding to each regular signature vector, where the second regular signature vector coverage degree corresponding to the ratio when the ratio is smaller than the preset threshold is larger than the second regular signature vector coverage degree corresponding to the second regular signature vector corresponding to the ratio when the ratio is larger than the set threshold The vector coverage is large.

For another example, for each regular signature vector in each consensus payment verification data, the average arrangement number point of the signature verification unit on the regular signature vector is determined according to the arrangement number of the signature verification unit on the regular signature vector, the site formation sequence corresponding to each associated regular signature vector is determined according to the relation of the average arrangement number points on each associated regular signature vector, the third regular signature vector coverage corresponding to each associated regular signature vector is determined according to the sequence association degree of the sequence of the site formation sequence and the time sequence corresponding to the data of the consensus payment verification data, and the regular signature vector coverage of the consensus payment verification data is determined according to the sum of the third regular signature vector coverage corresponding to each associated regular signature vector, wherein the larger the sequence association degree is, the larger the third regular signature vector coverage is, the sequence of the time corresponding to the data of the consensus payment verification data is a sequence of the consensus payment verification data along a forward time axis.

For another example, for each regular signature vector in each piece of consensus payment verification data, according to the arrangement number of the signature verification unit on the regular signature vector, an average arrangement number point of the signature verification unit on the regular signature vector is determined, a middle arrangement number point of the average arrangement number points on any two regular signature vectors in every three adjacent regular signature vectors is determined, and the matching degree between the average arrangement number point on the remaining regular signature vector and the middle arrangement number point is determined at the same time.

(3) And determining the coincidence degree of every three adjacent regular signature vectors according to the matching degree, wherein the greater the matching degree, the higher the coincidence degree is, or determining the middle ranking number point of the average ranking number point on two adjacent regular signature vectors in every three adjacent regular signature vectors, and determining the coincidence degree of every three adjacent regular signature vectors according to the sequence association degree of the two middle ranking number points to determine the fourth regular signature vector coverage degree corresponding to every three adjacent regular signature vectors, wherein the greater the sequence association degree, the higher the coincidence degree is.

(4) And determining the regular signature vector coverage of the consensus payment verification data according to the sum of the fourth regular signature vector coverage corresponding to every three adjacent regular signature vectors, wherein the higher the coincidence degree is, the larger the fourth regular signature vector coverage is.

(5) Or in another case, if the unit rule information of the signature verification unit includes a characteristic value of the signature verification unit, then for each consensus payment verification data, determining the feature value change features of the first signature verification unit and the last signature verification unit on each regular signature vector according to the feature value of the signature verification unit on each regular signature vector in the consensus payment verification data, determining the coverage degree of a fifth regular signature vector corresponding to each regular signature vector according to whether the change characteristic of the characteristic value meets the preset characteristic change rule or not, determining the regular signature vector coverage of the consensus payment verification data according to the sum of the fifth regular signature vector coverage corresponding to each regular signature vector, and the coverage of the corresponding fifth rule signature vector is greater when the preset feature change rule is met than when the preset feature change rule is not met.

For another example, for each piece of consensus payment verification data, a gradient value of the signature verification unit on each regular signature vector in the consensus payment verification data is determined according to a feature value of the signature verification unit on each regular signature vector in the consensus payment verification data, a sixth regular signature vector coverage corresponding to each regular signature vector is determined according to an average value of absolute values of the gradient values of the signature verification units on each regular signature vector, and a regular signature vector coverage of the consensus payment verification data is determined according to a sum of the sixth regular signature vector coverage corresponding to each regular signature vector, wherein the larger the average value is, the larger the sixth regular signature vector coverage is.

In a possible implementation manner, for step S130, the trained safety recognition artificial intelligence model may have a classification capability of the consensus prediction rule, and by performing safety recognition on payment response data information of each payment response object in the distributed ledger within a preset time period, a confidence level of a payment ledger information set corresponding to each payment response object in the distributed ledger within the preset time period under each calibration consensus prediction rule may be obtained, and then the calibration consensus prediction rule with the highest confidence level is selected as a final consensus prediction rule.

In one possible implementation, further to step S140, it may be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S141 of comparing whether the consensus prediction rule of the payment ledger information set corresponding to each payment response object is different from the predetermined consensus rule tag, and acquiring a target consensus prediction rule different from the predetermined consensus rule tag and a payment response object corresponding to the target consensus prediction rule according to the comparison result.

In this embodiment, for a target consensus prediction rule different from the predetermined consensus rule tag, it may be understood that there may be a tampering risk of payment security, and therefore, a target consensus prediction rule different from the predetermined consensus rule tag and a payment response object corresponding to the target consensus prediction rule may be obtained, so as to facilitate subsequent protection configuration processing.

And a substep S142, performing simulation verification on the target consensus prediction rule and the payment response object corresponding to the target consensus prediction rule according to a preset consensus payment verification strategy, and respectively generating payment verification strategy result information of each consensus payment verification strategy.

And a substep S143, generating at least one security protection updating script and a protection execution node sequence corresponding to each security protection updating script according to the payment verification policy result information of each consensus payment verification policy.

For example, in the substep S142, a preset signature verification unit corresponding to each predetermined consensus payment verification policy may be obtained, a signature verification unit sequence of each predetermined consensus payment verification policy is formed, and according to a preset unit quantity threshold corresponding to each consensus payment verification policy, a top-ranked target signature verification unit is selected from the signature verification unit sequence to obtain a target signature verification unit corresponding to each predetermined consensus payment verification policy. Then, the consensus prediction rule of the payment book information set corresponding to each payment response object is matched with the target signature verification unit corresponding to each preset consensus payment verification strategy, and the consensus prediction rule matched with each preset consensus payment verification strategy is determined according to the matching result, so that the payment verification strategy result information of each consensus payment verification strategy is generated.

Exemplarily, in the sub-step S143, the following embodiments may be implemented, which are described in detail as follows.

(1) And aiming at each consensus prediction rule of the payment verification strategy result information of each consensus payment verification strategy, respectively acquiring preset protection execution node information matched with the consensus prediction rules, acquiring a target protection execution node set associated with the preset protection execution node information and the consensus payment verification strategy, and determining the consensus payment verification strategy as a safety protection update script when the number of target protection execution nodes in the target protection execution node set is greater than a set number.

(2) On the basis of determining the consensus payment verification strategy as a safety protection updating script, calculating the target protection execution node set, acquiring protection verification information corresponding to the target protection execution node set, extracting protection features of each target protection execution node of the consensus prediction rule in the target protection execution node set, and acquiring the protection feature vector of each target protection execution node in the target protection execution node set.

(3) And determining the target protection execution node with the verification history frequency greater than a preset threshold value in the protection verification information corresponding to the target protection execution node set as a key target protection execution node.

(4) And calculating a first protection transaction parameter of the whole node sequence according to the protection feature vector of each target protection execution node in the target protection execution node set, and calculating a second protection transaction parameter of the key target protection execution node according to the protection feature vector of each target protection execution node in the key target protection execution node.

(5) Calculating preset weight coefficients corresponding to the first protection transaction parameter, the second protection transaction parameter, the first protection transaction parameter and the second protection transaction parameter respectively, obtaining a feature coefficient of the key target protection execution node, calculating a calculation result of a protection feature vector and the feature coefficient of each target protection execution node in the target protection execution node set, and obtaining a first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to the calculation result.

(6) And calculating the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set and the protection verification information to obtain the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set.

Or, in another example, the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set may be further obtained according to the calculation result of the protection feature vector and the feature coefficient of each target protection execution node in the target protection execution node set, and the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set is calculated according to a preset difference range, so as to obtain the second execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set.

Wherein the parameter difference between the second performed consensus algorithm reference and the first performed consensus algorithm reference is not in the difference range.

(7) And calculating the second execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set and the protection verification information to obtain the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set.

(8) And determining a target coefficient of each target protection execution node in the target protection execution node set according to the execution consensus algorithm reference degree and the protection verification information, and calculating a ratio of the execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set to a preset constant, wherein the target coefficient is a value obtained by dividing the execution consensus algorithm reference degree by a characteristic vector value of the protection verification information.

(9) And calculating the product of the ratio of the execution consensus algorithm reference degree of each target protection execution node to a preset constant and the corresponding target coefficient, and obtaining the screening degree of each target protection execution node in the target protection execution node set.

(10) And arranging the target protection execution nodes with the screening degrees larger than the set screening degree according to the screening degree of each target protection execution node, and determining the target protection execution nodes of the same command type as one protection consensus verification item so as to determine the protection execution node sequence corresponding to the safety protection updating script.

Based on the above description, in this embodiment, at least one security protection update script and a protection execution node sequence corresponding to each security protection update script may be sent to a payment encryption component in the blockchain verification service system 200 of the distributed ledger, so that the payment encryption component protects the payment security verification process corresponding to the blockchain request response component according to the security protection update script specified by the distributed ledger and according to the protection execution node sequence corresponding to the security protection update script. In other words, in the future payment process, the safety protection updating script obtained by intelligently learning the daily consensus payment verification habit of the distributed account book can be flexibly selected for automatic control, and then the collected payment response big data information is more and more, so that the safety recognition artificial intelligence model can be continuously learned and controlled, and the precision of the safety recognition artificial intelligence model is continuously improved.

Fig. 3 is a schematic functional module diagram of a security identification apparatus 300 based on blockchain big data according to an embodiment of the present disclosure, and this embodiment may divide the functional module of the security identification apparatus 300 based on blockchain big data according to the above method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the present application is schematic, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the block chain big data based security identification apparatus 300 shown in fig. 3 is only a schematic diagram of an apparatus. The security identification apparatus 300 based on blockchain big data may include an obtaining module 310, a learning control module 320, a security identification module 330, and a generating module 340, and the functions of the functional modules of the security identification apparatus 300 based on blockchain big data are described in detail below.

The obtaining module 310 is configured to obtain payment response big data information of the distributed ledger within each ledger distribution interval where a transaction is completed, where the payment response big data information is obtained after encryption of the blockchain request response component is completed through a payment encryption component in the blockchain verification service system 200 of the distributed ledger, the payment response big data information includes payment response objects and payment ledger information sets corresponding to each payment response object, the payment response objects are used to represent verification objects generated each time in the consensus payment verification process, and the payment ledger information sets are used to record consensus payment verification data under corresponding payment response objects. 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.

And the learning control module 320 is configured to obtain a corresponding safety identification artificial intelligence model according to the payment response big data information of the distributed ledger in each ledger distribution interval in which the transaction is completed and the preset consensus rule tag corresponding to each payment response object. The learning control module 320 may be configured to execute the step S120, and as for a detailed implementation of the learning control module 320, reference may be made to the detailed description of the step S120.

The safety identification module 330 is configured to perform safety identification on payment response data information of the distributed ledger under each payment response object in the preset time period according to the safety identification artificial intelligence model, so as to obtain a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period. The security identification module 330 may be configured to perform the step S130, and the detailed implementation of the security identification module 330 may refer to the detailed description of the step S130.

The generating module 340 is configured to generate at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to the consensus prediction rule of the payment book information set corresponding to each payment response object, where the protection execution node sequence includes at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item. 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.

Further, fig. 4 is a schematic structural diagram of an artificial intelligence cloud service platform 100 for executing the above block chain big data based security identification method according to the embodiment of the present application. As shown in fig. 4, the artificial intelligence cloud service platform 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The processor 130 may be one or more, and one processor 130 is illustrated in fig. 4 as an example. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified by the connection by the bus 140 in fig. 4.

The machine-readable storage medium 120 is a computer-readable storage medium, and can be used to store a software program, a computer-executable program, and modules, such as program commands/modules corresponding to the block chain big data based secure identification method in the embodiment of the present application (for example, the obtaining module 310, the learning control module 320, the secure identification module 330, and the generation module 340 of the block chain big data based secure identification apparatus 300 shown in fig. 3). The processor 130 executes various functional applications and data processing of the terminal device by detecting the software program, command and module stored in the machine-readable storage medium 120, that is, the above-mentioned security identification method based on the blockchain big data is implemented, and details are not described here.

The machine-readable storage medium 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memories of the systems and methods described herein are intended to comprise, without being limited to, these and any other suitable memory of a publishing node. In some examples, the machine-readable storage medium 120 may further include memory remotely located from the processor 130, which may be connected to the artificial intelligence cloud service platform 100 over a network. Examples of such networks include, but are not limited to, the internet, an intranet of items to be compiled, a local area network, a mobile communications network, and combinations thereof.

The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in the processor 130 or by commands in the form of software. The processor 130 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.

The artificial intelligence cloud service platform 100 can perform information interaction with other devices (such as the blockchain verification service system 200) through the network interface 110. Network interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may send and receive information using network interface 110.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. The safety identification method based on the blockchain big data is applied to an artificial intelligence cloud service platform, the artificial intelligence cloud service platform is in communication connection with blockchain verification service systems of a plurality of different distributed accounts, each blockchain verification service system comprises a blockchain request response component and a payment encryption component in communication connection with the blockchain request response component, the artificial intelligence cloud service platform is used for providing the distributed accounts with payment response big data information of the distributed accounts within different account distribution intervals, and the method comprises the following steps:

acquiring payment response big data information of a distributed account book in each account book distribution interval in which a transaction is completed, wherein the payment response big data information is obtained after encryption completion of a block chain request response component through a payment encryption component in a block chain verification service system of the distributed account book, the payment response big data information comprises payment response objects and payment account book information sets corresponding to the payment response objects, the payment response objects are used for representing verification objects generated in the process of consensus payment verification each time, the payment account book information sets are used for recording consensus payment verification data under the corresponding payment response objects, and the verification objects comprise behaviors of user biological characteristic verification and user payment environment verification;

configuring and obtaining a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed account book in each account book distribution interval which finishes transaction and a preset consensus rule tag corresponding to each payment response object, wherein the preset consensus rule tag is used for representing the type of consensus payment verification corresponding to each payment response object and comprises a workload certification mechanism, a rights and interests certification mechanism and a shares authorization certification mechanism, or the preset consensus rule tag is used for being obtained based on historical use condition setting;

and generating at least one safety protection updating script and a protection execution node sequence corresponding to each safety protection updating script according to a comparison relation between a consensus prediction rule of the payment book information set corresponding to each payment response object and a preset consensus rule tag, wherein the protection execution node sequence comprises at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item, and a control command is formed in the node sequence by taking a time axis as a direction and taking unit time as a protection unit for a subsequent payment verification process.

2. The safety identification method based on the blockchain big data according to claim 1, wherein the step of configuring a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed ledger within each ledger distribution interval where the transaction is completed and a preset consensus rule tag corresponding to each payment response object includes:

3. The blockchain big data-based security identification method according to claim 2, wherein the step of extracting transaction agreement feature information of the payment ledger information set corresponding to each payment response object includes:

4. The block chain big data-based security identification method according to any one of claims 1 to 3, wherein the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to a comparison relationship between a consensus prediction rule of a payment ledger information set corresponding to each payment response object and a predetermined consensus rule tag comprises:

comparing whether the consensus prediction rule of the payment book information set corresponding to each payment response object is different from a preset consensus rule label or not, and acquiring a target consensus prediction rule different from the preset consensus rule label and a payment response object corresponding to the target consensus prediction rule according to a comparison result;

5. The block chain big data-based security identification method according to claim 4, wherein the step of performing simulation verification on the target consensus prediction rule and the payment response object corresponding to the target consensus prediction rule according to a predetermined consensus payment verification policy to generate payment verification policy result information of each consensus payment verification policy respectively comprises:

6. The blockchain big data-based security identification method according to claim 4, wherein the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to the payment verification policy result information of each consensus payment verification policy includes:

or obtaining a first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to a calculation result of the protection feature vector and the feature coefficient of each target protection execution node in the target protection execution node set, and calculating the first execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set according to a preset difference range to obtain a second execution consensus algorithm reference degree of each target protection execution node in the target protection execution node set;

determining a target coefficient of each target protection execution node in the target protection execution node set according to the execution consensus algorithm reference and the protection verification information, and calculating a ratio of the execution consensus algorithm reference of each target protection execution node in the target protection execution node set to a preset constant;

7. The blockchain big data-based secure identification method according to claim 6, wherein a parameter difference between the second performed consensus algorithm reference and the first performed consensus algorithm reference is not in the difference range.

8. The blockchain big data-based security identification method according to claim 1, wherein the target coefficient is a value obtained by dividing the reference degree of the performed consensus algorithm by a characteristic vector value of the protection verification information.

9. The blockchain big data-based security identification method according to any one of claims 1 to 3, wherein after the step of generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script according to a comparison relationship between a consensus prediction rule of a payment ledger information set corresponding to each payment response object and a predetermined consensus rule tag, the method further comprises:

10. The safety identification system based on the blockchain big data is characterized by comprising an artificial intelligence cloud service platform and a blockchain verification service system of a plurality of different distributed accounts, wherein the blockchain verification service system is in communication connection with the artificial intelligence cloud service platform and comprises a blockchain request response component and a payment encryption component in communication connection with the blockchain request response component, and the artificial intelligence cloud service platform is used for providing payment response big data information of the distributed accounts in different account distribution intervals for the distributed accounts;

the artificial intelligence cloud service platform is used for acquiring payment response big data information of a distributed ledger in each ledger distribution interval in which transaction is completed, wherein the payment response big data information comprises payment response objects and payment ledger information sets corresponding to the payment response objects, the payment response objects are used for representing verification objects generated each time in the consensus payment verification process, the payment ledger information sets are used for recording consensus payment verification data under the corresponding payment response objects, and the verification objects comprise behaviors of user biological characteristic verification and user payment environment verification;

the artificial intelligence cloud service platform is used for configuring and obtaining a corresponding safety identification artificial intelligence model according to payment response big data information of the distributed account book in each account book distribution interval which is subjected to transaction and a preset consensus rule tag corresponding to each payment response object, wherein the preset consensus rule tag is used for representing the type of consensus payment verification corresponding to each payment response object and comprises a workload certification mechanism, a rights and interests certification mechanism and a shares authorization certification mechanism, or the preset consensus rule tag is used for being set and obtained based on historical use conditions;

the artificial intelligence cloud service platform is used for generating at least one safety protection updating script and a protection execution node sequence corresponding to each safety protection updating script according to a comparison relation between a consensus prediction rule of a payment book information set corresponding to each payment response object and a preset consensus rule tag, wherein the protection execution node sequence comprises at least one protection consensus verification item and a node sequence corresponding to each protection consensus verification item, a time axis is used as a direction in the node sequence, and a control command is formed by taking unit time as a protection unit for a subsequent payment verification process.