CN111695903B

CN111695903B - Information flow analysis method based on block chain and mobile internet and cloud computing platform

Info

Publication number: CN111695903B
Application number: CN202010585952.XA
Authority: CN
Inventors: 杨刘琴
Original assignee: Individual
Current assignee: Shanghai Baiyuan Jiahe Information Technology Co ltd
Priority date: 2020-06-24
Filing date: 2020-06-24
Publication date: 2021-09-14
Anticipated expiration: 2040-06-24
Also published as: CN111695903A; CN112529579A; CN112508572A

Abstract

The embodiment of the application provides an information flow analysis method and a cloud computing platform based on a block chain and a mobile internet, wherein a recorded dynamic link library file used for recording different pieces of protection analysis rule information for respectively executing protection is created according to a protection label of each protection consensus verification project, on the basis, a corresponding dynamic measurement table and payment verification measurement rules for each protection consensus verification project are configured according to the protection analysis rule information, each corresponding protection consensus verification project is configured, protection data flow analysis is executed, and the recorded dynamic link library file is further subjected to rule updating according to the obtained protection information flow analysis data. Therefore, in the payment verification process, protection can be performed based on the payment verification measurement rule of a plurality of different protection consensus verification projects, the rule is continuously updated according to the protection information flow analysis data, and a more accurate protection analysis rule mechanism can be realized so as to improve the payment safety.

Description

Information flow analysis method based on block chain and mobile internet and cloud computing platform

Technical Field

The application relates to the technical field of block chains and secure payment, in particular to an information flow analysis method and a cloud computing platform based on the block chains and a mobile internet.

Background

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism and encryption algorithm, in the payment security verification process based on the block chain, a protection analysis rule mechanism in the traditional scheme is usually protected based on the payment verification measurement rule of a unified protection consensus verification project, and the protection analysis rule mechanism is difficult to realize accurate payment protection, so that the payment security is influenced.

Disclosure of Invention

In view of the above, an object of the present application is to provide an information flow analysis method and a cloud computing platform based on a block chain and a mobile internet, by determining a protection execution node sequence composed of different protection consensus verification projects, then a recording dynamic link library file for recording different protection analysis rule information for executing protection of each protection consensus verification project is created according to the protection label of each protection consensus verification project, on the basis, according to the protection analysis rule information, configuring a corresponding dynamic measurement table and payment verification measurement rule aiming at each protection consensus verification item, and configuring to each corresponding protection consensus verification item, and then calling each protection consensus verification item to execute protection data flow analysis according to the corresponding dynamic measurement table and the payment verification measurement rule, and further carrying out rule updating on the record dynamic link library file according to the obtained protection information flow analysis data. Therefore, in the payment verification process, protection can be performed based on the payment verification measurement rule of a plurality of different protection consensus verification projects, the rule is continuously updated according to the protection information flow analysis data, and a more accurate protection analysis rule mechanism can be realized so as to improve the payment safety.

In a first aspect, the present application provides an information flow analysis method based on a blockchain and a mobile internet, which is applied to a cloud computing platform, where the cloud computing platform is communicatively connected to a blockchain verification service system, the blockchain verification service system includes a blockchain request response component and a payment encryption component communicatively connected to the blockchain request response component, and the method includes:

according to a payment security verification request which is requested to be verified by the blockchain verification service system, in the process of protecting the payment security verification process corresponding to the blockchain request response component, according to a protection label of each protection consensus verification item in a predetermined protection execution node sequence, creating a recording dynamic link library file for recording different protection analysis rule information of each protection consensus verification item for executing protection, wherein the protection label is used for representing the protection category and the protection plan of the protection consensus verification item;

configuring a corresponding dynamic measurement table and payment verification measurement rule aiming at each protection consensus verification project according to the protection analysis rule information, and configuring the dynamic measurement table and the payment verification measurement rule to each corresponding protection consensus verification project, wherein the dynamic measurement table comprises a plurality of payment verification projects and a plurality of measurement values, each payment verification project is associated with a single measurement value, and the payment verification measurement rule is in one-to-one correspondence with each payment verification project;

calling each corresponding protection consensus verification item to execute protection data flow analysis on payment verification information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule to obtain corresponding protection information flow analysis data;

and updating the rule of the recording dynamic link library file according to the protective information flow analysis data.

In a possible implementation manner of the first aspect, the configuring, according to the protection analysis rule information, a corresponding dynamic measurement table and a corresponding payment verification measurement rule for each protection consensus verification item includes:

determining a safety rule structure body aiming at each protection consensus verification project according to the protection analysis rule information, and determining a safety rule label of each protection consensus verification project and a rule relation between each safety rule label according to the safety rule structure body;

constructing a security rule structure as a dynamic metric table space by taking the security rule label as a rule path and the rule relation as a path execution object;

extracting a central regular path of a central safety rule label and an edge regular path in an edge safety rule label according to the dynamic metric table space, and sequentially combining regular path reconstruction spaces formed by the central regular path and the edge regular path;

converting each regular path reconstruction space into a space object set of the same security rule label type, wherein the space object comprises two security rule label types of filtering and defending;

analyzing a filtering rule relation and a defense rule relation between elements of each space object set to obtain a corresponding rule relation matrix, weighting the rule relation matrix to construct the space object set into a filtering level space object set, wherein the filtering rule relation refers to that filtering behavior characteristics, filtering process information and filtering characteristic identification of each filtering rule behavior are obtained to construct a filtering verification set of the filtering rule behavior, each filtering rule behavior is converted into a characteristic vector of the corresponding filtering verification set, an incidence relation between the characteristic vectors is calculated, and the defense rule relation is related to the incidence relation between data structures of each filtering rule behavior;

determining verification measurement occupation information of different security rule label type rule relation weight keys among rule paths in a filter level space object set of the security rule structure body, and distributing verification measurement occupation for the security rule structure body according to the verification measurement occupation information to obtain a measurement rule object corresponding to each verification measurement occupation;

sequentially traversing each filtering level space object of the filtering level space object set, and extracting filtering content of each filtering level space object according to the incidence relation among filtering to obtain the structure content of the safety rule structure, wherein the structure content of the safety rule structure comprises a measurement rule object corresponding to each verification measurement occupation;

and configuring a corresponding dynamic measurement table and payment verification measurement rules aiming at each protection consensus verification project according to the structure content of the safety rule structure.

In a possible implementation manner of the first aspect, the step of configuring, according to a structure content of the security rule structure, a corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item includes:

determining a dynamic measurement feature sequence of a dynamic measurement table containing each protection consensus verification item and a measurement rule feature sequence of a measurement rule object containing each protection consensus verification item according to the structure content of the safety rule structure;

generating a payment verification measurement rule corresponding to the measurement rule feature sequence of the payment verification measurement rule by using a first configuration component, the payment verification measurement rule being generated by using a first information configuration script, and generating and processing the first configuration component by using a second information configuration script by using the dynamic measurement feature sequence of the dynamic measurement table of each protection consensus verification item to obtain a second configuration component; the first configuration component is a rule configuration component used for generating and processing the payment verification measurement rule, and the second configuration component is a configuration component obtained by generating and processing the first configuration component by using the dynamic measurement feature sequence of the dynamic measurement table of each protection consensus verification item;

the cloud computing platform generates and processes payment verification measurement rules by using a first configuration component and a first information configuration script in advance, generates and processes a first configuration component by using a dynamic measurement feature sequence of a dynamic measurement table of each protection consensus verification item to generate a second configuration component, and fills the generated and processed payment verification measurement rules and the second configuration component into the dynamic measurement table of each protection consensus verification item, wherein the same payment verification measurement rules correspond to the same first configuration component, different payment verification measurement rules correspond to different first configuration components, and the dynamic measurement tables of different protection consensus verification items correspond to different second configuration components.

In a possible implementation manner of the first aspect, the step of invoking each corresponding protection consensus verification item to perform protection data flow analysis on the payment verification information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule, so as to obtain corresponding protection information flow analysis data includes:

calling each corresponding protection consensus verification item, and setting rule matching object information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule;

and executing protection data flow analysis on the payment verification information in sequence by each rule matching object in the rule matching object information in the payment safety verification process to obtain corresponding protection information flow analysis data.

In a possible implementation manner of the first aspect, the step of performing rule update on the record dynamic link library file according to the protection information flow analysis data includes:

analyzing process blocking information and security kernel calling information in the protection execution process of each protection consensus verification project according to the protection information flow analysis data, and constructing a rule updating model;

and acquiring each rule updating object of the rule updating model, and executing rule updating processing operation on the record dynamic link library file according to the rule updating data of the rule updating object.

In a possible implementation manner of the first aspect, the step of performing a rule update processing operation on the record dynamic link library file according to the rule update data of the rule update object includes:

determining the updating path information of the payment verification item where the updating rule is located from the rule updating object;

calling the rule updating model to acquire updating path information of the payment verification project area in the recorded dynamic link library file, and searching a corresponding rule updating strategy from the rule updating model;

and respectively fusing the updating path information of the payment verification project and the updating path information of the payment verification project area in the record dynamic link library file, using the fusing processing as updating path parameters, and calling the rule updating strategy to execute rule updating processing operation, wherein the rule updating strategy comprises updating rule configuration information corresponding to each updating path.

In a possible implementation manner of the first aspect, the predetermined guard execution node sequence is obtained by:

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 a plurality of protection consensus verification items and a node sequence corresponding to each protection consensus verification item.

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:

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;

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 second aspect, an embodiment of the present application further provides an information flow analysis apparatus based on a blockchain and a mobile internet, which is applied to a cloud computing platform, where the cloud computing platform is communicatively connected to a blockchain verification service system, the blockchain verification service system includes a blockchain request response component and a payment encryption component communicatively connected to the blockchain request response component, and the apparatus includes:

a creating module, configured to create, according to a payment security verification request requested to be verified by the blockchain verification service system, a record dynamic link library file for recording different protection analysis rule information for executing protection on each protection consensus verification item according to a protection tag of each protection consensus verification item in a predetermined protection execution node sequence in a process of protecting a payment security verification process corresponding to the blockchain request response component, where the protection tag is used to represent a protection category and a protection plan of the protection consensus verification item;

a configuration module, configured to configure, according to the protection analysis rule information, a corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item, and configure the dynamic measurement table and the payment verification measurement rule to each corresponding protection consensus verification item, where the dynamic measurement table includes a plurality of payment verification items and a plurality of measurement scores, each payment verification item is associated with a single measurement score, and the payment verification measurement rule corresponds to each payment verification item one to one;

the calling module is used for calling each corresponding protection consensus verification item to execute protection data flow analysis on the payment verification information in the payment safety verification process according to the corresponding dynamic measurement table and the payment verification measurement rule to obtain corresponding protection information flow analysis data;

and the updating module is used for regularly updating the record dynamic link library file according to the protective information flow analysis data.

In a third aspect, an embodiment of the present application further provides an information flow analysis system based on a blockchain and a mobile internet, where the information flow analysis system based on a blockchain and a mobile internet includes a cloud computing platform and a blockchain verification service system communicatively connected to the cloud computing platform, and the blockchain verification service system includes a blockchain request response component and a payment encryption component communicatively connected to the blockchain request response component;

the cloud computing platform is used for creating a recording dynamic link library file for recording different protection analysis rule information of each protection consensus verification project execution protection according to a protection label of each protection consensus verification project in a predetermined protection execution node sequence in the process of protecting the payment security verification process corresponding to the block chain request response component according to the payment security verification request requested to be verified by the block chain verification service system, wherein the protection label is used for representing the protection category and the protection plan of the protection consensus verification project;

the cloud computing platform is used for configuring a corresponding dynamic measurement table and a corresponding payment verification measurement rule aiming at each protection consensus verification project according to the protection analysis rule information, and configuring the dynamic measurement table and the payment verification measurement rule to each corresponding protection consensus verification project, wherein the dynamic measurement table comprises a plurality of payment verification projects and a plurality of measurement values, each payment verification project is associated with a single measurement value, and the payment verification measurement rule is in one-to-one correspondence with each payment verification project;

the cloud computing platform is used for calling each corresponding protection consensus verification item to execute protection data flow analysis on the payment verification information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule to obtain corresponding protection information flow analysis data;

and the cloud computing platform is used for carrying out rule updating on the record dynamic link library file according to the protection information flow analysis data.

In a fourth aspect, an embodiment of the present application further provides a cloud computing platform, where the cloud computing platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be in communication connection with at least one information flow analysis system based on a blockchain and a mobile internet, the machine-readable storage medium is configured to store a program, a command, or a code, and the processor is configured to execute the program, the command, or the code in the machine-readable storage medium to perform the method for information flow analysis based on a blockchain and a mobile internet in the first aspect or any possible implementation manner 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 caused to execute the method for analyzing an information flow based on a blockchain and a mobile internet in the first aspect or any one of the possible implementations of the first aspect.

According to any one of the aspects, a recording dynamic link library file for recording different protection analysis rule information for executing protection of each protection consensus verification item is created according to the protection label of each protection consensus verification item, on the basis, the corresponding dynamic measurement table and payment verification measurement rule aiming at each protection consensus verification item are configured according to the protection analysis rule information, and are configured to each corresponding protection consensus verification item, so that each protection consensus verification item is called to execute protection data flow analysis according to the corresponding dynamic measurement table and payment verification measurement rule, and the recording dynamic link library file is further subjected to rule updating according to the obtained protection information flow analysis data. Therefore, in the payment verification process, protection can be performed based on the payment verification measurement rule of a plurality of different protection consensus verification projects, the rule is continuously updated according to the protection information flow analysis data, and a more accurate protection analysis rule mechanism can be realized so as to improve the payment safety.

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 application scenario diagram of an information flow analysis system based on a block chain and a mobile internet according to an embodiment of the present application;

fig. 2 is a schematic flowchart of an information flow analysis method based on a blockchain and a mobile internet according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of an information flow analysis device based on a block chain and a mobile internet according to an embodiment of the present application;

fig. 4 is a schematic block diagram of a structure of a cloud computing platform for implementing the above information flow analysis method based on a block chain and a mobile internet according to an 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 an information flow analysis system 10 based on a block chain and a mobile internet according to an embodiment of the present invention. The blockchain and mobile internet based information flow analysis system 10 may include a cloud computing platform 100 and a blockchain verification service system 200 communicatively connected to the cloud computing platform 100. The blockchain and mobile internet based information flow analysis system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the blockchain and mobile internet based information flow analysis system 10 may also include only a portion of the components shown in fig. 1 or may also include other components.

In this embodiment, the cloud computing platform 100 and the blockchain verification service system 200 in the blockchain and mobile internet based information flow analysis system 10 may cooperatively perform the blockchain and mobile internet based information flow analysis method described in the following method embodiment, and for the specific steps of the cloud computing platform 100 and the blockchain verification service system 200, reference may be made to the detailed description of the following method embodiment.

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 analysis data of a payment response large protection information stream 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 specifically limited herein.

To solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information flow analysis method based on a block chain and a mobile internet according to an embodiment of the present invention, and the information flow analysis method based on a block chain and a mobile internet according to the present embodiment may be executed by the cloud computing platform 100 shown in fig. 1, and the information flow analysis method based on a block chain and a mobile internet is described in detail below.

Step S110, according to the payment security verification request requested to be verified by the blockchain verification service system 200, in the process of protecting the payment security verification process corresponding to the blockchain request response component, a record dynamic link library file for recording different protection analysis rule information for executing protection of each protection consensus verification item is created according to the protection tag of each protection consensus verification item in the predetermined protection execution node sequence.

Step S120, configuring a corresponding dynamic measurement table and payment verification measurement rule aiming at each protection consensus verification project according to the protection analysis rule information, and configuring the dynamic measurement table and the payment verification measurement rule to each corresponding protection consensus verification project.

Step S130, calling each corresponding protection consensus verification item to execute protection data flow analysis on the payment verification information in the payment safety verification process according to the corresponding dynamic measurement table and the payment verification measurement rule, and obtaining corresponding protection information flow analysis data.

And step S140, updating the rule of the dynamic link library file according to the analysis data of the protection information flow.

In this embodiment, in the payment process of the mobile internet, the terminal may send a payment security verification request to the cloud computing platform 100 through the blockchain verification service system 200, and the cloud computing platform 100 may detect a protection process of the payment security verification process corresponding to the blockchain request response component according to the payment security verification request requested to be verified by the blockchain verification service system 200.

The protection execution node sequence may include a plurality of protection consensus verification items and a node sequence corresponding to each protection consensus verification item, and a control command may be formed in the node sequences in the direction of a time axis and in a protection unit of unit time for a subsequent payment verification process. Each protection consensus verification item in the protection execution node sequence may be determined in advance by the cloud computing platform 100, for example, may be determined based on an artificial intelligence intelligent identification manner, which will be described in detail in the following embodiments. The protection consensus verification item may refer to protection verification content predetermined by the consensus rule, and the detailed protection verification content may be flexibly selected in advance according to the protection requirement, and the specific content selection is not a technical problem to be solved by the present invention and is not described herein again.

In this process, the protection tag of each protection consensus verification item may be used to represent a protection verification process or a protection verification type corresponding to the protection consensus verification item, and the cloud computing platform 200 may pre-configure protection parsing rule information of different protection verification processes or protection verification types. Therefore, based on the corresponding relation between different protection verification processes or protection verification types and protection analysis rule information in the configuration information, a recording dynamic link library file for recording different protection analysis rule information for executing protection of each protection consensus verification project can be respectively created. That is, the record dynamic link library file includes different protection parsing rule information for executing protection of each protection consensus verification item.

In this embodiment, the dynamic metric table may include a plurality of payment verification items and a plurality of metric scores, each payment verification item is associated with a single metric score, and the payment verification metric rule corresponds to each payment verification item in a one-to-one manner. The payment verification item may be, but is not limited to, a payment environment verification item, a payment application verification item, and the like, and the measurement score may be used to characterize a security level corresponding to the payment verification item, which is not specifically limited herein.

Based on the above design, in this embodiment, a record dynamic link library file for recording different protection analysis rule information for respectively executing protection is created according to the protection tag of each protection consensus verification item, on this basis, a corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item are configured according to the protection analysis rule information, and are configured to each corresponding protection consensus verification item, so as to execute protection data flow analysis, and further perform rule update on the record dynamic link library file according to the obtained protection information flow analysis data. Therefore, in the payment verification process, protection can be performed based on the payment verification measurement rule of a plurality of different protection consensus verification projects, the rule is continuously updated according to the protection information flow analysis data, and a more accurate protection analysis rule mechanism can be realized so as to improve the payment safety.

In one possible implementation, for step S110, the predetermined guard execution node sequence is implemented by the following specific sub-steps, which are described in detail below.

Step S111, obtaining payment response big data information of the distributed ledger in each ledger distribution interval in which the transaction is completed.

And step S112, 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.

And S113, performing safety identification on the 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 to obtain a consensus prediction rule of a payment ledger information set corresponding to each payment response object in the preset time period.

Step S114, according to the consensus prediction rule of the payment ledger information set corresponding to each payment response object, generating at least one security protection update script and a protection execution node sequence corresponding to each security protection update script.

In this embodiment, the cloud computing platform 100 may provide the payment response big data information of the distributed ledger in different ledger distribution intervals for the distributed ledger, and the distributed ledger may flexibly select the payment response big data information of a part or all of the ledger distribution intervals to complete a transaction, so that the cloud computing platform 100 may obtain the 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 the 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 cloud computing platform 100 for recording.

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 S112, 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 S1121 of extracting transaction agreement characteristic information of the payment ledger information set corresponding to each payment response object.

And a substep S1122, 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 a learnable characteristic of the transaction protocol characteristic information in the transaction protocol category through the model to be generated, wherein the learnable characteristic comprises 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 S1123, 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.

In sub-step S1124, the plurality of first update command contents are sorted in the order from high convergence to low convergence for each of the plurality of first update command contents, so as to obtain an update command content sequence.

And a substep S1125 of determining the updating command content of the mapping of the learning segmentation feature in the model to be generated in the plurality of learning segmentation features based on the preset similarity ratio threshold and the updating 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 S1126, 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 a target learnable feature, when the learnable feature is determined to be the target learnable feature, for each of a plurality of first updating command contents, controlling the learning segmentation feature after the segmentation processing is performed on a plurality of learnable feature segment sets obtained by the model to be generated by learning in the transaction protocol category according to the first updating command content, and the updating command content mapped by each learned segmentation feature after the segmentation processing in the model to be generated, and generating a corresponding prediction consensus rule after the learning control.

And a substep S1127 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 S1121, 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 S1121 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 S113, 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 of the distributed ledger within a preset time period, a confidence level of a payment ledger information set corresponding to each payment response object of 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.

Further to step S114, in one possible implementation, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1141 of comparing whether the consensus prediction rule of the payment book information set corresponding to each payment response object is different from the predetermined consensus rule tag, and obtaining 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 S1142 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 strategy, and respectively generating payment verification strategy result information of each consensus payment verification strategy.

And a substep S1143 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.

For example, in the sub-step S1142, 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 target signature verification unit with a top rank is selected from the signature verification unit sequence, so as 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.

In one possible implementation manner, for step S120, in order to fully configure the corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S121, determining a security rule structure for each protection consensus verification item according to the protection analysis rule information, and determining a security rule tag of each protection consensus verification item and a rule relationship between each security rule tag according to the security rule structure.

In this embodiment, the security rule tag of each protection consensus verification item and the rule relationship between each security rule tag may be recorded in a data segment other than the header information in the security rule structure, and the index identifier in the header information may be used to index and search the security rule tag of each protection consensus verification item and the rule relationship between each security rule tag.

And a substep S122, taking the security rule label as a rule path and taking the rule relation as a path execution object, and constructing a security rule structure as a dynamic metric table space.

For example, the dynamic metric table space may be a dimension space in which the security rule tags are used as the regular paths and the rule relationships are used as the path execution objects, and at this time, the dynamic metric table space may be constructed by analyzing the security rule tags in the security rule structure and the corresponding rule relationships.

And a substep S123 of extracting a center regular path of the center security rule tag and an edge regular path of the edge security rule tag according to the dynamic metric table space, and sequentially combining regular path reconstruction spaces formed by the center regular path and the edge regular path.

In this embodiment, the central regular path of the central security rule tag may refer to a regular path of a global security rule tag, and the edge regular path in the edge security rule tag may refer to a regular path of a security rule tag other than the global security rule tag.

And a substep S124, converting each rule path reconstruction space into a space object set of the same security rule label type, wherein the space object comprises two security rule label types of filtering and defending.

And a substep S125, analyzing the filtering rule relation and the defense rule relation between each spatial object set element to obtain a corresponding rule relation matrix, and weighting the rule relation matrix to construct the spatial object set into a filtering-level spatial object set.

In this embodiment, the filtering rule relationship may refer to that the filtering behavior feature, the filtering process information, and the filtering feature identifier of each filtering rule behavior are obtained to construct a filtering verification set of the filtering rule behavior, each filtering rule behavior is converted into a feature vector of a corresponding filtering verification set, an association relationship between feature vectors is calculated, and the defense rule relationship is related to an association relationship between data structures of each filtering rule behavior.

And a substep S126 of determining verification measurement occupation information of the rule relation weight keys of different security rule label types among the rule paths in the filtering level space object set of the security rule structure, and distributing verification measurement occupation to the security rule structure according to the verification measurement occupation information to obtain a measurement rule object corresponding to each verification measurement occupation.

And a substep S127 of traversing each filtering level space object of the filtering level space object set in sequence, and extracting the filtering content of each filtering level space object according to the incidence relation among the filters to obtain the structure content of the safety rule structure.

In this embodiment, the structure content of the security rule structure includes a measurement rule object corresponding to each verification measurement place.

And a substep S128, configuring a corresponding dynamic measurement table and payment verification measurement rule aiming at each protection consensus verification item according to the structure content of the safety rule structure.

Exemplarily, in the sub-step S128, the following detailed description can be implemented as follows.

(1) And determining a dynamic measurement feature sequence of the dynamic measurement table containing each protection consensus verification item and a measurement rule feature sequence of a measurement rule object containing each protection consensus verification item according to the structure content of the security rule structure.

(2) And generating the first configuration component by using the second information configuration script and obtaining a second configuration component by using the first configuration component, the payment verification measurement rule which is generated by using the first information configuration script and corresponds to the measurement rule characteristic sequence of the payment verification measurement rule and the dynamic measurement characteristic sequence of the dynamic measurement table of each protection consensus verification item.

In this embodiment, the first configuration component is a rule configuration component for generating and processing the payment verification measurement rule, and the second configuration component is a configuration component obtained by generating and processing the first configuration component by using a dynamic measurement feature sequence of a dynamic measurement table of each protection consensus verification item.

In this embodiment, the cloud computing platform 100 may use the first configuration component and the first information configuration script to generate the payment verification measurement rule in advance, use the dynamic measurement feature sequence of the dynamic measurement table of each protection consensus verification item to generate the first configuration component and generate the second configuration component, and fill the generated and processed payment verification measurement rule and the second configuration component into the dynamic measurement table of each protection consensus verification item.

The same payment verification measurement rule corresponds to the same first configuration component, different payment verification measurement rules correspond to different first configuration components, and the dynamic measurement tables of different protection consensus verification projects correspond to different second configuration components.

In one possible implementation, step S130 can be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S131, calling each corresponding protection consensus verification item, and setting rule matching object information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule.

And a substep S132 of executing protection data flow analysis on the payment verification information in sequence based on each rule matching object in the rule matching object information in the payment security verification process to obtain corresponding protection information flow analysis data.

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

And a substep S141 of analyzing the process blocking information and the security kernel calling information in the protection execution process of each protection consensus verification project according to the protection information flow analysis data and constructing a rule updating model.

And a substep S142, obtaining each rule updating object of the rule updating model, and executing rule updating processing operation on the record dynamic link library file according to the rule updating data of the rule updating object.

For example, in the sub-step S142, update path information of the payment verification item where the update rule is located may be determined from the rule update object, then the rule update model is invoked to obtain update path information of the payment verification item area in the record dynamic link library file, and a corresponding rule update policy is searched from the rule update model. Therefore, the updating path information of the payment verification project and the updating path information of the payment verification project area in the recorded dynamic link library file can be fused and used as updating path parameters respectively, and a rule updating strategy is called to execute rule updating processing operation.

It should be noted that the rule update policy includes update rule configuration information corresponding to each update path. Therefore, the rule is continuously updated according to the protection information flow analysis data, and a more accurate protection analysis rule mechanism can be realized so as to improve the payment safety.

Fig. 3 is a schematic functional module diagram of an information flow analysis apparatus 300 based on a block chain and a mobile internet according to an embodiment of the present application, where the present embodiment may perform functional module division on the information flow analysis apparatus 300 based on the block chain and the mobile internet according to the method embodiment executed by the cloud computing platform 100. 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 by corresponding functions, the information flow analysis apparatus 300 based on the block chain and the mobile internet shown in fig. 3 is only a schematic diagram of an apparatus. The device 300 for analyzing information flow based on blockchain and mobile internet may include a creation module 310, a configuration module 320, a calling module 330, and an updating module 340, and the functions of the functional modules of the device 300 for analyzing information flow based on blockchain and mobile internet are described in detail below.

A creating module 310, configured to create, according to a payment security verification request requested to be verified by the blockchain verification service system 200, a record dynamic link library file for recording different protection analysis rule information for executing protection on each protection consensus verification item according to a protection tag of each protection consensus verification item in a predetermined protection execution node sequence during a protection process of the payment security verification process corresponding to the blockchain request response component, where the protection tag is used to represent a protection category and a protection plan of the protection consensus verification item. The creating module 310 may be configured to execute the step S110, and the detailed implementation of the creating module 310 may refer to the detailed description of the step S110.

The configuration module 320 is configured to configure, according to the protection analysis rule information, a corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item, and configure the dynamic measurement table and the payment verification measurement rule to each corresponding protection consensus verification item, where the dynamic measurement table includes a plurality of payment verification items and a plurality of measurement scores, each payment verification item is associated with a single measurement score, and the payment verification measurement rule corresponds to each payment verification item one to one. The configuration module 320 may be configured to perform the step S120, and the detailed implementation of the configuration module 320 may refer to the detailed description of the step S120.

The invoking module 330 is configured to invoke each corresponding protection consensus verification item to perform protection data flow analysis on the payment verification information in the payment security verification process according to the corresponding dynamic measurement table and the payment verification measurement rule, so as to obtain corresponding protection information flow analysis data. The invoking module 330 may be configured to perform the step S130, and the detailed implementation of the invoking module 330 may refer to the detailed description of the step S130.

And the updating module 340 is configured to perform rule updating on the record dynamic link library file according to the protection information flow analysis data. The updating module 340 may be configured to perform the step S140, and the detailed implementation of the updating module 340 may refer to the detailed description of the step S140.

Further, fig. 4 is a schematic structural diagram of a cloud computing platform 100 for executing the above information flow analysis method based on a block chain and a mobile internet according to an embodiment of the present application. As shown in fig. 4, the cloud computing 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 software programs, computer-executable programs, and modules, such as program commands/modules corresponding to the methods for analyzing the information flow based on the blockchain and the mobile internet in the embodiments of the present application (for example, the creating module 310, the configuring module 320, the calling module 330, and the updating module 340 of the apparatus 300 for analyzing the information flow based on the blockchain and the mobile internet shown in fig. 3). The processor 130 executes various functional applications and data processing of the terminal device by detecting software programs, commands and modules stored in the machine-readable storage medium 120, that is, the above-mentioned information flow analysis method based on the block chain and the mobile internet is implemented, and details are not described herein again.

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 located remotely from the processor 130, which may be connected to the cloud computing 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 cloud computing platform 100 may interact with other devices (e.g., the blockchain verification service system 200) via 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 run entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer, or entirely on the remote computer or cloud computing platform. 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 cloud computing platform 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. An information flow analysis method based on a blockchain and a mobile internet is applied to a cloud computing platform, the cloud computing platform is connected with a blockchain verification service system in a communication mode, the blockchain verification service system comprises a blockchain request response component and a payment encryption component connected with the blockchain request response component in a communication mode, and the method comprises the following steps:

2. The information flow analysis method based on the blockchain and the mobile internet as claimed in claim 1, wherein the step of configuring the corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item according to the protection analysis rule information comprises:

3. The information flow analysis method based on the blockchain and the mobile internet as claimed in claim 2, wherein the step of configuring the corresponding dynamic measurement table and payment verification measurement rule for each protection consensus verification item according to the structure content of the security rule structure includes:

4. The method according to claim 1, wherein the step of invoking each corresponding protection consensus verification item to perform protection dataflow analysis on the payment verification information during the payment security verification process according to the corresponding dynamic measurement table and payment verification measurement rule to obtain corresponding protection dataflow analysis data includes:

5. The method for analyzing information flow based on blockchain and mobile internet according to any one of claims 1 to 4, wherein the step of regularly updating the record dynamic link library file according to the protection information flow analysis data comprises:

6. The blockchain and mobile internet based information flow analysis method according to claim 5, wherein the step of performing a rule update processing operation on the record dynamic link library file according to the rule update data of the rule update object includes:

7. The blockchain and mobile internet based information flow analysis method according to any one of claims 1 to 4, wherein the predetermined guard execution node sequence is obtained by:

8. The method according to claim 7, 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 the payment ledger information set corresponding to each payment response object and a predetermined consensus rule tag includes:

9. The blockchain and mobile internet based information flow analysis method according to claim 8, wherein the step of performing the simulation verification on the target consensus prediction rule and the payment response object corresponding to the target consensus prediction rule according to the predetermined consensus payment verification policy to generate the payment verification policy result information of each consensus payment verification policy respectively comprises:

10. A cloud computing platform, characterized in that the cloud computing platform comprises a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface and the processor are connected by a bus system, the network interface is used for being connected with at least one information flow analysis system based on a blockchain and a mobile internet, 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 information flow analysis method based on the blockchain and the mobile internet according to any one of claims 1 to 9.