CN114780932B - Cross-block chain data interaction verification method, system and equipment for management three-mode platform - Google Patents
Cross-block chain data interaction verification method, system and equipment for management three-mode platform Download PDFInfo
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- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
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- G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
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- G06Q20/3827—Use of message hashing
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- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
Abstract
The invention belongs to the field of block chain data interaction verification, and particularly relates to a cross-block chain data interaction verification method, a system and equipment for a management three-mode platform, aiming at solving the problems of low performance and low information safety and reliability of a block chain system caused by the fact that the number of interaction times is large and data synchronization is frequent in the existing cross-block chain data interaction. The invention comprises the following steps: taking any block chain as a main block chain, and taking the rest as auxiliary block chains; selecting n nodes as operation nodes from the main blockchain, and connecting n auxiliary blockchains with the main blockchain; the main block chain and the auxiliary block chain respectively acquire file hash and chain link; when the uplink behavior of the main block chain and the auxiliary block chain exceeds a preset number, generating a block, extracting block hash, and generating a first certificate and a second certificate; and verifying through a preset identity identification method, and performing data cross-link interaction by combining the first certificate and the second certificate through acquiring the authority account. The block chain has high performance, information safety and reliability.
Description
Technical Field
The invention belongs to the field of block chain data interactive verification, and particularly relates to a cross-block chain data interactive verification method, a cross-block chain data interactive verification system and cross-block chain data interactive verification equipment for a management three-mode platform.
Background
The blockchain is originally a unique mode of storing data by using encryption currencies such as bitcoins and the like, is a self-reference data structure, is an integrated application of technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like, is used for storing a large amount of information, is sequentially linked from back to front for each record, has the characteristics of openness and transparency, incapability of tampering and convenience in tracing, and increasingly becomes a hotspot frontier technology concerned globally.
In the prior art, when cross-chain data interaction and synchronization are performed between different blockchains, a data interaction mode is mainly adopted while transaction generation is performed. In this way, the number of data interactions in the network through the blockchain is large, and data synchronization is frequent, so that the performance of the blockchain system is low. Furthermore, data interaction in this manner may compromise the number of transactions, resulting in exposure of trade secrets.
Therefore, how to realize data interaction between block chains and ensure the safety and reliability of information in the data interaction become important factors restricting the cross-chain interaction of the block chains.
Disclosure of Invention
In order to solve the above problems in the prior art, that is, the existing inter-block chain data interaction has the problems of high interaction times, frequent data synchronization, low block chain system performance and low information security and reliability, the invention provides an inter-block chain data interaction verification method for a management three-mode platform, which comprises the following steps:
taking any one of a plurality of block chains of a management three-transformation platform as a main block chain of the rest of the block chains, taking the rest n block chains as auxiliary block chains of the main block chain, selecting n nodes from the main block chain as operation nodes, and connecting the n auxiliary block chains with the main block chain;
the main block chain processes the original file obtained by the main block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the main block chain; processing the original file acquired by the auxiliary block chain through a Hash algorithm by the auxiliary block chain to obtain the file Hash of the original file, and chaining in the corresponding auxiliary block chain;
when the uplink behavior of the main block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a first certificate; when the uplink behavior of the sub-block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
when the client performs data interaction with other block chains in a cross-chain mode, client identity verification is performed through a preset identity recognition method, an authority account is sent to the client passing verification, and the client performs data interaction through the authority account in combination with a corresponding first certificate or a corresponding second certificate.
In some preferred embodiments, the client data interaction with other blockchains across chains comprises cross-chain data lookup and cross-chain data retrieval.
In some preferred embodiments, the cross-chain data lookup includes:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client authority by combining the authentication failure information;
and if the verification passes, sending an authority account to the client side passing the verification, combining the client side with the corresponding first certificate or second certificate through the authority account, extracting the block hash of the block head where the data needing to be consulted is located, and retrieving and consulting the data based on the block hash.
In some preferred embodiments, the cross-chain data is called by:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client authority by combining the authentication failure information;
and if the verification passes, sending an authority account to the client side passing the verification, extracting the block hash of the block head of the data needing to be called by combining the client side with the corresponding first certificate or second certificate through the authority account, encrypting the block chain in which the block is located by a preset data encryption method, and sending the encrypted data to the block chain in which the client side is located.
In some preferred embodiments, the data encryption method is an asymmetric data encryption method.
In some preferred embodiments, the asymmetric data encryption method includes an RSA encryption method, an Elgamal encryption method, a knapsack encryption method, a Rabin encryption method, a D-H encryption method, or an ECC encryption method.
In some preferred embodiments, the preset identification method is as follows:
obtaining multi-modal identity recognition data of a client user; the multi-modal identification data comprises face video data, voice data and text data of the user;
the client user identity recognition is carried out through a multi-mode identity recognition model, and a video voice recognition result and a text recognition result recognized by the client user are obtained:
if the confidence values of the video voice recognition result and the text recognition result are higher than the set threshold value, the verification is passed, and identity verification success information is returned;
if the confidence values of the video voice recognition result and the text recognition result are lower than the set threshold value, the verification fails, and identity verification failure information is returned;
otherwise, restarting the acquisition and identification process of the modal data with the confidence value lower than the set threshold value, and when the times of restarting the acquisition and identification process is greater than the preset value, failing the verification and returning the identity verification failure information.
In some preferred embodiments, the multi-modal identity recognition model comprises a video recognition model, a speech recognition model, a video speech matching model, and a text recognition model;
the video voice matching model is used for identifying whether the sources of the current video voice data are the same user or not, and the matching method comprises the following steps:
step A10, acquiring user video voice data acquired by a client, and dividing the voice data in the video voice data into corresponding voice fragments according to a video timestamp;
step A20, performing mouth shape key point detection on each frame of the video voice data through a predefined mouth shape key point template, and generating a dynamic mouth shape based on the mouth shape key point of each frame;
respectively calculating the MFCC coefficient of each voice fragment, and generating a voice mouth shape of voice data based on the MFCC coefficient in combination with the time stamp of the corresponding video and the key point position of the dynamic mouth shape;
step A30, calculating the similarity between the dynamic mouth shape and the voice mouth shape of each frame of video voice data, if the ratio of the number of frames of the video voice data with the similarity larger than the preset threshold to the number of frames of the whole video voice data is larger than the preset ratio, the source of the current video voice data is the same user.
In another aspect of the present invention, a cross-blockchain data interaction verification system for a management three-model platform is provided, which includes the following modules:
the block chain network building module is configured to take any one of a plurality of block chains of the management three-way platform as a main block chain of the rest of the block chains, take the rest n block chains as auxiliary block chains of the main block chain, select n nodes as operation nodes, and connect the n auxiliary block chains with the main block chain;
the system comprises an original file Hash extraction and chain loading module, a data transmission module and a data transmission module, wherein the original file Hash extraction and chain loading module is configured to process an original file obtained by a main block chain through a Hash algorithm by the main block chain to obtain a file Hash of the original file, and chain loading is carried out in the main block chain; the sub-block chain processes the original file obtained by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the corresponding sub-block chain;
the block hash extraction and certificate generation module is configured to generate a block based on a chain loading behavior when the chain loading behavior of a main block chain exceeds a preset number of times, extract the block hash of a block head in the block, and generate a first certificate; when the uplink behavior of the sub-block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
and the identity authentication and data interaction module is configured to perform client identity authentication through a preset identity identification method when the client cross-links the data of other block chains, send an authority account to the client passing the authentication, and perform data interaction by combining the authority account with the corresponding first certificate or second certificate.
In a third aspect of the present invention, an electronic device is provided, including:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by the processor for execution by the processor to implement the cross blockchain data interaction validation method of the management triple platform described above.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, where the computer-readable storage medium stores computer instructions for being executed by the computer to implement the above cross-blockchain data interaction verification method for a management triple-play platform.
The invention has the beneficial effects that:
(1) the invention relates to a cross-block-chain data interaction verification method for a management three-way platform, which is characterized in that a plurality of block chains of the management three-way (management institutionalization, institutionalization form flow and form flow informatization) platform are connected into a block chain network, one block chain is used as a main block chain, other block chains are used as auxiliary block chains, and data interaction and data synchronization can be carried out only after the main block chain and the auxiliary block chains carry out uplink behaviors for preset times and form blocks, so that the times of data interaction and the frequency of data synchronization are effectively reduced, and the performance of a block chain system is improved. Meanwhile, the number of times of block chain winding is effectively shielded, and exposure of commercial secrets due to exposure of the number of times of block chain winding is avoided.
(2) The cross-block-chain data interaction verification method for the management three-way platform divides cross-chain data interaction into two types of lookup and calling, does not transmit data in a block chain piece only when data is looked up, and encrypts the data in the block chain where the data is located and transmits the data to the block chain where a client side for calling the data is located only when the data is called, so that the transmission frequency of the data among the chains is effectively reduced, and the resource consumption is reduced.
(3) The cross-block chain data interaction verification method for the management triple platform encrypts data transmitted among chains by adopting an asymmetric data encryption method, a public key used for encryption cannot be stolen to cause data leakage, a private key used for decryption is stored in a data decryption party, the private key does not need to be synchronized before communication, the risk of stealing the private key is effectively avoided, and the safety and the reliability of the data are improved.
(4) According to the cross-block chain data interaction verification method for the management three-way platform, the permission of data lookup and calling is issued to different permission accounts according to the openable object of the data, the permission accounts are respectively issued to different users, and after a client user logs in, corresponding data lookup and calling can be carried out only through the permission account received by the client user, so that the safety and the reliability of the data are further improved.
(5) According to the cross-block-chain data interaction verification method for the management three-way platform, when a client side confirms the user identity, the video mouth shape and the voice mouth shape of the user are matched firstly, the mouth shape matching is mainly used for identifying whether the source of the current video voice data is the same user, and the video identity identification, the voice identity identification and the text identity identification are further carried out only if the source of the current video voice data is the same user.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart illustrating a cross-blockchain data interaction verification method of a management triple-play platform according to the present invention;
FIG. 2 is a schematic diagram illustrating an identity recognition process according to an embodiment of a cross-blockchain data interaction verification method for a management triple-mode platform;
FIG. 3 is a schematic diagram of a structure of a voice mouth shape generation model according to an embodiment of a cross-blockchain data interaction verification method for a management triple-mode platform;
FIG. 4 is a block diagram of a computer system of a server for implementing embodiments of the method, system, and apparatus of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention discloses a cross-block chain data interaction verification method for a management three-way platform, which comprises the following steps:
taking any one of a plurality of block chains for managing a three-mode platform as a main block chain of the rest of the block chains, taking the rest n block chains as auxiliary block chains of the main block chain, selecting n nodes from the main block chain as operation nodes, and connecting the n auxiliary block chains with the main block chain;
the main block chain processes the original file obtained by the main block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the main block chain; the sub-block chain processes the original file obtained by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the corresponding sub-block chain;
when the uplink behavior of the main block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a first certificate; when the uplink behavior of the sub-block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
when the client performs data interaction with other block chains in a cross-chain mode, client identity verification is performed through a preset identity recognition method, an authority account is sent to the client passing verification, and the client performs data interaction through the authority account in combination with a corresponding first certificate or a corresponding second certificate.
In order to more clearly describe the cross-blockchain data interaction verification method of the management triple-platform of the present invention, details of steps in the embodiment of the present invention are described below with reference to fig. 1.
The method for cross-block chain data interaction verification of the management triple-platform in the first embodiment of the invention comprises the following steps S10-S40, and the following steps are described in detail:
step S10, any one of the block chains of the three-mode platform is taken as the main block chain of the rest block chains, the rest n block chains are taken as the auxiliary block chains of the main block chain, n nodes are selected from the main block chain as operation nodes, and the n auxiliary block chains are connected with the main block chain.
After the multi-block chain is divided into the main block chain and the auxiliary block chain, the block chains are involved in data interaction, authentication, identity recognition and the like in the data interaction, and the synchronization of cross-chain data is also involved.
When the data of the primary block chain needs to be synchronized to the secondary block chain, the data synchronization comprises the following steps:
step S11, generating a main block chain block by the main block chain, and transmitting the block head of the main block chain block to the auxiliary block chain by the operation node through the first processor corresponding to the main block chain;
step S12, after judging that the block head of the main block chain block is legal, the first processor stores the public key of the common node in the block head of the main block chain block into the second processor corresponding to the auxiliary block chain;
and step S13, repeating the step S11 to the step S12 until the chain-crossing data synchronized from the main block chain to the auxiliary block chain is not increased any more, and completing the process of synchronizing the data of the main block chain to the auxiliary block chain.
When the data of the auxiliary block chain needs to be synchronized to the main block chain, the data synchronization comprises the following steps:
step S21, the sub-block chain generates a sub-block chain block, and the operation node transmits the block head of the sub-block chain block to the main block chain through the second processor corresponding to the sub-block chain;
step S22, after the second processor judges that the block head of the auxiliary block chain block is legal, the public key of the common node in the block head of the auxiliary block chain block is stored into the first processor corresponding to the main block chain;
and step S23, repeating the step S21 to the step S22 until the chain crossing data synchronized by the auxiliary block chain to the main block chain is not increased any more, and completing the process of synchronizing the data of the auxiliary block chain to the main block chain.
When the data of the sub-block chain needs to be synchronized to other sub-block chains, taking the data of the r sub-block chain needs to be synchronized to the s sub-block chain as an example, the data synchronization includes:
step S31, the r sub-block chain generates an r sub-block chain block, the operation node transmits the block head of the r sub-block chain block to the S block chain through the second processor corresponding to the r sub-block chain;
step S32, after the second processor corresponding to the r sub-block chain judges that the block head of the r sub-block chain block is legal, the public key of the common node in the block head of the r sub-block chain block is stored into the second processor corresponding to the S block chain;
and step S33, repeating the step S31 to the step S32 until the cross-chain data synchronized from the r-th sub-block chain to the S-th sub-block chain is not increased, and completing the process of synchronizing the data of the r-th sub-block chain to the S-th sub-block chain.
Step S20, the master block chain processes the original file obtained by the master block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the master block chain; and the sub-block chain processes the original file acquired by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and uplinks the original file in the corresponding sub-block chain.
Step S30, when the uplink behavior of the main block chain exceeds the preset times, generating a block based on the uplink behavior, extracting the block hash of a block head in the block, and generating a first certificate; and when the uplink behavior of the sub-block chain exceeds the preset times, generating a block based on the uplink behavior, extracting the block hash of a block header in the block, and generating a second certificate.
The preset number of times (i.e. the number of uplink operations performed by a main Block chain or the number of uplink operations performed by a sub-Block chain included in a Block in a Block chain network) may be set by setting the Block size of the main Block chain or the sub-Block chain.
After 1 time of uplink activity, the main block chain and the sub-block chain do not perform data interaction and synchronization between the block chains, but after the uplink activity of preset times (adjusted according to specific setting or block size, generally the preset times are set to be 2-5 times), a block is formed based on information of the multiple uplink activities, and at this time, data interaction and synchronization are performed based on the block.
Therefore, the number of data interaction between the block chains can be greatly reduced, the data synchronization frequency is reduced, and the performance of the block chain system is improved. Meanwhile, the number of chaining times of the main blockchain and the auxiliary blockchain cannot be judged through the number of data interaction between the blockchains, so that the details of data chaining (namely transaction) are effectively shielded, and the business secret is prevented from being exposed due to the exposure of the number of chaining times of the blockchains.
Step S40, when the client performs data interaction with other block chains in a cross-chain manner, the client performs client identity verification through a preset identity recognition method, sends an authority account to the client passing the verification, and the client performs data interaction through the authority account in combination with the corresponding first certificate or second certificate.
The client performs data interaction with other block chains in a cross-chain mode, including cross-chain data lookup and cross-chain data retrieval.
Step S411, cross-link data is consulted, and the method comprises the following steps:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client permission by combining the authentication failure information;
and if the verification passes, sending an authority account to the client side passing the verification, combining the client side with the corresponding first certificate or second certificate through the authority account, extracting the block hash of the block head where the data needing to be consulted is located, and retrieving and consulting the data based on the block hash.
Step S412, cross-link data is called, and the method comprises the following steps:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client authority by combining the authentication failure information;
and if the verification is passed, sending an authority account to the client side passing the verification, extracting block hash of the block head of the data needing to be called by combining the authority account with the corresponding first certificate or second certificate by the client side, encrypting the block chain of the block by a preset data encryption method, and sending the encrypted data to the block chain of the client side.
The data encryption method is an asymmetric data encryption method and comprises an RSA encryption method, an Elgamal encryption method, a knapsack encryption method, a Rabin encryption method, a D-H encryption method or an ECC encryption method.
As shown in fig. 2, which is a schematic view of an identity recognition process according to an embodiment of the cross-blockchain data interaction verification method for a management triple-mode platform of the present invention, in step S413, the preset identity recognition method is:
step S4131, obtaining multi-modal identification data of the client user; the multi-modal identification data comprises face video data, voice data and text data of the user;
step S4132, performing client user identity recognition through the multi-modal identity recognition model to obtain a video voice recognition result and a text recognition result recognized by the client user:
if the confidence values of the video voice recognition result and the text recognition result are higher than the set threshold value, the verification is passed, and identity verification success information is returned;
if the confidence values of the video voice recognition result and the text recognition result are lower than the set threshold value, the verification fails, and identity verification failure information is returned;
otherwise, restarting the acquisition and identification process of the modal data with the confidence value lower than the set threshold value, and when the times of restarting the acquisition and identification process are larger than the preset value, failing the verification, and returning the identity verification failure information.
The multi-mode identity recognition model comprises a video recognition model, a voice recognition model, a video voice matching model and a text recognition model;
the video voice matching model is used for identifying whether the sources of the current video voice data are the same user or not, and the matching method comprises the following steps:
step A10, acquiring user video voice data acquired by a client, and dividing the voice data in the video voice data into corresponding voice fragments according to a video timestamp;
step A20, performing mouth shape key point detection on each frame of the video voice data through a predefined mouth shape key point template, and generating a dynamic mouth shape based on the mouth shape key point of each frame;
respectively calculating the MFCC coefficient of each voice fragment, and generating a voice mouth shape of voice data based on the MFCC coefficient in combination with the time stamp of the corresponding video and the key point position of the dynamic mouth shape;
step A30, calculating the similarity between the dynamic mouth shape and the voice mouth shape of each frame of video voice data, if the ratio of the number of frames of the video voice data with the similarity larger than the preset threshold to the number of frames of the whole video voice data is larger than the preset ratio, the source of the current video voice data is the same user.
In one embodiment of the invention, voice data is sampled at a sampling rate of 100Hz to obtain 35 discrete samples, MFCC coefficients of the samples are respectively calculated, and a 12 x 35-dimensional MFCC coefficient matrix is generated, wherein each column of the matrix is the MFCC characteristic of each sample.
Fig. 3 is a schematic diagram of a structure of a generation model of a voice mouth shape according to an embodiment of a cross-blockchain data interaction verification method for a management triple-platform of the present invention, where the generation model includes a generation module and a deblurring module.
The generation module comprises 3 branches: the first branch is used for extracting 256-dimensional features of the MFCC matrix, and comprises a1 st convolution layer (comprising 64 convolution kernels of 3 x 3), a2 nd convolution layer (comprising 128 convolution kernels of 3 x 3), a1 st pooling layer (comprising 1 convolution kernel of 3 x 3 and having a convolution kernel moving step size of 2), a3 rd convolution layer (comprising 256 convolution kernels of 3 x 3), a 4 th convolution layer (comprising 256 convolution kernels of 3 x 3), a 5 th convolution layer (comprising 512 convolution kernels of 3 x 3), a2 nd pooling layer (comprising 512 convolution kernels of 3 x 3 and having a convolution kernel moving step size of 2), a1 st fully-connected layer (comprising 512 convolution kernels) and a2 nd fully-connected layer (comprising 512 convolution kernels) which are connected in sequence, and the MFCC feature matrix is input into the first branch, so that the corresponding 256-dimensional features can be obtained; the second branch is used for extracting the dynamic mouth shape key points of the standard mouth shape and comprises a 6 th convolution layer (comprising 96 convolution kernels with 7 x 7 and the convolution kernel moving step size is 2), a3 rd pooling layer (comprising 1 convolution kernel with 3 x 3 and the convolution kernel moving step size is 2), a 7 th convolution layer (comprising 256 convolution kernels with 5 x 5 and the convolution kernel moving step size is 2), a 4 th pooling layer (comprising 1 convolution kernel with 3 x 3 and the convolution kernel moving step size is 2) which are connected in sequence, inputting the standard mouth shape image into a second branch to obtain corresponding 256-dimensional characteristics, wherein the 8 th convolution layer (comprising 512 convolution kernels of 3 x 3), the 9 th convolution layer (comprising 512 convolution kernels of 3 x 3), the 10 th convolution layer (comprising 512 convolution kernels of 3 x 3), the 3 rd fully-connected layer (comprising 512 convolution kernels) and the 4 th fully-connected layer (comprising 256 convolution kernels); the third branch is used for inputting the voice corresponding to the mouth shape, comprising the 5 th fully-connected layer (comprising 128 convolution kernels), the 11 th convolution layer (comprising 512 convolution kernels of 6 x 6 and having a convolution kernel moving step size of 2), the 12 th convolution layer (comprising 256 convolution kernels of 5 x 5 and having a convolution kernel moving step size of 2), the 13 th convolution layer (comprising 96 convolution kernels of 5 x 5 and having a convolution kernel moving step size of 2), the 14 th convolution layer (comprising 96 convolution kernels of 5 x 5 and having a convolution kernel moving step size of 2), the 15 th convolution layer (comprising 64 convolution kernels of 5 x 5 and having a convolution kernel moving step size of 2) and the 16 th convolution layer (comprising 3 convolution kernels of 5 x 5) which are connected in sequence, the 256-dimensional features corresponding to the MFCC feature matrix output by the first branch and the 256-dimensional features corresponding to the standard mouth shape image output by the second branch are input into the third branch, the voice mouth shape of the voice data can be obtained.
Step A30a, matching the dynamic mouth shape and the voice mouth shape of the video voice data, and matching by a key point curve comparison method:
step A31a, extracting each key point of the dynamic mouth shape and the voice mouth shape corresponding to each frame respectively;
step A32a, using the frame number of each frame as a time line, respectively fitting the dynamic mouth shape curve and the voice mouth shape curve corresponding to each key point;
step A33a, aiming at any key point, comparing the fitted dynamic mouth shape curve with the voice mouth shape curve, if the contact ratio of the fitted curve is more than a set value, comparing the current key point;
step S34a, traversing each key point, and if all the key points pass the comparison, the source of the current video voice data is the same user.
The mouth shape key points comprise a left mouth angular point, a right mouth angular point, two upper lip edge points and two lower lip edge points.
For any key point, the calculation of the contact ratio of the fitting curve can be carried out through a key point curve matching model:
step A331a, solving eigenvalues of a Helmholte equation in the region of a key point curve of the dynamic mouth shape and a key point curve of the voice mouth shape through a key point curve matching model respectively, and constructing curve descriptors respectively;
step a332a, performing a difference measurement on the curve descriptor by weighting the euclidean distance, where the measurement method is shown in formula (1):
wherein the content of the first and second substances,
representing the measure of the difference,
，
first of the curve descriptors of the key point curve for the dynamic die
The number of the components is one,
first of the curve descriptors of the key point curve for the speech mouth shape
The number of the components is one,
to represent a curve descriptor
A component and a
The ratio of the weights of the individual components,
the number of components that are curve descriptors;
in step a333a, if the difference metric is lower than the set threshold, the current keypoint comparison is passed.
The training method of the key point curve matching model is as follows:
step B10, acquiring video voice data corresponding to a plurality of users as a training data set, marking a training data label of the video voice from the same user as true (1), marking a training data label of the video voice from different users and video mouth shapes obviously not corresponding to the voice as false (0), processing the rest of the training data through the method of the steps A10-A20, calculating the similarity between the dynamic mouth shape and the voice mouth shape of each frame of video voice data, and normalizing the similarity value between (0-1) to be used as a soft label of the training data;
step B20, solving eigenvalues of Helmholte equation in the region of the key point curve of the dynamic mouth shape and the key point curve of the voice mouth shape through the key point curve matching model respectively for any training data, and respectively constructing curve descriptors;
step B30, performing difference measurement on the curve descriptor through the weighted Euclidean distance to obtain a difference measurement value;
step B40, calculating a probability distribution loss between the difference metric and the label corresponding to the training data, as shown in equation (2):
wherein, the first and the second end of the pipe are connected with each other,
representing the loss function between the model output and the training sample label,
as to the amount of training data in the current training batch,
the probability distribution of the sample labels for the training data of the current training batch,
probability distribution of difference metric values output for models of training data of a current training batch;
and step B50, adjusting network parameters in the descending direction of the probability distribution loss value and carrying out iterative training until a set training end condition is reached, so as to obtain a trained key point curve matching model.
The gradient function of the decrease in the probability distribution loss value is shown in equation (3):
wherein the content of the first and second substances,
is a gradient function of decreasing probability distribution loss values,
as a current parameter
Difference metric of model output underProbability distribution of
Probability distribution with sample labels
The value of the loss in between is,
for a predetermined gradient descent acceleration function,
the acceleration factor is decreased for the gradient.
Although the foregoing embodiments describe the steps in the above sequential order, those skilled in the art will understand that, in order to achieve the effect of the present embodiments, the steps may not be executed in such an order, and may be executed simultaneously (in parallel) or in an inverse order, and these simple variations are within the scope of the present invention.
The cross-block chain data interaction verification system for the management three-way platform in the second embodiment of the invention comprises the following modules:
the block chain network building module is configured to take any one of a plurality of block chains of the management three-transformation platform as a main block chain of the rest of the block chains, take the rest n block chains as auxiliary block chains of the main block chain, select n nodes as operation nodes, and connect the n auxiliary block chains with the main block chain;
the system comprises an original file Hash extraction and chain loading module, a data transmission module and a data transmission module, wherein the original file Hash extraction and chain loading module is configured to process an original file obtained by a main block chain through a Hash algorithm by the main block chain to obtain a file Hash of the original file, and chain loading is carried out in the main block chain; the sub-block chain processes the original file obtained by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the corresponding sub-block chain;
the block hash extraction and certificate generation module is configured to generate a block based on a chain loading behavior when the chain loading behavior of a main block chain exceeds a preset number of times, extract the block hash of a block head in the block, and generate a first certificate; when the uplink behavior of the secondary block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
and the identity authentication and data interaction module is configured to perform client identity authentication through a preset identity identification method when the client cross-links the data of other block chains, send an authority account to the client passing the authentication, and perform data interaction by combining the authority account with the corresponding first certificate or second certificate.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the cross-block chain data interaction verification system for a management triple platform provided in the foregoing embodiment is only illustrated by the division of the functional modules, and in practical applications, the functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. Names of the modules and steps related in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
An electronic apparatus according to a third embodiment of the present invention includes:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by the processor for execution by the processor to implement the cross blockchain data interaction validation method of the management triple platform described above.
A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions for being executed by the computer to implement the method for cross-blockchain data interaction verification of a management triple-play platform described above.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Referring now to FIG. 4, therein is shown a block diagram of a computer system of a server that may be used to implement embodiments of the method, system, and apparatus of the present application. The server shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the computer system includes a Central Processing Unit (CPU)601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. A driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that the computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar 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 case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (11)
1. A cross-block chain data interaction verification method for a management three-mode platform is characterized by comprising the following steps:
taking any one of a plurality of block chains for managing a three-mode platform as a main block chain of the rest of the block chains, taking the rest n block chains as auxiliary block chains of the main block chain, selecting n nodes from the main block chain as operation nodes, and connecting the n auxiliary block chains with the main block chain;
the main block chain processes the original file obtained by the main block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the main block chain; the sub-block chain processes the original file obtained by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the corresponding sub-block chain;
when the uplink behavior of the main block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a first certificate; when the uplink behavior of the sub-block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
when the client performs data interaction with other block chains in a cross-chain manner, the client identity is verified through a preset identity recognition method, an authority account is sent to the client passing verification, and the client performs data interaction through the authority account in combination with a corresponding first certificate or a corresponding second certificate;
when the client performs data interaction with other block chains in a cross-chain manner, the client identity authentication method comprises the following steps:
obtaining multi-modal identity recognition data of a client user, performing client user identity recognition through a multi-modal identity recognition model, obtaining a video voice recognition result and a text recognition result recognized by the client user, and performing client identity verification according to the video voice recognition result and the text recognition result; the multi-modal identification data comprises face video data, voice data and text data of the user; the multi-mode identity recognition model comprises a video recognition model, a voice recognition model, a video voice matching model and a text recognition model;
the video voice matching model is used for identifying whether the sources of the current video voice data are the same user, and the matching method comprises the following steps:
step A10, acquiring user video voice data acquired by a client, and dividing the voice data in the video voice data into corresponding voice fragments according to a video timestamp;
step A20, performing mouth shape key point detection on each frame of the video voice data through a predefined mouth shape key point template, and generating a dynamic mouth shape based on the mouth shape key point of each frame;
respectively calculating the MFCC coefficient of each voice fragment, and generating a voice mouth shape of voice data based on the MFCC coefficient in combination with the time stamp of the corresponding video and the key point position of the dynamic mouth shape;
step A30, matching the dynamic mouth shape and the voice mouth shape of the video voice data by a key point curve comparison method: extracting each key point of the dynamic mouth shape and the voice mouth shape corresponding to each frame respectively, taking the frame number of each frame as a time line, fitting the dynamic mouth shape curve and the voice mouth shape curve corresponding to each key point respectively, comparing the fitted dynamic mouth shape curve and the fitted voice mouth shape curve aiming at any key point, if the contact ratio of the fitted curves is greater than a set value, comparing the current key points, traversing each key point, and if all the key points are compared, determining that the current video voice data source is the same user.
2. The method of claim 1, wherein the client inter-chaining data interaction validation with other blockchains comprises inter-chaining data lookup and inter-chaining data retrieval.
3. The method for cross-blockchain data interaction validation of management triple-purpose platform according to claim 2, wherein the cross-chain data lookup comprises:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client authority by combining the authentication failure information;
and if the verification passes, sending an authority account to the client side passing the verification, combining the client side with the corresponding first certificate or second certificate through the authority account, extracting the block hash of the block head where the data needing to be consulted is located, and retrieving and consulting the data based on the block hash.
4. The method for cross-blockchain data interaction validation of a management triple-purpose platform according to claim 2, wherein the cross-chain data is retrieved by:
the client collects user identity information, carries out user identity verification through a preset identity identification method, and executes:
if the verification fails, returning authentication failure information, calling an authentication log of the block chain where the client is located by the client, and locking the client authority by combining the authentication failure information;
and if the verification passes, sending an authority account to the client side passing the verification, extracting the block hash of the block head of the data needing to be called by combining the client side with the corresponding first certificate or second certificate through the authority account, encrypting the block chain in which the block is located by a preset data encryption method, and sending the encrypted data to the block chain in which the client side is located.
5. The method as claimed in claim 4, wherein the data encryption method is an asymmetric data encryption method.
6. The method as claimed in claim 5, wherein the asymmetric data encryption method comprises RSA encryption, Elgamal encryption, knapsack encryption, Rabin encryption, D-H encryption or ECC encryption.
7. The method for cross-block-chain data interaction verification of a management triple-purpose platform according to claim 1, wherein the client authentication is performed according to a video voice recognition result and a text recognition result, and the method comprises:
if the confidence values of the video voice recognition result and the text recognition result are higher than the set threshold value, the verification is passed, and identity verification success information is returned;
if the confidence values of the video voice recognition result and the text recognition result are lower than the set threshold value, the verification fails, and identity verification failure information is returned;
otherwise, restarting the acquisition and identification process of the modal data with the confidence value lower than the set threshold value, and when the times of restarting the acquisition and identification process is greater than the preset value, failing the verification and returning the identity verification failure information.
8. The method of claim 7, wherein the dynamic mouth shape and the voice mouth shape of the video and voice data are matched by similarity:
and calculating the similarity between the dynamic mouth shape and the voice mouth shape of each frame of video voice data, wherein if the ratio of the frame number of the video voice data with the similarity larger than a preset threshold value to the frame number of the whole video voice data is larger than a preset ratio, the sources of the current video voice data are the same user.
9. A cross-block chain data interaction verification system for a management three-mode platform is characterized by comprising the following modules:
the block chain network building module is configured to take any one of a plurality of block chains of the management three-way platform as a main block chain of the rest of the block chains, take the rest n block chains as auxiliary block chains of the main block chain, select n nodes as operation nodes, and connect the n auxiliary block chains with the main block chain;
the system comprises an original file Hash extraction and chain loading module, a data transmission module and a data transmission module, wherein the original file Hash extraction and chain loading module is configured to process an original file obtained by a main block chain through a Hash algorithm by the main block chain to obtain a file Hash of the original file, and chain loading is carried out in the main block chain; the sub-block chain processes the original file obtained by the sub-block chain through a Hash algorithm to obtain the file Hash of the original file, and links the chain in the corresponding sub-block chain;
the block hash extraction and certificate generation module is configured to generate a block based on a chain loading behavior when the chain loading behavior of a main block chain exceeds a preset number of times, extract the block hash of a block head in the block, and generate a first certificate; when the uplink behavior of the sub-block chain exceeds a preset number of times, generating a block based on the uplink behavior, extracting block hash of a block head in the block, and generating a second certificate;
the identity authentication and data interaction module is configured to perform client identity authentication through a preset identity identification method when a client cross-links to transfer data of other block chains, send an authority account to a client passing authentication, and perform data interaction by combining the corresponding first certificate or second certificate through the authority account;
when the client performs data interaction with other block chains in a cross-chain manner, the client identity authentication method comprises the following steps:
obtaining multi-mode identity recognition data of a client user, performing client user identity recognition through a multi-mode identity recognition model, obtaining a video voice recognition result and a text recognition result recognized by the client user, and performing client identity verification according to the video voice recognition result and the text recognition result; the multi-modal identification data comprises face video data, voice data and text data of the user; the multi-mode identity recognition model comprises a video recognition model, a voice recognition model, a video voice matching model and a text recognition model;
the video voice matching model is used for identifying whether the sources of the current video voice data are the same user or not, and the matching method comprises the following steps:
step A10, acquiring user video voice data acquired by a client, and dividing the voice data in the video voice data into corresponding voice fragments according to a video timestamp;
step A20, performing mouth shape key point detection on each frame of the video voice data through a predefined mouth shape key point template, and generating a dynamic mouth shape based on the mouth shape key point of each frame;
respectively calculating the MFCC coefficient of each voice fragment, and generating the voice mouth shape of the voice data based on the MFCC coefficient in combination with the time stamp of the corresponding video and the key point position of the dynamic mouth shape;
step A30, matching the dynamic mouth shape and the voice mouth shape of the video voice data by a key point curve comparison method: and respectively extracting each key point of the dynamic mouth shape and the voice mouth shape corresponding to each frame, respectively fitting the dynamic mouth shape curve and the voice mouth shape curve corresponding to each key point by taking the frame number of each frame as a time line, comparing the fitted dynamic mouth shape curve and the fitted voice mouth shape curve aiming at any key point, if the contact ratio of the fitted curves is greater than a set value, comparing and passing the current key points, traversing each key point, and if all the key points are compared and passed, determining that the source of the current video voice data is the same user.
10. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by the processor for execution by the processor to implement the method of cross blockchain data interaction validation for a management triple platform of any of claims 1-8.
11. A computer-readable storage medium storing computer instructions for execution by the computer to implement the method for cross blockchain data interaction validation for a management triple-enabled platform according to any one of claims 1 to 8.
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| CN112733178A (en) * | 2020-11-23 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Cross-chain trust method, device, equipment and medium based on digital certificate authentication |
| CN112989299A (en) * | 2021-03-11 | 2021-06-18 | 恒睿(重庆)人工智能技术研究院有限公司 | Interactive identity recognition method, system, device and medium |
| CN114499898A (en) * | 2022-04-15 | 2022-05-13 | 北京邮电大学 | Block chain cross-chain secure access method and device |
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