Data processing method and device

Abstract

The invention provides a data processing method and device. The data processing method comprises the following steps: acquiring at least one media content; performing hard binding processing according to the at least one media content to obtain first hard binding; and generating a block according to the first hard binding, wherein the block is recorded in a block chain system. According to the application, the diversity of the Non in the PoW process can be increased.

Description

Technical Field

[0001] The present application relates to PoW technology, and in particular to a data processing method and device. Background Art

[0002] Proof of Work (PoW) is a commonly used method for reaching consensus (agreement) on who will be the next node to add a new block to a public blockchain. The PoW mechanism requires participants (nodes) in the network to complete some computing tasks, which often require a lot of computing resources and time.

[0003] In the Bitcoin network, the random value (Nonce), the collected transactions (i.e., the transaction list to be packaged into the new block (Collected TXs)) and the hash value of the previous block (Hash of Prev.Block) are input into the hash function (Hash Function), and the output hash value is used to determine whether the conditions are met (for example, is the hash value less than a target value (Less than a target value?)). When the conditions are met, a new block (New Block) is generated; when the conditions are not met, a new Nonce (Chang a nonce) is changed. The role of Nonce is to change the input of the Hash Function so that the output of the Hash Function changes accordingly, and then determine whether the conditions are met in the changed hash value to generate a new block. It can be seen that Nonce is an important factor in generating new blocks. Summary of the invention

[0004] The present application provides a data processing method and device, which can increase the diversity of Nonce in the PoW process.

[0005] In a first aspect, the present application provides a data processing method, including: obtaining at least one media content; performing hard binding processing based on the at least one media content to obtain a first hard binding; generating a block based on the first hard binding, and the block is recorded in a blockchain system.

[0006] The embodiment of the present application combines PoW with media content, and can use a large amount of computing resources of PoW to generate media content, and then obtain Nonce based on the media content to implement the PoW process, thereby improving the diversity of Nonce in the PoW process.

[0007] The Proof of Work (PoW) mechanism requires network participants (i.e., consensus nodes) to complete some computing tasks, which often require a lot of computing resources and time. In order to avoid wasting these computing resources and time, the embodiments of the present application can realize the computing tasks completed by using computing resources and time to generate at least one media content.

[0008] In one possible implementation, the method for consensus nodes to generate media content can use AI technology, for example, a neural network model is constructed based on the above computing resources, and the neural network model can output media content, such as AIGC pictures, videos, etc. The technical principles and types of neural network models can refer to the above description, which will not be repeated here. Media content may include content or metadata of content, etc., and media content can be generated in the form of media files, which is not specifically limited in the embodiments of the present application.

[0009] In an embodiment of the present application, hard binding processing can be implemented using a one-way function, and the processing result can be called hard binding. Exemplarily, the one-way function can be a hash function, and its output result is a hash value, so the hard binding can be a hash value.

[0010] After the consensus node generates at least one media content, it performs hard binding processing according to the at least one media content to obtain a first hard binding. In the embodiment of the present application, the first hard binding is represented as (Number once, Nonce), also known as a random number. Usually, Nonce can represent an arbitrary or non-repeating random value that is used only once. The role of Nonce is to change the input of the Hash Function so that the hash value of the output of the Hash Function changes accordingly, and then judge whether the condition is met based on the changed hash value to generate a new block. It can be seen that as the Nonce changes, the output hash value also changes, thereby avoiding the hash value used as the judgment condition from being reused.

[0011] In the embodiment of the present application, according to the amount of media content generated by the consensus node, there are several ways:

[0012] (1) The consensus node generates a media content

[0013] The consensus node generates a media content through AI technology, and then obtains Nonce based on the media content, for example, through a hash function.

[0014] At this time, if the second hard binding meets the preset conditions, the above media content is recorded; if the second hard binding does not meet the preset conditions, the above media content is discarded. It can also be considered that only the media content corresponding to the second hard binding used to generate a new block will be recorded by the blockchain system. The preset condition may include that the second hard binding is less than a preset threshold, that is, when the second hard binding is less than the preset threshold, it means that the preset condition is met. It should be noted that the preset condition can also be set to other content, which is not specifically limited.

[0015] In an embodiment of the present application, the second hard binding (eg, a hash value) is obtained based on at least the first hard binding. In addition, the second hard binding can also be obtained based on at least one of the transaction data (Collected TXs) and the hard binding of the previous block (Hash ofPrev.Block).

[0016] (2) The consensus node generates a media content

[0017] The consensus node generates a media content through AI technology and records it directly without judgment.

[0018] In the process of obtaining Nonce, firstly obtain the third hard binding according to the media content; then obtain the fourth hard binding according to the second media content generated before the aforementioned media content; and finally obtain Nonce according to the third hard binding and the fourth hard binding.

[0019] (3) Consensus nodes generate multiple media content

[0020] The consensus node generates multiple media contents through AI technology and records them directly without judgment.

[0021] In the process of obtaining Nonce, a plurality of fifth hard bindings are firstly obtained according to the plurality of media contents respectively; and then Nonce is obtained according to the plurality of fifth hard bindings.

[0022] It should be noted that in the embodiment of the present application, the blockchain system records media content. In addition to recording the media content itself as described above, such as recording picture media content, it can also record the Hash value corresponding to the media content, as well as the Hash function, etc., which can be based on actual needs or storage resources, etc., and there is no specific limitation on this.

[0023] Optionally, the block includes the first hard binding described above.

[0024] Optionally, the block is generated when the second hard binding satisfies a preset condition.

[0025] In a possible implementation, at least one media content generated by a consensus node can be used to obtain a random number (Nonce) (first hard binding), and then the random number (Nonce) together with the collected transactions (i.e., the transaction list (Collected TXs) to be packaged into the new block) and the hash value (Hash of Prev.Block) of the previous block are input into the hash function (Hash Function), and the hash value (second hard binding) is output. Based on the hash value, it is judged whether the preset conditions are met to implement the PoW process. When the hash value meets the preset conditions, a block is generated; when the hash value does not meet the preset conditions, a Nonce is changed (Chang a nonce) and the aforementioned process is performed again. The role of Nonce here is to change the input of the Hash Function, so that the hash value of the output of the Hash Function changes accordingly, and then based on the changed hash value, it is judged whether the conditions are met to generate a new block. It can be seen that as the Nonce changes, the output hash value also changes accordingly, thereby avoiding the hash value used as the judgment condition from being reused.

[0026] In one possible implementation, the media content is also used to generate non-fungible tokens (NFTs). The essence of NFTs is a trusted digital equity certificate with unique characteristics in the blockchain network. It is a data object that can record and process multi-dimensional and complex attributes on the blockchain. For example, NFTs may include pictures, cryptocurrencies, etc. The combination of PoW and AIGC in the embodiment of the present application includes two branches. The first branch includes: generating and recording AIGC media content through AI technology, calculating the Hash value of AIGC media content, obtaining Nonce based on the Hash value, executing PoW based on Nonce, and applying the PoW result; the second branch includes: identity authentication based on AIGC media content, and using the media content parsing results for result matching and confirmation, thereby determining the identity of the verification object. The aforementioned two branches establish an association with each other through the interaction of requesting AIGC media content and sending AIGC media content, thereby realizing the organic combination of PoW and AIGC.

[0027] The first branch can be executed by the consensus node to implement the PoW process. The second branch can be executed by the consensus node to authenticate the verification object based on the AIGC media content generated by the first branch; or, the second branch can also be executed by the service platform of the business manufacturer (such as the server of Huawei Mall), in cooperation with the consensus node, the consensus node generates AIGC media content, and sends the AIGC media content to the service platform, and the service platform authenticates the verification object based on the AIGC media content. In this case, the service platform and the consensus node can interact through any form of communication mechanism, or information and data can be transmitted through an intermediate medium, without specific limitation.

[0028] It can be seen that by combining PoW and AIGC, AIGC media content can be used to obtain the Nonce required to execute PoW and complete the established calculation of PoW to improve the diversity of Nonce in PoW. PoW computing resources can also be used to obtain AIGC media content, which is then used to authenticate the verification object, so as to more effectively utilize PoW computing resources, improve the efficiency and security of identity authentication, and avoid media content attacks.

[0029] In the embodiment of the present application, the media content used for identity authentication of the verification object meets the following three conditions:

[0030] 1. Generated by consensus nodes in the blockchain system;

[0031] 2. Generated through AI technology;

[0032] 3. Records and blockchain systems.

[0033] The verification request may be triggered by an operation of the verification object, for example, when the verification object wants to log in, the login control is clicked on the login interface, thereby triggering the generation of the verification request. The specific operation and method of triggering the verification request are not specifically limited in the embodiment of the present application.

[0034] The verification information is matched with the pre-made information. If the match is successful, it means that the verification object is a real human user. If the match fails, it means that the verification object is a robot or script.

[0035] For example, compare multiple sequential characters in the verification information with multiple pre-prepared characters to see whether the characters and the order of characters are consistent. If they are consistent, it means that the match is successful; compare the final position in the verification information with the pre-prepared position to see whether the two are the same. If they are the same, it means that the match is successful; compare a string of characters in the verification information with a pre-prepared string of characters to see whether the characters and the order of characters are consistent. If they are consistent, it means that the match is successful.

[0036] In the embodiment of the present application, the first media content (AIGC media content) is used to authenticate the identity of the verification object, and the aforementioned first media content can be obtained by using the computing resources of PoW. In this way, the computing resources of PoW can be used more effectively, and the efficiency and security of identity authentication can be improved, and the media content can be prevented from being attacked.

[0037] In a second aspect, the present application provides a data processing device, comprising: an acquisition module for acquiring at least one media content; a processing module for performing hard binding processing based on the at least one media content to obtain a first hard binding; a generation module for generating a block based on the first hard binding, and the block is recorded in the blockchain system.

[0038] In a possible implementation manner, the block includes the first hard binding.

[0039] In a possible implementation manner, the block is generated when a second hard binding satisfies a preset condition, and the second hard binding is obtained at least according to the first hard binding.

[0040] In a possible implementation, the second hard binding is also obtained based on at least one of transaction data and a hard binding of a previous block.

[0041] In a possible implementation, the processing module is further used to record a media content when a media content is generated and the second hard binding satisfies the preset condition; or, when a media content is generated and the second hard binding does not satisfy the preset condition, discard the media content.

[0042] In a possible implementation, the processing module is also used to record a media content when generating the media content; obtain a third hard binding based on the media content; obtain a fourth hard binding based on a second media content, the second media content being generated before the media content; and obtain the first hard binding based on the third hard binding and the fourth hard binding.

[0043] In a possible implementation, the processing module is further configured to, when generating a plurality of media contents, record the plurality of media contents; obtain a plurality of fifth hard bindings respectively according to the plurality of media contents; and obtain the first hard binding according to the plurality of fifth hard bindings.

[0044] In a possible implementation manner, the preset condition includes that the second hard binding is less than a preset threshold.

[0045] In a possible implementation, the acquisition module is specifically configured to acquire the at least one media content through artificial intelligence (AI) technology.

[0046] In one possible implementation, the media content is also used to generate non-fungible tokens NFT.

[0047] In a possible implementation, it also includes: a verification module, which is used to provide the NFT to the verification object to obtain the verification information input by the verification object when a verification request is received; and determine the identity of the verification object based on the verification information.

[0048] In one possible implementation, the NFT includes an image verification code; and the verification information is generated based on an operation performed by the verification object on the image verification code.

[0049] In a third aspect, the present application provides a network node, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement a method as described in any one of the above-mentioned first aspects.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program, wherein when the computer program is executed on a computer, the computer executes any one of the methods described in the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program code, and when the computer program code is executed on a computer, the computer executes any one of the methods in the first aspect.

[0052] In a sixth aspect, the present application provides a chip comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute a method as described in any one of the above-mentioned first aspects.

[0053] In a seventh aspect, the present application provides a blockchain system, comprising: multiple network nodes, wherein any one of the network nodes is used to execute any of the methods described in the first aspect above.

[0054] In an eighth aspect, the present application provides a blockchain system, comprising a first network node and a second network node, the first network node generates at least one media content, sends at least one media content to the second network node, and the second network node is used to execute any one of the methods in the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the PoW mechanism;

[0056] Figure 2 It is a schematic diagram of the structure of the network node;

[0057] Figure 3 A flowchart of a process 300 of a data processing method according to an embodiment of the present application;

[0058] Figure 4a A schematic diagram of the PoW mechanism of an embodiment of the present application;

[0059] Figure 4b A schematic diagram of the PoW mechanism of an embodiment of the present application;

[0060] Figure 4c A schematic diagram of the PoW mechanism of an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the combination of PoW and AIGC in an embodiment of the present application;

[0062] Figure 6 A flowchart of a process 600 of an identity authentication method according to an embodiment of the present application;

[0063] Figure 7 Schematic diagram of the structure of the data processing device 700 according to an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] The terms "first", "second", etc. in the specification embodiments, claims, and drawings of the present application are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, including a series of steps or units. The method, system, product, or device is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0066] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0067] Before introducing the technical solution of the present application, the relevant technologies involved in the embodiments of the present application are first described.

[0068] 1. Application-layer digital identity

[0069] Application-layer digital identity refers to the identity information of a user, device, or system in a specific application environment, including user name, email address, password, device identification code, etc. This information is the basis for identity authentication, enabling applications to confirm and verify the identity of the user and perform access control based on the user's permissions and roles. Application-layer digital identity plays an important role in protecting user security and privacy. By using digital identity information, applications can detect and prevent unauthorized access and activities. In addition, application-layer digital identity can also be used for auditing and monitoring. For example, if a security incident occurs, it can assist in the investigation by viewing the activities related to a specific digital identity.

[0070] 2. Verification code

[0071] The full name of CAPTCHA is "Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA)", which is used to distinguish between humans and computers and is an important part of digital identity at the application layer. Its main purpose is to prevent automated robots or scripts from performing malicious activities. For example, when a user registers an account, submits a form, or performs other operations that require verification, the system will ask the user to complete the test. For example, enter a randomly generated sequence of characters or perform other types of operations. If the user can complete the test correctly, the system will consider the user to be a real human, not a robot or script. It can be seen that CAPTCHA is an important tool for network security, designed to prevent automated attacks and ensure that only real human users can access and use specific website functions.

[0072] There are many forms of CAPTCHAs. The most common type is an image CAPTCHA, which contains randomly generated characters that users need to enter. These characters are usually distorted or covered, making them difficult for machines to recognize, but humans can see and enter them. There are also sliding CAPTCHAs, puzzle CAPTCHAs, and image CAPTCHAs that only require users to click on a specific picture or mark an object in the image.

[0073] 3. AI Generated Content (AIGC)

[0074] AIGC refers to content created through artificial intelligence (AI) technology, including text, images, audio, video and other forms. With the emergence of generative adversarial networks (GAN), denoising diffusion models, etc., AIGC's underlying technology continues to break through, enabling it to generate corresponding text and realistic images or videos according to prompts. AI-generated content has a wide range of applications, from providing a richer user experience to improving the efficiency of content creation to creating new art forms. Its possibilities are endless and it has great development prospects in generating personalized, customized content, and real-time content. Using AIGC, a slider verification code without obvious gaps can be designed. To determine the target position, it is necessary to understand the semantics of the image, thereby increasing the difficulty of cracking; AIGC can quickly generate an unlimited number of corresponding images through text descriptions, reducing time costs and workload, making the cracking method based on traversing the gallery invalid, and greatly enhancing the difficulty of cracking the verification code.

[0075] 4. Blockchain

[0076] For example, blockchain is a chain of blocks. Each block stores certain information, and they are connected into a chain in the order of their generation. This chain is stored in all servers. As long as there is one server in the entire system that can work, the entire blockchain is safe. These servers are called nodes in the blockchain system, and they provide storage space and computing power support for the entire blockchain system. Blockchain has two core characteristics: one is that data is difficult to tamper with; the other is decentralization. Based on these two characteristics, the information recorded in the blockchain is more authentic and reliable, which can help solve the problem of people's distrust.

[0077] For example, each block in the blockchain contains a batch of transaction records. Once a block is added to the chain, it cannot be changed, which creates a persistent and immutable transaction history record.

[0078] For example, each block of the blockchain contains the hash value of the previous block, and these blocks are linked together to form the so-called "chain".

[0079] For example, a blockchain network (also called a blockchain system) is composed of nodes. Each node holds a copy of the entire blockchain, which enables the blockchain network to operate without a central server and prevents data from being tampered with.

[0080] For example, consensus mechanisms are the rules used by a blockchain network to decide how new blocks are added to the chain. The most famous consensus mechanisms include Proof of Work (PoW) and Proof of Stake (PoS).

[0081] For example, blockchain uses cryptographic algorithms to ensure the security and privacy of transactions, which usually include the use of public and private keys, and hash functions.

[0082] For example, Smart Contracts: In some blockchains (e.g., Bitcoin, Ethereum, etc.), a smart contract is a protocol that automatically executes transactions. When predetermined conditions are met, the smart contract will be automatically executed without any third-party intervention.

[0083] 5. PoW

[0084] Proof of Work (PoW) is a commonly used method for reaching consensus (agreement) on who will be the next node to add a new block to a public blockchain. The PoW mechanism requires participants (nodes) in the network to complete some computing tasks, which often require a lot of computing resources and time.

[0085] For example, Figure 1 This is a schematic diagram of the PoW mechanism, Figure 1 As shown, in the Bitcoin network, the random value (Nonce), the collected transactions (i.e., the transaction list to be packaged into the new block (Collected TXs)) and the hash value of the previous block (Hash of Prev.Block) are input into the hash function (Hash Function), and the output hash value is used to determine whether the conditions are met (for example, whether the hash value is less than a target value (Less than a target value?)). When the conditions are met, a new block (New Block) is generated; when the conditions are not met, a Nonce (Chang a nonce) is changed. The role of Nonce is to change the input of the Hash Function, so that the hash value of the output of the Hash Function changes accordingly, and then judge whether the conditions are met based on the changed hash value to generate a new block. It can be seen that as the Nonce changes, the output hash value also changes, thereby avoiding the hash value used as the judgment condition from being reused. Solving the above problems requires a lot of computing resources, but once a solution is found, other participants can quickly verify the correctness of the solution. This is why it is called "proof of work": participants must prove that they have invested a certain amount of work to solve the problem. One of the main benefits of the PoW mechanism is that it helps protect the network from the “Byzantine Generals Problem.” PoW ensures that only participants who have invested sufficient computing resources can influence the network, thus protecting the network from attacks.

[0086] 6. AI

[0087] (1) Neural Network

[0088] Neural Network (NN) is a machine learning model. A neural network can be composed of neural units. A neural unit can refer to an operation unit with xs and intercept 1 as input. The output of the operation unit can be:

[0089]

[0090] Where s=1, 2, ...n, n is a natural number greater than 1, Ws is the weight of xs, and b is the bias of the neural unit. f is the activation function of the neural unit, which is used to introduce nonlinear characteristics into the neural network to convert the input signal in the neural unit into the output signal. The output signal of the activation function can be used as the input of the next convolution layer. The activation function can be a sigmoid function. A neural network is a network formed by connecting many of the above-mentioned single neural units together, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract the characteristics of the local receptive field. The local receptive field can be an area composed of several neural units.

[0091] (2) Deep Neural Networks

[0092] Deep Neural Network (DNN), also known as multi-layer neural network, can be understood as a neural network with many hidden layers. There is no special metric for "many" here. From the position of different layers of DNN, the neural network inside DNN can be divided into three categories: input layer, hidden layer, and output layer. Generally speaking, the first layer is the input layer, the last layer is the output layer, and the layers in between are all hidden layers. The layers are fully connected, that is, any neuron in the i-th layer must be connected to any neuron in the i+1-th layer. Although DNN looks complicated, the work of each layer is actually not complicated. Simply put, it is the following linear relationship expression: in, is the input vector, is the output vector, is the offset vector, W is the weight matrix (also called coefficient), and α() is the activation function. Each layer is just an input vector After such a simple operation, the output vector Since DNN has many layers, the coefficient W and the offset vector The definition of these parameters in DNN is as follows: Take coefficient W as an example: Assume that in a three-layer DNN, the linear coefficient from the 4th neuron in the second layer to the 2nd neuron in the third layer is defined as The superscript 3 represents the layer number of the coefficient W, while the subscripts correspond to the output third layer index 2 and the input second layer index 4. In summary, the coefficients from the kth neuron in the L-1th layer to the jth neuron in the Lth layer are defined as It should be noted that the input layer does not have a W parameter. In a deep neural network, more hidden layers allow the network to better describe complex situations in the real world. Theoretically, the more parameters a model has, the higher its complexity and the greater its "capacity", which means it can complete more complex learning tasks. Training a deep neural network is the process of learning the weight matrix, and its ultimate goal is to obtain the weight matrix of all layers of the trained deep neural network (the weight matrix formed by many layers of vector W).

[0093] (3) Convolutional Neural Network

[0094] Convolutional Neuron Network (CNN) is a deep neural network with a convolutional structure. It is a deep learning architecture. A deep learning architecture refers to multiple levels of learning at different levels of abstraction through machine learning algorithms. As a deep learning architecture, CNN is a feed-forward artificial neural network in which each neuron can respond to the image input into it. The convolutional neural network contains a feature extractor consisting of a convolution layer and a pooling layer. The feature extractor can be regarded as a filter, and the convolution process can be regarded as using a trainable filter to convolve an input image or convolution feature plane (feature map).

[0095] A convolutional layer is a layer of neurons in a convolutional neural network that performs convolution on the input signal. A convolutional layer can include many convolution operators, also known as kernels. In image processing, the convolution operator is essentially a weight matrix, which is usually predefined. During the convolution operation on the image, the weight matrix is ​​usually processed horizontally on the input image one pixel after another (or two pixels after two pixels... depending on the value of the stride) to complete the work of extracting specific features from the image. The size of the weight matrix should be related to the size of the image. It should be noted that the depth dimension of the weight matrix is ​​the same as the depth dimension of the input image. During the convolution operation, the weight matrix will extend to the entire depth of the input image. Therefore, convolution with a single weight matrix will produce a convolution output with a single depth dimension, but in most cases, instead of using a single weight matrix, multiple weight matrices of the same size (rows × columns) are applied, that is, multiple isotype matrices. The output of each weight matrix is ​​stacked to form the depth dimension of the convolution image, where the dimension can be understood as being determined by the "multiple" mentioned above. Different weight matrices can be used to extract different features in the image, for example, one weight matrix is ​​used to extract image edge information, another weight matrix is ​​used to extract specific colors of the image, and another weight matrix is ​​used to blur unnecessary noise points in the image. The multiple weight matrices have the same size (rows × columns), and the size of the feature maps extracted by the multiple weight matrices of the same size is also the same. The extracted multiple feature maps of the same size are then merged to form the output of the convolution operation. The weight values ​​in these weight matrices need to be obtained through a lot of training in practical applications. The weight matrices formed by the weight values ​​obtained through training can be used to extract information from the input image, so that the convolutional neural network can make correct predictions. When the convolutional neural network has multiple convolutional layers, the initial convolutional layer often extracts more general features, which can also be called low-level features; as the depth of the convolutional neural network deepens, the features extracted by the later convolutional layers become more and more complex, such as high-level semantic features. Features with higher semantics are more suitable for the problem to be solved.

[0096] Since it is often necessary to reduce the number of training parameters, it is often necessary to periodically introduce a pooling layer after the convolution layer. It can be a convolution layer followed by a pooling layer, or multiple convolution layers followed by one or more pooling layers. In the image processing process, the only purpose of the pooling layer is to reduce the spatial size of the image. The pooling layer can include an average pooling operator and / or a maximum pooling operator to sample the input image to obtain an image of smaller size. The average pooling operator can calculate the pixel values ​​in the image within a specific range to produce an average value as the result of average pooling. The maximum pooling operator can take the pixel with the largest value in the range as the result of maximum pooling within a specific range. In addition, just as the size of the weight matrix used in the convolution layer should be related to the image size, the operator in the pooling layer should also be related to the image size. The size of the image output after processing by the pooling layer can be smaller than the size of the image input to the pooling layer. Each pixel in the image output by the pooling layer represents the average value or maximum value of the corresponding sub-region of the image input to the pooling layer.

[0097] After being processed by the convolution layer / pooling layer, the convolution neural network is not sufficient to output the required output information. Because as mentioned above, the convolution layer / pooling layer will only extract features and reduce the parameters brought by the input image. However, in order to generate the final output information (the required class information or other related information), the convolution neural network needs to use the neural network layer to generate one or a group of outputs of the required number of classes. Therefore, the neural network layer may include multiple hidden layers, and the parameters contained in the multiple hidden layers can be pre-trained according to the relevant training data of the specific task type. For example, the task type may include image recognition, image classification, image super-resolution reconstruction, etc.

[0098] Optionally, after the multiple hidden layers in the neural network layer, an output layer of the entire convolutional neural network is also included. The output layer has a loss function similar to the classification cross entropy, which is specifically used to calculate the prediction error. Once the forward propagation of the entire convolutional neural network is completed, the back propagation will begin to update the weight values ​​and biases of the aforementioned layers to reduce the loss of the convolutional neural network and the error between the result output by the convolutional neural network through the output layer and the ideal result.

[0099] (4) Recurrent Neural Network

[0100] Recurrent Neural Networks (RNN) are used to process sequence data. In the traditional neural network model, from the input layer to the hidden layer and then to the output layer, the layers are fully connected, and the nodes within each layer are disconnected. Although this ordinary neural network has solved many difficult problems, it is still powerless for many problems. For example, if you want to predict the next word in a sentence, you generally need to use the previous word, because the previous and next words in a sentence are not independent. The reason why RNN is called a recurrent neural network is that the current output of a sequence is also related to the previous output. The specific manifestation is that the network will remember the previous information and apply it to the calculation of the current output, that is, the nodes between the hidden layers are no longer disconnected but connected, and the input of the hidden layer includes not only the output of the input layer but also the output of the hidden layer at the previous moment. In theory, RNN can process sequence data of any length. The training of RNN is the same as the training of traditional CNN or DNN. The same error back propagation algorithm is used, but there is a difference: that is, if the RNN is expanded, the parameters, such as W, are shared; this is not the case with the traditional neural network mentioned above. And when using the gradient descent algorithm, the output of each step depends not only on the network at the current step, but also on the state of the network at the previous steps. This learning algorithm is called the Back Propagation Through Time (BPTT).

[0101] Since we already have convolutional neural networks, why do we need recurrent neural networks? The reason is simple. In convolutional neural networks, there is a premise that the elements are independent of each other, and the input and output are also independent, such as cats and dogs. But in the real world, many elements are interconnected, such as the change of stocks over time, or someone said: I like traveling, and my favorite place is Yunnan. I must go there if I have the chance in the future. Fill in the blank here, humans should all know to fill in "Yunnan". Because humans will make inferences based on the content of the context, but how can machines do this? RNN came into being. RNN is designed to give machines the ability to remember like humans. Therefore, the output of RNN needs to rely on the current input information and historical memory information.

[0102] (5) Loss Function

[0103] In the process of training a deep neural network, because we hope that the output of the deep neural network is as close as possible to the value we really want to predict, we can compare the predicted value of the current network with the target value we really want, and then update the weight vector of each layer of the neural network according to the difference between the two (of course, there is usually an initialization process before the first update, that is, pre-configuring parameters for each layer in the deep neural network). For example, if the predicted value of the network is high, adjust the weight vector to make it predict a lower value, and keep adjusting until the deep neural network can predict the target value we really want or a value very close to the target value we really want. Therefore, it is necessary to pre-define "how to compare the difference between the predicted value and the target value", which is the loss function or objective function, which are important equations used to measure the difference between the predicted value and the target value. Among them, taking the loss function as an example, the higher the output value (loss) of the loss function, the greater the difference, so the training of the deep neural network becomes a process of minimizing this loss as much as possible.

[0104] (6) Back propagation algorithm

[0105] Convolutional neural networks can use the error back propagation (BP) algorithm to correct the size of the parameters in the initial super-resolution model during the training process, so that the reconstruction error loss of the super-resolution model becomes smaller and smaller. Specifically, the forward transmission of the input signal to the output will generate error loss, and the error loss information is back-propagated to update the parameters in the initial super-resolution model, so that the error loss converges. The back propagation algorithm is a back propagation movement dominated by error loss, aiming to obtain the optimal parameters of the super-resolution model, such as the weight matrix.

[0106] (7) Generative Adversarial Networks

[0107] Generative Adversarial Networks (GAN) is a deep learning model. The model includes at least two modules: one module is the generative model, and the other module is the discriminative model. Through the game learning between these two modules, better output is generated. Both the generative model and the discriminative model can be neural networks, specifically deep neural networks or convolutional neural networks. The basic principle of GAN is as follows: Taking the GAN that generates pictures as an example, suppose there are two networks, G (Generator) and D (Discriminator), where G is a network that generates pictures. It receives a random noise z and generates pictures through this noise, denoted as G(z); D is a discriminative network, which is used to determine whether a picture is "real". Its input parameter is x, x represents a picture, and the output D(x) represents the probability that x is a real picture. If it is 1, it means that it is 100% a real picture, and if it is 0, it means that it cannot be a real picture. In the process of training the generative adversarial network, the goal of the generative network G is to generate realistic images as much as possible to deceive the discriminative network D, while the goal of the discriminative network D is to distinguish the images generated by G from the real images as much as possible. In this way, G and D constitute a dynamic "game" process, which is also the "adversary" in the "generative adversarial network". The final result of the game is that, under ideal conditions, G can generate images G(z) that are "real" enough, while D has difficulty determining whether the images generated by G are real, that is, D(G(z)) = 0.5. In this way, an excellent generative model G is obtained, which can be used to generate images.

[0108] In the current network environment, verification codes are widely used in various network services as an effective means to prevent malware and automatic robots. However, traditional verification code technology relies on simple graphics and text recognition, which is easily recognized and cracked by advanced machine learning algorithms, greatly reducing the protection effect of verification codes. On the other hand, the PoW mechanism in the blockchain requires a lot of computing work to obtain an effective solution to ensure the security and stability of the blockchain. However, this computing process does not generate actual value, but only wastes a lot of computing resources to maintain the security of the blockchain.

[0109] The embodiment of the present application provides a data processing method, which organically combines PoW with AIGC, and can use a large amount of computing resources of PoW to generate AIGC verification codes for identity authentication. In addition, Nonce can be obtained based on the AIGC verification code for the PoW process. The technical solution of the present application will be described below.

[0110] In one possible implementation, the blockchain system includes multiple network nodes. The blockchain is a chain of blocks, each of which stores certain information. They are connected into a chain in the order of their generation. This chain is stored in the aforementioned network nodes. As long as there is a network node in the entire system that can work, the entire blockchain is safe. The network node provides storage space and computing power support for the entire blockchain system.

[0111] Figure 2 It is a schematic diagram of the structure of the network node, such as Figure 2 As shown, the network node includes at least one processor 211, at least one memory 212, at least one transceiver 213, at least one network interface 214 and one or more antennas 215. The processor 211, the memory 212, the transceiver 213 and the network interface 214 are connected, for example, through a bus. In the embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 215 is connected to the transceiver 213. The network interface 214 is used to connect the network node to other communication devices through a communication link. For example, the network interface 214 may include a network interface between the network node and other network nodes.

[0112] The processor 211 is mainly used to process data, control the entire network node, execute software programs, and process the data of the software programs, for example, to support the network node to perform the actions described in the embodiment. The network node may include a baseband processor and a central processor. The baseband processor is mainly used to process the communication protocol and communication data, and the central processor is mainly used to control the entire network node, execute software programs, and process the data of the software programs. The processor 211 can integrate the functions of the baseband processor and the central processor. Those skilled in the art can understand that the baseband processor and the central processor can also be independent processors, interconnected by technologies such as buses. Those skilled in the art can understand that the network node can include multiple baseband processors to adapt to different network formats, and the network node can include multiple central processors to enhance its processing capabilities. The various components of the network node can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processor can also be described as a central processing circuit or a central processing chip. The function of processing data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0113] The memory 212 is mainly used to store software programs and data. The memory 212 can be independent and connected to the processor 211. Optionally, the memory 212 can be integrated with the processor 211, for example, integrated into a chip. Among them, the memory 212 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 211. The various types of computer program codes executed can also be regarded as drivers of the processor 211.

[0114] Figure 2 Only one memory and one processor are shown. In an actual network node, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.

[0115] The transceiver 213 can be used to support the reception or transmission of radio frequency signals between the network node and other network nodes, and the transceiver 213 can be connected to the antenna 215. The transceiver 213 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 215 can receive radio frequency signals, and the receiver Rx of the transceiver 213 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 211, so that the processor 211 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation processing and decoding processing. In addition, the transmitter Tx in the transceiver 213 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 211, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 215.

[0116] In one possible implementation, Figure 2 The network nodes shown can be used as consensus nodes in the blockchain system. The consensus nodes have the consensus algorithm in the consensus mechanism described above (including the algorithm for implementing the PoW mechanism). They are nodes in the blockchain that have downloaded the complete algorithm and can decide who will keep accounts. The network nodes corresponding to the consensus nodes can be called light nodes, which do not have a complete algorithm. The consensus nodes can generate verification codes and implement identity verification of the verification object based on the verification code.

[0117] Based on this, Figure 3 Flow chart of process 300 of the data processing method of the present invention. Process 300 may be performed by the above network node (i.e., consensus node). Process 300 is described as a series of steps or operations. It should be understood that process 300 may be performed in various orders and / or occur simultaneously, not limited to Figure 3 The execution order shown. Process 300 may include:

[0118] Step 301: Obtain at least one media content.

[0119] As mentioned above, the PoW mechanism requires participants in the network (i.e., consensus nodes) to complete some computing tasks, which often require a lot of computing resources and time. In order to avoid wasting these computing resources and time, the embodiments of the present application can realize the computing tasks completed by using computing resources and time to generate at least one media content.

[0120] In one possible implementation, the method for consensus nodes to generate media content can use AI technology, for example, a neural network model is constructed based on the above computing resources, and the neural network model can output media content, such as AIGC pictures, videos, etc. The technical principles and types of neural network models can refer to the above description, which will not be repeated here. Media content may include content or metadata of content, etc., and media content can be generated in the form of media files, which is not specifically limited in the embodiments of the present application.

[0121] In one possible implementation, at least one media content may also be obtained from other network nodes. For example, a first network node generates at least one media content and sends the at least one media content to a second network node. The first network node and the second network node are different network nodes in the same blockchain system, and the second network node executes process 300 of the data processing method.

[0122] Step 302: Perform hard binding processing according to at least one media content to obtain a first hard binding.

[0123] In an embodiment of the present application, hard binding processing can be implemented using a one-way function, and the processing result can be called hard binding. Exemplarily, the one-way function can be a hash function, and its output result is a hash value, so the hard binding can be a hash value.

[0124] After the consensus node generates at least one media content, hard binding processing is performed according to the at least one media content to obtain a first hard binding. In the embodiment of the present application, the first hard binding is represented as (Number once, Nonce), also known as a random number. Usually, Nonce can represent an arbitrary or non-repetitive random value that is used only once.

[0125] According to the amount of media content generated by the consensus node, there are three implementation examples:

[0126] (1) The consensus node generates a media content

[0127] The consensus node generates a media content through AI technology, and then obtains Nonce based on the media content, for example, through a hash function.

[0128] At this time, if the second hard binding meets the preset conditions, the above media content is recorded; if the second hard binding does not meet the preset conditions, the above media content is discarded. It can also be considered that only the media content corresponding to the second hard binding used to generate a new block will be recorded by the blockchain system. The preset condition may include that the second hard binding is less than a preset threshold, that is, when the second hard binding is less than the preset threshold, it means that the preset condition is met. It should be noted that the preset condition can also be set to other content, which is not specifically limited.

[0129] In an embodiment of the present application, the second hard binding (eg, a hash value) is obtained based on at least the first hard binding. In addition, the second hard binding can also be obtained based on at least one of the transaction data (Collected TXs) and the hard binding of the previous block (Hash ofPrev.Block).

[0130] For example, Figure 4a A schematic diagram of the PoW mechanism of an embodiment of the present application is shown in FIG. Figure 4a As shown, the following steps are included:

[0131] a. The consensus node generates an AIGC image through AI technology;

[0132] b. The consensus node performs hash operation on the AIGC image to obtain the hash value of the AIGC image;

[0133] c. The consensus node uses the Hash value of the AIGC image as the Nonce;

[0134] d. The consensus node inputs Nonce, Collected TXs and Hash of Prev.Block into HashFunction;

[0135] e. The consensus node determines whether the output result of the Hash Function is less than the preset threshold (also called the target value);

[0136] f. When the output result is less than the preset threshold, generate a new block and record the AIGC image generated in step a; otherwise, return to step a.

[0137] (2) The consensus node generates a media content

[0138] The consensus node generates a media content through AI technology and records it directly without judgment.

[0139] In the process of obtaining Nonce, firstly obtain the third hard binding according to the media content; then obtain the fourth hard binding according to the second media content generated before the aforementioned media content; and finally obtain Nonce according to the third hard binding and the fourth hard binding.

[0140] For example, Figure 4b A schematic diagram of the PoW mechanism of an embodiment of the present application is shown in FIG. Figure 4b As shown, the following steps are included:

[0141] a. The consensus node generates an AIGC image through AI technology;

[0142] b. The consensus node performs a hash operation on the AIGC image to obtain the hash value of the AIGC image, and records the aforementioned AIGC image in the AIGC chain;

[0143] c. Combine the Hash value of the AIGC picture (third hard binding) with the Hash value of the previous AIGC picture recorded in the AIGC chain (fourth hard binding), and calculate the Hash value as Nonce;

[0144] d. The consensus node inputs Nonce, Collected TXs and Hash of Prev.Block into HashFunction;

[0145] e. The consensus node determines whether the output result of the Hash Function is less than the preset threshold (also called the target value);

[0146] f. When the output result is less than the preset threshold, generate a new block and record the AIGC image generated in step a; otherwise, return to step a.

[0147] Figure 4b The embodiment shown and Figure 4a The differences between the shown embodiments are: Figure 4a The embodiment shown can only obtain the AIGC picture corresponding to the Nonce that meets the preset conditions in the end, and Figure 4b The illustrated embodiment uses an AIGC chain to record each generated AIGC picture, so that each calculation is not wasted, and each generated AIGC picture can be used.

[0148] (3) Consensus nodes generate multiple media content

[0149] The consensus node generates multiple media contents through AI technology and records them directly without judgment.

[0150] In the process of obtaining Nonce, a plurality of fifth hard bindings are firstly obtained according to the plurality of media contents respectively; and then Nonce is obtained according to the plurality of fifth hard bindings.

[0151] For example, Figure 4c A schematic diagram of the PoW mechanism of an embodiment of the present application is shown in FIG. Figure 4c As shown, the following steps are included:

[0152] a. The consensus node generates multiple AIGC images through AI technology;

[0153] b. The consensus node performs hash operations on the multiple AIGC images to obtain hash values ​​of the multiple AIGC images (fifth hard binding), and uses the aforementioned multiple AIGC images as child nodes of a tree (e.g., a Merkle tree) and stores them in the tree;

[0154] c. Multiple child nodes are paired in pairs, and the Hash values ​​corresponding to the two child nodes in each pair are then Hash-calculated to obtain the Hash value of the parent node, and so on, until the Hash value of the root node of the tree is obtained as the Nonce;

[0155] For example, the consensus node generates 4 AIGC images, and obtains 4 Hash values ​​corresponding to 4 child nodes, which are marked as 1, 2, 3, and 4 for ease of description. Child nodes 1 and 2 are paired, and child nodes 3 and 4 are paired. In the first pair, 1 and 2 are hashed to obtain the Hash value of parent node 1', and in the second pair, 3 and 4 are hashed to obtain the Hash value of parent node 2'. Parent nodes 1' and 2' are hashed to obtain the Hash value of the root node as Nonce.

[0156] For another example, the consensus node generates three AIGC images, and obtains three hash values ​​corresponding to three child nodes, which are marked as 1, 2, and 3 for ease of description. Child nodes 1 and 2 are paired, and child node 3 is not paired. In the first pair, 1 and 2 are hashed to obtain the hash value of parent node 1', and 3 can be hashed with the padding 0 to obtain the hash value of parent node 2', or the hash value of 3 can be directly used as the hash value of parent node 2'. Parent nodes 1' and 2' are hashed to obtain the hash value of the root node as Nonce.

[0157] It should be noted that the embodiment of the present application may also use other methods to obtain Nonce, and there is no specific limitation on this.

[0158] d. The consensus node inputs Nonce, Collected TXs and Hash of Prev.Block into HashFunction;

[0159] e. The consensus node determines whether the output result of the Hash Function is less than the preset threshold (also called the target value);

[0160] f. When the output result is less than the preset threshold, generate a new block and record the AIGC image generated in step a; otherwise, return to step a.

[0161] Figure 4c The embodiment shown and Figure 4b The differences between the shown embodiments are: Figure 4b The illustrated embodiment adopts a serial method and generates only one AIGC picture at a time, which is time-consuming and has low time utilization rate; Figure 4c The illustrated embodiment adopts a parallel approach and can generate multiple AIGC pictures at one time, thus saving time costs.

[0162] It should be noted that in the embodiment of the present application, the blockchain system records media content. In addition to recording the media content itself as described above, such as recording picture media content, it can also record the Hash value corresponding to the media content, as well as the Hash function, etc., which can be based on actual needs or storage resources, etc., and there is no specific limitation on this.

[0163] Step 303: Generate a block according to the first hard binding, and the block is recorded in the blockchain system.

[0164] Optionally, the block includes the first hard binding described above.

[0165] Optionally, the block is generated when the second hard binding satisfies a preset condition.

[0166] In a possible implementation, at least one media content generated by a consensus node can be used to obtain a random number (Nonce) (first hard binding), and then the random number (Nonce) together with the collected transactions (i.e., the transaction list (Collected TXs) to be packaged into the new block) and the hash value (Hash of Prev.Block) of the previous block are input into the hash function (Hash Function), and the hash value (second hard binding) is output. Based on the hash value, it is judged whether the preset conditions are met to implement the PoW process. When the hash value meets the preset conditions, a block is generated; when the hash value does not meet the preset conditions, a Nonce is changed (Chang a nonce) and the aforementioned process is performed again. The role of Nonce here is to change the input of the Hash Function, so that the hash value of the output of the Hash Function changes accordingly, and then based on the changed hash value, it is judged whether the conditions are met to generate a new block. It can be seen that as the Nonce changes, the output hash value also changes accordingly, thereby avoiding the hash value used as the judgment condition from being reused.

[0167] In combination with the above description, the embodiment of the present application combines PoW with media content, and can use a large amount of computing resources of PoW to generate media content, and then obtain Nonce based on the media content to implement the PoW process, thereby improving the diversity of Nonce in the PoW process.

[0168] In one possible implementation, media content is also used to generate non-fungible tokens (NFTs). The essence of NFTs is a unique and trustworthy digital equity certificate in the blockchain network. It is a data object with multi-dimensional and complex attributes that can be recorded and processed on the blockchain. For example, NFTs can include pictures, cryptocurrencies, etc. Figure 5 This is a schematic diagram of the combination of PoW and AIGC in an embodiment of the present application, as shown in Figure 5 As shown, it includes two branches. The first branch includes: generating and recording AIGC media content through AI technology, calculating the Hash value of AIGC media content, obtaining Nonce based on the Hash value, executing PoW based on Nonce, and applying the PoW result; the second branch includes: performing identity authentication based on AIGC media content, using the media content parsing results for result matching and confirmation, thereby determining the identity of the verified object. The above two branches establish an association with each other through the interaction of requesting AIGC media content and sending AIGC media content, thereby realizing the organic combination of PoW and AIGC.

[0169] The first branch can be executed by the network node (i.e., consensus node) to implement the PoW process. The second branch can be executed by the network node (i.e., consensus node) to authenticate the verification object based on the AIGC media content generated by the first branch; or, the second branch can also be executed by the service platform of the business vendor (e.g., the server of Huawei Mall), in cooperation with the consensus node, the consensus node generates AIGC media content, and sends the AIGC media content to the service platform, and the service platform authenticates the verification object based on the AIGC media content. In this case, the service platform and the consensus node can interact through any form of communication mechanism, or information and data can be transmitted through an intermediate medium, without specific limitation.

[0170] It can be seen that by combining PoW and AIGC, AIGC media content can be used to obtain the Nonce required to execute PoW and complete the established calculation of PoW to improve the diversity of Nonce in PoW. PoW computing resources can also be used to obtain AIGC media content, which is then used to authenticate the verification object, so as to more effectively utilize PoW computing resources, improve the efficiency and security of identity authentication, and avoid media content attacks.

[0171] based on Figure 5 The second branch in Figure 6 Flowchart of process 600 of the identity authentication method of an embodiment of the present application. Process 600 may be performed by the above-mentioned network node (i.e., consensus node) or the service platform of the business vendor (e.g., the server of Huawei Mall). Process 600 is described as a series of steps or operations. It should be understood that process 600 may be performed in various orders and / or occur simultaneously, not limited to Figure 6 The execution order shown. Process 600 may include:

[0172] Step 601: When a verification request is received, first media content is obtained. The first media content is generated by the consensus node through AI technology and recorded in the blockchain system.

[0173] Optionally, the first media content may be an image. For example, an image of a login interface, which includes multiple characters, and the verification object is required to click the multiple characters in order; or an image of a login interface, which has a gap, similar to a small piece in a puzzle, and the verification object is required to slide the small piece to the gap to complete the image; or an image of a login interface, the background color of which is a high-saturation multiple colors, which contains a string of characters, and the verification object is required to enter this string of characters; and so on. It can be seen that image media content can have various forms and is used for verification objects to log in to websites, applications, etc., to identify whether it is a real human logging in or a machine attack.

[0174] As mentioned above, the PoW mechanism requires the participants in the network (i.e., consensus nodes) to complete some computing tasks, and these tasks often require a large amount of computing resources and time. If only Nonce is generated, it is a waste of these computing resources and time. Therefore, the present application embodiment can realize the computing tasks completed using computing resources and time to generate media content. Since media content is generated based on the computing resources and time of the PoW mechanism, it is difficult for an attacker to generate media content that is as effective as the aforementioned media content without sufficient computing resources. And since each media content is unique, it is also difficult for an attacker to attack through a pre-generated picture library. Based on this, the media content generated by the consensus node can be used to improve the efficiency and security of identity authentication.

[0175] In the embodiment of the present application, the media content (i.e., the first media content) used for identity authentication of the verification object satisfies three conditions:

[0176] 1. Generated by consensus nodes in the blockchain system;

[0177] 2. Generated through AI technology;

[0178] 3. Records and blockchain systems.

[0179] For a description of the media content, see Figure 3 The embodiments shown will not be described in detail here.

[0180] The verification request may be triggered by an operation of the verification object, for example, when the verification object wants to log in, the login control is clicked on the login interface, thereby triggering the generation of the verification request. The specific operation and method of triggering the verification request are not specifically limited in the embodiment of the present application.

[0181] Step 602: Provide the first media content to the verification object to obtain verification information input by the verification object.

[0182] The first media content may be displayed on the interactive interface in the form of a web page, a pop-up window, etc., which is not specifically limited. The verification information may be generated based on the operation of the verification object on the interactive interface.

[0183] As in the example above, if the first media content is image media content, then the first media content displayed on the interactive interface may include multiple characters, and the verification object is required to click on the multiple characters in sequence. At this time, the verification information may include multiple characters with sequence input by multiple click operations; or, the first media content has a gap, similar to a small piece in a puzzle, and the verification object is required to slide the small piece to the gap to complete the image. At this time, the verification information may include the final position of the small piece input by the sliding operation; or, the background color of the first media content is a variety of highly saturated colors, which implies a string of characters, and the verification object is required to input this string of characters. At this time, the verification information may include a string of characters input by the input operation. In addition, the first media content can also be presented in other ways, and the embodiments of the present application do not specifically limit this.

[0184] Step 603: Determine the identity of the verification object according to the verification information.

[0185] The verification information is matched with the pre-made information. If the match is successful, it means that the verification object is a real human user. If the match fails, it means that the verification object is a robot or script.

[0186] For example, compare multiple sequential characters in the verification information with multiple pre-prepared characters to see whether the characters and the order of characters are consistent. If they are consistent, it means that the match is successful; compare the final position in the verification information with the pre-prepared position to see whether the two are the same. If they are the same, it means that the match is successful; compare a string of characters in the verification information with a pre-prepared string of characters to see whether the characters and the order of characters are consistent. If they are consistent, it means that the match is successful.

[0187] In the embodiment of the present application, the first media content (AIGC media content) is used to authenticate the identity of the verification object, and the aforementioned first media content can be obtained by using the computing resources of PoW. In this way, the computing resources of PoW can be used more effectively, and the efficiency and security of identity authentication can be improved, and the media content can be prevented from being attacked.

[0188] Figure 7 Schematic diagram of the structure of the data processing device 700 according to the embodiment of the present application. Figure 7 As shown, the data processing device 700 of this embodiment can be applied to the above network node (ie, consensus node). The data processing device 700 may include: an acquisition module 701, a processing module 702, a generation module 703, and a verification module 704.

[0189] The acquisition module 701 is used to acquire at least one media content; the processing module 702 is used to perform hard binding processing according to the at least one media content to obtain a first hard binding; the generation module 703 is used to generate a block according to the first hard binding, and the block is recorded in the blockchain system.

[0190] In a possible implementation manner, the block includes the first hard binding.

[0191] In a possible implementation manner, the block is generated when a second hard binding satisfies a preset condition, and the second hard binding is obtained at least according to the first hard binding.

[0192] In a possible implementation, the second hard binding is also obtained based on at least one of transaction data and a hard binding of a previous block.

[0193] In a possible implementation, the processing module 702 is further used to record a media content when a media content is generated and the second hard binding satisfies the preset condition; or, when a media content is generated and the second hard binding does not satisfy the preset condition, discard the media content.

[0194] In a possible implementation, the processing module 702 is also used to record a media content when generating the media content; obtain a third hard binding based on the media content; obtain a fourth hard binding based on a second media content, the second media content being generated before the media content; and obtain the first hard binding based on the third hard binding and the fourth hard binding.

[0195] In a possible implementation, the processing module 702 is further configured to, when generating multiple media contents, record the multiple media contents; obtain multiple fifth hard bindings respectively according to the multiple media contents; and obtain the first hard binding according to the multiple fifth hard bindings.

[0196] In a possible implementation manner, the preset condition includes that the second hard binding is less than a preset threshold.

[0197] In a possible implementation, the acquisition module 701 is specifically configured to acquire the at least one media content through artificial intelligence (AI) technology.

[0198] In one possible implementation, the media content is also used to generate non-fungible tokens NFT.

[0199] In one possible implementation, the verification module 704 is used to provide the NFT to the verification object to obtain the verification information input by the verification object when a verification request is received; and determine the identity of the verification object based on the verification information.

[0200] In one possible implementation, the NFT includes an image verification code; and the verification information is generated based on an operation performed by the verification object on the image verification code.

[0201] The device of this embodiment can be used to perform Figure 3 The technical solution of the method embodiment shown has similar implementation principles and technical effects, which will not be repeated here.

[0202] In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The processor can be a general-purpose processor, a digital signal processor (digital signal processor, DSP), an application-specific integrated circuit (application-specific integrated circuit, ASIC), a field programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware coding processor to be executed, or the hardware and software modules in the coding processor are combined to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0203] The memory mentioned in the above embodiments may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0204] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0205] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0206] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0207] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0208] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0209] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0210] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: include: Obtain at least one media content; Performing hard binding processing according to the at least one media content to obtain a first hard binding; A block is generated according to the first hard binding, and the block is recorded in the blockchain.

2. The method according to claim 1, characterized in that The block includes the first hard binding.

3. The method according to claim 1 or 2, characterized in that: The block is generated when a second hard binding satisfies a preset condition, and the second hard binding is obtained at least according to the first hard binding.

4. The method according to claim 3, characterized in that: The second hard binding is also obtained based on at least one of the transaction data and the hard binding of the previous block.

5. The method according to claim 3 or 4, characterized in that: Also includes: When a media content is generated and the second hard binding satisfies the preset condition, recording the media content; or, When a piece of media content is generated and the second hard binding does not satisfy the preset condition, the piece of media content is discarded.

6. The method according to any one of claims 1 to 4, characterized in that Also includes: When a media content is generated, recording the media content; The performing hard binding processing according to the at least one media content to obtain a first hard binding includes: Acquire a third hard binding according to the one media content; Acquire a fourth hard binding according to second media content, the second media content being generated before the one media content; The first hard binding is obtained according to the third hard binding and the fourth hard binding.

7. The method according to any one of claims 1 to 4, characterized in that Also includes: When a plurality of media contents are generated, recording the plurality of media contents; The performing hard binding processing according to the at least one media content to obtain a first hard binding includes: Acquire a plurality of fifth hard bindings respectively according to the plurality of media contents; The first hard binding is obtained according to the plurality of fifth hard bindings.

8. The method according to claim 3 or 5, characterized in that: The preset condition includes that the second hard binding is less than a preset threshold.

9. The method according to any one of claims 1 to 8, characterized in that The obtaining of at least one media content comprises: The at least one media content is acquired through artificial intelligence (AI) technology.

10. The method according to any one of claims 1 to 9, characterized in that The media content is also used to generate non-fungible tokens NFT.

11. The method according to claim 10, characterized in that Also includes: When receiving a verification request, providing the NFT to the verification object to obtain verification information input by the verification object; The identity of the verification object is determined according to the verification information.

12. The method according to claim 11, characterized in that The NFT includes an image verification code; the verification information is generated based on the operation of the verification object on the image verification code.

13. A data processing device, characterized in that: include: An acquisition module, used to acquire at least one media content; A processing module, configured to perform hard binding processing according to the at least one media content to obtain a first hard binding; A generation module is used to generate a block according to the first hard binding, and the block is recorded in the blockchain system.

14. The device according to claim 13, characterized in that The block includes the first hard binding.

15. The device according to claim 13 or 14, characterized in that The block is generated when a second hard binding satisfies a preset condition, and the second hard binding is obtained at least according to the first hard binding.

16. The device according to claim 15, characterized in that The preset condition includes that the second hard binding is less than a preset threshold.

17. The device according to any one of claims 13 to 16, characterized in that The acquisition module is specifically used to acquire the at least one media content through artificial intelligence AI technology.

18. A network node, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 12.

19. A computer-readable storage medium, characterized in that: The invention comprises a computer program, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 12.

20. A computer program product, characterized in that The computer program product comprises a computer program code, and when the computer program code is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 12.

21. A blockchain system, characterized in that: include: A plurality of network nodes, wherein a first network node among the plurality of network nodes comprises: a memory and one or more processors; the memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-12.

22. A blockchain system, characterized in that: include: A first network node and a second network node, wherein the first network node generates at least one media content, the first network node sends the at least one media content to the second network node, and the second network node is configured to execute the method according to any one of claims 1 to 12.

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