Blockchain-based semantic communication methods and systems, storage media, and devices

Abstract

This disclosure relates to a blockchain-based semantic communication method, system, storage medium, and device, belonging to the field of communication technology. The method includes: an edge device performing fuzzy processing on original semantic data based on a preset circuit compiler to obtain a data transformation proof of the original semantic data and the transformed semantic data, and calculating a public reference string based on the circuit compiler; the edge device determining a zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploading the zero-knowledge verification result, the verification key in the reference string, and the transformed semantic data to the blockchain; the blockchain verifying the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, obtaining a data verification result; and a virtual service provider determining the authenticity of the transformed semantic data based on the data verification result. This disclosure can effectively defend against malicious attacks during semantic communication transmission.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and more specifically, to a blockchain-based semantic communication method, a blockchain-based semantic communication system, a computer-readable storage medium, and an electronic device. Background Technology

[0002] In existing semantic communication methods, virtual service providers cannot verify the authenticity of semantic data sent by edge devices, resulting in low authenticity of the data received by virtual service providers.

[0003] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a blockchain-based semantic communication method, a blockchain-based semantic communication system, a computer-readable storage medium, and an electronic device, thereby overcoming, to at least some extent, the problem of low authenticity of data received by virtual service providers due to limitations and defects in related technologies.

[0005] According to one aspect of this disclosure, a blockchain-based semantic communication method is provided, comprising:

[0006] The edge device performs fuzzing processing on the original semantic data based on a preset circuit compiler to obtain the data transformation proof of the original semantic data and the transformed semantic data, and calculates a common reference string based on the circuit compiler;

[0007] The edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain.

[0008] The blockchain verifies the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, and obtains the data verification result.

[0009] The virtual service provider determines the authenticity of the converted semantic data based on the data verification results.

[0010] In one exemplary embodiment of this disclosure, the original semantic data is fuzzed based on a preset circuit compiler to obtain a data transformation proof of the original semantic data and the transformed semantic data, including:

[0011] The original image data is acquired and cropped to obtain the original semantic data in the original image data.

[0012] The original semantic data is subjected to bilinear interpolation based on a preset circuit compiler to obtain the transformed semantic data. The relationship between the original semantic data and the transformed semantic data is calculated based on the circuit compiler to obtain the data transformation proof.

[0013] In one exemplary embodiment of this disclosure, cropping the original image data to obtain the original semantic data from the original image data includes:

[0014] Based on a preset semantic segmentation model, key components in the original image data are cropped, and the original semantic data in the original image data is obtained based on the cropped key components.

[0015] In one exemplary embodiment of this disclosure, calculating a common reference string based on the circuit compiler includes:

[0016] Obtain preset security parameters and input the preset security parameters and the circuit compiler into the key generation model to obtain the public reference string; wherein, the reference string includes the evaluation authorization key and the verification key.

[0017] In one exemplary embodiment of this disclosure, determining the zero-knowledge verification result based on the evaluation authorization key in the reference string includes:

[0018] The evaluation authorization key, data transformation proof, and transformed semantic data from the reference string are input into the data proof model to obtain the zero-knowledge verification result.

[0019] In one exemplary embodiment of this disclosure, the authenticity of the transformation process of the transformed semantic data is proven based on the zero-knowledge verification result and the verification key to obtain the data verification result, including:

[0020] The zero-knowledge verification result and the verification key are input into the data verification model, and the authenticity of the transformation process of the transformed semantic data is proved based on the output of the data verification model to obtain the data verification result.

[0021] Specifically, if the output of the data verification model is 1, the data verification result indicates that the authenticity verification of the transformation process of the transformed semantic data is successful; if the output of the data verification model is 0, the data verification result indicates that the authenticity verification of the transformation process of the transformed semantic data is unsuccessful.

[0022] In one exemplary embodiment of this disclosure, determining the data authenticity of the converted semantic data based on the data verification result includes:

[0023] If the data verification result indicates that the authenticity of the transformation process of the transformed semantic data is successfully verified, then the authenticity of the transformed semantic data is determined to be true.

[0024] If the data verification result indicates that the authenticity verification of the transformation process of the transformed semantic data fails, then the authenticity of the transformed semantic data is determined to be false.

[0025] In one exemplary embodiment of this disclosure, the blockchain-based semantic communication method further includes:

[0026] When the authenticity of the transformed semantic data is determined to be true, the virtual service provider obtains the transformed semantic data and data transformation proof provided by the edge device from the blockchain, and obtains the original semantic data based on the transformed semantic data and data transformation proof.

[0027] If the authenticity of the converted semantic data is determined to be false, the virtual service provider will reject the converted semantic data and data conversion proof provided by the edge device.

[0028] In one exemplary embodiment of this disclosure, the blockchain-based semantic communication method further includes:

[0029] The virtual service provider digitizes the original semantic data based on a preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device.

[0030] In one exemplary embodiment of this disclosure, the original semantic data is digitized based on a preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device, including:

[0031] Call the artificial intelligence-generated content model in the preset metaverse virtual space;

[0032] Based on the AI-generated content model, the landmark semantic data included in the original semantic data is extracted, and the virtual environment scene is rendered according to the landmark semantic data.

[0033] According to one aspect of this disclosure, a blockchain-based semantic communication system is provided, comprising:

[0034] An edge device is used to perform fuzzing processing on the original semantic data based on a preset circuit compiler to obtain the data transformation proof of the original semantic data and the transformed semantic data, and to calculate a common reference string based on the circuit compiler;

[0035] The edge device is also configured to determine the zero-knowledge verification result based on the evaluation authorization key in the reference string, and upload the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain;

[0036] The blockchain is communicatively connected to the edge device and is used to prove the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, so as to obtain the data verification result.

[0037] A virtual service provider, communicating with the blockchain, is used to determine the authenticity of the transformed semantic data based on the data verification results.

[0038] According to one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the blockchain-based semantic communication method described in any one of the preceding claims.

[0039] According to one aspect of this disclosure, an electronic device is provided, comprising:

[0040] Processor; and

[0041] Memory for storing the executable instructions of the processor;

[0042] The processor is configured to execute any of the above-described blockchain-based semantic communication methods by executing the executable instructions.

[0043] This disclosure provides a blockchain-based semantic communication method. Firstly, an edge device performs fuzzy processing on the original semantic data using a preset circuit compiler to obtain a data transformation proof of the original semantic data and the transformed semantic data. A reference string is then calculated based on the circuit compiler. Next, the edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string and uploads the zero-knowledge verification result, the verification key in the reference string, and the transformed semantic data to the blockchain. Then, the blockchain verifies the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, obtaining a data verification result. Finally, a virtual service provider determines the transformed semantic data based on the data verification result. Regarding the authenticity of the data, since the authenticity of the converted semantic data can be determined based on the data verification results, this solves the problem in existing technologies where virtual service providers cannot verify the authenticity of semantic data sent by edge devices, resulting in low authenticity of the data received by virtual service providers. This improves the authenticity of the converted semantic data received by virtual service providers. On the other hand, since the authenticity of the conversion process of the converted semantic data can be proven by blockchain based on zero-knowledge verification results and verification keys, data verification results can be obtained. Finally, the authenticity of the converted semantic data can be determined by the virtual service provider based on the data verification results, thus effectively defending against malicious attacks during semantic communication transmission.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0046] Figure 1 The flowchart illustrates a blockchain-based semantic communication method according to an example embodiment of the present disclosure.

[0047] Figure 2 The illustration shows an example diagram of a blockchain-based semantic communication system according to an example embodiment of the present disclosure.

[0048] Figure 3 The illustration shows an example scenario of an application scenario of a blockchain-based semantic communication method according to an example embodiment of the present disclosure.

[0049] Figure 4 The diagram schematically illustrates an example structure of a convolutional neural network according to an exemplary embodiment of the present disclosure.

[0050] Figure 5 The diagram schematically illustrates a structural example of a preset semantic segmentation model according to an exemplary embodiment of the present disclosure.

[0051] Figure 6 A flowchart illustrating a semantic communication method based on multi-sided interaction according to an example embodiment of the present disclosure is shown schematically.

[0052] Figure 7 The diagram schematically illustrates a block diagram of a blockchain-based semantic communication device according to an exemplary embodiment of the present disclosure.

[0053] Figure 8 An electronic device for implementing the above-described blockchain-based semantic communication method is illustrated according to an example embodiment of this disclosure. Detailed Implementation

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0055] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0056] In order to construct a sufficiently realistic digital mirror space of the physical world in the metaverse, a massive number of distributed edge devices are needed to transmit large amounts of data to virtual service providers in order to facilitate interaction between the virtual and the real.

[0057] Semantic communication is a technology that represents and transmits information semantically. It addresses the meaning expression and transmission of information at the semantic level, moving some or all of the understanding of information meaning to the sending end. It extracts semantic data from raw data and expresses the desired meaning. This source-side compression reduces information redundancy, transmission volume, and bandwidth requirements. Simultaneously, through this source-side compression, providers of metaverse virtual services can directly collect semantic information from edge devices, accelerating the generation of AIGC (Artificial Intelligence Generated Content) and the construction of virtual worlds. However, the use of semantic communication raises a security issue, as attackers might send malicious semantic data with similar semantic information but different expected content, causing erroneous AIGC outputs and disrupting users' access to metaverse services.

[0058] Based on this, the present disclosure first provides a blockchain-based semantic communication method. Specifically, refer to... Figure 1 As shown, this blockchain-based semantic communication method may include the following steps:

[0059] Step S110. The edge device performs fuzzing processing on the original semantic data based on a preset circuit compiler to obtain the data conversion proof of the original semantic data and the converted semantic data, and calculates the common reference string based on the circuit compiler;

[0060] Step S120. The edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain;

[0061] Step S130. The blockchain proves the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, and obtains the data verification result;

[0062] Step S140. The virtual service provider determines the authenticity of the converted semantic data based on the data verification results.

[0063] In the aforementioned blockchain-based semantic communication method, on the one hand, the edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain the data transformation proof of the original semantic data and the transformed semantic data, and calculates a reference string based on the circuit compiler; then, the edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the transformed semantic data to the blockchain; subsequently, the blockchain proves the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, obtaining the data verification result; finally, the virtual service provider determines the data of the transformed semantic data based on the data verification result. Authenticity is improved because the authenticity of the converted semantic data can be determined based on the data verification results. This addresses the problem in existing technologies where virtual service providers (VISPs) cannot verify the authenticity of semantic data sent by edge devices, resulting in lower authenticity of the data received by VISPs. Furthermore, the authenticity of the converted semantic data received by VISPs can be improved by using blockchain to prove the authenticity of the conversion process based on zero-knowledge verification results and verification keys. Finally, VISPs can determine the authenticity of the converted semantic data based on the data verification results, effectively defending against malicious attacks during semantic communication transmission.

[0064] The following will provide a detailed explanation and description of the blockchain-based semantic communication method described in the exemplary embodiments of this disclosure, in conjunction with the accompanying drawings.

[0065] First, the technical implementation principles of the exemplary embodiments of this disclosure will be explained and described. Specifically, the semantic communication method based on blockchain described in the exemplary embodiments of this disclosure designs a semantic defense scheme based on blockchain and zero-knowledge proof. It can use zero-knowledge proof to record the transformation of semantic data and use blockchain to track and verify the mutation of semantic data. At the same time, blockchain-assisted semantic communication can establish trust between distributed unknown edge devices and VSPs. However, considering that attackers may tamper with the data before the semantic data is uploaded to the blockchain, making it semantically similar (almost identical descriptors) but with different expected meanings; for example, a malicious edge device may modify the pixels of a "sunflower" image to make it semantically similar to a "snow mountain" image, but the two are visually significantly different. This will greatly affect the output of the AIGC model in virtual space, and it will be difficult for VSPs to detect adversarial semantic data and true semantic data. The differences between real semantic data; therefore, to solve the above problems, a semantic defense scheme based on blockchain and zero-knowledge proof is designed. Zero-knowledge proof can be used to record the transformation of semantic data, and blockchain can be used to track and verify mutations in semantic data. Simultaneously, the blockchain and zero-knowledge proof-based semantic defense scheme can also help VSP identify whether images transmitted in the metaverse have been maliciously tampered with by attackers. In practical applications, edge devices can also use bilinear interpolation algorithms to transform or process semantic data, and use zero-knowledge proof to record and verify the data transformation, instead of directly submitting the extracted semantic data to VSP. This improves the authenticity and security of the semantic data obtained by VSP, ensuring that the semantic data received by VSP is not malicious data sent by malicious edge devices.

[0066] Secondly, the blockchain-based semantic communication system involved in the exemplary embodiments of this disclosure will be explained and described.

[0067] For details, please refer to Figure 2As shown, the blockchain-based semantic communication system may include an edge device 210, a blockchain 220, and a virtual service provider 230. The edge device can communicate with the blockchain and the virtual service provider via wired or wireless communication, and the blockchain can also communicate with the virtual service provider via wired or wireless communication. In practical applications, the edge device described herein can be used to perform fuzzy processing on the original semantic data based on a preset circuit compiler, obtaining a data transformation proof of the original semantic data and the transformed semantic data, and calculating a public reference string based on the circuit compiler. The edge device can also be used to determine the zero-knowledge verification result based on the evaluation authorization key in the reference string, and upload the zero-knowledge verification result, the verification key in the reference string, and the transformed semantic data to the blockchain. The blockchain described herein can be used to prove the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, obtaining a data verification result. Furthermore, the virtual service provider described herein can be used to determine the data authenticity of the transformed semantic data based on the data verification result.

[0068] In one example embodiment, the edge device described herein may include a user equipment (UE), a wireless terminal device, a mobile terminal device, a device-to-device (D2D) terminal device, a vehicle-to-everything (V2X) terminal device, a machine-to-machine / machine-type communications (M2M / MTC) terminal device, an Internet of Things (IoT) terminal device, a subscriber unit, a subscriber station, a mobile station, a remote station, an access point (AP), a remote terminal, an access terminal, a user terminal, a user agent, or a user device, etc. Alternatively, the edge device may also include a mobile phone (or "cellular" phone), a computer with a mobile terminal device, a portable, pocket-sized, handheld, or computer-embedded mobile device, etc. Examples of edge devices include Personal Communication Service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Furthermore, edge devices can also include limited devices, such as those with low power consumption, limited storage capacity, or limited computing power. Examples include information sensing devices such as barcode scanners, Radio Frequency Identification (RFID), sensors, Global Positioning System (GPS), and laser scanners. Moreover, by way of example and not limitation, edge devices can also include wearable devices. Wearable devices, also known as wearable smart devices or smart wearable devices, are a general term for devices developed by applying wearable technology to intelligently implement everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories.Wearable devices are not merely hardware devices; they achieve powerful functionality through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those with comprehensive functions, large sizes, and the ability to perform complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses. They also include devices focused on a specific application function that require interaction with other devices like smartphones, such as smart bracelets, smart helmets, and smart jewelry for vital sign monitoring. All the terminals described above, if located in a vehicle (e.g., placed inside or installed within the vehicle), can be considered in-vehicle terminal devices, also known as on-board units (OBUs).

[0069] In one example embodiment, the blockchain-based semantic communication system described above can ensure the authenticity of semantic data transmitted from edge devices to the metaverse virtual service provider, thereby facilitating interaction between physical space and the metaverse virtual space. Simultaneously, by using blockchain and zero-knowledge proofs to distinguish the semantic similarity between adversarial semantic data and real semantic data, and by checking the authenticity of semantic data transformation, malicious attacks during semantic communication transmission can be effectively defended. Furthermore, referring to… Figure 3 As shown, this blockchain-based semantic communication system can achieve specific semantic communication in the following ways: First, semantic extraction and transmission of interactive data between physical space (e.g., edge device 210) and virtual space (virtual device provider 230, which can also be understood as metaverse virtual space); for example, the edge device extracts semantic data from the raw information and transmits it to the Virtualization Service Provider (VSP); then, the VSP can use the received semantic data to quickly digitize the physical domain; in practical applications, this solution can effectively handle high-frequency, massive data interaction between the VSP and distributed edge devices; Second, semantic conversion and verification; specifically, in practical applications, attackers may tamper with the data before the semantic data is uploaded to the blockchain, making it maintain semantic similarity (almost identical descriptors) but with different expected meanings; for example, refer to Figure 4As shown, the semantic data of an image can be mapped to a high-dimensional descriptor using the convolutional layer 410, pooling layer 420, and fully connected layer 430 in a CNN (Convolutional Neural Network). The resulting high-dimensional descriptor can then be used to distinguish semantic similarities. To address this technical problem, this exemplary embodiment designs a circuit generated by a zero-knowledge algorithm to record and verify the transformation performed on the semantic data. Edge devices use the extracted semantic data as input to generate a proof of the transformation and output the transformed semantic data. The proof and semantic data are then transmitted to a VSP for verification via a blockchain network. Simultaneously, applying blockchain and zero-knowledge proofs to record and verify semantic data transformations between the VSP and distributed edge devices effectively prevents data mutations.

[0070] In one example embodiment, the VSP can also receive semantic data (images) from edge devices deployed in different locations, and then render images, perceive landmarks, and create artistic content through AIGC (Artificial Intelligence Generated Content) services in the metaverse. For example, the VSP can use landmark semantic data from different angles to render 3D scenes, providing users with a seamless virtual world experience. The VSP can also use landmark information to generate digital artworks. Meanwhile, since AIGC services play an important role in the interaction between the physical world and the real world, promoting the use of data resources and enriching metaverse applications, accurate semantic data is crucial for subsequent AIGC services, and its quality may affect the content generated by AIGC. At the same time, the accuracy of the content generated by AIGC will determine the accuracy of the virtual world's replication of the physical world.

[0071] The following will combine Figures 2-4 right Figure 1 The blockchain-based semantic communication method shown will be further explained and illustrated. Specifically:

[0072] In step S110, the edge device performs fuzzing processing on the original semantic data based on a preset circuit compiler to obtain the data conversion proof of the original semantic data and the converted semantic data, and calculates a common reference string based on the circuit compiler.

[0073] In this example embodiment, firstly, the original data memory is fuzzed by the edge device based on a preset circuit compiler, thereby obtaining the data conversion proof of the original semantic data and the converted semantic data; wherein, the preset circuit compiler described here may be, for example, circuit C, that is, circuit compiler Circom; in the actual application process, the arithmetic expression mapping relationship f can be used to start the logic in the computation circuit C to obtain the data conversion proof of the original semantic data (private witness w) and the converted semantic data (public statement s).

[0074] In one example embodiment, the original semantic data is fuzzed based on a preset circuit compiler to obtain a data conversion proof and converted semantic data. This can be achieved as follows: First, original image data is acquired and cropped to obtain the original semantic data. Second, bilinear interpolation is performed on the original semantic data based on the preset circuit compiler to obtain the converted semantic data. The relationship between the original semantic data and the converted semantic data is calculated based on the circuit compiler to obtain the data conversion proof. The cropping of the original image data to obtain the original semantic data can be achieved as follows: key components in the original image data are cropped based on a preset semantic segmentation model, and the original semantic data is obtained based on the cropped key components.

[0075] The following section will further explain and illustrate the data transformation proof and the specific implementation process of the transformed semantic data. Specifically, firstly, the raw image data collected by the edge device based on the image acquisition component can be obtained. The raw image data described here has different image categories in different application scenarios. For example, in the autonomous driving scenario, the raw image data described here may include, but is not limited to, images of landmark buildings, road traffic conditions, and traffic lights. In the art scenario, the raw image data may include statues, artworks, etc. In practical applications, the corresponding raw image data can be collected according to the actual scenario; this example does not impose any special restrictions on this.

[0076] Secondly, key components in the original image data can be cropped based on a pre-defined semantic segmentation model, and the original semantic data in the original image data can be obtained based on the cropped key components. The semantic segmentation model described here can also be referred to as a semantic segmentation module; further, refer to... Figure 5As shown, the semantic segmentation model may include a backbone feature extraction network 510, a neck feature fusion network 520, and a head feature detection network 530. In practical applications, the specific semantic segmentation process can be implemented as follows: First, the backbone feature extraction network is used to downsample the original image data to obtain local features. Second, the neck feature fusion network is used to perform bidirectional fusion of the local features from deep to shallow and then from shallow to deep to obtain global features. Then, the head feature detection network is used to detect the category information and location information of the target object included in the global features to obtain the key components in the original image data. Finally, the key components are spliced ​​together to obtain the original semantic data in the original image data.

[0077] Furthermore, after obtaining the original semantic data, bilinear interpolation can be performed on the original semantic data based on the preset circuit compiler to obtain the transformed semantic data; that is, the circuit Circom can be controlled to implement the logic of bilinear interpolation through the circuit compiler in zero-knowledge verification, and then the original semantic data can be fuzzed based on the logic of bilinear interpolation to obtain the transformed semantic data; finally, the original semantic data and the transformed semantic data are input into the circuit compiler to obtain the relationship between the original semantic data and the transformed semantic data, thereby obtaining the corresponding data transformation proof; the specific implementation process can be shown in the following formula (1):

[0078] Extract(f)→(s,w); Formula (1)

[0079] Where f represents the specific mapping process, s represents the data transformation proof, and w represents the transformed semantic data.

[0080] It should be further explained here that by using bilinear interpolation to transform the original semantic data, the goal of blurring the extracted image (i.e., the original semantic data), increasing visual invariance, and distinguishing between adversarial and real semantic extraction can be achieved. However, since VSP has difficulty verifying whether the blurry semantic data comes from a real transformation, attackers may adjust some pixels to make the descriptor of the tampered adversarial image resemble the real image. Therefore, this disclosure introduces a circuit compiler from zero-knowledge proof. At the same time, based on this circuit compiler, the relationship between the input and output semantic data can be obtained, enabling verifiable computation of the transformed semantic data without revealing the content of the input semantic data, thereby avoiding the possibility of tampering.

[0081] Secondly, after obtaining the data conversion proof and the converted semantic data, it is also necessary to calculate the common reference string based on the circuit compiler. Specifically, this can be achieved as follows: obtain preset security parameters, and input the preset security parameters and the circuit compiler into the key generation model to obtain the common reference string; wherein, the reference string includes the evaluation authorization key and the verification key. Specifically, in practical applications, the security parameters described here can be set according to actual needs; this disclosure uses a security parameter of 1. λ Let's take an example to explain and illustrate. Meanwhile, the key generation model described here, namely the Setup function, can directly input security parameters and the circuit compiler into the Setup function to obtain a Common Reference String (CRS); this CRS includes the evaluation authorization key crs.ek and the verification key crs.vk; crs.ek can be used for proof, and crs.vk can be used for verification. In practical applications, the specific generation process of the Common Reference String can be shown in the following formula (2):

[0082]

[0083] In step S120, the edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain.

[0084] Specifically, the zero-knowledge verification result can be determined based on the evaluation authorization key in the reference string as follows: the evaluation authorization key, data transformation proof, and transformed semantic data in the reference string are input into the data proof model to obtain the zero-knowledge verification result. That is, in practical applications, the evaluation authorization key crs.ek corresponding to the transformed original semantic data, the transformed semantic data s, and the data transformation proof w can be input into the data proof model to generate a zero-knowledge proof π, and then the relationship can be reflected and verified through zero-knowledge proof; where the data proof model recorded here is the Prover function; at the same time, since the transformed semantic data s and the data transformation proof w represent public information and private information corresponding to the transformation relationship, a zero-knowledge proof π can be generated to reflect and verify this relationship; where the specific generation process of the zero-knowledge verification result can be shown in the following formula (3):

[0085]

[0086] It should be noted that the transformed semantic data s and the data transformation proof w recorded here can satisfy the following relationship: C(s, w) = 1.

[0087] In step S130, the blockchain verifies the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, and obtains the data verification result.

[0088] Specifically, the authenticity of the transformation process of the converted semantic data is proven based on the zero-knowledge verification result and the verification key to obtain the data verification result. This can be achieved as follows: the zero-knowledge verification result and the verification key are input into the data verification model, and the authenticity of the transformation process of the converted semantic data is proven based on the output of the data verification model to obtain the data verification result. If the output of the data verification model is 1, the data verification result indicates that the authenticity of the transformation process of the converted semantic data has been successfully verified; if the output of the data verification model is 0, the data verification result indicates that the authenticity of the transformation process of the converted semantic data has failed to be verified. That is, in practical applications, in the blockchain, the zero-knowledge verification result and the verification key can be directly input into the data verification model (Verifier function) to obtain the corresponding data verification result. The specific implementation process is as follows:

[0089] VSP utilizes smart contracts deployed on the blockchain to verify the authenticity of the conversion; simultaneously, the verification process is implemented within the blockchain, allowing edge devices and VSP to query the verification results and build trust in a decentralized manner; in practical applications, the verification key crs.vk, the declaration s, and the proof π generated in the previous step can be used as inputs to the smart contract to obtain the data verification result. The proof verification process (i.e., the specific generation process of the data verification result) can be implemented using the following formula (4):

[0090] Verify(CRS.vk,s,π)→{0,1}; Formula (4)

[0091] In step S140, the virtual service provider determines the authenticity of the converted semantic data based on the data verification results.

[0092] Specifically, the authenticity of the transformed semantic data can be determined based on the data verification results in the following way: if the data verification result indicates that the authenticity verification of the transformation process of the transformed semantic data is successful, then the authenticity of the transformed semantic data is determined to be true; if the data verification result indicates that the authenticity verification of the transformation process of the transformed semantic data fails, then the authenticity of the transformed semantic data is determined to be false.

[0093] In one example embodiment, the blockchain-based semantic communication method may further include: when the authenticity of the transformed semantic data is determined to be true, the Virtual Service Provider (VSP) obtains the transformed semantic data and data transformation proof provided by the edge device from the blockchain, and obtains the original semantic data based on the transformed semantic data and data transformation proof; when the authenticity of the transformed semantic data is determined to be false, the VSP rejects the transformed semantic data and data transformation proof provided by the edge device. That is, the VSP can determine whether to accept or reject the proof based on the output; wherein, when the output state of the data verification model is 1, the proof verification is successful, and the VSP accepts the transformed semantic data and data transformation proof provided by the edge device; otherwise, the VSP rejects the proof corresponding to the semantic data generated by the malicious edge device, and also rejects the transformed semantic data.

[0094] In one example embodiment, the blockchain-based semantic communication method further includes: the virtual service provider digitizing the original semantic data based on a preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device. This digitization of the original semantic data based on the preset metaverse virtual space to obtain the virtual environment scene corresponding to the original image data collected by the edge device can be achieved as follows: calling an AI-generated content model in the preset metaverse virtual space; extracting landmark semantic data included in the original semantic data based on the AI-generated content model; and rendering the virtual environment scene based on the landmark semantic data. That is, in practical applications, the metaverse virtual space in the VSP can digitize the original semantic data to obtain the corresponding virtual environment scene; for example, rendering images, perceiving landmarks, and creating artistic content through AIGC services, etc. In practical applications, corresponding virtual environment scenes can be generated according to actual needs, and this example does not impose any special limitations on this.

[0095] In one example embodiment, the blockchain-based semantic communication method described in this disclosure can also be directly applied to scenarios where a virtual road environment is constructed in a metaverse for autonomous driving testing. For example, edge devices at different locations on a physical road can collect photos of traffic conditions from multiple angles. Simultaneously, to reduce information redundancy, a semantic segmentation module can be used to crop key components of the image into semantic data. Furthermore, the semantic data can be transmitted to a Virtual Service Platform (VSP) to report traffic conditions on the road. Further, the VSP collects semantic data from multiple edge devices to train virtual road environments under different conditions. Based on this method, the VSP can use blockchain and zero-knowledge proofs to verify the authenticity of the semantic data, ensuring reliable data reception from edge devices for realistic road condition simulation. Moreover, the blockchain-based semantic communication method described in this disclosure can also be directly applied to fields such as industrial metaverses, utilizing blockchain smart contracts and zero-knowledge proofs to drive the establishment of a large-scale, highly reliable, and scalable metaverse network infrastructure, serving scenarios such as industrial digital twins and AIGC+office collaboration.

[0096] The following will combine Figure 6 The semantic communication method based on blockchain described in the exemplary embodiments of this disclosure will be further explained and illustrated. Specifically, refer to... Figure 6 As shown, this blockchain-based semantic communication method may include the following steps:

[0097] In step S610, the edge device acquires the original image data and crops the original image data to obtain the original semantic data in the original image data;

[0098] In step S620, the edge device performs bilinear interpolation on the original semantic data based on a preset circuit compiler to obtain the transformed semantic data.

[0099] Step S630: The edge device calculates the relationship between the original semantic data and the transformed semantic data based on the circuit compiler to obtain the data transformation proof, and calculates the common reference string based on the circuit compiler;

[0100] In step S640, the edge device determines the zero-knowledge verification result based on the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain;

[0101] Step S650: The blockchain verifies the authenticity of the transformation process of the transformed semantic data based on the zero-knowledge verification result and the verification key, and obtains the data verification result.

[0102] Step S660: The virtual service provider determines the authenticity of the converted semantic data based on the data verification results;

[0103] In step S670, when the virtual service provider determines that the data authenticity of the converted semantic data is true, the virtual service provider obtains the converted semantic data and data conversion proof provided by the edge device from the blockchain, and obtains the original semantic data based on the converted semantic data and data conversion proof.

[0104] In step S680, when the virtual service provider determines that the authenticity of the converted semantic data is false, the virtual service provider rejects the converted semantic data and data conversion proof provided by the edge device.

[0105] In step S690, the virtual service provider digitizes the original semantic data based on the preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device.

[0106] Thus, the blockchain-based semantic communication method described in the exemplary embodiments of this disclosure has been fully implemented. Based on the foregoing description, it can be understood that the blockchain-based semantic communication method provided in the exemplary embodiments of this disclosure, on the one hand, can ensure the authenticity of semantic data transmitted from edge devices to the Metaverse Virtual Service Provider (VSP), effectively defending against malicious attacks during semantic communication transmission; on the other hand, the integration of blockchain and semantic communication enables more efficient and secure exchange of semantic information between distributed edge devices and the Metaverse VSP; simultaneously, blockchain can establish trust and share semantic information between anonymous edge devices, preventing third-party attackers from manipulating and modifying the semantic information.

[0107] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0108] This disclosure also provides an example embodiment of a blockchain-based semantic communication device. Specifically, refer to... Figure 7 As shown, the blockchain-based semantic communication device may include a semantic data processing module 710, a zero-knowledge verification result determination module 720, an authenticity verification module 730, and a data authenticity determination module 740. Wherein:

[0109] The semantic data processing module 710 can be used to perform fuzzing processing on the original semantic data through an edge device based on a preset circuit compiler, to obtain the data conversion proof of the original semantic data and the converted semantic data, and to calculate a common reference string based on the circuit compiler.

[0110] The zero-knowledge verification result determination module 720 can be used to determine the zero-knowledge verification result through the edge device based on the evaluation authorization key in the reference string, and upload the zero-knowledge verification result, the verification key in the reference string, and the converted semantic data to the blockchain.

[0111] The authenticity verification module 730 can be used to verify the authenticity of the transformation process of the transformed semantic data through the blockchain based on the zero-knowledge verification result and the verification key, and obtain the data verification result.

[0112] The data authenticity determination module 740 can be used to determine the data authenticity of the transformed semantic data by a virtual service provider based on the data verification result.

[0113] In one exemplary embodiment of this disclosure, the original semantic data is fuzzed based on a preset circuit compiler to obtain a data conversion proof of the original semantic data and the converted semantic data. This includes: acquiring original image data and cropping the original image data to obtain the original semantic data in the original image data; performing bilinear interpolation on the original semantic data based on the preset circuit compiler to obtain the converted semantic data; and calculating the relationship between the original semantic data and the converted semantic data based on the circuit compiler to obtain the data conversion proof.

[0114] In one exemplary embodiment of this disclosure, cropping the original image data to obtain original semantic data in the original image data includes: cropping key components in the original image data based on a preset semantic segmentation model, and obtaining the original semantic data in the original image data based on the cropped key components.

[0115] In one exemplary embodiment of this disclosure, calculating a public reference string based on the circuit compiler includes: obtaining preset security parameters and inputting the preset security parameters and the circuit compiler into a key generation model to obtain the public reference string; wherein, the reference string includes an evaluation authorization key and a verification key.

[0116] In one exemplary embodiment of this disclosure, determining the zero-knowledge verification result based on the evaluation authorization key in the reference string includes: inputting the evaluation authorization key, data transformation proof, and transformed semantic data in the reference string into a data proof model to obtain the zero-knowledge verification result.

[0117] In one exemplary embodiment of this disclosure, proving the authenticity of the conversion process of the converted semantic data based on the zero-knowledge verification result and the verification key to obtain a data verification result includes: inputting the zero-knowledge verification result and the verification key into a data verification model, and proving the authenticity of the conversion process of the converted semantic data based on the output result of the data verification model to obtain a data verification result; wherein, if the output result of the data verification model is 1, the data verification result indicates that the authenticity of the conversion process of the converted semantic data has been successfully verified; if the output result of the data verification model is 0, the data verification result indicates that the authenticity of the conversion process of the converted semantic data has failed to be verified.

[0118] In one exemplary embodiment of this disclosure, determining the data authenticity of the converted semantic data based on the data verification result includes: if the data verification result indicates that the authenticity verification of the conversion process of the converted semantic data is successful, then the data authenticity of the converted semantic data is determined to be true; if the data verification result indicates that the authenticity verification of the conversion process of the converted semantic data fails, then the data authenticity of the converted semantic data is determined to be false.

[0119] In one exemplary embodiment of this disclosure, the blockchain-based semantic communication device further includes:

[0120] The semantic data receiving module can be used to obtain the converted semantic data and data conversion proof provided by the edge device from the blockchain through the virtual service provider when it is determined that the data authenticity of the converted semantic data is true, and obtain the original semantic data based on the converted semantic data and data conversion proof.

[0121] The semantic data rejection module can be used to reject the converted semantic data and data conversion proof provided by the edge device through the virtual service provider when it is determined that the data authenticity of the converted semantic data is false.

[0122] In one exemplary embodiment of this disclosure, the blockchain-based semantic communication device further includes:

[0123] The digitization processing module can be used to digitize the original semantic data through the virtual service provider based on a preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device.

[0124] In one exemplary embodiment of this disclosure, the original semantic data is digitized based on a preset metaverse virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device. This includes: calling an artificial intelligence-generated content model in the preset metaverse virtual space; extracting landmark semantic data included in the original semantic data based on the artificial intelligence-generated content model; and rendering the virtual environment scene according to the landmark semantic data.

[0125] The specific details of each module in the aforementioned blockchain-based semantic communication device have been described in detail in the corresponding blockchain-based semantic communication method, so they will not be repeated here.

[0126] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0127] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0128] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0129] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0130] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0131] like Figure 8As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including storage unit 820 and processing unit 810), and a display unit 840.

[0132] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 810 can perform actions such as... Figure 1 Step S110: The edge device performs fuzzing processing on the original semantic data based on a preset circuit compiler to obtain the data transformation proof of the original semantic data and the transformed semantic data, and calculates a public reference string based on the circuit compiler; Step S120: The edge device determines the zero-knowledge verification result according to the evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, the verification key in the reference string, and the transformed semantic data to the blockchain; Step S130: The blockchain proves the authenticity of the transformation process of the transformed semantic data according to the zero-knowledge verification result and the verification key, and obtains a data verification result; Step S140: The virtual service provider determines the data authenticity of the transformed semantic data according to the data verification result.

[0133] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0134] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0135] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0136] Electronic device 800 can also communicate with one or more external devices 900 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0137] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0138] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Exemplary Methods" section above.

[0139] The program product for implementing the above-described method according to embodiments of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0140] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0141] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0142] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0143] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0144] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0145] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention described herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not invented by this disclosure. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A blockchain-based semantic communication method, characterized in that, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device determines a zero-knowledge verification result according to an evaluation authorization key in the reference string, and uploads the zero-knowledge verification result, a verification key in the reference string, and the converted semantic data to a blockchain. The blockchain proves the authenticity of the conversion process of the converted semantic data according to the zero-knowledge verification result and the verification key to obtain a data verification result. A virtual service provider determines the data authenticity of the converted semantic data according to the data verification result. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. 2.The blockchain-based semantic communication method of claim 1, wherein, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. 3.The blockchain-based semantic communication method of claim 1, wherein, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. 4.The blockchain-based semantic communication method of claim 1, wherein, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. 5.The blockchain-based semantic communication method of claim 1, wherein, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. 6.The blockchain-based semantic communication method of claim 1, wherein, The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof of the original semantic data and converted semantic data, and calculates a public reference string based on the circuit compiler. The edge device performs fuzzy processing on the original semantic data based If the data verification result is that the authenticity verification of the conversion process of the converted semantic data succeeds, it is determined that the data authenticity of the converted semantic data is true; If the data verification result is that the authenticity verification of the conversion process of the converted semantic data fails, it is determined that the data authenticity of the converted semantic data is false. 7.The blockchain-based semantic communication method of claim 6, wherein, The blockchain-based semantic communication method further comprises: When it is determined that the data authenticity of the converted semantic data is true, the virtual service provider obtains the converted semantic data and the data conversion proof provided by the edge device from the blockchain, and obtains the original semantic data according to the converted semantic data and the data conversion proof; When it is determined that the data authenticity of the converted semantic data is false, the virtual service provider rejects the converted semantic data and the data conversion proof provided by the edge device. 8.The blockchain-based semantic communication method of claim 7, wherein, The blockchain-based semantic communication method further comprises: The virtual service provider digitally processes the original semantic data based on a preset meta-universe virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device. 9.The blockchain-based semantic communication method of claim 8, wherein, The virtual service provider digitally processes the original semantic data based on a preset meta-universe virtual space to obtain a virtual environment scene corresponding to the original image data collected by the edge device, comprising: Calling an artificial intelligence generated content model in the preset meta-universe virtual space; Based on the artificial intelligence generated content model, extracting landmark semantic data included in the original semantic data, and rendering the virtual environment scene according to the landmark semantic data. 10.A blockchain-based semantic communication system, characterized in that, Comprise: The edge device is configured to perform fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof and converted semantic data of the original semantic data, and calculate a public reference string based on the circuit compiler; The edge device is further configured to determine a zero-knowledge verification result based on an evaluation authorization key in the reference string, and upload the zero-knowledge verification result, a verification key in the reference string, and the converted semantic data to a blockchain; The blockchain, in communication connection with the edge device, is configured to prove the authenticity of the conversion process of the converted semantic data based on the zero-knowledge verification result and the verification key to obtain a data verification result; The virtual service provider, in communication connection with the blockchain, is configured to determine the data authenticity of the converted semantic data based on the data verification result; The blockchain, in communication connection with the edge device, is configured to prove the authenticity of the conversion process of the converted semantic data based on the zero-knowledge verification result and the verification key to obtain a data verification result; The blockchain, in communication connection with the edge device, is configured to prove the authenticity of the conversion process of the converted semantic data based on the zero-knowledge verification result and the verification key to obtain a data verification result; The edge device is configured to perform fuzzy processing on the original semantic data based on a preset circuit compiler to obtain data conversion proof and converted semantic data of the original semantic data, and calculate a public reference string based on the circuit compiler; Obtaining original image data and cropping the original image data to obtain original semantic data in the original image data; Based on the preset circuit compiler, the original semantic data is subjected to bilinear interpolation processing to obtain converted semantic data, and the relationship between the original semantic data and the converted semantic data is calculated based on the circuit compiler to obtain the data conversion proof.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the blockchain-based semantic communication method according to any one of claims 1-9.

12. An electronic device, comprising: Comprise: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the blockchain-based semantic communication method according to any one of claims 1-9 via executing the executable instructions.

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