Semantic communication method and device, equipment and storage medium

Abstract

The invention provides a semantic communication method and device, equipment and a storage medium, and relates to the technical field of communication. According to the specific implementation scheme, a sending end carries out fusion and superposition processing on information source data and channel data on an application layer to obtain a multi-user semantic vector, and the channel data is obtained through a data feedback link connecting the application layer and a physical layer; the multi-user semantic vector is encrypted, the encrypted multi-user semantic vector is sent to a receiving end, and the receiving end is matched with the multi-user semantic vector; and the receiving end carries out comprehensive decoding on the encrypted multi-user semantic vector to obtain the information source data. The data transmission efficiency of the communication system is effectively improved while the load capacity of the communication system is remarkably reduced, and development and application of the semantic communication technology in the communication system are promoted.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a semantic communication method, apparatus, device, and storage medium. Background Art

[0002] With the development of the intelligent era, the demand for data transmission bandwidth from various intelligent applications has increased rapidly, and the capacity of existing communication systems is gradually approaching the Shannon limit. Semantic communication technology can significantly reduce data transmission bandwidth by filtering redundant information and extracting the meaning of effective information. However, when semantic communication technology is applied to existing communication systems, data transmission efficiency is reduced. Summary of the Invention

[0003] The present disclosure provides a semantic communication method, apparatus, device and storage medium.

[0004] According to a first aspect of the present disclosure, a method for semantic communication is provided, comprising:

[0005] The transmitting end performs fusion and superposition processing on the source data and the channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through the data feedback link connecting the application layer and the physical layer;

[0006] encrypting the multi-user semantic vector and sending the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector;

[0007] The receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0008] According to a second aspect of the present disclosure, there is provided a semantic communication device, comprising:

[0009] a fusion and overlay processing module, configured to perform fusion and overlay processing on the source data and the channel data at the application layer at the transmitting end to obtain a multi-user semantic vector, wherein the channel data is obtained via a data feedback link connecting the application layer and the physical layer;

[0010] an encryption transmission module, configured to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector;

[0011] The integrated decoding module is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

[0012] According to the third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the semantic communication method described in the first aspect of the present disclosure.

[0013] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the semantic communication method according to the first aspect of the present disclosure.

[0014] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the semantic communication method according to the first aspect of the present disclosure.

[0015] The technology disclosed in the present invention solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. While significantly reducing the load of the communication system, it effectively improves the data transmission efficiency of the communication system and promotes the development and application of semantic communication technology in the communication system.

[0016] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0018] Figure 1 This is a flowchart of a semantic communication method provided by the first embodiment of the present disclosure;

[0019] Figure 2 This is a flowchart of a semantic communication method provided by the second embodiment of the present disclosure;

[0020] Figure 3 This is a flowchart of a semantic communication method provided by the third embodiment of the present disclosure;

[0021] Figure 4 This is a flowchart of a semantic communication method provided by the fourth embodiment of the present disclosure;

[0022] Figure 5 is a structural block diagram of a semantic communication device provided by the fifth embodiment of the present disclosure;

[0023] Figure 6This is a schematic diagram of a process protocol interface provided by the fifth embodiment of the present disclosure;

[0024] Figure 7 It is a block diagram of an electronic device used to implement the semantic communication method of the sixth embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0026] Semantic communication technology is a new type of communication technology that integrates semantic information into communication engineering. By selectively extracting features from raw signals, compressing them, and transmitting them, it understands the sender's intended meaning before transmitting the data. This significantly reduces the bandwidth requirements of intelligent applications and reduces the load on communication systems.

[0027] Transmission efficiency is an important indicator for evaluating the performance of a communication system, including the speed and quality of data transmission.

[0028] When applying semantic communication technology to existing communication systems, the need for compatibility and coordination with existing communication protocols increases implementation difficulty and complexity. For example, the implementation location of semantic communication systems based on the joint source-channel coding framework within the existing protocol layer architecture is unclear. Furthermore, source coding and channel coding are primarily performed at the application layer and physical layer, respectively, leading to cross-layer protocol conflicts. Existing communication protocols cannot effectively support joint source-channel coding technology, resulting in reduced data transmission speeds and limiting the development and application of semantic communication technology in communication systems.

[0029] During data transmission, encryption technology must be used to protect the transmitted data to prevent theft or tampering. However, traditional encryption technology itself destroys the semantic characteristics of the source information. Since the encrypted data cannot be correctly understood and parsed, it cannot effectively achieve information transmission and the quality of data transmission is reduced.

[0030] The semantic communication method, apparatus, device, and storage medium provided by the present disclosure are described below with reference to the accompanying drawings.

[0031] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] First embodiment

[0033] Figure 1 This is a flow chart of a semantic communication method provided by an embodiment of the present disclosure. It should be noted that the semantic communication method of the embodiment of the present disclosure can be executed by an electronic device. Figure 1 As shown, the semantic communication method may include but is not limited to the following steps.

[0034] To address the above issues, the present disclosure provides a semantic communication method. In this embodiment, a multi-user communication system with one transmitter and multiple receivers is employed. A data feedback link connecting the application layer and the physical layer is added to the communication protocol architecture.

[0035] In step 101, the transmitting end performs fusion and superposition processing on the source data and the channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0036] The source data is located at the application layer and can be collected from a base station or any other device that can be used to collect source data, which is not limited here.

[0037] The channel data at the physical layer is fed back from the physical layer to the application layer through the data feedback link.

[0038] The multi-user semantic vector is a vector representation of the semantic information of multiple users. The meaning and importance of each vector can be intuitively explained. As user behavior data continues to increase, the multi-user semantic vector can be continuously updated and optimized to maintain its accuracy.

[0039] In this step, the channel data used for fusion and overlay processing is directly transmitted from the physical layer to the application layer via a data feedback link. This channel data feedback from the physical layer provides information about the channel state and the channel status between users, enabling better signal detection and error correction in the subsequent decryption and decoding steps. Furthermore, by fusion and overlaying the source and channel data at the application layer to obtain multi-user semantic vectors, and using the channel data to encode and modulate the source data, redundant information can be reduced, thereby reducing the amount of transmitted data and improving the quality and speed of data transmission.

[0040] In step 102, the multi-user semantic vector is encrypted and the encrypted multi-user semantic vector is sent to a receiving end, wherein the receiving end matches the multi-user semantic vector.

[0041] Before sending the multi-user semantic vectors, the transmitter performs semantic encryption on them at the application layer or intermediate layer. This ensures compatibility with different communication protocols and system platforms, facilitating data transmission and processing. Furthermore, compared to traditional encryption methods, semantic encryption at the application layer or intermediate layer allows for efficient encryption and decryption using modern computing resources, with minimal computational overhead, thereby improving data transmission efficiency. The encrypted multi-user semantic vectors are then sent to matching receivers. Each receiver classifies and filters the data before decoding the multi-user semantic vectors. During subsequent data processing and decryption, each receiver focuses only on the data relevant to it, enabling faster identification and parsing of each user's data, avoiding unnecessary full data parsing. This reduces computational complexity, resource consumption, and data processing time. Through this matching mechanism, if the multi-user semantic vector received by a receiver does not match the expected semantic vector, the receiver can quickly detect and correct the error, thereby improving data transmission accuracy.

[0042] In step 103, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0043] In this step, the encrypted multi-user semantic vector is decrypted through comprehensive decoding to restore the original semantic information, and then the restored semantic information is decoded so that the user at the receiving end can obtain the corresponding source data.

[0044] The semantic communication method provided by the embodiments of the present disclosure solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. While significantly reducing the load of the communication system, it effectively improves the data transmission efficiency of the communication system, promotes the development and application of semantic communication technology in communication systems, and promotes the development of the 6G era.

[0045] Second embodiment

[0046] In another embodiment proposed in the present disclosure, Figure 2 As shown, the semantic communication method may include but is not limited to the following steps.

[0047] In step 201, the source data and the channel data are fused to obtain a plurality of semantic vectors.

[0048] Source data and channel data are both important sources of information. Fusing them to generate semantic vectors can provide a comprehensive perspective, enabling the system to better understand the meaning and context of the data, improve the accuracy of data transmission, filter out irrelevant information, and increase transmission speed.

[0049] Optionally, in this step, the source data and channel data matching the semantic coding model can be jointly source-channel coded based on a preset semantic coding model to obtain multiple semantic vectors, wherein the semantic coding models are independent of each other in the semantic space.

[0050] Joint source-channel coding can make full use of the intrinsic connection between source data and channel data, reduce redundant information through collaborative coding, compress data more efficiently, and improve data robustness and adaptability by adaptively coding different source and channel characteristics.

[0051] Semantic encoding models that are independent of each other in the semantic space can more specifically capture and represent independent semantic information in source data and channel data, and generate richer and more accurate semantic vector representations.

[0052] Optionally, the joint source-channel coding includes but is not limited to at least one of the following: link coding, convolutional coding, and channel coding.

[0053] This embodiment performs joint source-channel coding on the source data and channel data at the application layer. Compared to the existing communication system, in which the coding mode of the source data and channel data is performed at the application layer and the physical layer respectively, this eliminates cross-layer protocol conflicts and is therefore not restricted by the type of joint source-channel coding. Depending on the actual scenario requirements, one or more of link coding, convolutional coding, and channel coding can be selected, but not limited to. For example, link coding is mainly used in low-speed communication systems, while convolutional coding is widely used in high-speed digital communication systems. Channel coding can resist channel noise by increasing redundancy, which is not limited here.

[0054] In step 202, a plurality of the semantic vectors are superimposed to obtain the multi-user semantic vector.

[0055] The plurality of semantic vectors are superimposed based on a modular division multiple access mode to generate the multi-user semantic vector.

[0056] By superimposing multiple semantic vectors using modular division multiple access (MDM), we can effectively integrate semantic information from different users, generating rich multi-user semantic vectors that provide a foundation for further multi-user analysis and processing. Furthermore, we can more efficiently utilize limited transmission resources, enabling the parallel transmission of semantic information from multiple users. This allows multi-user semantic vectors to reach the receiving end in a shorter time, thereby improving transmission efficiency.

[0057] In step 203, the multi-user semantic vector is encrypted, and the encrypted multi-user semantic vector is sent to a receiving end, wherein the receiving end matches the multi-user semantic vector.

[0058] Optionally, step 203 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0059] In step 204, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0060] Optionally, step 204 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0061] Third embodiment

[0062] In another embodiment proposed in the present disclosure, Figure 3 As shown, the semantic communication method may include but is not limited to the following steps.

[0063] In step 301, the transmitter performs fusion and superposition processing on the source data and the channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0064] Optionally, step 301 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0065] In step 302, the multi-user semantic vector is encrypted.

[0066] Encryption can be performed at the application layer and / or at the middle layer.

[0067] Encrypting multi-user semantic vectors ensures data security and privacy during transmission. Encryption prevents unauthorized access and theft, protecting the confidentiality and integrity of user data. It also provides authentication and tamper-proofing, ensuring secure and reliable data transmission.

[0068] Optionally, the encryption processing includes but is not limited to at least one of the following: semantic encryption, sequence encryption, and public key encryption.

[0069] In practical applications, encryption methods can be selected based on actual needs. For example, semantic encryption focuses on ensuring the semantic security of encrypted data. Even if the ciphertext is stolen, the attacker cannot obtain the true meaning of the plaintext from it. Sequence encryption encrypts information character by character in sequence, encrypting only one character at a time, combining the characteristics of symmetric encryption and block encryption. Public key encryption uses asymmetric keys for encryption, allowing anyone to encrypt with a public key, but only those holding the corresponding private key can decrypt. It is suitable for applications such as secure communications and digital signatures. The specific encryption processing method is not limited here.

[0070] In step 303, the encrypted multi-user semantic vector is sent to the receiving end through the physical layer.

[0071] The physical layer handles the transmission of underlying physical signals, including signal modulation, transmission, and reception. By sending the encrypted multi-user semantic vector to the receiving end through the physical layer, the transmission capabilities and technical means of the physical layer can be fully utilized, further improving the security and reliability of data transmission.

[0072] Optionally, the sending of the encrypted multi-user semantic vector to the physical layer uses a communication protocol including but not limited to at least one of the following:

[0073] HTTP protocol, XML protocol, RDF protocol, SPARQL protocol, and Dublin Core protocol.

[0074] Since the compatibility issues between semantic communication and existing communication systems have been eliminated in the previous steps, the communication protocols between protocol layer architectures can be flexibly selected according to actual needs. For example, the HTTP protocol supports the client / server mode, which is simple, fast, flexible, connectionless, and stateless; the XML protocol is concise and effective, easy to learn and use, and an open international standard; the RDF protocol is used to describe the data structure of "subject-predicate-object" triples, and is simple, flexible, and extensible; the SPARQL protocol: allows users to write queries to access RDF data; the Dublin Core protocol defines a simple metadata standard for describing digital resources, which is universal, extensible, and simple, and is not limited here.

[0075] In step 304, the receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

[0076] Optionally, step 304 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0077] Fourth embodiment

[0078] In another embodiment of the present disclosure, Figure 4 As shown, the semantic communication method may include but is not limited to the following steps.

[0079] In step 401, the transmitting end performs fusion and superposition processing on the source data and the channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0080] Optionally, step 401 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0081] In step 402, the multi-user semantic vector is encrypted and the encrypted multi-user semantic vector is sent to a receiving end, wherein the receiving end matches the multi-user semantic vector.

[0082] Optionally, step 402 may be implemented in any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0083] In step 403, the receiving end sends the encrypted multi-user semantic vector to the application layer for decryption processing.

[0084] Decryption is a critical step in protecting user data security and privacy, preventing unauthorized access and theft, and ensuring data security and reliability. Through application-layer security mechanisms and key management, encrypted multi-user semantic vectors can be effectively decrypted at the application layer to obtain the original semantic vectors.

[0085] Optionally, the encrypted multi-user semantic vector is sent to the application layer using a communication protocol including but not limited to at least one of the following:

[0086] HTTP protocol, XML protocol, RDF protocol, SPARQL protocol, Dublin Core protocol. The specific selected protocol must correspond to the protocol used in step 303.

[0087] In step 404, the decrypted multi-user semantic vector is decoded based on a preset semantic decoding model to obtain a decoded semantic vector.

[0088] The preset semantic decoding model matches the semantic encoding model preset in step 202. Decoding the decrypted multi-user semantic vector based on the preset semantic decoding model can accurately restore the decoded semantic vector, restore the encrypted and decrypted data to an understandable form, and provide accurate semantic information for subsequent application processing.

[0089] In step 405, joint source-channel decoding is performed on the decoded semantic vector to obtain the source data.

[0090] Performing joint source-channel decoding on the decoded semantic vectors can more accurately recover the original source data. Joint source-channel decoding utilizes the jointly coded information of the source and channel, better handling data redundancy and errors during the decoding process. This facilitates extracting more accurate source data from the decoded semantic vectors, improving the performance and reliability of multi-user communication systems.

[0091] Optionally, in this step, a semantic decoder corresponding to the joint source-channel coding is first determined by a preset matching method.

[0092] The preset matching method can select the most suitable decoder according to the characteristics of the source data and the encoding method, thereby improving the decoding accuracy and efficiency, which is conducive to better restoring the original source data, reducing decoding errors and data distortion, and improving the performance and reliability of multi-user communication systems.

[0093] Optionally, the matching method includes but is not limited to at least one of the following:

[0094] Rule-based matching, similarity-based matching, deep learning-based matching, context-based matching, intent-based matching, and pattern recognition-based matching.

[0095] In practical applications, the specific matching method is determined according to actual needs and is not limited here.

[0096] Finally, the decoded semantic vector is subjected to joint source-channel decoding by the semantic decoder.

[0097] A semantic decoder is a tool that restores encoded data to its original semantic information. Common semantic decoders include dynamic decoders, finite weighted state converters, and original dynamic decoders.

[0098] The semantic decoder selects the most appropriate algorithm based on different encoding methods and data characteristics to better understand the semantic meaning of the semantic vector, decode the semantic vector, and handle data redundancy and errors to improve decoding accuracy and efficiency. This decoding method helps to better restore the original source data and enhance the performance and reliability of multi-user communication systems.

[0099] The following combination Figure 5 The operation steps of the device provided in this embodiment are described in detail.

[0100] Figure 5This is a schematic diagram of the process protocol architecture provided by the embodiment of the present disclosure, such as Figure 5 As shown in FIG, in this protocol architecture, from bottom to top are the high layer (including the application layer, the middle layer, etc.), the link access control sublayer (LAC), the media access control sublayer (MAC) and the physical layer.

[0101] The application layer is primarily responsible for: acquiring source information for multiple users to transmit at the base station, pairing semantic models that do not interfere with each other in the semantic space, receiving channel information from the physical layer feedback interface, and performing joint source-channel coding. Semantic signals obtained from different semantic models are superimposed using Modular Division Multiple Access (MDMA). Furthermore, the superimposed multi-user semantic vectors are packetized and encrypted.

[0102] The link access control sublayer is mainly used to: process signaling.

[0103] The media access control sublayer is mainly used to: normally add CRC check bits to the subpacketized data stream and implement the retransmission mechanism.

[0104] The physical layer is mainly used for: feedback of channel signals to the application layer, transmission of underlying physical signals, traditional coding, modulation, layer mapping, etc.

[0105] First, the base station matches multiple different semantic encoding and decoding models for the information sources that need to be transmitted by multiple users at the application layer.

[0106] Then, the base station uses the matching semantic coding model at the application layer to perform joint source-channel coding on the image source to obtain a semantic vector.

[0107] Next, the base station superimposes the semantic vectors of multiple users through modular division multiple access technology to obtain a multi-user semantic vector.

[0108] Next, the base station passes the data from the application layer down to the physical layer. During the transmission process, the application layer and the intermediate layer perform encryption operations.

[0109] Then, the base station sends the encrypted semantic information to the corresponding other users at the physical layer.

[0110] Next, each receiving end first receives the signal and performs a decryption operation.

[0111] Finally, each receiver uses the paired semantic decoder to recover the source.

[0112] Fifth embodiment

[0113] In order to implement the above embodiment, the present disclosure also proposes a semantic communication device 600 . Figure 6 This is a structural block diagram of a semantic communication device 600 provided by the present disclosure. Figure 6As shown, the semantic communication device 600 may include:

[0114] The fusion and overlay processing module 601 is used to perform fusion and overlay processing on the source data and the channel data at the application layer at the transmitting end to obtain a multi-user semantic vector, wherein the channel data is obtained through a data feedback link connecting the application layer and the physical layer.

[0115] Optionally, the functions of the fusion and overlay processing module 601 may be implemented in a distributed manner using any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0116] The encryption transmission module 602 is configured to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector.

[0117] Optionally, the functions of the encryption transmission module 602 can be distributed and implemented using any one of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0118] The integrated decoding module 603 is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

[0119] Optionally, the functions of the integrated decoding module 603 may be implemented in a distributed manner using any of the implementation methods in the embodiments of the present disclosure. The embodiments provided in the present disclosure do not limit this and will not be described in detail.

[0120] The semantic communication device 600 provided by the embodiment of the present disclosure solves the technical problem of reduced data transmission efficiency when semantic communication technology is applied to existing communication systems. While significantly reducing the load of the communication system, it effectively improves the data transmission efficiency of the communication system, promotes the development and application of semantic communication technology in the communication system, and promotes the development of the 6G era.

[0121] Sixth embodiment

[0122] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0123] Figure 7A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0124] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0125] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0126] The computing unit 701 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as method XXX. For example, in some embodiments, method XXX can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of method XXX described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform method XXX in any other appropriate manner (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0132] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0133] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0134] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A semantic communication method, comprising: The transmitting end performs fusion and superposition processing on the source data and the channel data at the application layer to obtain a multi-user semantic vector, wherein the channel data is obtained through the data feedback link connecting the application layer and the physical layer; encrypting the multi-user semantic vector and sending the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector; The receiving end performs comprehensive decoding on the encrypted multi-user semantic vector to obtain the source data.

2. The method according to claim 1, wherein The fusing and superimposing processing of the source data and the channel data to obtain the multi-user semantic vector includes: fusing the source data and the channel data to obtain a plurality of semantic vectors; A plurality of the semantic vectors are superimposed to obtain the multi-user semantic vector.

3. The method according to claim 2, wherein: The fusing the source data and the channel data to obtain a plurality of semantic vectors includes: Based on a preset semantic coding model, the source data and channel data matching the semantic coding model are jointly source-channel coded to obtain a plurality of semantic vectors, wherein the semantic coding models are independent of each other in the semantic space.

4. The method according to claim 3, wherein: The joint source-channel coding includes but is not limited to at least one of the following: link coding, convolutional coding, and channel coding.

5. The method according to claim 2, wherein: The superimposing the plurality of semantic vectors to obtain the multi-user semantic vector includes: The plurality of semantic vectors are superimposed based on a modular division multiple access mode to generate the multi-user semantic vector.

6. The method according to claim 1, wherein The encryption process includes but is not limited to at least one of the following: semantic encryption, sequence encryption, and public key encryption.

7. The method according to claim 1, wherein The step of sending the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector, includes: The encrypted multi-user semantic vector is sent to the receiving end through the physical layer.

8. The method according to claim 1, wherein The receiving end comprehensively decodes the encrypted multi-user semantic vector to obtain the source data, including: The receiving end sends the encrypted multi-user semantic vector to the application layer for decryption processing; Based on a preset semantic decoding model, the decrypted multi-user semantic vector is decoded to obtain a decoded semantic vector; Perform joint source-channel decoding on the decoded semantic vector to obtain the source data.

9. The method according to claim 7 or 8, wherein The sending of the encrypted multi-user semantic vector to the physical layer, or the sending of the encrypted multi-user semantic vector to the application layer, employs a communication protocol including but not limited to at least one of the following: HTTP protocol, XML protocol, RDF protocol, SPARQL protocol, and Dublin Core protocol.

10. The method according to claim 8, wherein The performing joint source-channel decoding on the decoded semantic vector includes: Determining a semantic decoder corresponding to the joint source-channel coding by a preset matching method; The decoded semantic vector is subjected to joint source-channel decoding by the semantic decoder.

11. The method according to claim 10, wherein: The matching method includes but is not limited to at least one of the following: Rule-based matching, similarity-based matching, deep learning-based matching, context-based matching, intent-based matching, and pattern recognition-based matching.

12. A semantic communication device comprising: a fusion and overlay processing module, configured to perform fusion and overlay processing on the source data and the channel data at the application layer at the transmitting end to obtain a multi-user semantic vector, wherein the channel data is obtained via a data feedback link connecting the application layer and the physical layer; an encryption transmission module, configured to encrypt the multi-user semantic vector and send the encrypted multi-user semantic vector to a receiving end, wherein the receiving end matches the multi-user semantic vector; The integrated decoding module is used to perform integrated decoding on the encrypted multi-user semantic vector at the receiving end to obtain the source data.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-11.

15. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 11.

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