Semantic communication method and device, electronic equipment and storage medium

Abstract

The invention provides a semantic communication method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of semantic communication. The method is applied to a sending end, and the specific implementation scheme is as follows: acquiring information source data; performing semantic coding on the information source data to obtain semantic coding features; performing encryption processing on the semantic coding feature by adopting a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature; and modulating the encrypted semantic coding feature, sending the modulated encrypted semantic coding feature to a receiving end, and recovering the information source data at the receiving end to obtain the recovered information source data. According to the method, the semantic features are extracted first and then encrypted, so that the data security is ensured, and the accuracy of semantic feature extraction is ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of semantic communication technology, and in particular to semantic communication methods, devices, electronic devices and storage media. Background Art

[0002] In the context of the rapid development of current communication technology, with the advent of the 6G era, semantic communication, as an innovative communication paradigm, is gradually becoming a research hotspot. The core advantage of semantic communication is that it is no longer limited to the precise transmission of data bits in traditional communication technology, but pays more attention to the actual meaning and use of information, that is, the "semantic similarity" of information. This meaning-centered transmission method can not only improve communication efficiency, but also maintain good performance in a changing communication environment, effectively alleviating the "cliff effect" problem in traditional communication systems.

[0003] Although semantic communication has shown great potential, it still faces many technical challenges in practical applications. Among them, how to ensure the accuracy and security of semantic information during transmission is one of the key issues. Summary of the invention

[0004] The present disclosure provides a semantic communication method, device, electronic device and storage medium.

[0005] According to one aspect of the present disclosure, a semantic communication method is provided, which is applied to a sending end, and the method includes:

[0006] Obtain source data;

[0007] Performing semantic coding on the source data to obtain semantic coding features;

[0008] The semantic coding feature is encrypted by using a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature;

[0009] The encrypted semantic coding feature is modulated, and the modulated encrypted semantic coding feature is sent to a receiving end, and the restored source data is obtained by restoring the source data at the receiving end.

[0010] According to another aspect of the present disclosure, a semantic communication method is provided, which is applied to a receiving end, and the method includes:

[0011] Receiving the modulated encrypted semantic coding feature sent by the sending end, the sending end obtains source data, performs semantic coding on the source data to obtain the semantic coding feature, encrypts the semantic coding feature using a chaotic pseudo-random sequence to obtain the encrypted semantic coding feature, and modulates the encrypted semantic coding feature;

[0012] The encrypted semantic coding features are demodulated, decrypted and decoded in sequence to obtain restored source data.

[0013] According to a third aspect of the present disclosure, a semantic communication method is provided, the method comprising:

[0014] The sending end obtains source data, performs semantic coding on the source data to obtain semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, modulates the encrypted semantic coding features, and sends the modulated encrypted semantic coding features to the receiving end;

[0015] The receiving end sequentially demodulates, decrypts and decodes the modulated encrypted semantic coding features to obtain restored source data.

[0016] According to a fourth aspect of the present disclosure, a semantic communication device is provided, which is applied to a sending end and includes:

[0017] An acquisition module, used to acquire source data;

[0018] An encoding module, used for performing semantic encoding on the source data to obtain semantic encoding features;

[0019] An encryption module, used for encrypting the semantic coding feature by using a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature;

[0020] The modulation module is used to modulate the encrypted semantic coding features and send the modulated encrypted semantic coding features to a receiving end, and recover the source data at the receiving end to obtain the recovered source data.

[0021] According to a fifth aspect of the present disclosure, a semantic communication device is provided, which is applied to a receiving end and includes:

[0022] A receiving module is used to receive the modulated encrypted semantic coding features sent by a sending end, wherein the sending end obtains source data, performs semantic coding on the source data to obtain semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, and modulates the encrypted semantic coding features;

[0023] The recovery module is used to sequentially demodulate, decrypt and decode the encrypted semantic coding features to obtain recovered source data.

[0024] According to a sixth aspect of the present disclosure, there is provided an electronic device, including:

[0025] at least one processor; and

[0026] a memory communicatively connected to the at least one processor; wherein,

[0027] 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 so that the at least one processor can execute any method in any of the above technical solutions.

[0028] According to a seventh 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 any one of the methods described in the above technical solutions.

[0029] According to an eighth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements any one of the methods described in the above technical solutions when executed by a processor.

[0030] The present disclosure provides a semantic communication method, device, equipment and storage medium. The present disclosure obtains source data and performs semantic coding to accurately extract key semantic features, effectively reduce data redundancy, and improve the efficiency and quality of data transmission. On this basis, a chaotic pseudo-random sequence is used to encrypt the semantic coding features, which makes full use of the high randomness, unpredictability and sensitivity to initial conditions of the chaotic sequence, greatly enhancing the security of the data during transmission. Even if the data is intercepted, it is difficult for unauthorized third parties to crack the semantic information therein. Subsequently, the encrypted semantic coding features are modulated and sent to the receiving end. This process further optimizes the transmission characteristics of the signal, enables it to better adapt to different channel environments, and improves the reliability and anti-interference ability of communication. Finally, the source data is restored at the receiving end through steps such as demodulation, decryption and decoding, ensuring the integrity and accuracy of the data. This innovative design of semantic extraction before encryption not only solves the problem that traditional encryption methods are difficult to balance security and accuracy in semantic communication, but also realizes efficient and secure semantic communication.

[0031] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0033] Figure 1 is a schematic diagram of the steps of the semantic communication method in an embodiment of the present disclosure;

[0034] Figure 2 It is a flowchart corresponding to the semantic communication method in one embodiment of the present disclosure.

[0035] Figure 3 A principle block diagram of a semantic communication device in an embodiment of the present disclosure;

[0036] Figure 4 It is a block diagram of an electronic device used to implement the semantic communication method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art 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, the description of well-known functions and structures is omitted in the following description.

[0038] In the prior art, semantic communication mainly adopts the following methods: In the first semantic communication information transmission method, the method first extracts and encodes the semantic information, then transmits it using the traditional bit-level error-free transmission method, and finally performs semantic decoding and information recovery at the receiving end. Although this method can achieve basic transmission of semantic information, since no encryption technology is used during the transmission process, there are problems with information privacy and security, and it is vulnerable to the risk of eavesdropping and interception.

[0039] The second method is to introduce encryption technology in the process of semantic information transmission, but it adopts the method of encrypting the data first and then extracting semantic features. The problem with this method is that traditional encryption operations will destroy the semantic relevance within the data, making it difficult to obtain correct semantic information in the semantic extraction link, thus affecting the accuracy of the entire semantic communication transmission process.

[0040] In summary, the existing technology has obvious deficiencies in the encrypted transmission of semantic communication and cannot strike a good balance between encrypted communication and semantic feature extraction. Therefore, there is an urgent need for a new semantic communication encrypted transmission scheme that can achieve secure encrypted transmission of semantic information while ensuring semantic feature extraction.

[0041] In order to solve the above technical problems, the present disclosure provides a new semantic communication method, see Figure 1 As shown, Figure 1 : is a schematic diagram of the steps of a semantic communication method in an embodiment of the present disclosure, the method is applied to a sending end, and the method includes:

[0042] Step S101, obtaining source data.

[0043] Specifically, "obtaining source data" is the starting step of the entire communication process, which involves collecting and extracting raw data for transmission from various information sources. Source data refers to the core information that needs to be transmitted during the communication process. This information can be in the form of text, images, audio, video, etc., and its form depends on its corresponding application scenario. For example, in the intelligent voice assistant scenario, the source data is the voice command issued by the user; in the telemedicine scenario, the source data may be the patient's physiological monitoring data or medical images.

[0044] The specific implementation process of the scheme includes: first, clarify the source type of the communication system and determine the form of data to be collected. For example, for an intelligent voice assistant system, the source is the user's voice input; for a satellite remote sensing system, the source is the image of the earth's surface taken by the satellite. Then, according to the source type, select the appropriate data acquisition device or sensor. For voice data, use a microphone for collection; for image data, use a camera; for physiological monitoring data, use a medical sensor, etc. After the source data is collected, since the collected raw data may contain noise or redundant information, preprocessing is required to improve the efficiency and accuracy of subsequent processing. For example, perform noise reduction on voice data, perform denoising and format conversion on image data, etc. Convert the preprocessed data into a format suitable for subsequent processing. For example, convert voice data into digital signals, convert image data into pixel matrices, etc. Finally, store the formatted source data in a temporary buffer or directly pass it to the subsequent processing module to prepare for steps such as semantic encoding and encryption. Through this process, the source data can be obtained efficiently and accurately, laying the foundation for subsequent steps such as semantic encoding, encryption and transmission.

[0045] Step S102: semantically encode the source data to obtain semantic encoding features.

[0046] Specifically, source data refers to the original information that needs to be transmitted during the communication process, which can be in various forms such as text, images, audio, video, etc., while semantic coding features are compact and information-rich vectors or symbolic forms extracted from these data, which can accurately reflect the core semantics of the data.

[0047] The specific implementation process of the scheme includes: after obtaining the source data, semantic analysis is performed on the source data to understand its core semantics. For text data, natural language processing (NLP) technology can be used to extract key semantic information of the text. For image data, convolutional neural network (CNN) can be used to identify key objects and scenes in the image. For audio data, speech recognition technology can be used to convert speech into text, and then semantic analysis is performed. After understanding the semantics of the source data, the next step is to extract semantic features. This step involves converting complex source data into compact feature vectors. For example, for text data, text can be converted into word vectors or sentence vectors; for image data, feature maps of the image can be extracted; for audio data, Mel spectrum features of the audio can be extracted. Since the extracted semantic features may contain redundant information, further optimization is required to improve transmission efficiency. This can be achieved through dimensionality reduction techniques (such as principal component analysis PCA) or feature selection algorithms to ensure that the extracted feature vectors are both compact and can accurately represent the semantics of the original data. Finally, the optimized feature vectors are output as semantic encoding features. These feature vectors can not only efficiently represent the core semantics of the source data, but also facilitate subsequent encryption and transmission processing. Through this process, semantic encoding not only improves the efficiency of data transmission, but also provides a basis for subsequent encryption and secure transmission. This method of semantic extraction followed by encoding ensures that the core semantics of the data are preserved during transmission, while reducing redundant information and improving the overall performance of the system.

[0048] Step S103, using a chaotic pseudo-random sequence to encrypt the semantic coding feature to obtain an encrypted semantic coding feature.

[0049] Specifically, a chaotic pseudo-random sequence is a sequence generated based on chaos theory, which has high randomness, unpredictability and sensitivity to initial conditions, making it an ideal encryption key. The semantic coding feature is a compact and information-rich vector extracted from the source data, which can accurately represent the core semantics of the data. By encrypting the semantic coding feature using a chaotic pseudo-random sequence, the semantic coding feature is effectively encrypted to generate an encrypted semantic coding feature. In this way, the core semantics of the original data is retained and the security during transmission is ensured. The high randomness and unpredictability of the chaotic sequence make the encrypted data difficult to crack. Even if the data is intercepted, it is difficult for unauthorized third parties to restore the original semantic information. This method not only improves the security of communication, but also maintains the efficient transmission characteristics of data.

[0050] Step S104, modulating the encrypted semantic coding features, and sending the modulated encrypted semantic coding features to a receiving end, and recovering the source data at the receiving end to obtain the recovered source data.

[0051] Specifically, the encrypted semantic coding feature refers to the data after semantic coding and chaotic encryption processing, which not only retains the core semantics of the original source data, but also ensures the security of the transmission process through encryption. Modulation processing is the process of converting the encrypted data into a signal form suitable for physical channel transmission, while source data recovery is the process of restoring the received signal to the original source data at the receiving end.

[0052] The specific implementation process of this scheme includes: selecting a suitable modulation method, such as QPSK (quadrature phase shift keying), 16-QAM (16-order quadrature amplitude modulation), etc., and mapping the encrypted bit stream into a signal form suitable for physical channel transmission. The modulated signal is sent to the receiving end through the physical channel. The receiving end first demodulates the received signal and restores the modulated signal to a bit stream. Then, the encrypted bit stream is decrypted using the same chaotic pseudo-random sequence as the sending end to restore the semantic coding features. Finally, the restored semantic coding features are decoded by the semantic decoder and restored to the original source data. Through this process, not only the security of data during transmission is ensured, but also the transmission efficiency and anti-interference ability of the signal are optimized through modulation and demodulation technology. Finally, the complete source data is restored through decryption and decoding at the receiving end, realizing efficient and secure semantic communication.

[0053] The present disclosure provides a semantic communication method, device, equipment and storage medium. The present disclosure obtains source data and performs semantic coding to accurately extract key semantic features, effectively reduce data redundancy, and improve the efficiency and quality of data transmission. On this basis, a chaotic pseudo-random sequence is used to encrypt the semantic coding features, which makes full use of the high randomness, unpredictability and sensitivity to initial conditions of the chaotic sequence, greatly enhancing the security of the data during transmission. Even if the data is intercepted, it is difficult for unauthorized third parties to crack the semantic information therein. Subsequently, the encrypted semantic coding features are modulated and sent to the receiving end. This process further optimizes the transmission characteristics of the signal, enables it to better adapt to different channel environments, and improves the reliability and anti-interference ability of communication. Finally, the source data is restored at the receiving end through steps such as demodulation, decryption and decoding, ensuring the integrity and accuracy of the data. This innovative design of semantic extraction before encryption not only solves the problem that traditional encryption methods are difficult to balance security and accuracy in semantic communication, but also realizes efficient and secure semantic communication.

[0054] In some optional embodiments, semantic encoding is performed on the source data to obtain semantic encoding features, including:

[0055] Extract key semantic information of source data to obtain key semantic information;

[0056] The key semantic information is converted into vector form to obtain semantic encoding features.

[0057] Specifically, source data refers to the original information that needs to be transmitted during the communication process, which can be in various forms such as text, images, audio, etc. Key semantic information refers to the most core and valuable content extracted from the source data, which can accurately reflect the semantic essence of the source data. Semantic encoding features convert these key semantic information into a compact vector form that is easy to process and transmit. It is usually a numerical vector that can efficiently represent the core semantics of the original data.

[0058] The specific implementation process of this solution includes: obtaining the original source data, such as a piece of text, an image, or a piece of audio. Using semantic analysis technology, such as natural language processing (NLP) model (for text data), convolutional neural network (CNN) (for image data) or speech recognition technology (for audio data), the source data is deeply analyzed to extract key semantic information. For example, for text data, the BERT model can be used to extract the core semantics of a sentence or paragraph; for image data, CNN can be used to identify the main objects and scenes in the image; for audio data, the audio can be converted into text first, and then semantic analysis can be performed.

[0059] After the key semantic information is extracted, it is converted into a vector form. For text data, word embedding technology (such as Word2Vec, GloVe) or sentence embedding technology (such as Sentence-BERT) can be used to convert text into vectors. For image data, the feature map extracted by CNN can be further processed into a compact vector. For audio data, the Mel spectrum features of the audio can be extracted and converted into vectors to obtain semantic encoding features, that is, compact vector forms. These vectors can efficiently represent key semantic information and facilitate subsequent encryption, transmission and decoding processing.

[0060] In this way, the extraction of key semantic information can accurately remove redundant data and retain only the most valuable core content, thereby greatly reducing the amount of data and improving the efficiency of data transmission. Secondly, converting these key semantic information into vector form not only facilitates subsequent encryption and transmission processing, but also ensures that the original semantic information can be accurately restored at the receiving end. This efficient data processing method enables the system to operate more stably in the face of complex communication environments, while reducing bandwidth requirements and transmission delays, providing strong support for efficient and secure semantic communication.

[0061] In some optional embodiments, a chaotic pseudo-random sequence is used to encrypt the semantic coding feature to obtain the encrypted semantic coding feature, including:

[0062] Get chaotic pseudo-random sequence;

[0063] The obfuscation algorithm is used to rearrange the semantic coding features to obtain the scrambled semantic coding features;

[0064] The chaotic pseudo-random sequence is XOR-ed with the scrambled semantic coding feature to obtain the encrypted semantic coding feature.

[0065] Specifically, in semantic communication systems, in order to ensure the security and efficiency of data transmission, an encryption scheme combining chaotic pseudo-random sequences and obfuscation algorithms is adopted. Chaotic pseudo-random sequences are sequences generated based on chaos theory, which are highly random, unpredictable, and sensitive to initial conditions, making them ideal encryption keys. Semantic encoding features are compact and information-rich vectors extracted from source data that can accurately represent the core semantics of the data. Obfuscation algorithms are algorithms used to rearrange data, which increases the complexity of encryption by destroying the spatial structure of the data.

[0066] The specific implementation process of the scheme includes: first inputting the parameters of the chaotic system, such as the initial value x0 and the control parameter r of the Logistic map, selecting a suitable chaotic system (such as the Logistic map), and generating a chaotic sequence of sufficient length by iterating the chaotic equation. For example, a chaotic sequence {x1, x2, …, xN} of length N is generated. The chaotic sequence is converted into binary form to generate a chaotic pseudo-random sequence K. After obtaining the chaotic pseudo-random sequence K, a confusion algorithm (such as a zigzag confusion algorithm) is selected to convert the semantic coding feature F into a one-dimensional array A, and then the array A is rearranged according to the zigzag path to generate a scrambled array A′. The scrambled array A′ is reconverted into an image format or a vector form F′ to obtain the scrambled semantic coding feature F′. Finally, the chaotic pseudo-random sequence is XORed with the scrambled semantic coding feature to obtain the encrypted semantic coding feature.

[0067] In this way, by obtaining a chaotic pseudo-random sequence and combining it with an obfuscation algorithm to rearrange the semantic coding features, and then performing an XOR operation on the chaotic pseudo-random sequence and the scrambled semantic coding features, this series of steps significantly enhances the security and anti-attack capability of the semantic communication system. Specifically, the high randomness and unpredictability of the chaotic pseudo-random sequence ensure the complexity of the encryption key, making the encrypted data difficult to crack. The use of the obfuscation algorithm further destroys the spatial structure of the data and increases the chaos of the data, making it difficult to restore the original information even if the attacker obtains part of the data. Finally, by combining the chaotic sequence with the scrambled features through an XOR operation, the generated encrypted semantic coding features not only retain the core semantics of the original data, but also greatly improve the confidentiality of the data during transmission. This encryption method optimizes transmission efficiency while ensuring data security.

[0068] In some optional embodiments, the semantic coding features are rearranged using an obfuscation algorithm to obtain scrambled semantic coding features, including:

[0069] The zigzag confusion algorithm is used to rearrange the semantic coding features to obtain the scrambled semantic coding features.

[0070] Specifically, semantic coding features are compact and information-rich vectors extracted from source data, which can accurately represent the core semantics of the data. The zigzag obfuscation algorithm is a specific obfuscation technique that rearranges data elements along a zigzag path, destroying the original spatial structure of the data, thereby increasing the confusion and encryption complexity of the data.

[0071] The specific implementation process of this scheme includes: inputting the semantic coding feature F, which is usually a vector or matrix data, representing the key semantic information extracted from the source data. Next, define the zigzag path. The zigzag path is a specific arrangement method in which the data elements are rearranged in a zigzag order. For example, for a two-dimensional matrix, the zigzag path starts from the upper left corner, moves to the right first, then moves down, and then moves to the left, and so on until all elements are rearranged. Convert the semantic coding feature F into a one-dimensional number A, and then rearrange the array A according to the zigzag path to generate the scrambled array A′.

[0072] In this way, the zigzag obfuscation algorithm is used to rearrange the semantic coding features, and the data elements are rearranged through a zigzag path, which destroys the original spatial structure of the data and makes the distribution of the data more complex and unpredictable. This scrambling method increases the confusion of the data. Even if the attacker obtains part of the data, it is difficult to restore the original semantic information, thereby effectively protecting the privacy and integrity of the data. In addition, the implementation of the zigzag obfuscation algorithm is relatively simple and computationally efficient, and will not significantly increase the computational burden of the system.

[0073] In some optional embodiments, performing an XOR operation on the chaotic pseudo-random sequence and the scrambled semantic coding feature to obtain an encrypted semantic coding feature includes:

[0074] Converting the scrambled semantic coding features into a bit stream format to obtain a scrambled bit stream;

[0075] Converting the chaotic pseudo-random sequence into a bit stream with the same length as the scrambled bit stream to obtain a pseudo-sequence bit stream;

[0076] The scrambled bit stream is XOR-ed with the pseudo-sequence bit stream to obtain an encrypted bit stream, and the encrypted bit stream is used as an encryption semantic coding feature.

[0077] Specifically, the scrambled semantic coding features refer to the semantic coding features that are rearranged by the zigzag obfuscation algorithm, which aims to increase the complexity of the data by destroying the spatial structure of the data. Chaotic pseudo-random sequence is a sequence generated based on chaos theory, which has a high degree of randomness and unpredictability and is suitable for encryption operations. Encrypted semantic coding features refer to the semantic coding features that have been encrypted and are used for secure transmission.

[0078] The specific implementation process of this scheme includes: converting the scrambled semantic coding feature F′ into a one-dimensional bit stream B. This step usually involves converting each feature value into binary form and concatenating all binary values ​​into a continuous bit stream. Next, convert the chaotic pseudo-random sequence K into a bit stream K′ with the same length as the scrambled bit stream B. This can be achieved by truncating or repeating the chaotic sequence to ensure that the length of K′ is the same as B. Perform a bit-by-bit XOR operation on the scrambled bit stream B and the pseudo-sequence bit stream K′ to generate an encrypted bit stream C, that is, the encrypted semantic coding feature. The specific operation is ci=bi⊕ki, where bi represents the i-th bit of B, ki is the i-th bit of K′, and ⊕ represents the XOR operation.

[0079] In this way, by converting the scrambled semantic coding features into a bit stream format and performing an XOR operation with a length-adjusted chaotic pseudo-random sequence to generate an encrypted bit stream as an encrypted semantic coding feature, this process significantly improves the security and confidentiality of data transmission. Specifically, the scrambled semantic coding features destroy the original spatial structure of the data, increase the chaos of the data, and make it difficult for attackers to directly extract useful information from the data. Secondly, the high randomness and unpredictability of the chaotic pseudo-random sequence further enhance the strength of encryption, making it difficult to crack the encrypted bit stream even if it is intercepted. Finally, the scrambled bit stream is combined with the chaotic sequence through an XOR operation, and the generated encrypted semantic coding features not only retain the core semantics of the original data, but also ensure a high degree of security during the transmission process. This method effectively improves the confidentiality and anti-attack capability of data without significantly increasing the complexity of the system.

[0080] The present disclosure provides a semantic communication method, which is applied to a receiving end and includes:

[0081] The modulated encrypted semantic coding features sent by the receiving end are received, the sending end obtains the source data, performs semantic coding on the source data to obtain the semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain the encrypted semantic coding features, and modulates the encrypted semantic coding features;

[0082] The encrypted semantic coding features are demodulated, decrypted and decoded in sequence to obtain the restored source data.

[0083] In this way, the sender first semantically encodes the source data, extracts key semantic features, reduces data redundancy and improves transmission efficiency; then uses a chaotic pseudo-random sequence to encrypt the semantic coding features, and enhances data security with the help of the high randomness and unpredictability of the chaotic sequence; finally, the encrypted semantic coding features are modulated to adapt to channel transmission. The receiver accurately restores the original source data through demodulation, decryption and decoding. By semantically encoding, chaotically encrypting and modulating the source data at the sender, and demodulating, decrypting and decoding at the receiver, efficient and secure semantic communication is achieved. This process not only ensures the integrity and accuracy of the data during transmission, but also effectively prevents the risk of data leakage and tampering.

[0084] In some optional embodiments, the encrypted semantic coding features are sequentially demodulated, decrypted, and decoded to obtain restored source data, including:

[0085] Converting the encrypted semantic features into digital bit streams to obtain demodulated encrypted semantic features;

[0086] Decrypting the demodulated encrypted semantic features to obtain decrypted semantic features;

[0087] The decrypted semantic features are decoded to obtain the restored source data.

[0088] Specifically, in semantic communication systems, in order to achieve secure, efficient data transmission and accurate recovery, the receiving end needs to perform a series of processing on the encrypted semantic features. Encrypted semantic features refer to the semantic coding features after chaotic encryption processing, which retains the core semantic information of the original data and ensures the security of the transmission process through encryption. The digital bit stream converts the encrypted semantic features into a binary form suitable for digital processing, which facilitates subsequent decryption and decoding operations. The demodulation process is to restore the received modulated signal to a digital bit stream, the decryption process is to use the same chaotic pseudo-random sequence as the sender to restore the semantic features before encryption, and the decoding is to restore the decrypted semantic features to the original source data form.

[0089] The specific implementation process of this scheme includes: demodulating the received modulated signal, using the inverse process of the same modulation method as the transmitter (such as QPSK, 16-QAM, etc.) to demodulate the modulated signal into a digital bit stream. Then, the demodulated bit stream is decrypted using the same chaotic pseudo-random sequence as the transmitter to obtain the decrypted semantic features. That is, through the bit-by-bit XOR operation bi′=ci′⊕ki, where ci′ is the i-th bit of the demodulated bit stream and ki is the i-th bit of the chaotic pseudo-random sequence, the decrypted semantic features are restored. Finally, the decrypted semantic features are restored to the original source data using the same semantic decoder as the transmitter.

[0090] In this way, first, the encrypted semantic features are converted into digital bit streams, which are convenient for processing and transmission in digital systems, ensuring the integrity and consistency of the data. Then, the same chaotic pseudo-random sequence as the sender is used for decryption processing. With the help of the high randomness and unpredictability of the chaotic sequence, the data is effectively prevented from being cracked by unauthorized third parties during transmission, thereby ensuring the security of the data. Finally, the decrypted semantic features are restored to the original source data through decoding processing, ensuring the accuracy and availability of the data after transmission. This series of steps not only improves the security of data transmission, but also ensures the integrity and accuracy of the data during transmission.

[0091] In some optional embodiments, the demodulated encrypted semantic features are decrypted to obtain the decrypted semantic features, including:

[0092] Get the inverse vector of the chaotic pseudo-random sequence;

[0093] Perform an XOR operation on the inverse vector and the demodulated encrypted semantic feature to obtain an XOR operation result;

[0094] The inverse obfuscation algorithm is used to process the XOR operation result to obtain the decrypted semantic features.

[0095] Specifically, in the semantic communication system, in order to achieve secure decryption and accurate recovery of encrypted semantic features, a decryption scheme combining the inverse vector of the chaotic pseudo-random sequence and the inverse obfuscation algorithm is adopted. The inverse vector of the chaotic pseudo-random sequence refers to the inverse sequence corresponding to the chaotic pseudo-random sequence used in the encryption process, which is used to restore the original data in the decryption process. The demodulated encrypted semantic features refer to the data that has been modulated, transmitted and demodulated, which is still in an encrypted state and needs further processing to restore the original semantic information. The inverse obfuscation algorithm is the inverse process of the obfuscation algorithm in the encryption process, which is used to restore the original structure and order of the data.

[0096] The specific implementation process of the scheme includes: inputting the initial parameters of the chaotic pseudo-random sequence (such as the initial value x0 and control parameter r of the Logistic map). Using the same chaotic system and initial parameters as the encryption end, a chaotic pseudo-random sequence is generated. Then, according to the operation in the encryption process, the corresponding inverse vector is generated. For example, if a simple XOR operation is used in the encryption process, the inverse vector is the same chaotic pseudo-random sequence. The demodulated encrypted semantic feature C′ and the inverse vector K are subjected to a bit-by-bit XOR operation to obtain the XOR operation result B′. The specific operation is bi′=ci′⊕ki′, where ci′ is the i-th bit of C′, ki′ is the i-th bit of K inverse, and ⊕ represents the XOR operation. Finally, the inverse process of the same obfuscation algorithm as the encryption end is used to process the XOR operation result B′. For example, if a zigzag obfuscation algorithm is used in the encryption process, a zigzag inverse obfuscation algorithm is used in the decryption process to restore the data to its original arrangement order.

[0097] In this way, by obtaining the inverse vector of the chaotic pseudo-random sequence, performing an XOR operation on it and the demodulated encrypted semantic features, and then using the inverse obfuscation algorithm to process the XOR operation results, this process achieves efficient decryption and accurate recovery of the encrypted semantic features. Specifically, the inverse vector of the chaotic pseudo-random sequence ensures the reversibility of the encryption process, so that the decryption operation can accurately restore the original data. The XOR operation utilizes the high randomness and unpredictability of the chaotic sequence, further enhancing the security of the data, making the encrypted data difficult to crack during transmission. The inverse obfuscation algorithm restores the original structure and order of the data, ensuring the consistency of the decrypted semantic features with the original semantic features. This series of steps not only improves the security of data transmission, but also ensures the integrity and accuracy of the data during transmission, providing an efficient and secure data recovery mechanism for the semantic communication system.

[0098] To facilitate an overall understanding of the technical solution of the present application, the presently disclosed embodiment describes the communication process of the solution as a whole. First, the transmitting end obtains source data, semantically encodes the source data to obtain semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, modulates the encrypted semantic coding features, and sends the modulated encrypted semantic coding features to the receiving end; the receiving end demodulates, decrypts, and decodes the modulated encrypted semantic coding features in turn to obtain recovered source data.

[0099] Specifically, see Figure 2 , Figure 2 It is a flowchart corresponding to the semantic communication method in one embodiment of the present disclosure. The flowchart shows the workflow of a semantic communication encryption system. The system first uses a semantic encoder to extract key semantic information from the original source data in the training phase, and then encodes this information into a format that is easy to transmit through a semantic decoder; in the variational phase, the system designs and constructs a chaotic circuit to generate a chaotic sequence, which is used as a key in the encryption process, and performs an XOR operation on the semantic coding features and the chaotic pseudo-random sequence to obtain the encrypted semantic coding features, thereby ensuring the security of the data during transmission; the receiving end performs the opposite operation, uses the same key for decryption, and restores the original source data through an inverse obfuscation algorithm and decoding processing. Finally, the decoded semantic information reaches the destination, allowing the receiver to obtain complete and accurate semantic information, realizing a semantic communication method that is both safe and efficient.

[0100] The entire flowchart describes a communication method that combines semantic coding and chaotic encryption. The semantic encoder and decoder in the training phase extract and restore semantic information, and the pseudo-random sequence generated by the chaotic system is used in the variational phase to encrypt and decrypt data, thereby achieving secure data transmission. This method not only ensures data security, but also improves transmission efficiency through semantic coding.

[0101] The following describes an apparatus embodiment of the present application, which can be used to execute the semantic communication method in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned embodiment of the semantic communication method of the present application.

[0102] The present disclosure also provides a semantic communication device 300, such as Figure 3 As shown, it is applied to the sending end and includes:

[0103] An acquisition module 301 is used to acquire information source data;

[0104] The encoding module 302 is used to perform semantic encoding on the source data to obtain semantic encoding features;

[0105] An encryption module 303 is configured to encrypt the semantic coding features by using a chaotic pseudo-random sequence to obtain encrypted semantic coding features;

[0106] A modulation module 304 is configured to modulate the encrypted semantic coding features and send the modulated encrypted semantic coding features to a receiving end, and restore the source data at the receiving end to obtain the restored source data.

[0107] In some alternative embodiments, the coding module 302 performs semantic coding on the source data to obtain semantic coding features, including:

[0108] Extract the key semantic information of the source data to obtain the key semantic information;

[0109] Convert the key semantic information into a vector form to obtain the semantic coding features.

[0110] In some alternative embodiments, the encryption module 303 encrypts the semantic coding features by using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, including:

[0111] Obtain a chaotic pseudo-random sequence;

[0112] Rearrange the semantic coding features by using a confusion algorithm to obtain the scrambled semantic coding features;

[0113] Perform an exclusive OR operation on the chaotic pseudo-random sequence and the scrambled semantic coding features to obtain the encrypted semantic coding features.

[0114] In some alternative embodiments, the encryption module 303 rearranges the semantic coding features by using a confusion algorithm to obtain the scrambled semantic coding features, including:

[0115] Rearrange the semantic coding features by using a Z-shaped confusion algorithm to obtain the scrambled semantic coding features.

[0116] In some alternative embodiments, the encryption module 303 performs an exclusive OR operation on the chaotic pseudo-random sequence and the scrambled semantic coding features to obtain the encrypted semantic coding features, including:

[0117] Convert the scrambled semantic coding features into a bitstream format to obtain the scrambled bitstream;

[0118] Convert the chaotic pseudo-random sequence into a bitstream with the same length as the scrambled bitstream to obtain a pseudo-sequence bitstream;

[0119] Perform an exclusive OR operation on the scrambled bitstream and the pseudo-sequence bitstream to obtain the encrypted bitstream, and use the encrypted bitstream as the encrypted semantic coding features.

[0120] The present disclosure also provides a semantic communication device, applied to a receiving end, comprising:

[0121] The receiving module is used to receive the modulated encrypted semantic coding features sent by the sending end, the sending end obtains the source data, performs semantic coding on the source data to obtain the semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain the encrypted semantic coding features, and modulates the encrypted semantic coding features;

[0122] The recovery module is used to demodulate, decrypt and decode the encrypted semantic coding features in sequence to obtain the recovered source data.

[0123] In some optional embodiments, the recovery module sequentially demodulates, decrypts, and decodes the encrypted semantic coding features to obtain recovered source data, including:

[0124] Converting the encrypted semantic features into digital bit streams to obtain demodulated encrypted semantic features;

[0125] Decrypting the demodulated encrypted semantic features to obtain decrypted semantic features;

[0126] The decrypted semantic features are decoded to obtain the restored source data.

[0127] In some optional embodiments, the recovery module decrypts the demodulated encrypted semantic features to obtain decrypted semantic features, including:

[0128] Get the inverse vector of the chaotic pseudo-random sequence;

[0129] Perform an XOR operation on the inverse vector and the demodulated encrypted semantic feature to obtain an XOR operation result;

[0130] The inverse obfuscation algorithm is used to process the XOR operation result to obtain the decrypted semantic features.

[0131] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0132] 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.

[0133] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, 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 required herein.

[0134] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0135] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0136] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 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 running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as semantic communication methods. For example, in some embodiments, the semantic communication method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the applet distribution described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the semantic communication method in any other appropriate manner (e.g., by means of firmware).

[0137] Various implementations of the systems and techniques described above 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), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including 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.

[0138] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0139] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 may 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.

[0140] 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).

[0141] The systems and techniques described herein may 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 with 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 may 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.

[0142] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0144] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A semantic communication method, applied to a sending end, the method comprising: Obtain source data; Performing semantic coding on the source data to obtain semantic coding features; The semantic coding feature is encrypted by using a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature; The encrypted semantic coding feature is modulated, and the modulated encrypted semantic coding feature is sent to a receiving end, and the restored source data is obtained by restoring the source data at the receiving end.

2. The method according to claim 1, wherein: The semantic encoding of the source data to obtain semantic encoding features includes: Extracting key semantic information of the source data to obtain key semantic information; The key semantic information is converted into a vector form to obtain the semantic encoding feature.

3. The method according to claim 1, wherein: The method of encrypting the semantic coding feature using a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature includes: Get chaotic pseudo-random sequence; Rearranging the semantic coding features by using an obfuscation algorithm to obtain scrambled semantic coding features; An XOR operation is performed on the chaotic pseudo-random sequence and the scrambled semantic coding feature to obtain an encrypted semantic coding feature.

4. The method according to claim 3, wherein: The method of rearranging the semantic coding features by using an obfuscation algorithm to obtain scrambled semantic coding features includes: The semantic coding features are rearranged using a zigzag confusion algorithm to obtain scrambled semantic coding features.

5. The method according to claim 3, wherein: The step of performing an XOR operation on the chaotic pseudo-random sequence and the scrambled semantic coding feature to obtain an encrypted semantic coding feature includes: Converting the scrambled semantic coding features into a bit stream format to obtain a scrambled bit stream; Converting the chaotic pseudo-random sequence into a bit stream having the same length as the scrambled bit stream to obtain a pseudo-sequence bit stream; An XOR operation is performed on the scrambled bit stream and the pseudo-sequence bit stream to obtain an encrypted bit stream, and the encrypted bit stream is used as an encryption semantic coding feature.

6. A semantic communication method, applied to a receiving end, the method comprising: Receiving the modulated encrypted semantic coding feature sent by the sending end, the sending end obtains source data, performs semantic coding on the source data to obtain the semantic coding feature, encrypts the semantic coding feature using a chaotic pseudo-random sequence to obtain the encrypted semantic coding feature, and modulates the encrypted semantic coding feature; The encrypted semantic coding features are demodulated, decrypted and decoded in sequence to obtain restored source data.

7. The method according to claim 6, wherein: The demodulating, decrypting and decoding the encrypted semantic coding features in sequence to obtain the restored source data includes: Converting the encrypted semantic features into a digital bit stream to obtain a demodulated encrypted semantic feature; Decrypting the demodulated encrypted semantic features to obtain decrypted semantic features; The decrypted semantic features are decoded to obtain restored source data.

8. The method according to claim 7, wherein: The step of decrypting the demodulated encrypted semantic features to obtain the decrypted semantic features includes: Obtaining an inverse vector of the chaotic pseudo-random sequence; Performing an XOR operation on the inverse vector and the demodulated encrypted semantic feature to obtain an XOR operation result; The XOR operation result is processed by using an inverse obfuscation algorithm to obtain a decrypted semantic feature.

9. A semantic communication method, the method comprising: The sending end obtains source data, performs semantic coding on the source data to obtain semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, modulates the encrypted semantic coding features, and sends the modulated encrypted semantic coding features to the receiving end; The receiving end sequentially demodulates, decrypts and decodes the modulated encrypted semantic coding features to obtain restored source data.

10. A semantic communication device, applied to a sending end, comprising: An acquisition module, used to acquire source data; An encoding module, used for performing semantic encoding on the source data to obtain semantic encoding features; An encryption module, used for encrypting the semantic coding feature by using a chaotic pseudo-random sequence to obtain an encrypted semantic coding feature; The modulation module is used to modulate the encrypted semantic coding features and send the modulated encrypted semantic coding features to a receiving end, and recover the source data at the receiving end to obtain the recovered source data.

11. The device according to claim 10, wherein: The encoding module performs semantic encoding on the source data to obtain semantic encoding features, including: Extracting key semantic information of the source data to obtain key semantic information; The key semantic information is converted into a vector form to obtain the semantic encoding feature.

12. The device according to claim 10, wherein: The encryption module uses a chaotic pseudo-random sequence to encrypt the semantic coding feature to obtain an encrypted semantic coding feature, including: Get chaotic pseudo-random sequence; Rearranging the semantic coding features by using an obfuscation algorithm to obtain scrambled semantic coding features; An XOR operation is performed on the chaotic pseudo-random sequence and the scrambled semantic coding feature to obtain an encrypted semantic coding feature.

13. The device according to claim 12, wherein: The encryption module uses an obfuscation algorithm to rearrange the semantic coding features to obtain scrambled semantic coding features, including: The semantic coding features are rearranged using a zigzag confusion algorithm to obtain scrambled semantic coding features.

14. The device according to claim 12, wherein: The encryption module performs an XOR operation on the chaotic pseudo-random sequence and the scrambled semantic coding feature to obtain an encrypted semantic coding feature, including: Converting the scrambled semantic coding features into a bit stream format to obtain a scrambled bit stream; Converting the chaotic pseudo-random sequence into a bit stream having the same length as the scrambled bit stream to obtain a pseudo-sequence bit stream; An XOR operation is performed on the scrambled bit stream and the pseudo-sequence bit stream to obtain an encrypted bit stream, and the encrypted bit stream is used as an encryption semantic coding feature.

15. A semantic communication device, applied to a receiving end, comprising: A receiving module is used to receive the modulated encrypted semantic coding features sent by a sending end, wherein the sending end obtains source data, performs semantic coding on the source data to obtain semantic coding features, encrypts the semantic coding features using a chaotic pseudo-random sequence to obtain encrypted semantic coding features, and modulates the encrypted semantic coding features; The recovery module is used to sequentially demodulate, decrypt and decode the encrypted semantic coding features to obtain recovered source data.

16. The device according to claim 15, wherein: The recovery module sequentially demodulates, decrypts and decodes the encrypted semantic coding features to obtain recovered source data, including: Converting the encrypted semantic features into a digital bit stream to obtain a demodulated encrypted semantic feature; Decrypting the demodulated encrypted semantic features to obtain decrypted semantic features; The decrypted semantic features are decoded to obtain restored source data.

17. The device according to claim 16, wherein: The recovery module decrypts the demodulated encrypted semantic features to obtain decrypted semantic features, including: Obtaining an inverse vector of the chaotic pseudo-random sequence; Performing an XOR operation on the inverse vector and the demodulated encrypted semantic feature to obtain an XOR operation result; The XOR operation result is processed by using an inverse obfuscation algorithm to obtain a decrypted semantic feature.

18. 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 9.

19. 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 1-9.

20. 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 9.

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