CN118264482A - File semantic information fusion one-text one-secret security encryption method and device - Google Patents

Abstract

The embodiment of the application provides a one-text-one-secret secure encryption method and device for fusing file semantic information, wherein the method comprises the following steps: receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm; the application can effectively improve the refinement degree and pertinence of encryption and simultaneously improve the encryption efficiency and security.

Description

File semantic information fusion one-text one-secret security encryption method and device

Technical Field

The application relates to the field of information security, in particular to a one-text one-secret security encryption method and device for fusing file semantic information.

Background

In the field of information transmission and storage, a secure encryption method is always an important means for protecting data privacy and confidentiality. However, the conventional encryption method is often capable of encrypting the whole file, but cannot perform fine processing according to semantic information of the file content, which may cause problems such as redundant encryption of information, low encryption efficiency, and the like.

The current encryption method is often based on traditional encryption algorithms, such as AES, RSA and the like, and the algorithms can provide higher security, but have certain limitation in fusing file semantic information. The traditional encryption method lacks deep understanding and analysis capability of file semantic information, and cannot conduct differentiation processing according to the characteristics of file content, so that the file cannot be encrypted in a refined mode.

In the existing data encryption field, in order to protect information from unauthorized access, various encryption algorithms are generally used to encrypt data. Currently, the common encryption methods include symmetric encryption, asymmetric encryption and the like. However, these conventional encryption methods generally encrypt the entire file with a uniform key, and once the key is compromised, the security of the entire file is compromised. In addition, the method cannot take the difference of file contents into consideration, and cannot dynamically adjust the encryption strategy according to different file contents, so that potential safety hazards exist.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a one-text one-secret security encryption method and device for fusing file semantic information, which can effectively improve the refinement degree and pertinence of encryption, simultaneously improve the encryption efficiency and security, and provide a brand new solution for information protection.

In order to solve at least one of the problems, the application provides the following technical scheme:

In a first aspect, the present application provides a secure encryption method for fusing file semantic information, including:

receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

Generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

Further, the semantic extraction is performed on the message data pre-marked as the text type through a preset natural language processing model to obtain a corresponding key word, a theme concept and an overall semantic, and multi-factor semantic fusion is performed according to the key word, the theme concept and the overall semantic to obtain a corresponding first file semantic, which comprises:

Performing text sequence word segmentation processing on the message data pre-marked as the text type, and converting text words obtained after the text sequence word segmentation processing into word vector models in a high-dimensional space;

And determining a key word, a theme concept and an overall semantic corresponding to the word vector model through a preset text classification model and a preset entity recognition model, and performing multi-factor semantic fusion according to the association relationship among the key word, the theme concept and the overall semantic to obtain a corresponding first file semantic.

Further, the semantic extraction is performed on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a corresponding appearance object, scene characteristics and action behaviors, and multi-factor semantic fusion is performed according to the appearance object, the scene characteristics and the action behaviors to obtain a corresponding second file semantic, which comprises:

dividing the video stream fragments of the message data which is pre-marked as the video stream type, extracting the image frames of each video fragment obtained by dividing the video stream fragments, inputting the image frames into a preset convolutional neural network for feature extraction, and obtaining corresponding appearance objects, scene features and action behaviors;

and carrying out multi-factor semantic fusion according to the semantic weights of the appearance object, the scene feature and the action behavior to obtain corresponding second file semantics.

Further, the generating the corresponding encryption key according to the first file semantic or the second file semantic, the device type of the internet of things device, and the MD5 file corresponding to the message data through a key derivation function includes:

Constructing a serial input sequence according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data;

And inputting the serial input sequence and the preset random salt value into a preset key derivation function to obtain a corresponding encryption key.

Further, the encrypting the message data by the encryption key and setting a self-adaptive encryption algorithm to obtain the message data after the message encryption, where the self-adaptive encryption algorithm includes a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics, and the method includes:

If the encryption key corresponds to the first file semantics, dividing the message data into byte sequences according to a preset rule, and carrying out confusion and scrambling on the byte sequences through the encryption key to obtain the message data after message encryption;

If the encryption key corresponds to the second file semantics, dividing the message data into continuous data stream blocks, and carrying out message encryption on each data stream block through a stream cipher algorithm and the encryption key to obtain the message data after message encryption.

Further, the receiving the message data sent by the internet of things device, and pre-marking the message data according to the message type of the message data, includes:

Receiving message data sent by Internet of things equipment, carrying out message analysis, and determining a corresponding message type, wherein the message type comprises a text type and a video stream type;

And generating a corresponding pre-mark according to the message type and adding the pre-mark into corresponding message data.

Further, after generating the corresponding encryption key according to the first file semantic or the second file semantic, the device type of the internet of things device, and the MD5 file corresponding to the message data through a key derivation function, the method includes:

Establishing an association binding relation between the encryption key and corresponding message data;

and storing the association binding relation into a preset key management database.

In a second aspect, the present application provides a secure one-text-to-one-secret encryption device for fusing semantic information of a file, including:

the message pre-marking module is used for receiving message data sent by the Internet of things equipment and pre-marking the message data according to the message type of the message data;

The semantic analysis fusion module is used for carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

The message encryption module is used for generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and an MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the one-text-one-secret secure encryption method of fusing file semantic information when the program is executed.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the one-text-to-one-secret secure encryption method of fusing file semantic information.

In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the one-text-to-one secure encryption method of fusing file semantic information.

According to the technical scheme, the application provides a one-text one-key secure encryption method and device for fusing file semantic information, which are used for pre-marking message data according to the message type of the message data by receiving the message data sent by internet of things equipment; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantic or the second file semantic, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the encryption refinement degree and pertinence can be effectively improved, the encryption efficiency and the encryption security are improved, and a brand new solution is provided for information protection.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a method for secure encryption of one text-to-one key that fuses semantic information of a file according to an embodiment of the present application;

FIG. 2 is a second flow chart of a first-text-to-first-text secure encryption method for fusing file semantic information according to an embodiment of the present application;

FIG. 3 is a third flow chart of a one-text-to-one-secret security encryption method for fusing file semantic information according to an embodiment of the present application;

FIG. 4 is a flowchart of a method for secure encryption of one text-to-one key that fuses semantic information of a document according to an embodiment of the present application;

FIG. 5 is a flowchart of a method for secure encryption of text-to-text messages in accordance with an embodiment of the present application;

FIG. 6 is a flowchart of a method for secure encryption of one text-to-one key that merges semantic information of a document according to an embodiment of the present application;

FIG. 7 is a flowchart of a method for secure encryption with one text-to-one key for fusing semantic information of a document according to an embodiment of the present application;

FIG. 8 is a block diagram of a one-text-to-one-secret secure encryption apparatus that fuses file semantic information in an embodiment of the present application;

fig. 9 is a schematic structural diagram of an electronic device in an embodiment of the application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.

Considering that in the prior art, various encryption algorithms are generally used to encrypt data in order to protect information from unauthorized access. Currently, the common encryption methods include symmetric encryption, asymmetric encryption and the like. However, these conventional encryption methods generally encrypt the entire file with a uniform key, and once the key is compromised, the security of the entire file is compromised. In addition, the method fails to consider the difference of file contents and can not realize the dynamic adjustment of encryption strategies according to different file contents, so that the problem of potential safety hazards exists; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

In order to effectively improve the refinement degree and pertinence of encryption and improve the encryption efficiency and the security, and provide a brand-new solution for information protection, the application provides an embodiment of a secure encryption method for fusing file semantic information, referring to fig. 1, the secure encryption method for fusing file semantic information specifically comprises the following contents:

Step S101: receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Optionally, in this embodiment, the data may be pre-marked according to access types of the internet of things device, such as a media device, an access control device, and a common sensor, according to message data reported by different device types, such as a device type, a message size range, and so on.

Specifically, in this step, the embodiment may first receive packet data from the internet of things device. The internet of things device may be a variety of sensors, controllers, or other types of devices that transmit data to a central processing system over a network. Once the present embodiment receives these message data, the present embodiment will pre-label according to its message type for subsequent processing and analysis.

Pre-marking refers to the preliminary classification or marking of data for subsequent processing. In this step, the present embodiment needs to identify the type of the message data, such as sensor data, control instructions, status information, and the like. Through pre-marking the message data, the embodiment can better understand the meaning and the purpose of the data and provide guidance for subsequent data processing and analysis.

In the pre-marking process, the present embodiment may utilize various techniques and methods to identify the type of message data. For example, regular expressions may be used to match messages of a particular format, or machine learning models may be used to automatically identify different types of messages.

By receiving and pre-marking the message data, the embodiment can better understand and utilize the data sent by the Internet of things equipment, and provide basic support for subsequent data analysis and application.

Step S102: carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

optionally, in this embodiment, semantic extraction and fusion may be performed for different types of packet data respectively. For the message data pre-marked as the text type, the embodiment uses a preset natural language processing model to perform semantic extraction so as to obtain the key words, the theme concepts and the whole semantics in the message data. The information is subjected to multi-factor semantic fusion, and all semantic elements in the message are comprehensively considered to obtain corresponding first file semantics.

Specifically, in the processing of text type message data, the present embodiment will use a natural language processing model, such as BERT, to perform semantic analysis on the text. The models have strong capability in the aspect of natural language understanding after pre-training, and key words, theme concepts and whole semantics in the text can be extracted. By comprehensively considering and fusing the semantic elements, the embodiment can obtain the comprehensive semantic expression of the text message.

Meanwhile, for the message data pre-marked as the video stream type, the embodiment uses a preset convolutional neural network to perform semantic extraction so as to identify the objects, scene features and action behaviors appearing in the message. And comprehensively considering the semantic elements through multi-factor semantic fusion to obtain corresponding second file semantics.

Specifically, for the video stream type message data, the embodiment uses Convolutional Neural Network (CNN) to perform semantic extraction. CNN has wide application in the field of image processing, and can effectively identify objects, scene features and action behaviors in images. By extracting the semanteme of each element in the video stream and carrying out multi-factor semantic fusion, the embodiment can obtain the comprehensive semantic expression of the video stream message.

The goal of this step is to extract the semantic information from the message data and integrate and fuse the semantic information from different sources for subsequent data analysis and application. By carrying out semantic extraction and fusion on the text and video stream type message data, the embodiment can more fully understand the information contained in the message and provide more powerful support for subsequent data processing and decision making.

Step S103: generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

Optionally, in this embodiment, the corresponding encryption key is generated by using the key derivation function according to the semantic information (the first file semantic or the second file semantic) of the message data, the device type of the internet of things device, and the MD5 file of the message data. The key derivation function can generate a unique key according to the input parameters, so that the security and the uniqueness of the encryption process are ensured.

Next, the present embodiment encrypts the message data using an adaptive encryption algorithm. The adaptive encryption algorithm comprises two different encryption modes, and the proper encryption mode is selected according to the semantic information of the message data. If the message data corresponds to the first file semantics, adopting a byte order random scrambling encryption algorithm; if the second file semantics are corresponding, a stream encryption algorithm is employed. Therefore, a proper encryption mode can be flexibly selected according to different semantic requirements, and the efficiency and the safety of the encryption process are improved.

Through this step, the embodiment can protect the security of the message data and prevent the message data from being stolen or tampered in the transmission process. The encrypted message data can be decrypted only through the corresponding decryption key, so that confidentiality and integrity of the message data are ensured.

As can be seen from the above description, the one-text one-key secure encryption method for fusing file semantic information provided by the embodiment of the application can perform pre-marking on the message data according to the message type of the message data by receiving the message data sent by the internet of things device; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

In an embodiment of the method for one-text one-time password security encryption of fusion file semantic information of the present application, referring to fig. 2, the method may further specifically include the following:

step S201: performing text sequence word segmentation processing on the message data pre-marked as the text type, and converting text words obtained after the text sequence word segmentation processing into word vector models in a high-dimensional space;

Step S202: and determining a key word, a theme concept and an overall semantic corresponding to the word vector model through a preset text classification model and a preset entity recognition model, and performing multi-factor semantic fusion according to the association relationship among the key word, the theme concept and the overall semantic to obtain a corresponding first file semantic.

Optionally, in this embodiment, for step S201, the text sequence word segmentation processing is performed on the message data pre-marked as the text type. Text sequence segmentation is a basic task in natural language processing that segments a continuous text sequence into discrete words or phrases. The purpose of this step is to convert the text data into a form that can be understood and processed by the computer, which lays a foundation for subsequent semantic extraction and analysis.

In the text sequence word segmentation process, various natural language processing tools and techniques are used in the embodiment to segment text content in message data. These tools and techniques include part-of-speech tagging, named entity recognition, disabling word filtering, and the like. Through the processing, the embodiment can segment the text data into words or phrases with semantic meanings, remove irrelevant words and improve the accuracy and efficiency of subsequent semantic analysis.

In step S202, the embodiment further extracts the semantics of the text word obtained after the text sequence word segmentation processing through the preset text classification model and entity recognition model. The text classification model may identify topics and content in the text data, while the entity recognition model may identify specific entities and their attributes in the text. Through the combined action of the two models, the embodiment can determine key words, theme concepts and overall semantics in the text data.

Certain association relation exists among the semantic elements, and the semantic elements are comprehensively considered in a multi-factor semantic fusion mode to obtain corresponding first file semantics. The multi-factor semantic fusion can be determined by weighting fusion, logic relationship fusion and the like according to specific scenes and requirements. The meaning and information of the text data are more comprehensively reflected by the obtained first file semantics, and a basis is provided for subsequent data processing and application.

In an embodiment of the method for encrypting file semantic information by using one text-to-one secret security according to the present application, referring to fig. 3, the method may further specifically include the following:

step S301: dividing the video stream fragments of the message data which is pre-marked as the video stream type, extracting the image frames of each video fragment obtained by dividing the video stream fragments, inputting the image frames into a preset convolutional neural network for feature extraction, and obtaining corresponding appearance objects, scene features and action behaviors;

step S302: and carrying out multi-factor semantic fusion according to the semantic weights of the appearance object, the scene feature and the action behavior to obtain corresponding second file semantics.

Optionally, in this embodiment, the message data pre-marked as a video stream type is processed. First, the embodiment performs segmentation of video stream segments, and segments a continuous video stream into a plurality of independent video segments. The purpose of this is to make the analysis and processing of the video data more fine-grained, facilitating the subsequent feature extraction and semantic analysis.

For each video clip, the embodiment extracts image frames therein, and inputs the image frames into a preset Convolutional Neural Network (CNN) for feature extraction. CNN is a deep learning model specially used for image processing and recognition, and can effectively extract abundant feature information from images. By extracting the features of the image frames in the video clips, the embodiment can obtain the appearance object, the scene feature and the action behavior corresponding to each video clip.

In step S302, the embodiment performs multi-factor semantic fusion according to the extracted appearance object, scene feature and action behavior to obtain a corresponding second file semantic. In the process of multi-factor semantic fusion, the embodiment considers the importance and the relevance of each semantic factor and gives corresponding weight to each semantic factor. The purpose of this is to more accurately reflect the meaning and information of the video data and to improve the effect of subsequent data analysis and application.

Through the processing of step S301 and step S302, the embodiment can extract abundant semantic information from the video stream, including appearance objects, scene features, action behaviors, and the like, and provides an important basis for subsequent data analysis and application.

In an embodiment of the method for encrypting file semantic information by using one text-to-one secret security according to the present application, referring to fig. 4, the method may further specifically include the following:

step S401: constructing a serial input sequence according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data;

Step S402: and inputting the serial input sequence and the preset random salt value into a preset key derivation function to obtain a corresponding encryption key.

Optionally, in this embodiment, a serial input sequence may be constructed according to the first file semantics or the second file semantics, the device type of the internet of things device, and the MD5 file corresponding to the packet data. This sequence is formed by concatenating the information in a certain order and format. Firstly, in this embodiment, the first file semantic meaning or the second file semantic meaning is used as a part of the sequence, and then the device type information of the internet of things device and the MD5 file corresponding to the message data are added. The tandem input sequence thus constructed will contain a number of key information, providing the basis for subsequent key derivation.

In step S402, the present embodiment inputs the constructed serial input sequence and the preset random salt value into a preset key derivation function, so as to generate a corresponding encryption key. A key derivation function is an algorithm that generates a series of output data by computing input data. By inputting the series input sequence and the random salt value into the key derivation function, the present embodiment can obtain a unique encryption key. This encryption key will be generated from a combination of the plurality of information inputs with a high degree of security and randomness.

Through the processing of steps S401 and S402, the present embodiment successfully generates an encryption key for message data. The key is used for the subsequent message encryption process, and the security and confidentiality of the message data are ensured. Meanwhile, the uniqueness and unpredictability of the key are ensured in the key generation process, and the security of the encryption system is improved.

In an embodiment of the method for one-text one-time password security encryption of fusion file semantic information of the present application, referring to fig. 5, the method may further specifically include the following:

Step S501: if the encryption key corresponds to the first file semantics, dividing the message data into byte sequences according to a preset rule, and carrying out confusion and scrambling on the byte sequences through the encryption key to obtain the message data after message encryption;

Step S502: if the encryption key corresponds to the second file semantics, dividing the message data into continuous data stream blocks, and carrying out message encryption on each data stream block through a stream cipher algorithm and the encryption key to obtain the message data after message encryption.

Alternatively, in the present embodiment, in step S501, the present embodiment deals with the case when the encryption key corresponds to the first file semantics, that is, the text type semantic information. Firstly, the embodiment divides the message data into byte sequences according to a preset rule. This process may involve some specific segmentation rules, such as segmentation according to a certain length or a specific character. Then, the present embodiment uses the encryption key to obfuscate and scramble the byte sequences, that is, rearrange or transform the byte sequences by the encryption algorithm, so that the encrypted data cannot be easily interpreted. This scrambling process may employ various encryption algorithms, such as permutation and substitution operations in symmetric encryption algorithms, to enhance confidentiality and security of data. Finally, the obtained message data after message encryption is a string of byte sequences after confusion processing, so that confidentiality and integrity of the message data can be effectively protected.

In step S502, however, the present embodiment deals with the case when the encryption key corresponds to the second file semantics, that is, the semantic information of the video stream type. In this case, the present embodiment divides the message data into successive data stream blocks, each representing a segment or frame in the video stream. Then, the present embodiment encrypts the message for each data stream block using the stream cipher algorithm and the encryption key. The stream cipher algorithm is an encryption algorithm suitable for continuous data streams that uses a key and each part of the data stream to mix and transform to achieve encryption and decryption of the data stream. By encrypting each data stream block, the present embodiment can protect the privacy and confidentiality of video stream data from unauthorized access and theft.

Through the processing of step S501 and step S502, the present embodiment can perform an effective encryption operation on text-type message data or video-stream-type message data. Therefore, even if the message is intercepted or intercepted in the transmission process, an attacker cannot directly acquire the content of the message, so that the safety and confidentiality of communication are ensured.

In an embodiment of the method for one-text one-time password security encryption of fusion file semantic information of the present application, referring to fig. 6, the following may be further specifically included:

Step S601: receiving message data sent by Internet of things equipment, carrying out message analysis, and determining a corresponding message type, wherein the message type comprises a text type and a video stream type;

step S602: and generating a corresponding pre-mark according to the message type and adding the pre-mark into corresponding message data.

Optionally, in this embodiment, in step S601, the embodiment first receives the message data sent by the internet of things device, and performs message parsing. In the parsing process, the embodiment analyzes the content of the message to determine the corresponding message type. According to the setting of the present embodiment, the message types can be classified into text types and video stream types. For text type messages, the embodiment identifies text content therein and classifies the text content as text type messages; for the video stream type message, the present embodiment identifies the video stream data therein and classifies the video stream data as the video stream type message. Thus, the embodiment can clearly distinguish different types of message data and prepare for subsequent processing.

In step S602, the present embodiment generates a corresponding pre-label according to the identified message type, and adds the pre-label to the corresponding message data. The pre-label is a label to the message data that may help this embodiment better understand and process the message data. For text type message data, the embodiment generates corresponding text pre-marks, including information such as key words of words, phrases or sentences, theme concepts and the like; for the message data of the video stream type, the embodiment generates a corresponding video pre-marker, which includes information such as objects, scene features, action behaviors and the like appearing in the video. By adding the pre-mark, the embodiment can analyze and apply the message data more quickly and accurately in the subsequent processing process.

In summary, in step S601 and step S602, the embodiment effectively distinguishes different types of message data by analyzing and preprocessing the message data sent by the internet of things device, and provides powerful support and foundation for subsequent processing steps.

In an embodiment of the method for one-text one-time password security encryption of fusion file semantic information of the present application, referring to fig. 7, the method may further specifically include the following:

step S701: establishing an association binding relation between the encryption key and corresponding message data;

step S702: and storing the association binding relation into a preset key management database.

Optionally, in this embodiment, an association binding relationship between the encryption key and the corresponding packet data may be established. This means that the present embodiment assigns a unique identifier to each encryption key and associates it with the message data used in generating the key. Through the association binding relationship, the embodiment can ensure that each encryption key has an explicit source, namely corresponding message data. By the method, the management and tracing efficiency of the encryption key is improved, and meanwhile, the safety of message data is enhanced.

In step S702, the present embodiment stores the association binding relationship in a preset key management database. The key management database is a database system dedicated to storing and managing encryption keys and their associated information. In this database, the present embodiment assigns a unique identifier to each encryption key and associates it with the corresponding message data. By storing the associated information in the database, the embodiment can search and retrieve the message data corresponding to the specific encryption key at any time and any place, thereby realizing effective management and control of the key.

In summary, in step S701 and step S702, in this embodiment, by establishing the association binding relationship between the encryption key and the message data and storing these relationship information in the key management database, necessary support and basis are provided for the subsequent message encryption and decryption processes, and meanwhile, the management and traceability of the encryption key is enhanced.

In order to effectively improve the refinement degree and pertinence of encryption and improve the encryption efficiency and security, and provide a brand-new solution for information protection, the application provides an embodiment of a secure encryption device for fusing file semantic information for realizing all or part of the contents of the secure encryption method for fusing file semantic information, referring to fig. 8, the secure encryption device for fusing file semantic information specifically comprises the following contents:

the message pre-marking module 10 is used for receiving message data sent by the internet of things equipment and pre-marking the message data according to the message type of the message data;

The semantic analysis fusion module 20 is configured to perform semantic extraction on the message data pre-marked as a text type through a preset natural language processing model to obtain a corresponding key word, a topic concept and an overall semantic, perform multi-factor semantic fusion according to the key word, the topic concept and the overall semantic to obtain a corresponding first file semantic, perform semantic extraction on the message data pre-marked as a video stream type through a preset convolutional neural network to obtain a corresponding appearance object, a scene feature and an action behavior, and perform multi-factor semantic fusion according to the appearance object, the scene feature and the action behavior to obtain a corresponding second file semantic;

The message encryption module 30 is configured to generate a corresponding encryption key according to the first file semantics or the second file semantics, the device type of the internet of things device, and an MD5 file corresponding to the message data through a key derivation function, and encrypt the message data through the encryption key and a set adaptive encryption algorithm to obtain the message data after the message encryption, where the adaptive encryption algorithm includes a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

As can be seen from the above description, the one-text-one-key secure encryption device for fusing file semantic information provided by the embodiment of the present application can perform pre-marking on the message data according to the message type of the message data by receiving the message data sent by the internet of things device; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

In order to effectively improve the refinement degree and pertinence of encryption and improve the encryption efficiency and security at the same time, and provide a brand-new solution for information protection, the application provides an embodiment of an electronic device for realizing all or part of contents in a one-text one-time-password secure encryption method of fused file semantic information, wherein the electronic device specifically comprises the following contents:

A processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between the one-text one-secret security encryption device fusing the file semantic information and related equipment such as a core service system, a user terminal and a related database; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the method for encrypting the file semantic information by using a secure encryption method and an embodiment of the device for encrypting the file semantic information by using a secure encryption method, and the contents of the embodiments are incorporated herein and are not repeated here.

It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical application, the part of the one-text one-time-pad secure encryption method for fusing the file semantic information can be executed on the electronic equipment side as described in the above description, or all operations can be completed in the client equipment. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.

The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.

Fig. 9 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 9, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 9 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.

In one embodiment, the one-text-to-one secure encryption method functionality that merges the file semantic information may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:

Step S101: receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Step S102: carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

Step S103: generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

As can be seen from the above description, the electronic device provided by the embodiment of the present application performs pre-marking on the message data according to the message type of the message data by receiving the message data sent by the internet of things device; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

In another embodiment, the one-text-to-one secure encryption device for fusing the file semantic information may be configured separately from the central processor 9100, for example, the one-text-to-one secure encryption device for fusing the file semantic information may be configured as a chip connected to the central processor 9100, and the one-text-to-one secure encryption method function for fusing the file semantic information is implemented under the control of the central processor.

As shown in fig. 9, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 9; in addition, the electronic device 9600 may further include components not shown in fig. 9, and reference may be made to the related art.

As shown in fig. 9, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.

The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.

The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.

The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.

The embodiment of the present application further provides a computer readable storage medium capable of implementing all steps in the one-text one-time secure encryption method in which the execution subject is the fusion file semantic information of the server or the client, where the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all steps in the one-text one-time secure encryption method in which the execution subject is the fusion file semantic information of the server or the client, for example, the processor implements the following steps when executing the computer program:

Step S101: receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Step S102: carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

Step S103: generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

As can be seen from the above description, the computer readable storage medium provided by the embodiment of the present application performs pre-marking on the message data according to the message type of the message data by receiving the message data sent by the internet of things device; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

The embodiment of the present application further provides a computer program product capable of implementing all the steps in the one-text-one-secret secure encryption method of the fused file semantic information in which the execution subject is the server or the client in the above embodiment, where the computer program/instructions implement the steps of the one-text-one-secret secure encryption method of the fused file semantic information when executed by the processor, for example, the computer program/instructions implement the steps of:

Step S101: receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Step S102: carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

Step S103: generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

As can be seen from the above description, the computer program product provided by the embodiment of the present application performs pre-marking on the message data according to the message type of the message data by receiving the message data sent by the internet of things device; carrying out semantic extraction on the message data pre-marked as the text type through a preset natural language processing model to obtain a first file semantic, and carrying out semantic extraction on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain a second file semantic; generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm, so that the refinement degree and pertinence of encryption can be effectively improved.

It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (10)

1. A secure one-text-to-one encryption method for fusing file semantic information, the method comprising:

receiving message data sent by Internet of things equipment, and pre-marking the message data according to the message type of the message data;

Carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

Generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

2. The method for one-text-to-one-secret secure encryption of fusion file semantic information according to claim 1, wherein the semantic extraction is performed on the message data pre-marked as text types through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, and multi-factor semantic fusion is performed according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, and the method comprises the following steps:

Performing text sequence word segmentation processing on the message data pre-marked as the text type, and converting text words obtained after the text sequence word segmentation processing into word vector models in a high-dimensional space;

And determining a key word, a theme concept and an overall semantic corresponding to the word vector model through a preset text classification model and a preset entity recognition model, and performing multi-factor semantic fusion according to the association relationship among the key word, the theme concept and the overall semantic to obtain a corresponding first file semantic.

3. The method for one-text-to-one-secret secure encryption of fusion file semantic information according to claim 1, wherein the semantic extraction is performed on the message data pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and multi-factor semantic fusion is performed according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics, and the method comprises the following steps:

dividing the video stream fragments of the message data which is pre-marked as the video stream type, extracting the image frames of each video fragment obtained by dividing the video stream fragments, inputting the image frames into a preset convolutional neural network for feature extraction, and obtaining corresponding appearance objects, scene features and action behaviors;

and carrying out multi-factor semantic fusion according to the semantic weights of the appearance object, the scene feature and the action behavior to obtain corresponding second file semantics.

4. The method for secure encryption with one text and one secret of the fused file semantic information according to claim 1, wherein the generating the corresponding encryption key according to the first file semantic or the second file semantic, the device type of the internet of things device and the MD5 file corresponding to the message data through a key derivation function includes:

Constructing a serial input sequence according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and the MD5 file corresponding to the message data;

And inputting the serial input sequence and the preset random salt value into a preset key derivation function to obtain a corresponding encryption key.

5. The method for one-text-to-one secure encryption of fused file semantic information according to claim 1, wherein the step of performing the message encryption on the message data by the encryption key and setting an adaptive encryption algorithm to obtain the message data after the message encryption, wherein the adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantic and a stream encryption algorithm corresponding to the second file semantic, and comprises the following steps:

If the encryption key corresponds to the first file semantics, dividing the message data into byte sequences according to a preset rule, and carrying out confusion and scrambling on the byte sequences through the encryption key to obtain the message data after message encryption;

If the encryption key corresponds to the second file semantics, dividing the message data into continuous data stream blocks, and carrying out message encryption on each data stream block through a stream cipher algorithm and the encryption key to obtain the message data after message encryption.

6. The method for one-text-to-one-key secure encryption of fusion file semantic information according to claim 1, wherein the step of receiving the message data sent by the internet of things device, and pre-marking the message data according to the message type of the message data comprises the following steps:

Receiving message data sent by Internet of things equipment, carrying out message analysis, and determining a corresponding message type, wherein the message type comprises a text type and a video stream type;

And generating a corresponding pre-mark according to the message type and adding the pre-mark into corresponding message data.

7. The one-text-to-one secure encryption method for fusing file semantic information according to claim 1, wherein after the generating the corresponding encryption key according to the first file semantic or the second file semantic, the device type of the internet of things device, and the MD5 file corresponding to the message data by a key derivation function, the method comprises:

Establishing an association binding relation between the encryption key and corresponding message data;

and storing the association binding relation into a preset key management database.

8. A one-text-to-one-secret secure encryption device for fusing file semantic information, the device comprising:

the message pre-marking module is used for receiving message data sent by the Internet of things equipment and pre-marking the message data according to the message type of the message data;

The semantic analysis fusion module is used for carrying out semantic extraction on the message data which is pre-marked as the text type through a preset natural language processing model to obtain corresponding key words, theme concepts and overall semantics, carrying out multi-factor semantic fusion according to the key words, the theme concepts and the overall semantics to obtain corresponding first file semantics, carrying out semantic extraction on the message data which is pre-marked as the video stream type through a preset convolutional neural network to obtain corresponding appearance objects, scene features and action behaviors, and carrying out multi-factor semantic fusion according to the appearance objects, the scene features and the action behaviors to obtain corresponding second file semantics;

The message encryption module is used for generating a corresponding encryption key through a key derivation function according to the first file semantics or the second file semantics, the equipment type of the Internet of things equipment and an MD5 file corresponding to the message data, and carrying out message encryption on the message data through the encryption key and a set self-adaptive encryption algorithm to obtain the message data after the message encryption, wherein the self-adaptive encryption algorithm comprises a byte order random scrambling encryption algorithm corresponding to the first file semantics and a stream encryption algorithm corresponding to the second file semantics.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the one-text-to-one secure encryption method of fusing file semantic information as claimed in any one of claims 1 to 7 when said program is executed by said processor.

10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of a one-text-to-one secure encryption method of fusing file semantic information as claimed in any one of claims 1 to 7.