Method, device, equipment and storage medium for secure encryption of parameters and messages

Abstract

The present invention relates to the field of message encryption, and in particular to a method, device, equipment and storage medium for secure encryption of parameters and messages. The method comprises the following steps: obtaining a plaintext message to be encrypted; performing adaptive filtering and noise reduction on the plaintext message to be encrypted, and filtering and removing outliers, thereby obtaining a filtered optimized message; performing parameter information identification on the filtered optimized message, and performing segment-by-segment parameter encryption, thereby obtaining multiple message segment encryption parameters; performing deep semantic feature analysis on multiple message data segments, and then performing segment-by-segment key field mining, thereby extracting the key semantic fields of each segment; performing semantic feature sensitivity mining on the key semantic fields of each segment, and performing sensitivity judgment, thereby extracting multiple highly sensitive semantic fields; performing segment-by-segment symmetrical encryption on multiple highly sensitive semantic fields, and performing distributed encryption storage, thereby constructing a distributed encryption storage framework. The present invention achieves highly secure message encryption.

Description

Technical Field

[0001] The present invention relates to the field of message encryption, and in particular to a method, device, equipment and storage medium for secure encryption of parameters and messages. Background Art

[0002] Messages, as a crucial vehicle for data transmission, are widely used in various fields, including computer networks, communications systems, financial transactions, and the Internet of Things. During information exchange, messages often contain sensitive data, such as user personal information, transaction records, and passwords. Theft or tampering of this data can lead to serious privacy breaches, financial losses, or system security issues. Therefore, ensuring the security and integrity of messages during transmission has become a crucial issue in modern information communications.

[0003] As cyberattacks continue to evolve, traditional message security methods, while able to guarantee data confidentiality and integrity to a certain extent, face challenges with increasing computing power and the sophistication of attack techniques, such as insufficient encryption strength and complex key management. Ensuring that every message is protected from eavesdropping, tampering, or forgery during transmission has become an even more pressing challenge, especially in large-scale distributed systems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method, device, equipment and storage medium for secure encryption of parameters and messages to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for securely encrypting parameters and messages, comprising the following steps:

[0006] Step S1: Obtain a plaintext message to be encrypted; perform adaptive filtering and noise reduction on the plaintext message to be encrypted, and perform outlier filtering and elimination to obtain a filtered optimized message;

[0007] Step S2: Identify parameter information of the filtering optimization message and encrypt the parameters segment by segment, thereby obtaining encryption parameters of multiple message segments;

[0008] Step S3: Perform deep semantic feature analysis on multiple message data segments, and then perform segment-by-segment key field mining to extract the key semantic fields of each segment;

[0009] Step S4: Mining the semantic feature sensitivity of the key semantic fields of each segment, performing sensitivity judgment, and extracting multiple highly sensitive semantic fields;

[0010] Step S5: symmetrically encrypt multiple highly sensitive semantic fields segment by segment, perform distributed encrypted storage, and build a distributed encrypted storage framework;

[0011] Step S6: Based on multiple message segment encryption parameters, the distributed encryption storage framework is randomly embedded and reconstructed to construct a randomly embedded distributed encryption framework to complete the secure encryption operation of parameters and messages.

[0012] The present invention also provides a parameter and message security encryption device, comprising:

[0013] The filtering optimization module is used to obtain the plaintext message to be encrypted; perform adaptive filtering and noise reduction on the encrypted plaintext message, and filter out outliers to obtain the filtered optimized message;

[0014] The parameter encryption module is used to identify the parameter information of the filtering optimization message and encrypt the parameters segment by segment to obtain encryption parameters of multiple message segments;

[0015] The semantic feature analysis module is used to perform deep semantic feature analysis on multiple message data segments, and then mine the key fields segment by segment to extract the key semantic fields of each segment;

[0016] The sensitivity judgment module is used to mine the semantic feature sensitivity of the key semantic fields of each segment, perform sensitivity judgment, and extract multiple highly sensitive semantic fields;

[0017] Distributed encryption module, used to perform segment-by-segment symmetrical encryption on multiple highly sensitive semantic fields, perform distributed encrypted storage, and build a distributed encrypted storage framework;

[0018] The embedding and reconstruction module is used to randomly embed and reconstruct the distributed encryption storage framework based on multiple message segment encryption parameters, and build a randomly embedded distributed encryption framework to complete the secure encryption of parameters and messages.

[0019] The present invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for securely encrypting parameters and messages described in any one of the above items are implemented.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for securely encrypting parameters and messages described in any one of the above items are implemented.

[0021] The beneficial effects of the present invention are as follows: adaptive filtering noise reduction and outlier filtering can improve the quality and accuracy of plaintext messages and reduce errors that may occur after encryption; filtering and optimizing messages can reduce the noise of encrypted data and improve the efficiency and accuracy of subsequent encryption and decryption; parameter information identification and segment-by-segment parameter encryption can ensure that sensitive information is effectively protected and improve data security; the generation of encryption parameters for multiple message segments can increase the difficulty of encryption and enhance data security; deep semantic feature analysis and key field mining can help identify important information in messages and provide an effective basis for subsequent encryption; extracting key semantic fields can ensure that encryption does not lose important information while protecting sensitive data and semantic feature sensitivity. Mining and extraction of highly sensitive fields can help identify and focus on protecting sensitive data, strengthen the targeted nature of encryption, and sensitivity judgment helps determine which fields require stricter encryption measures, thereby enhancing data security. Symmetric encryption and distributed encryption storage of highly sensitive semantic fields can ensure the security of data during transmission and storage. Building a distributed encryption storage framework can improve data's anti-attack capabilities and reliability, avoid single point failures and data leakage risks, and random embedding reconstruction and building a randomly embedded distributed encryption framework can increase the complexity of the encryption algorithm and improve data security. Random embedding reconstruction based on message segment encryption parameters can customize encryption schemes and enhance data confidentiality and integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of the steps of a method for securely encrypting parameters and messages according to the present invention;

[0023] Figure 2 Detailed implementation flow chart of step S1;

[0024] Figure 3 Detailed implementation flow chart of step S2;

[0025] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] This application example provides a method, apparatus, device, and storage medium for secure encryption of parameters and messages. The execution subjects of the method, apparatus, device, and storage medium for secure encryption of parameters and messages include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. equipped with the system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of: an audio and image management system, an information management system, and a cloud data management system.

[0028] See also Figures 1 to 4 The present invention provides a method for securely encrypting parameters and messages, the method comprising the following steps:

[0029] Step S1: Obtain a plaintext message to be encrypted; perform adaptive filtering and noise reduction on the plaintext message to be encrypted, and perform outlier filtering and elimination to obtain a filtered optimized message;

[0030] Step S2: Identify parameter information of the filtering optimization message and encrypt the parameters segment by segment, thereby obtaining encryption parameters of multiple message segments;

[0031] Step S3: Perform deep semantic feature analysis on multiple message data segments, and then perform segment-by-segment key field mining to extract the key semantic fields of each segment;

[0032] Step S4: Mining the semantic feature sensitivity of the key semantic fields of each segment, performing sensitivity judgment, and extracting multiple highly sensitive semantic fields;

[0033] Step S5: symmetrically encrypt multiple highly sensitive semantic fields segment by segment, perform distributed encrypted storage, and build a distributed encrypted storage framework;

[0034] Step S6: Based on multiple message segment encryption parameters, the distributed encryption storage framework is randomly embedded and reconstructed to construct a randomly embedded distributed encryption framework to complete the secure encryption operation of parameters and messages.

[0035] The present invention can improve the quality and accuracy of plaintext messages through adaptive filtering noise reduction and outlier filtering, reduce the errors that may occur after encryption, filter and optimize messages to reduce the noise of encrypted data, improve the efficiency and accuracy of subsequent encryption and decryption, parameter information identification and segment-by-segment parameter encryption can ensure that sensitive information is effectively protected and improve data security, the generation of encryption parameters for multiple message segments can increase the encryption difficulty and enhance data security, deep semantic feature analysis and key field mining can help identify important information in the message, provide an effective basis for subsequent encryption, extract key semantic fields can ensure that encryption does not lose important information, while protecting sensitive data, semantic feature sensitivity mining and high Sensitivity field extraction can help identify and focus on protecting sensitive data, strengthen the targeted nature of encryption, and sensitivity judgment helps determine which fields require stricter encryption measures, thereby enhancing data security. Segment-by-segment symmetric encryption and distributed encryption storage of highly sensitive semantic fields can ensure the security of data during transmission and storage. Building a distributed encryption storage framework can improve data's anti-attack capabilities and reliability, avoid single point failures and data leakage risks, and random embedding reconstruction and building a random embedding distributed encryption framework can increase the complexity of the encryption algorithm and improve data security. Random embedding reconstruction based on message segment encryption parameters can customize encryption schemes and enhance data confidentiality and integrity.

[0036] In the embodiment of the present invention, see Figure 1 , is a flowchart of the steps of a method for securely encrypting parameters and messages of the present invention. In this example, the steps of the method for securely encrypting parameters and messages include:

[0037] Step S1: Obtain a plaintext message to be encrypted; perform adaptive filtering and noise reduction on the plaintext message to be encrypted, and perform outlier filtering and elimination to obtain a filtered optimized message;

[0038] In this embodiment, raw message data to be encrypted is read from a business system or other data source and cached in memory for subsequent processing and analysis. Statistical characteristics of the message data, such as amplitude distribution and frequency characteristics, are analyzed. Based on the characteristics of message noise, an appropriate adaptive filtering algorithm, such as Wiener filtering or Kalman filtering, is selected. Filter parameters are dynamically adjusted to effectively suppress interference components such as random noise and impulse noise in the message. Adaptive filtering significantly improves the signal-to-noise ratio of the message, enhancing the reliability of subsequent processing. Statistical distribution analysis is used to identify outliers and glitches in the message. Based on the normal distribution characteristics of the message data, an adaptive outlier detection threshold is set. Abnormal data points exceeding the threshold are repaired using methods such as interpolation and smoothing to eliminate outliers from the message, ensuring data continuity and smoothness, and providing high-quality input for subsequent semantic analysis. After adaptive filtering and outlier filtering, a high-quality optimized message is obtained. The noise components of the message data have been significantly reduced, and outliers have been effectively eliminated. This pre-processed optimized message serves as input data for subsequent semantic analysis and encryption.

[0039] Step S2: Identify parameter information of the filtering optimization message and encrypt the parameters segment by segment, thereby obtaining encryption parameters of multiple message segments;

[0040] In this embodiment, the optimized message data is deeply analyzed to identify various types of parameter information contained therein. Natural language processing techniques, such as named entity recognition and keyword extraction, are used to detect numerical parameters and code parameters in the message. Based on the semantic characteristics and contextual relationships of the parameters, each type of parameter is finely classified and labeled to establish a structured representation of the message parameter information, providing a clear target for subsequent encryption processing. Different encryption algorithms and keys are used to encrypt each identified parameter information. Appropriate encryption methods, such as symmetric encryption and asymmetric encryption, are selected for different parameter types. The encrypted parameter information is dynamically combined with the original message segments to form encrypted message segments. During the combination, random embedding, segmented encryption, and other methods can be used to increase the complexity of the final message. After the above parameter identification and segment-by-segment encryption processing, a group of encrypted message segments are obtained. Each message segment contains encrypted key parameter information. These encrypted message segments will serve as input data for the subsequent comprehensive encryption scheme for further processing.

[0041] Step S3: Perform deep semantic feature analysis on multiple message data segments, and then perform segment-by-segment key field mining to extract the key semantic fields of each segment;

[0042] In this embodiment, a pre-trained deep learning semantic analysis model is used to perform fine-grained semantic analysis on each message data segment. Advanced natural language processing models such as BERT and GPT are used to extract features from the message at the lexical, syntactic, and semantic levels. Rich semantic feature information such as keywords, named entities, and sentiment trends is identified from the message. These semantic features provide an important basis for subsequent key field mining and message association analysis. Based on the semantic features extracted in the previous step, key field mining is performed for each message data segment. In combination with domain knowledge and business rules, corresponding rules and algorithms are designed to identify the core semantic information in the message. For example, key fields related to personal privacy and sensitive transactions can be focused on. Through this targeted key field mining, the key semantic information in the message is highly focused. After deep semantic feature analysis and key field mining, the key semantic fields of each message segment are ultimately obtained. These key semantic fields contain the most core and sensitive information content in the message. These key fields will serve as the focus of subsequent encryption processing to ensure their security.

[0043] Step S4: Mining the semantic feature sensitivity of the key semantic fields of each segment, performing sensitivity judgment, and extracting multiple highly sensitive semantic fields;

[0044] In this embodiment, the sensitivity characteristics of the extracted key semantic fields of each message segment need to be further explored. In combination with domain knowledge and security requirements, a set of sensitivity assessment indicators for semantic features is designed. These indicators include: the sensitivity level related to privacy, the importance of critical business, the necessity of regulatory compliance, etc. By comparing these sensitivity indicators, each key semantic field is scored and evaluated. Based on the above sensitivity assessment, a dynamically adjustable sensitivity threshold is set for each key semantic field. This sensitivity threshold can be adjusted according to different business scenarios, security requirements, and other factors. Key semantic fields that exceed the sensitivity threshold are marked as high-sensitivity fields and require key encryption protection. Through this adaptive sensitivity judgment mechanism, the encryption strategy can be flexibly controlled to be consistent with actual needs. After the above sensitivity analysis and adaptive judgment, a set of high-sensitivity key semantic fields are ultimately extracted. These high-sensitivity fields contain the most critical and private core information content in the message. These high-sensitivity fields will serve as the main targets of subsequent encryption processing to ensure their effective security protection.

[0045] Step S5: symmetrically encrypt multiple highly sensitive semantic fields segment by segment, perform distributed encrypted storage, and build a distributed encrypted storage framework;

[0046] In this embodiment, the highly sensitive semantic fields extracted in step S4 are encrypted segment by segment using an efficient symmetric encryption algorithm, such as AES or ChaCha20, to encrypt each semantic field. An independent encryption key is generated for each semantic field to ensure encryption independence and isolation. The encrypted semantic fields are reassembled into the original message data segment structure and the encrypted message data segments are distributed and stored in multiple independent encryption storage units. Each encryption storage unit is an independent computing node, which can be a physical machine, a virtual machine, or a container. To achieve heterogeneous encryption, each storage unit is equipped with a different encryption algorithm and key. Fault-tolerant technologies such as redundant backup and sharding reconstruction are used to ensure that even if some storage units fail, integrity is not compromised. Based on the above-mentioned distributed storage and heterogeneous encryption design, a distributed encryption storage framework is constructed. The framework includes multiple independent encryption storage units and a central control system for coordinating and managing these units. The central control system is responsible for dynamically scheduling storage strategies, monitoring fault tolerance status, coordinating encryption keys, and other functions. Through this distributed architecture, the security and availability of the entire encryption system are greatly improved.

[0047] Step S6: Based on multiple message segment encryption parameters, the distributed encryption storage framework is randomly embedded and reconstructed to construct a randomly embedded distributed encryption framework to complete the secure encryption operation of parameters and messages.

[0048] In this embodiment, a coding index table is established for message data segments and encryption parameters. This index table records the correspondence between each message data segment and the corresponding encryption parameter. This index table ensures that the original message can be correctly restored during decryption. Based on the establishment of the coding index, a random embedding method is used to dynamically reconstruct the encoded parameters with the message. Each time encryption is performed, a different embedding position and order are randomly selected, resulting in a high degree of uncertainty in the final encrypted message. This random reconstruction method increases the difficulty for attackers to crack the encrypted message. To further increase the uncertainty of the encrypted message, more complex reconstruction rules are designed. In addition to simple random embedding, other coding transformations can be introduced, such as segmented encryption and multi-level nesting. The superposition of multiple complex transformations will ultimately generate an encrypted message with extremely high uncertainty and obfuscation. For the receiver, only by using the pre-established coding index table can the encrypted message be correctly decrypted and restored. The receiver first identifies the correspondence between the message data segment and the encryption parameter based on the index table. Then, according to the correct reconstruction rules, the encrypted message is gradually disassembled and restored, ultimately obtaining a clear original message.

[0049] In this embodiment, refer to Figure 2, is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0050] Step S11: Obtain the plaintext message to be encrypted;

[0051] Step S12: performing multi-band wavelet transform decomposition on the encrypted plaintext message to obtain message data of multiple frequencies;

[0052] Step S13: Analyze the noise characteristics of the message data of multiple frequencies one by one, and extract the message noise characteristics of each frequency;

[0053] Step S14: Adaptively filter and reduce noise based on the noise characteristics of the message at each frequency, thereby obtaining a filtered and reduced-noise message;

[0054] Step S15: Detect abnormal burst points on the filtered and denoised message and mark abnormal burst values;

[0055] Step S16: Filter out the abnormal burst values to obtain a filtered optimized message.

[0056] In this embodiment, the source of the plaintext message to be encrypted is determined, such as network transmission, database extraction or user input, to ensure the reliability and integrity of the data source so that the subsequent encryption process can proceed smoothly. The plaintext message is obtained from the data source using an appropriate interface or protocol (such as HTTP, TCP / IP) and stored in memory or a file. The obtained data is preliminarily verified to ensure that its format and content meet expectations. The plaintext message is formatted into a standard format suitable for subsequent processing (such as JSON, XML or binary format) to facilitate processing in subsequent steps. An appropriate wavelet basis (such as Haar, Daubechies, Coiflet, etc.) is selected for wavelet transform to extract message features in different frequency ranges. Characteristic, use wavelet transform function (such as wavedec in PyWavelets library) to perform multi-band decomposition on the encrypted plaintext message, decompose the message data into multiple frequency sub-bands, obtain high-frequency and low-frequency components, store the message data of multiple frequencies in a data structure for subsequent processing, ensure that the sub-band data of each frequency can be accessed and analyzed separately, calculate the relevant noise characteristics for the message data of each frequency, such as signal-to-noise ratio (SNR), root mean square value (RMS), peak factor, etc., and use statistical analysis methods (such as standard deviation, average) to evaluate the noise level in each frequency sub-band, compare the extracted noise characteristics with the predefined threshold to identify the significance of the noise, and record the noise characteristics of each frequency Data is collected to facilitate the implementation of subsequent adaptive filtering. According to the analyzed noise characteristics, a suitable adaptive filter (such as Kalman filter, adaptive mean filter, etc.) is selected. The filter parameters are dynamically adjusted according to the noise characteristics of each frequency to ensure that it can effectively remove noise and retain the signal. The adaptive filter is applied to the message data of each frequency to obtain the filtered denoised message. The filtering effect of each frequency is recorded during the filtering process for subsequent evaluation. The definition criteria of abnormal protrusion points are clarified, such as instantaneous values exceeding a certain threshold or sharp changes within a certain frequency range. The abnormal detection algorithm (such as Z-score method, moving average method) is applied to analyze the filtered denoised message, detect abnormal protrusion values, and record the detected All abnormal protrusion points and their related information (such as timestamps, amplitudes, frequencies, etc.) are marked to ensure that these abnormal values can be identified and processed in the subsequent processing stage. The criteria for filtering outliers are determined, such as setting a threshold range or using statistical methods (such as the quartile method) to determine the filtering conditions. The marked abnormal protrusion values are traversed and filtered according to the set criteria to eliminate those message data identified as abnormal. The eliminated abnormal values are recorded during the filtering process for subsequent analysis and verification. The message data that has been filtered out of the abnormal value is saved as a filtered optimized message to ensure that its format is consistent with the original data. Metadata (such as processing time and filtering criteria) is added to the optimized message to facilitate subsequent access and use.

[0057] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0058] Step S21: performing semantic structure analysis on the filtered optimized message and extracting message semantic structure data;

[0059] Step S22: performing complete semantic multi-segment decomposition of the filtered optimized message according to the message semantic structure data, thereby obtaining multiple message data segments;

[0060] Step S23: Identifying parameter information of multiple message data segments and extracting parameter information of each message segment;

[0061] Step S24: encrypt the parameter information of each message segment segment by segment, thereby obtaining multiple message segment encryption parameters.

[0062] In this embodiment, a suitable semantic analysis tool or library (such as NLTK, spaCy, or a custom regular expression) is selected to process and analyze the content of the filtering optimization message. The filtering optimization message is preprocessed, including removing redundant spaces, punctuation marks, and special characters, and converting the message into a unified format (such as lowercase) to improve the accuracy of subsequent analysis. Natural language processing (NLP) technology is applied to the message to perform word segmentation, part-of-speech tagging, and syntactic analysis to extract semantic structure data, identify key components in the message (such as subject, verb, object, etc.), and record their positions and relationships in the message. The extracted semantic structure data is stored in a data structure for subsequent processing, such as using a dictionary or data frame (such as PandasDataFrame). Decomposition rules are defined based on the extracted semantic structure data, such as dividing the message segments according to specific keywords, sentence structure, or logic. The filtering optimization message is decomposed into multiple segments using the decomposition rules to generate multiple message data segments, ensuring that each data segment retains complete semantic information to facilitate subsequent analysis and processing. The decomposed message segments are verified to confirm Ensure that it conforms to the expected semantic structure, store multiple message data segments in a list or array for subsequent processing, determine the type of parameter information that needs to be extracted, such as timestamp, user ID, operation code, status code, etc., use regular expressions, NLP technology or custom parsers to extract the required parameter information from each message segment, traverse each message segment, apply parameter extraction methods, identify and extract the parameter information of each segment, record the extracted parameter information, including its name, value and source segment, select a suitable encryption algorithm (such as AES, RSA or other symmetric / asymmetric encryption algorithms), develop an encryption scheme based on security requirements, generate or obtain the keys required for encryption, and ensure the secure storage and management of the keys to prevent unauthorized access, encrypt the parameter information of each message segment one by one, ensure the security of each parameter, apply the selected encryption algorithm, encrypt the parameters, generate encrypted parameter data, store the encrypted parameter data in a secure location, ensure its availability and integrity, and generate an encryption result report containing the encrypted parameters and their original values for subsequent verification and auditing.

[0063] The specific encryption of the parameters is as follows:

[0064] Extract parameter information of the first message segment;

[0065] Convert the parameter information of the first message segment into a JSON string to obtain multiple JSON strings;

[0066] Perform MD5 encryption on multiple JSON strings to generate multiple encrypted strings;

[0067] Generate a 16-bit random character code and the current user's identity authentication data;

[0068] symmetrically encrypting multiple encrypted character strings using the 16-bit random character code to obtain symmetrically encrypted character strings;

[0069] Encapsulate the current user identity authentication data into a request header to obtain a user identity request header unit;

[0070] The user identity request header unit is used to embed the symmetric encryption string into the header, thereby obtaining the message segment encryption parameters;

[0071] All message segment encryption parameters are iteratively encrypted to obtain multiple message segment encryption parameters.

[0072] In this embodiment, the first message segment is traversed to identify and extract the required parameter information, such as timestamp, user ID, operation code, etc. The extracted parameter information is stored in a dictionary or similar data structure for subsequent processing. An applicable JSON processing library (such as the json module in Python) is introduced. The function of the JSON library is used to convert the extracted parameter information dictionary into a JSON string, format the output, and ensure that the structure of the JSON string is clear and meets the standards. If there are multiple message segments, the above process is repeated to generate corresponding JSON strings and store them in a list. An applicable hash library (such as hashlib in Python) is introduced to traverse the multiple stored JSON strings, encrypt each JSON string using the MD5 algorithm, generate an encrypted string using the hash function, record each encryption result, and store all MD5 encrypted strings in a list for subsequent use. A random number generation library (such as the random or secrets library in Python) is introduced to use the random function to generate a 16 16-bit random character code. The characters can include letters and numbers. Ensure the uniqueness and unpredictability of the random character code. Obtain the current user's identification data (such as a user ID or session token) from the user authentication system or session. Ensure that the obtained data is valid and up-to-date. Select an appropriate symmetric encryption algorithm (such as AES) and define the key generation and padding method. Use the generated 16-bit random character code as the key to symmetrically encrypt multiple MD5-encrypted strings one by one. Record each encryption result to form a list of symmetric encrypted strings. Determine the structure of the request header, which typically includes content type and user identification information. Encapsulate the obtained user identification data into the request header to form a complete request header unit. Combine the user identity request header unit with the symmetric encrypted string to form the final message segment encryption parameters. Ensure that the embedding process does not destroy the integrity of the encrypted string. Repeat the above encryption steps for all extracted message segments, processing the parameter information of each message segment one by one. Store all message segment encryption parameters in an appropriate data structure, such as a list or dictionary, for subsequent use or transmission.

[0073] In this embodiment, refer to Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0074] Step S31: performing deep semantic feature analysis on multiple message data segments to extract message semantic features of each segment;

[0075] Step S32: performing contextual logical association analysis on the message semantic features of each segment to obtain message semantic logical association data;

[0076] Step S33: performing intelligent semantic smoothing on the multiple message data segments based on the message semantic logic association data, thereby generating multiple semantically smoothed message segments;

[0077] Step S34: mining key fields of multiple semantically smoothed message segments one by one, thereby extracting key semantic fields of each segment.

[0078] In this embodiment, a suitable natural language processing tool or library (such as spaCy, NLTK, Transformers, etc.) is selected to perform deep semantic feature analysis, and multiple message data segments are preprocessed, including removing redundant spaces, punctuation marks, converting to lowercase, etc. to improve the accuracy of the analysis. Each message segment is analyzed using the selected semantic parsing tool to extract semantic features, such as keywords, entity recognition (such as names, places, dates), word meanings and dependencies, etc. The extracted semantic features are stored in a data structure (such as a dictionary or data frame) for subsequent processing, and the criteria for contextual logical analysis are determined to clarify which features or fields have logical associations, which may include time, causal relationships, topic relevance, etc. The extracted semantic features are subjected to contextual logical analysis using a logistic regression model, a Bayesian network, or graph theory-related methods. The logical relationship between each message feature is identified and recorded to form message semantic logical association data, and the logical association data obtained from the analysis is stored in an appropriate data structure for subsequent use (such as a list, a dictionary, or a data frame). ), select an appropriate semantic smoothing algorithm (such as conditional generative adversarial network (CGAN) and variational autoencoder (VAE)) to achieve semantic smoothing, use previously extracted semantic features and logical association data to train the selected model to ensure that the model can understand and generate natural language that conforms to the context, input multiple message segments into the trained semantic smoothing model to generate smoothed semantic message segments, and ensure that the generated message segments are more semantically coherent and logical, determine the types of key fields to be extracted, such as keywords, important parameters, specific entities, etc., use methods such as TF-IDF, LDA topic model, and text similarity calculation to identify the key fields in each semantically smoothed message segment, perform key field mining on each semantically smoothed message segment one by one, and extract content that matches the defined key field type, store the extracted key fields in a structured data format (such as a dictionary or list) for subsequent use, verify the extracted key fields to ensure their accuracy and relevance, and aggregate all extracted key fields to form the final dataset for subsequent analysis and use.

[0079] In this embodiment, step S4 includes the following steps:

[0080] Step S41: performing application scenario analysis on the plaintext message to be encrypted to obtain message application scenario data;

[0081] Step S42: Identify the security requirements of the message application scenario data to obtain the current message security requirement data;

[0082] Step S43: Calculate the dynamic security sensitivity threshold based on the current message security requirement data to generate the current scenario security sensitivity threshold;

[0083] Step S44: performing semantic feature sensitivity mining on the key semantic fields of each segment, thereby generating the sensitivity of the key semantic fields;

[0084] Step S45: Perform sensitivity judgment on the sensitivity of the key semantic field based on the current scenario security sensitivity threshold. When the sensitivity of the key semantic field is greater than or equal to the current scenario security sensitivity threshold, it is judged as a high-sensitivity semantic field. All key semantic fields are traversed to extract multiple high-sensitivity semantic fields.

[0085] In this embodiment, the message application scenarios that need to be analyzed are clearly defined, such as financial transactions, medical information, user authentication, etc., and contextual data related to the plaintext messages to be encrypted are collected, including usage environment, data type, user needs, etc. Information can be obtained through questionnaires, user interviews or historical data analysis, and the collected data are classified and organized to extract key features of the application scenarios, such as data sensitivity, usage frequency, legal and regulatory requirements, etc. The extracted features are recorded in a data structure (such as a dictionary or data frame) to form message application scenario data, and standards for identifying security requirements are formulated to clarify which features and factors will affect the security requirements of messages (such as data confidentiality, integrity, and availability). Based on the collected message application scenario data, the security requirements in the current environment are analyzed, including exposed risks, potential attack surfaces, etc. The analysis results are compared with the security requirement standards to identify the security requirement data of the current message and record it in the data structure. An appropriate model or algorithm (such as fuzzy logic, machine learning algorithm) is selected to calculate the dynamic security sensitivity threshold, and collect Parameter data that affects security sensitivity, such as application scenario characteristics, security requirement levels, historical event data, etc., performs dynamic security sensitivity threshold calculation based on input parameters to generate the security sensitivity threshold of the current scenario, records the calculation results for subsequent use, determines the indicators used to evaluate the sensitivity of key semantic fields, such as data type, privacy level, legal compliance, etc., selects appropriate analysis tools or algorithms (such as decision trees, feature importance analysis) to evaluate the sensitivity of each key semantic field, analyzes the key semantic fields of each segment, calculates and generates its sensitivity value, stores the sensitivity results in a data structure to ensure its traceability, compares the sensitivity of each key semantic field with the security sensitivity threshold of the current scenario, and based on the comparison results, determines that when the sensitivity of the key semantic field is greater than or equal to the security sensitivity threshold of the current scenario, it is judged to be a high-sensitivity semantic field, traverses all key semantic fields, extracts multiple high-sensitivity semantic fields, and stores them in an appropriate data structure (such as a list or dictionary) for subsequent security processing.

[0086] In this embodiment, step S5 includes the following steps:

[0087] Step S51: symmetrically encrypting multiple highly sensitive semantic fields segment by segment to obtain multiple encrypted semantic fields;

[0088] Step S52: performing independent encapsulation processing on multiple encrypted semantic fields to generate multiple independent encryption units;

[0089] Step S53: Perform distributed encryption storage on multiple independent encryption units to build a distributed encryption storage framework.

[0090] In this embodiment, the symmetric encryption algorithm used is determined, such as AES, DES, or ChaCha20, and an appropriate mode (such as CBC, GCM) is selected to improve security. The key required for encryption is generated, ensuring that its length and complexity meet security standards. The key is securely stored to prevent unauthorized access. Each highly sensitive semantic field is traversed and encrypted using the selected symmetric encryption algorithm. The encrypted result is stored in a list or array to form multiple encrypted semantic fields. The encryption result is integrity checked to ensure that no errors occur in the encryption process. The encapsulation structure of the independent encryption unit is designed, including metadata such as field name, encrypted value, and timestamp for subsequent use. Each encrypted semantic field is encapsulated to create an independent encryption unit. The encapsulation format can use JSON, XML, or other structured formats to ensure that each independent encryption unit is complete and self-contained for easy access and management. All independent encryption units are encapsulated. The encryption unit is stored in a data structure (such as a list or dictionary) to ensure that it is easy to access and manage. Select a suitable distributed storage system (such as Apache Cassandra, MongoDB, Amazon S3, etc.) to ensure that it supports high availability and data redundancy. Design a distributed encryption storage framework, determine the data sharding strategy and access control mechanism to achieve efficient storage and secure management of data, distribute multiple independent encryption units in the designed distributed storage system, ensure that data is evenly distributed across multiple nodes to improve access speed and fault tolerance, formulate a data backup and recovery strategy to prevent data loss or damage, ensure regular backup of stored data, and test the effectiveness of the recovery process. Configure access permissions for the storage system to ensure that only authorized users can access and manage stored data. Implement monitoring and auditing mechanisms to track all access and modification operations to stored data.

[0091] In this embodiment, step S6 includes the following steps:

[0092] Step S61: Identify unencrypted data in the filtered optimized message based on multiple highly sensitive semantic fields and parameter information of each message segment, and extract all unencrypted data in the message;

[0093] Step S62: performing data fragmentation processing on all unencrypted data in the message to obtain unencrypted data fragments;

[0094] Step S63: performing sequential encoding processing on the unencrypted data slices and the encryption parameters of the multiple message segments to obtain encoded unencrypted data slices and encoded encryption parameters;

[0095] Step S64: performing obfuscated serialization integration on the encoded unencrypted data slices and the encoded encryption parameters, thereby obtaining an obfuscated data sequence;

[0096] Step S65: randomly embed and reconstruct the obfuscated data sequence into the distributed encryption storage framework to construct a randomly embedded distributed encryption framework to complete the secure encryption of parameters and messages.

[0097] In this embodiment, the message is traversed and filtered to optimize, and the highly sensitive semantic fields are used as keywords to identify the unencrypted data in the message that matches them. The location information of all unencrypted data (such as the number of lines, character positions) is recorded for subsequent processing. The extracted unencrypted data is stored in a list or dictionary for subsequent data sharding processing. The rules and sizes of the sharding are determined, for example, based on the number of bytes, the number of lines, or the specific character length, to ensure the rationality and effectiveness of the sharding. The stored unencrypted data is traversed and sharded according to the set rules to generate multiple unencrypted data slices, ensuring that each data slice contains sufficient information for subsequent processing. All generated unencrypted data slices are stored in a list to facilitate subsequent encoding processing. A sequential encoding method is determined, such as UTF-8 encoding, Base64 encoding, or a custom encoding scheme to improve data security and transmission efficiency. Each unencrypted data slice and the corresponding message segment encryption parameter are sequentially encoded to ensure that the encoded data can retain the original information structure for subsequent obfuscation processing. The encoded unencrypted data slice and the encoded data are separated. The encoded encryption parameters are stored in an appropriate data structure to facilitate subsequent operations. Appropriate obfuscation algorithms (such as AES obfuscation, XOR obfuscation, etc.) are selected to improve data security and prevent unauthorized access. The encoded unencrypted data pieces and the encoded encryption parameters are serialized and integrated to generate an obfuscated data sequence. This ensures that the obfuscation process can effectively mix the data, increase the complexity and incomprehensibility of the data, and store the generated obfuscated data sequence in an appropriate data structure for subsequent random embedding processing. The random embedding reconstruction strategy is determined to ensure that the data can be randomly distributed in the distributed storage framework to avoid the risks brought by centralized storage. The obfuscated data sequence is randomly embedded and reconstructed in the distributed encryption storage framework to ensure that each data piece and encryption parameter is stored in a dispersed manner during the storage process. A load balancing algorithm is used to ensure that data is evenly distributed among the nodes. The integrity of the embedded data is verified to ensure that all data pieces and encryption parameters are successfully embedded and accessible. A monitoring mechanism is implemented to track the health status and access status of the storage system to ensure data security and availability.

[0098] In this embodiment, the present invention further provides a device for securely encrypting parameters and messages, including:

[0099] The filtering optimization module is used to obtain the plaintext message to be encrypted; perform adaptive filtering and noise reduction on the encrypted plaintext message, and filter out outliers to obtain the filtered optimized message;

[0100] The parameter encryption module is used to identify the parameter information of the filtering optimization message and encrypt the parameters segment by segment to obtain encryption parameters of multiple message segments;

[0101] The semantic feature analysis module is used to perform deep semantic feature analysis on multiple message data segments, and then mine the key fields segment by segment to extract the key semantic fields of each segment;

[0102] The sensitivity judgment module is used to mine the semantic feature sensitivity of the key semantic fields of each segment, perform sensitivity judgment, and extract multiple highly sensitive semantic fields;

[0103] Distributed encryption module, used to perform segment-by-segment symmetrical encryption on multiple highly sensitive semantic fields, perform distributed encrypted storage, and build a distributed encrypted storage framework;

[0104] The embedding and reconstruction module is used to randomly embed and reconstruct the distributed encryption storage framework based on multiple message segment encryption parameters, and build a randomly embedded distributed encryption framework to complete the secure encryption of parameters and messages.

[0105] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the above-mentioned methods for securely encrypting parameters and messages when executing the computer program.

[0106] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of the method for securely encrypting parameters and messages described in any one of the above items.

[0107] Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution is embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media for storing program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0109] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0110] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for secure encryption of parameters and messages, characterized in that: The following steps are involved: Step S1: Obtain a plaintext message to be encrypted; perform adaptive filtering and noise reduction on the plaintext message to be encrypted, and perform outlier filtering and elimination to obtain a filtered optimized message; Step S2: Identify parameter information of the filtering optimization message and encrypt the parameters segment by segment, thereby obtaining encryption parameters of multiple message segments; Step S3: Perform deep semantic feature analysis on multiple message data segments, and then perform segment-by-segment key field mining to extract the key semantic fields of each segment; Step S4: Mining the semantic feature sensitivity of the key semantic fields of each segment, performing sensitivity judgment, and extracting multiple highly sensitive semantic fields; Step S5: symmetrically encrypt multiple highly sensitive semantic fields segment by segment, perform distributed encrypted storage, and build a distributed encrypted storage framework; Step S6: Based on multiple message segment encryption parameters, the distributed encryption storage framework is randomly embedded and reconstructed to construct a randomly embedded distributed encryption framework to complete the secure encryption operation of parameters and messages.

2. The method for secure encryption of parameters and messages according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtain the plaintext message to be encrypted; Step S12: performing multi-band wavelet transform decomposition on the encrypted plaintext message to obtain message data of multiple frequencies; Step S13: Analyze the noise characteristics of the message data of multiple frequencies one by one, and extract the message noise characteristics of each frequency; Step S14: Adaptively filter and reduce noise based on the noise characteristics of the message at each frequency, thereby obtaining a filtered and reduced-noise message; Step S15: Detect abnormal burst points on the filtered and denoised message and mark abnormal burst values; Step S16: Filter out the abnormal burst values to obtain a filtered optimized message.

3. The method for secure encryption of parameters and messages according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing semantic structure analysis on the filtered optimized message and extracting message semantic structure data; Step S22: performing complete semantic multi-segment decomposition of the filtered optimized message according to the message semantic structure data, thereby obtaining multiple message data segments; Step S23: Identifying parameter information of multiple message data segments and extracting parameter information of each message segment; Step S24: encrypting the parameter information of each message segment segment by segment, thereby obtaining multiple message segment encryption parameters; The specific encryption of the parameters is as follows: Extract parameter information of the first message segment; Convert the parameter information of the first message segment into a JSON string to obtain multiple JSON strings; Perform MD5 encryption on multiple JSON strings to generate multiple encrypted strings; Generate a 16-bit random character code and the current user's identity authentication data; symmetrically encrypting multiple encrypted character strings using the 16-bit random character code to obtain symmetrically encrypted character strings; Encapsulate the current user identity authentication data into a request header to obtain a user identity request header unit; The user identity request header unit is used to embed the symmetric encryption string into the header, thereby obtaining the message segment encryption parameters; All message segment encryption parameters are iteratively encrypted to obtain multiple message segment encryption parameters.

4. The method for secure encryption of parameters and messages according to claim 1, wherein: The specific steps of step S3 are: Step S31: performing deep semantic feature analysis on multiple message data segments to extract message semantic features of each segment; Step S32: performing contextual logical association analysis on the message semantic features of each segment to obtain message semantic logical association data; Step S33: performing intelligent semantic smoothing on the multiple message data segments based on the message semantic logic association data, thereby generating multiple semantically smoothed message segments; Step S34: mining key fields of multiple semantically smoothed message segments one by one, thereby extracting key semantic fields of each segment.

5. The method for secure encryption of parameters and messages according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing application scenario analysis on the plaintext message to be encrypted to obtain message application scenario data; Step S42: Identify the security requirements of the message application scenario data to obtain the current message security requirement data; Step S43: Calculate the dynamic security sensitivity threshold based on the current message security requirement data to generate the current scenario security sensitivity threshold; Step S44: performing semantic feature sensitivity mining on the key semantic fields of each segment, thereby generating the sensitivity of the key semantic fields; Step S45: Perform sensitivity judgment on the sensitivity of the key semantic field based on the current scenario security sensitivity threshold. When the sensitivity of the key semantic field is greater than or equal to the current scenario security sensitivity threshold, it is judged as a high-sensitivity semantic field. All key semantic fields are traversed to extract multiple high-sensitivity semantic fields.

6. The method for secure encryption of parameters and messages according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: symmetrically encrypting multiple highly sensitive semantic fields segment by segment to obtain multiple encrypted semantic fields; Step S52: performing independent encapsulation processing on multiple encrypted semantic fields to generate multiple independent encryption units; Step S53: Perform distributed encryption storage on multiple independent encryption units to build a distributed encryption storage framework.

7. The method for secure encryption of parameters and messages according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Identify unencrypted data in the filtered optimized message based on multiple highly sensitive semantic fields and parameter information of each message segment, and extract all unencrypted data in the message; Step S62: performing data fragmentation processing on all unencrypted data in the message to obtain unencrypted data fragments; Step S63: performing sequential encoding processing on the unencrypted data slices and the encryption parameters of the multiple message segments to obtain encoded unencrypted data slices and encoded encryption parameters; Step S64: performing obfuscated serialization integration on the encoded unencrypted data slices and the encoded encryption parameters, thereby obtaining an obfuscated data sequence; Step S65: randomly embed and reconstruct the obfuscated data sequence into the distributed encryption storage framework to construct a randomly embedded distributed encryption framework to complete the secure encryption of parameters and messages.

8. A device for securely encrypting parameters and messages, characterized in that: The method for executing the secure encryption of parameters and messages according to claim 1 comprises: The filtering optimization module is used to obtain the plaintext message to be encrypted; perform adaptive filtering and noise reduction on the encrypted plaintext message, and filter out outliers to obtain the filtered optimized message; The parameter encryption module is used to identify the parameter information of the filtering optimization message and encrypt the parameters segment by segment to obtain encryption parameters of multiple message segments; The semantic feature analysis module is used to perform deep semantic feature analysis on multiple message data segments, and then mine the key fields segment by segment to extract the key semantic fields of each segment; The sensitivity judgment module is used to mine the semantic feature sensitivity of the key semantic fields of each segment, perform sensitivity judgment, and extract multiple highly sensitive semantic fields; Distributed encryption module, used to perform segment-by-segment symmetrical encryption on multiple highly sensitive semantic fields, perform distributed encrypted storage, and build a distributed encrypted storage framework; The embedding and reconstruction module is used to randomly embed and reconstruct the distributed encryption storage framework based on multiple message segment encryption parameters, and build a randomly embedded distributed encryption framework to complete the secure encryption of parameters and messages.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method for securely encrypting parameters and messages according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for securely encrypting parameters and messages according to any one of claims 1 to 7 are implemented.

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