Blockchain-based Salmonella Resistance Monitoring Data Security Management System

Abstract

The application provides a Salmonella drug resistance monitoring data security management system based on a blockchain, and belongs to the technical field of data transmission management.The hierarchical encryption mechanism is constructed, the characteristics of the blockchain technology are fused, the standardized feature combination template architecture is designed, and the hierarchical transmission and smart contract verification system are matched, so that the multi-dimensional collaborative security management advantages are formed:the differential encryption strategy is implemented based on the data sensitivity, the symmetric and asymmetric encryption technologies are combined to guarantee the security of the data at all levels, the feature parameter modularization recombination and template association identification are used to optimize the transmission efficiency while guaranteeing the data integrity, the dispersed data can be accurately restored to the structured data set, the consensus node verification and the on-chain state updating driven by the smart contract are finally implemented, the whole-process standardized control of the data from the collection, the transmission to the storage is realized, and the Salmonella drug resistance monitoring data security management mode with the safety, the efficiency and the traceability is constructed.

Description

Technical Field

[0001] This invention relates to the field of data transmission management technology, and in particular to a blockchain-based data security management system for monitoring Salmonella drug resistance. Background Technology

[0002] In the field of public health surveillance and pathogenic microorganism research, the full-cycle safe management of Salmonella drug resistance data faces multiple technical challenges, including the following issues:

[0003] Salmonella resistance data includes multi-dimensional features such as gene sequences, strain phenotypes, and clinical sample metadata, with significant differences in sensitivity (e.g., genotypic features are involved in pathogen tracing and evolutionary analysis, while sample metadata is associated with patient privacy). Existing systems generally adopt a uniform encryption strategy, resulting in insufficient encryption strength for highly sensitive data and redundant encryption for low-sensitivity data, which wastes computing resources and fails to meet compliance requirements.

[0004] Highly sensitive data such as Salmonella drug resistance gene maps and strain traceability information involve public health safety and patient privacy. Traditional management systems are vulnerable to hacker attacks or unauthorized operations by internal personnel during transmission, leading to frequent data leaks and malicious tampering incidents.

[0005] Therefore, it is necessary to provide a blockchain-based data security management system for Salmonella resistance monitoring to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a blockchain-based data security management system for Salmonella resistance monitoring to solve the issues of easy data leakage, low security, resource waste, and low management efficiency.

[0007] The present invention provides a blockchain-based data security management system for Salmonella resistance monitoring, the security management system comprising:

[0008] The data acquisition module is used to acquire Salmonella drug resistance data obtained during the experiment, analyze and extract feature data to form a feature set;

[0009] The security classification module is used to set the security coefficient of features based on data sensitivity, assign security coefficients to each feature, and classify the security level of each feature according to the preset security threshold range to obtain the security level result.

[0010] The feature identification module is used to identify different types of parameters of each feature. Based on the obtained security level results, it identifies the different types of parameters of the feature and generates corresponding unique identifiers.

[0011] The template generation module is used to generate corresponding feature combination templates in sequence based on the identified different types of parameters;

[0012] The encrypted transmission module is used to transmit features hierarchically according to security levels and encrypt the transmission using blockchain technology during the transmission process; the association between the unique identifier and the feature combination template is preserved during the transmission process;

[0013] The receiving and storage module is used to receive the transmitted features, match the feature combination template according to the unique identifier, restore the type parameters of each feature, and integrate them into a complete dataset for storage on the blockchain.

[0014] Preferably, the specific steps for obtaining Salmonella drug resistance data during the experiment, analyzing and extracting feature data to form a feature set are as follows:

[0015] Acquire Salmonella resistance data obtained during the experiment and preprocess it, including cleaning the raw data, removing invalid or duplicate records, and standardizing the data format.

[0016] Bioinformatics was used to analyze and extract various types of features from Salmonella resistance data, including genotypic features, phenotypic features, and sample metadata, and these features were compiled into a feature set.

[0017] Preferably, the specific steps for setting security coefficients for features based on data sensitivity, assigning security coefficients to each feature, and classifying the security level of each feature according to a preset security threshold range are as follows:

[0018] The data sensitivity of each type of feature is identified, and different safety coefficients are set for each feature based on the data sensitivity. The data sensitivity includes high sensitivity, medium sensitivity and low sensitivity. The safety coefficient set for high sensitivity features is ≥0.8, the safety coefficient set for medium sensitivity features is 0.5-0.7, and the safety coefficient set for low sensitivity features is ≤0.3.

[0019] The preset security threshold ranges include 0-0.3 as Level 1, 0.3-0.7 as Level 2, and 0.7-1 as Level 3, and the features are mapped to the corresponding security levels according to the preset security threshold ranges.

[0020] Preferably, the specific steps for identifying different types of parameters for each feature, identifying the different types of parameters for the feature based on the obtained security level results, and generating corresponding unique identifiers are as follows:

[0021] Identify different types of parameters for each feature, including core parameters, auxiliary parameters, and related parameters.

[0022] Generate a hash value for each parameter as a corresponding unique identifier and associate it with the security level.

[0023] Preferably, the specific steps for generating corresponding feature combination templates sequentially based on the identified different types of parameters are as follows:

[0024] The parameters are sorted according to their type, namely core parameters, auxiliary parameters, and related parameters, to obtain the parameter sorting result.

[0025] Create a feature combination template to accommodate feature parameters. Reserve a space for each parameter within the framework of the feature combination template and label it with the feature name, parameter type, and security level information.

[0026] Based on the result of parameter sorting, and combined with the correspondence between the unique identifier and the parameter, the parameters are sequentially filled into the framework of the feature combination template. After the feature combination template is filled with parameters, a hash calculation is performed to generate a unique hash value, which serves as the template identifier.

[0027] Preferably, the specific steps of transmitting features hierarchically according to security levels and encrypting the transmission using blockchain technology during the transmission process are as follows:

[0028] Symmetric encryption is used for features with security level 1, and asymmetric encryption is used for features with security levels 2 and 3.

[0029] The encrypted feature data, unique identifier, and template hash value are packaged into a transaction and written into a smart contract for transmission, while the mapping relationship between the template and the unique identifier is preserved in the blockchain.

[0030] Preferably, the specific steps for receiving and transmitting features, matching feature combination templates based on unique identifiers, restoring the type parameters of each feature, and integrating them into a complete dataset for storage on the blockchain are as follows:

[0031] The receiver decrypts the data using its private key and compares the unique identifier with the template hash recorded on the blockchain.

[0032] Based on the parameter type definition in the unique identifier matching template, the features of the scattered transmission are reassembled according to the original structure;

[0033] After the complete dataset is verified by the consensus nodes, it is appended to the blockchain in the form of blocks, and the smart contract state is updated.

[0034] Compared with related technologies, the blockchain-based Salmonella resistance monitoring data security management system provided by this invention has the following beneficial effects:

[0035] This invention establishes a multi-dimensional collaborative security management advantage by constructing a hierarchical encryption mechanism, integrating blockchain technology characteristics, designing a standardized feature combination template architecture, and providing a supporting hierarchical transmission and smart contract verification system. It implements differentiated encryption strategies based on data sensitivity, combining symmetric and asymmetric encryption technologies to ensure data security at all levels, while leveraging the immutability and consensus traceability of blockchain to enhance the credibility of data throughout its entire lifecycle. Through modular recombination of feature parameters and template association identification, it optimizes transmission efficiency while ensuring data integrity, enabling the accurate reconstruction of dispersed data into structured datasets. Finally, through smart contract-driven consensus node verification and on-chain state updates, it achieves standardized control over the entire process of data collection, transmission, and storage, constructing a secure data management method for Salmonella resistance monitoring that combines security, efficiency, and traceability. Attached Figure Description

[0036] Figure 1 This is a system block diagram of the blockchain-based Salmonella antibiotic resistance monitoring data security management system of the present invention. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] Example

[0039] like Figure 1 As shown, the blockchain-based Salmonella resistance monitoring data security management system includes:

[0040] The data acquisition module is used to acquire Salmonella drug resistance data obtained during the experiment, analyze and extract feature data to form a feature set;

[0041] The security classification module is used to set the security coefficient of features based on data sensitivity, assign security coefficients to each feature, and classify the security level of each feature according to the preset security threshold range to obtain the security level result.

[0042] The feature identification module is used to identify different types of parameters of each feature. Based on the obtained security level results, it identifies the different types of parameters of the feature and generates corresponding unique identifiers.

[0043] The template generation module is used to generate corresponding feature combination templates in sequence based on the identified different types of parameters;

[0044] The encrypted transmission module is used to transmit features hierarchically according to security levels and encrypt the transmission using blockchain technology during the transmission process; the association between the unique identifier and the feature combination template is preserved during the transmission process;

[0045] The receiving and storage module is used to receive the transmitted features, match the feature combination template according to the unique identifier, restore the type parameters of each feature, and integrate them into a complete dataset for storage on the blockchain.

[0046] In the specific implementation process, the steps of the data acquisition module include:

[0047] Acquire Salmonella resistance data obtained during the experiment and preprocess them, including cleaning the raw data, removing invalid or duplicate records, and standardizing the data format.

[0048] Specifically, Salmonella resistance data are collected from experimental databases, experimental record files, and other sources. The data is then checked for missing values, outliers, or errors. For example, some experimental records may contain missing records of bacterial growth; these need to be corrected or deleted. Invalid records refer to those that are not meaningful for analyzing Salmonella resistance, such as invalid test data caused by operational errors during the experiment. Duplicate records refer to identical data entries. Data is uniformly converted to a standard format for subsequent data analysis and processing. For example, timestamps are uniformly converted to the format YYYY-MM-DDHH:MM:SS, and drug names are uniformly used in full or standard abbreviations.

[0049] Bioinformatics was used to analyze and extract various types of features from Salmonella resistance data, including genotypic features, phenotypic features, and sample metadata.

[0050] Specifically, tools and methods in bioinformatics include gene sequencing analysis software and bioinformatics databases. Gene sequencing analysis software is used to analyze the gene sequences of Salmonella, extracting characteristics such as gene mutations and gene expression levels related to drug resistance. For example, mutations in certain genes may lead to bacterial resistance to specific drugs. Phenotypic characteristics are also identified, including observing the growth of Salmonella under different drug environments and the size of inhibition zones; these intuitive manifestations are called phenotypic features. Obtaining sample metadata includes information such as the sample's origin (e.g., animal, food, environment), collection time, and collection location. Although sample metadata does not directly reflect drug resistance, it is of great significance for analyzing the transmission patterns and influencing factors of drug resistance.

[0051] In the specific implementation process, the steps for security partitioning modules include:

[0052] The data sensitivity of each type of feature is identified, and different safety coefficients are set for each feature based on the data sensitivity. The data sensitivity includes high sensitivity, medium sensitivity and low sensitivity. The safety coefficient set for high sensitivity features is ≥0.8, the safety coefficient set for medium sensitivity features is 0.5-0.7, and the safety coefficient set for low sensitivity features is ≤0.3.

[0053] Specifically, regarding the data sensitivity of different types of features, in genotypic features: gene mutation information directly related to drug resistance is highly sensitive. For example, the presence of certain drug-resistant genes or mutations at specific gene loci, once leaked, could be exploited by criminals to develop targeted drug-resistant strains or evade drug detection, thus posing a serious threat to public health and safety. Simultaneously, genetic information involving the personal privacy of patients or sample providers (such as sequences potentially related to the human genome) is also highly sensitive. In phenotypic features: the degree of bacterial resistance to different drugs is of medium sensitivity. In sample metadata: features such as collection location, time, and source are of low sensitivity. In this embodiment, genotypic features include feature A: the detection of a novel drug-resistant gene in Salmonella, which is associated with resistance to multiple antibiotics and has not yet been publicly reported. This feature involves highly sensitive drug-resistant gene information, therefore a safety factor of 0.9 is set. Phenotypic characteristics include characteristic B: the inhibition zone diameter of a certain Salmonella sample against multiple commonly used antibiotics (such as penicillins and cephalosporins) is smaller than the standard value, indicating that the strain has multidrug resistance. Although this characteristic reflects the current drug resistance status of bacteria, it does not involve specific genetic information, so the sensitivity is medium, and a safety factor of 0.6 is set. The sample metadata includes information that the sample was collected from a large-scale poultry farm. If the information on drug-resistant strains is leaked, it may affect the local livestock industry and food safety. However, the sensitivity of this metadata alone is low, and a safety factor of 0.5 is set.

[0054] The preset security threshold ranges include 0-0.3 as Level 1, 0.3-0.7 as Level 2, and 0.7-1 as Level 3, and the features are mapped to the corresponding security levels according to the preset security threshold ranges.

[0055] Specifically, based on the needs and actual situation of data security management, preset security threshold ranges are established, dividing the security coefficient range into different levels. In this embodiment, the preset security threshold ranges are: 0-0.3 for Level 1, 0.3-0.7 for Level 2, and 0.7-1 for Level 3. Based on the security coefficient set for each feature, it is mapped to the preset security threshold range, thereby determining the security level of each feature. The higher the security level, the more sensitive the data of that feature is. In this embodiment, feature A has a security coefficient of 0.9, falling within the 0.7-1 security threshold range. Therefore, feature A has a security level of Level 3, belonging to a high-security feature. For such features, the highest level of security protection measures need to be taken in subsequent data processing and transmission, such as using strong encryption algorithms and multi-factor authentication.

[0056] In the specific implementation process, the steps of the feature identification module include:

[0057] Identify different types of parameters for each feature, including core parameters, auxiliary parameters, and related parameters.

[0058] Specifically, different types of parameters for each feature are identified. The identification criteria are as follows: Core parameters are key data describing the drug resistance characteristics of Salmonella, directly determining the core attributes and significance of the feature. For genotypic features, core parameters are gene sequences and mutation sites directly related to drug resistance; for phenotypic features, core parameters are quantitative indicators reflecting the degree of drug resistance, including minimum inhibitory concentration (MIC) and inhibition zone diameter; for sample metadata, core parameters are information specifying key characteristics such as sample source and time. Auxiliary parameters supplement and explain the core parameters. For example, gene expression levels and regulatory element information in genotypic features; bacterial growth environmental conditions (such as temperature and pH) and drug action time in phenotypic features; and sample collection methods and storage conditions in sample metadata. Related parameters are data that are related to the current feature but are not directly used to describe that feature; they are specifically identified using data mining techniques.

[0059] Generate a hash value for each parameter as a corresponding unique identifier and associate it with the security level.

[0060] Specifically, a hash algorithm, including SHA-256 or MD5, is selected. The original data for each parameter is used as input, and the chosen hash algorithm is used to calculate the corresponding hash value. The hash value is a fixed-length string that is unique and irreversible; different input data will generate different hash values, and the original data cannot be deduced from the hash value. Next, a mapping relationship is established: a data structure (such as a dictionary or database table) is created to associate the hash value (unique identifier) ​​generated for each parameter with the security level of that parameter's characteristic. During data processing, whenever a new parameter unique identifier is generated, its association with the security level is promptly stored in the corresponding data structure.

[0061] In the specific implementation process, the steps of the template generation module include:

[0062] The parameters are sorted according to their type, namely core parameters, auxiliary parameters, and related parameters, to obtain the parameter sorting results.

[0063] Specifically, the parameters are sorted in the order of core parameters, auxiliary parameters, and related parameters. Different types of parameters for each feature are identified, and the core, auxiliary, and related parameters are categorized and organized separately. For example, by analyzing the feature definition and extraction process, it is determined which parameters belong to core parameters, which to auxiliary parameters, and which to related parameters, and these are stored in different datasets. The categorized parameters are then arranged sequentially according to a predetermined sorting rule to obtain the parameter sorting result.

[0064] Create a feature combination template to accommodate feature parameters. Reserve a space for each parameter within the framework of the feature combination template and label it with the feature name, parameter type, and security level information.

[0065] Specifically, design a general feature combination template framework that can accommodate various types of feature parameters, using a structured data format, including JSON or XML. At each parameter's reserved location, label the parameter's feature name, parameter type (core parameter, auxiliary parameter, related parameter), and security level information. Define the designed feature combination template as a reusable data structure and store it for repeated use.

[0066] Based on the result of parameter sorting, and combined with the correspondence between the unique identifier and the parameter, the parameters are sequentially filled into the framework of the feature combination template. After the feature combination template is filled with parameters, a hash calculation is performed to generate a unique hash value, which serves as the template identifier.

[0067] Specifically, based on the parameter sorting results and the correspondence between unique identifiers and parameters, the unique identifier of each parameter is sequentially filled into the "parameter value" field of the corresponding parameter in the feature combination template. For example, for the first parameter after sorting, its corresponding unique identifier is found and written into the "parameter value" position of the corresponding parameter in the template. A hash calculation is performed on the feature combination template after parameter filling to generate a unique hash value as the template identifier. In this embodiment, the SHA-256 hash algorithm is selected, and the entire feature combination template data is used as input to calculate a fixed-length hash value. The generated template hash value is associated with the feature combination template and stored so that the feature combination template can be quickly located and verified through the template hash value during subsequent data transmission and reception.

[0068] In the specific implementation process, the steps of the encrypted transmission module include:

[0069] Symmetric encryption is used for features with a security level of 1, and asymmetric encryption is used for features with a security level of 2 and 3.

[0070] Specifically, for features with a security level of 1 (low sensitivity, security factor ≤ 0.3), the AES algorithm, a symmetric encryption algorithm known for its high computational efficiency and ease of implementation, is typically chosen. The feature data with a security level of 1 is used as plaintext, and encrypted using the generated symmetric key and the selected AES algorithm to obtain ciphertext data. For features with security levels of 2 and 3, the ECC algorithm, an asymmetric encryption algorithm already in use, is employed. The feature data with a security level of 2 is used as plaintext, and encrypted using the recipient's public key to obtain ciphertext data.

[0071] The encrypted feature data, unique identifier, and template hash value are packaged into a transaction and written into a smart contract for transmission, while the mapping relationship between the template and the unique identifier is preserved in the blockchain.

[0072] Specifically, the encrypted feature data of different security levels are integrated to form a complete data packet. The unique identifier generated for each parameter in step 3 and the unique hash value of the feature combination template generated in step 4 are added to the data packet. The unique identifier is used to match the corresponding parameter when the receiver restores the data, and the template hash value is used to verify whether the received data is consistent with the original feature combination template. Next, a smart contract specifically designed for Salmonella resistance monitoring data transmission is defined. The smart contract defines the rules and logic for data reception, verification, and storage. In this embodiment, the smart contract stipulates that the receiver must use the correct private key to decrypt the data, verify the matching relationship between the unique identifier and the template hash value, and how to update the state of the smart contract after successful data verification. Finally, the packaged data packet is used as the data part of a transaction, a blockchain transaction is created, the smart contract address is specified as the receiver in the transaction, and the transaction is sent to the blockchain network.

[0073] In the specific implementation process, the steps for receiving the storage module include:

[0074] The recipient decrypts the data using their private key and compares the unique identifier with the template hash recorded on the blockchain.

[0075] Specifically, after the receiver receives the encrypted feature data, it uses its private key to decrypt the encrypted data and extracts the unique identifier corresponding to each feature parameter from the decrypted data. Then, it compares the unique identifier with the template hash recorded on the chain.

[0076] Based on the parameter type definition in the unique identifier matching template, the features of the scattered transmission are reassembled according to the original structure.

[0077] Specifically, detailed information about the feature combination template is obtained from the blockchain, including the position of each parameter in the template, the feature name, the parameter type (core parameter, auxiliary parameter, related parameter), and the security level. Based on the extracted unique identifier, the corresponding parameter is found in the feature combination template. Then, the decrypted, scattered feature parameters are organized according to their position and parameter type in the feature combination template. According to the framework of the feature combination template, each parameter is sequentially filled into the corresponding reserved position to restore the original structure of the feature.

[0078] After the complete dataset is verified by the consensus nodes, it is appended to the blockchain in the form of blocks, and the smart contract state is updated.

[0079] Specifically, the recipient submits the reconstructed complete dataset to the blockchain network. Upon submission, the data is sent to blockchain nodes in the form of transactions. Consensus nodes in the blockchain network verify the submitted data. Once the data passes the verification of the consensus nodes, a node in the blockchain network packages the data along with other valid transactions within the same time period into a new block. After the complete dataset is successfully appended to the blockchain, the smart contract updates these state variables.

[0080] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0082] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1.A blockchain-based Salmonella drug resistance monitoring data security management system, characterized in that, The security management system comprises: A data acquisition module for acquiring Salmonella drug resistance data obtained during the experiment, analyzing and extracting feature data to form a feature set; A security division module for setting a security coefficient for the features based on data sensitivity, assigning a security coefficient to each feature, and dividing the security level of each feature according to a pre-set security threshold section to obtain a security level result; A feature identification module for identifying different types of parameters of each feature, identifying different types of parameters of each feature according to the obtained security level result, and generating a corresponding unique symbol; A template generation module for generating a corresponding feature combination template in sequence according to the identified different types of parameters; An encrypted transmission module for hierarchical transmission of features according to the security level, and encrypted transmission through blockchain technology during transmission; the association between the unique symbol and the feature combination template is preserved during transmission; A receiving and storing module for receiving the transmitted features, matching the feature combination template according to the unique symbol, restoring the type parameters of each feature, and integrating them into a complete data set for storage in the blockchain; The specific steps of identifying different types of parameters of each feature, identifying different types of parameters of each feature according to the obtained security level result, and generating a corresponding unique symbol are as follows: Identify different types of parameters of each feature, wherein different types of parameters include core parameters, auxiliary parameters and associated parameters; Generate a hash value for each parameter as a corresponding unique symbol, and associate it with the security level; The specific steps of generating a corresponding feature combination template in sequence according to the identified different types of parameters are as follows: Sort the feature parameter types, i.e. core parameters, auxiliary parameters and associated parameters, in sequence to obtain a parameter sorting result; Create a feature combination template for containing feature parameters, reserve a position for each parameter in the framework of the feature combination template, and label the feature name, parameter type and security level information; Based on the parameter sorting result, combine the unique symbol with the parameter correspondence, fill the parameters into the framework of the feature combination template in sequence, and perform hash calculation on the feature combination template with filled parameters to generate a unique hash value as the template identifier. 2.The blockchain-based Salmonella drug resistance monitoring data security management system according to claim 1, characterized in that, The specific steps of acquiring Salmonella drug resistance data obtained during the experiment, analyzing and extracting feature data to form a feature set are as follows: Acquire Salmonella drug resistance data obtained during the experiment, and preprocess it, wherein preprocessing includes cleaning raw data, removing invalid or duplicate records, and standardizing data format; Analyze and extract various types of features from the Salmonella drug resistance data through bioinformatics, including genotype features, phenotype features and sample metadata, and organize them into a feature set. 3.The blockchain-based Salmonella drug resistance monitoring data security management system according to claim 2, characterized in that, The specific steps of setting a security coefficient for the features based on data sensitivity, assigning a security coefficient to each feature, are as follows: Identify the data sensitivity of each type of feature, and set different security coefficients for each feature based on data sensitivity, wherein data sensitivity includes high sensitivity, medium sensitivity and low sensitivity. 4.The blockchain-based Salmonella drug resistance monitoring data security management system of claim 1, wherein, The specific steps of hierarchical transmission of features according to the security level, and encrypted transmission through blockchain technology during transmission are as follows: Symmetric encryption is performed on features with a security level of one, and asymmetric encryption is performed on features with a security level of two and three; The encrypted feature data, unique symbol, and template identification are packaged as a transaction and written to the smart contract for transmission, and the mapping relationship between the feature combination template and the unique symbol is retained on the blockchain. 5.The blockchain-based Salmonella drug resistance monitoring data security management system according to claim 4, characterized in that, The received features are transmitted, and the type parameters of each feature are restored according to the unique symbol matching the feature combination template, and integrated into a complete data set for storage on the blockchain, and the specific steps are as follows: The receiver decrypts the data using a private key and compares the unique symbol with the template identification recorded on the chain; According to the parameter type definition in the feature combination template, the dispersed features are reorganized according to the original structure according to the unique symbol matching the feature combination template; The complete data set is verified by the consensus node, and is appended to the blockchain in the form of a block, and the smart contract state is updated.

Images