Encrypted data security synchronization sharing method and transmission system

Abstract

The invention relates to the technical field of data security, in particular to an encrypted data security synchronization sharing method and transmission system, comprising: acquiring various data in supply chain participants; processing the standardized supply chain data through an encryption algorithm, and uploading the processed supply chain data to a block chain network; designing a smart contract of the block chain based on the data trust degree and the data security degree, and defining shared access control of the data; according to the secure synchronous sharing method for the encrypted data provided by the invention, a secure, credible, efficient and transparent supply chain data sharing mode is realized by utilizing an encryption algorithm, an intelligent contract and data security evaluation, the credibility and security of the data are effectively improved, a powerful support is provided for supply chain management and decision making, and the security of the supply chain is improved. According to the method, a block chain technology is combined with supply chain management, and safe and efficient sharing of supply chain data is realized through a specific intelligent contract design and a data processing flow.

Description

Technical Field

[0001] This invention relates to the field of data security technology, specifically to an encrypted data security synchronization and sharing method and transmission system. Background Technology

[0002] In current supply chain management, the secure sharing of encrypted data is a core challenge. Existing technical solutions mostly employ centralized databases or cloud platforms to store and share encrypted supply chain data, such as ERP (Enterprise Resource Planning) systems and SCM (Supply Chain Management) platforms. These systems ensure the security of encrypted data through methods such as setting access permissions and encrypting transmission. However, centralized data management architectures inherently possess security risks, such as single points of failure, data tampering, and unauthorized access. Especially in complex supply chain environments involving multiple parties, the security and trustworthiness of data cannot be fully guaranteed.

[0003] Existing technologies ensure the security of encrypted data access through access permissions, public keys, and private keys. However, they neglect the security of the data itself. Encrypted data is easily tampered with during its existence and use, which can lead to users receiving incorrect data and causing losses. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method and transmission system for secure synchronous sharing of encrypted data.

[0005] This invention employs the following technical solution: a method for secure synchronous sharing of encrypted data, comprising:

[0006] Step S01: Obtain various types of data from supply chain participants, record them as supply chain data, and standardize the supply chain data.

[0007] Step S02: After the standardized supply chain data is processed by encryption algorithm, it is uploaded to the blockchain network;

[0008] Step S03: Obtain the data trustworthiness and data security of the supply chain data;

[0009] Step S04: Design a blockchain smart contract based on data trust and data security, define data sharing access control, and use the blockchain smart contract to realize data sharing access control.

[0010] As a further description of the above technical solution: the method of uploading the standardized supply chain data to the blockchain network after processing it with an encryption algorithm includes:

[0011] Select the hash algorithm as the encryption algorithm, input supply chain data to generate hash values;

[0012] Upload the complete supply chain data to an external storage system and obtain the storage address;

[0013] Save the hash value and store it together with the storage address in the blockchain to ensure the integrity of verifiable data.

[0014] As a further description of the above technical solution: the standardization process includes unifying the storage format of supply chain data to ensure compatibility of data from different sources, setting unified data fields, and adopting a standardized data model;

[0015] The storage format includes JSON, XML, CSV, or a database table structure.

[0016] As a further description of the above technical solution: the supply chain data includes production information data, logistics information data, quality inspection report data, warehousing data, raw material data, production plan data, and supplier information data.

[0017] As a further description of the above technical solution: the method for obtaining data trust includes:

[0018] Trust characteristic parameters of the collected data;

[0019] The trust feature parameters of the data include data transparency, data consistency, and verification count. The data transparency and data consistency are quantified and rated using a score of 1-10.

[0020] The trust coefficient of the data is obtained by analyzing the aforementioned trust feature parameters;

[0021] The acquisition method, trust coefficient, and historical credit of the data provider are recorded as the initial data.

[0022] The collected initial data is input into a pre-built first machine learning model, which outputs the data confidence level.

[0023] As a further description of the above technical solution: the method for obtaining the data security level includes:

[0024] S3.1 Obtain the number of participants in the data verification, the number of verification attempts, and the number of verification failures and successes, and establish a verification dataset;

[0025] Validate the dataset:

[0026] YH p YZC represents the p-th verification participant. p T represents the number of verifications associated with the p-th verification participant. pThis represents the success rate of the p-th verification participant;

[0027] S3.2, Obtain data access permissions, and generate quantization coefficient one based on the access permissions;

[0028] S3.3, Obtain the data storage method, and generate quantization coefficient two based on the storage method;

[0029] S3.4, the number of data verification participants, the success rate of verification participants, the number of verifications, quantization coefficient one and quantization coefficient two are weighted and summed to obtain the data security level.

[0030] As a further description of the above technical solution: the method of generating quantization coefficient one based on access permissions includes: establishing a relationship between access permissions and access security levels, i.e., access control method → ​​description → security level assignment; obtaining the corresponding access security level assignment based on the established relationship between access permissions and security levels; when data has multiple access permissions, calculating the weighted average value as the access security level assignment.

[0031] The method for generating quantization coefficient 2 based on storage method includes: establishing a relationship between storage method and storage security level assignment, i.e., storage method → ​​description → storage security level assignment; obtaining the corresponding storage security level assignment based on the established relationship between storage method and storage security level assignment; and when data has different storage mechanisms, calculating the weighted average as the storage security level assignment.

[0032] As a further description of the above technical solution: the method for obtaining the trust coefficient of data based on the trust feature parameters includes: performing a weighted calculation on the data transparency, data consistency and verification count to obtain the trust coefficient of the data.

[0033] As a further description of the above technical solution: the method for designing blockchain smart contracts based on data trust and data security, and defining data sharing access control, includes:

[0034] Preset data trust threshold F and data security threshold H;

[0035] When the data trust level is greater than or equal to the data trust level threshold F, and the data security level is greater than or equal to the data security level threshold H, automatic sharing is allowed.

[0036] When the data trust level is greater than or equal to the data trust level threshold F and the data security level is less than the data security level threshold H, or when the data trust level is less than the data trust level threshold F and the data security level is greater than or equal to the data security level threshold H, data generation requires additional verification before sharing.

[0037] When the data trust level is less than the data trust level threshold F and the data security level is less than the data security level threshold H, sharing is prohibited.

[0038] A secure synchronous sharing and transmission system for encrypted data is provided to implement the aforementioned secure synchronous sharing method for encrypted data. The system includes:

[0039] The data acquisition module acquires various types of data from supply chain participants, records them as supply chain data, and performs standardized processing on the supply chain data.

[0040] The data upload module processes the standardized supply chain data using encryption algorithms and then uploads it to the blockchain network.

[0041] The data analysis module assesses the trustworthiness and security of supply chain data.

[0042] The shared configuration module designs blockchain smart contracts based on data trust and data security, defines data sharing access control, and uses blockchain smart contracts to realize data sharing access control.

[0043] Beneficial effects:

[0044] This invention provides a secure, synchronous, and shared encrypted data method and transmission system. By utilizing encryption algorithms, smart contracts, and data security assessment, it achieves a secure, reliable, efficient, and transparent supply chain data sharing model, effectively improving data credibility and security. This provides strong support for supply chain management and decision-making. Specifically, it combines blockchain technology with supply chain management, and through specific smart contract design and data processing procedures, achieves secure and efficient sharing of supply chain data.

[0045] Furthermore, by calculating data security through weighted summation, the evaluation system can comprehensively consider data verification, access control, and storage security, thereby providing a more refined security management mechanism for supply chain data. Through multi-dimensional data quantification, the data security assessment becomes more scientific and reasonable, while enhancing the system's automated processing capabilities, reducing human intervention, improving the efficiency and reliability of supply chain data management, and enhancing the security of the data itself. This provides a more robust security guarantee for blockchain data sharing and smart contract execution.

[0046] Finally, by quantifying trust characteristics in multiple dimensions, the calculation of data trustworthiness becomes more scientific and reasonable. Combined with machine learning optimization models, the automation level of the system is improved, human intervention is reduced, and the credibility of supply chain data is enhanced. Attached Figure Description

[0047] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0048] Figure 1A flowchart illustrating a secure synchronous sharing method for encrypted data provided in an embodiment of the present invention;

[0049] Figure 2 A flowchart illustrating the method for obtaining data security level provided in an embodiment of the present invention;

[0050] Figure 3 A flowchart illustrating the method for obtaining data trust level provided in an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of an encrypted data security synchronous sharing and transmission system provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the technical means, creative features, objectives, and effects of this invention readily understandable, the invention is further described below with reference to specific illustrations. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0053] Example 1

[0054] Please see Figure 1 This invention provides a technical solution: a method for secure synchronous sharing of encrypted data, comprising:

[0055] Step S01: Obtain various types of data from supply chain participants, record them as supply chain data, and standardize the supply chain data.

[0056] The standardization process includes unifying the storage format of supply chain data to ensure compatibility of data from different sources, setting unified data fields, and adopting a standardized data model.

[0057] The storage format includes JSON, XML, CSV, or a database table structure.

[0058] It should be noted that the standardized data models include GS1 (Global Standard 1) and EPCIS (Electronic Product Code Information Service), which enhance interoperability through standardized data models.

[0059] In this embodiment, standardized processing ensures the consistency, integrity, and security of supply chain data, providing a reliable foundation for subsequent blockchain storage, smart contract execution, and data sharing.

[0060] The supply chain data includes production information data, logistics information data, quality inspection report data, warehousing data, raw material data, production plan data, and supplier information data.

[0061] Step S02: After the standardized supply chain data is processed by encryption algorithm, it is uploaded to the blockchain network;

[0062] Methods for uploading standardized supply chain data to a blockchain network after processing it with encryption algorithms include:

[0063] Select the hash algorithm as the encryption algorithm, input supply chain data to generate hash values;

[0064] Upload the complete supply chain data to an external storage system and obtain the storage address;

[0065] Save the hash value and store it together with the storage address in the blockchain to ensure the integrity of the data that can be verified later.

[0066] Step S03: Obtain the data security and data trustworthiness of the supply chain data;

[0067] Step S04: Design a blockchain smart contract based on data trust and data security, define data sharing access control, and use the blockchain smart contract to realize data sharing access control.

[0068] Based on data trust and data security, the design of blockchain smart contracts and the methods for defining data sharing access control include:

[0069] Preset data trust threshold F and data security threshold H;

[0070] When the data trust level is greater than or equal to the data trust level threshold F, and the data security level is greater than or equal to the data security level threshold H, automatic sharing is allowed.

[0071] When the data trust level is greater than or equal to the data trust level threshold F and the data security level is less than the data security level threshold H, or when the data trust level is less than the data trust level threshold F and the data security level is greater than or equal to the data security level threshold H, data generation requires additional verification before sharing.

[0072] When the data trust level is less than the data trust level threshold F and the data security level is less than the data security level threshold H, sharing is prohibited.

[0073] In this embodiment, the blockchain-based supply chain data security sharing method utilizes encryption algorithms, smart contracts, and data security assessments to achieve a secure, reliable, efficient, and transparent supply chain data sharing model. This effectively enhances the credibility, traceability, and security of the data, providing strong support for supply chain management and decision-making. It combines blockchain technology with supply chain management, and through specific smart contract design and data processing procedures, achieves secure and efficient sharing of supply chain data.

[0074] It should be noted that data trust refers to the trustworthiness of each type of data in the supply chain, while data security refers to the security of each type of data in the supply chain.

[0075] Example 2

[0076] Please see Figure 1 and Figure 2 The present invention provides a technical solution:

[0077] The methods for obtaining the data security level include:

[0078] S3.1 Obtain the number of participants in the data verification, the number of verification attempts, and the number of verification failures and successes, and establish a verification dataset;

[0079] Validate the dataset:

[0080] YH p YZC represents the p-th verification participant. p T represents the number of verifications associated with the p-th verification participant. p This represents the success rate of the p-th verification participant;

[0081] It should be noted that the more times data is successfully verified, the higher its security level; conversely, the more times data fails verification, the higher the failure rate may indicate that the data has been tampered with or that its source is unreliable.

[0082] By acquiring the number of participants in data verification, the number of verifications, and the number of successful and failed verifications, the authenticity and stability of the data can be objectively assessed. The more times the data is successfully verified, the higher the security level. A high failure rate may mean that the data is abnormal or has been tampered with. This mechanism can help the supply chain system automatically identify untrusted data and avoid the impact of low-quality data on the decision-making process.

[0083] S3.2, Obtain data access permissions, and generate quantization coefficient one based on the access permissions;

[0084] The method for generating quantization coefficient one based on access permissions includes:

[0085] Establish the relationship between access permissions and access security levels, i.e., access control method → ​​description → security level assignment;

[0086] Example:

[0087] No access control → Data is completely public and anyone can access it → 1;

[0088] Password-based access → Only users with the password can access the data → 2;

[0089] Based on whitelist / IP restrictions → Only specific IPs or whitelisted users can access → 3;

[0090] RBAC (Role-Based Access Control) → Users are assigned permissions according to their roles (such as administrator, user, auditor) → 4;

[0091] ABAC (Attribute-Based Access Control) → Access permissions are dynamically determined by user attributes (identity, location, device) → 5;

[0092] Zero Trust Access Control → Employing Dynamic Authentication, Multi-Factor Authentication (MFA) + Behavioral Analysis → 6;

[0093] The corresponding access security level value is obtained based on the established relationship between access permissions and security level assignment.

[0094] When data has multiple access permissions, the weighted average value is calculated and assigned as the access security level.

[0095] It should be noted that by quantitatively assessing access permissions, data security levels can be managed in a more refined manner based on data sharing strategies. Different access control methods correspond to different security levels; for example, public data has the lowest security level, while zero-trust access control has the highest. For data with multiple access permissions, a weighted average is used to calculate the security level, making the assessment more accurate.

[0096] S3.3, Obtain the data storage method, and generate quantization coefficient two based on the storage method;

[0097] Methods for generating quantization coefficients based on storage methods include:

[0098] Establish the relationship between storage method and storage security level assignment, i.e., storage method → ​​description → storage security level assignment;

[0099] Example:

[0100] No backup, single-point storage → only one copy of the data, easily lost → 1;

[0101] Regular backups, single-point storage → Data is backed up regularly, but still stored centrally → 2;

[0102] Master-Slave replication → with a master database + a standby database → 3;

[0103] RAID Redundancy Storage → Use RAID (such as RAID1 / 5 / 10) to provide storage redundancy → 4;

[0104] Distributed storage (such as IPFS, HDFS) → Data is stored on multiple nodes to prevent single point of failure → 5;

[0105] Blockchain storage (such as public chains / consortium chains) → Data is recorded on the blockchain and cannot be tampered with → 6.

[0106] The corresponding storage security level value is obtained based on the established relationship between storage method and storage security level assignment.

[0107] When data has different storage mechanisms, a weighted average value is calculated and assigned as the storage security level.

[0108] It should be noted that through quantitative evaluation of storage methods, different storage strategies correspond to different security levels. Single-point storage offers the lowest security, while distributed storage and blockchain storage provide higher security and reliability, ensuring that data is not lost due to single-point failures or malicious tampering. When data storage involves multiple technical solutions, a weighted average is used to calculate the storage security level, ensuring that the data security assessment more accurately reflects the actual situation.

[0109] S3.4, the number of data verification participants, the success rate of verification participants, the number of verifications, quantization coefficient one and quantization coefficient two are weighted and summed to obtain the data security level.

[0110] In this embodiment, data security is calculated by weighted summation, enabling the evaluation system to comprehensively consider data verification, access control, and storage security. This provides a more refined security management mechanism for supply chain data. Through multi-dimensional data quantification, the data security assessment becomes more scientific and reasonable. At the same time, it enhances the system's automated processing capabilities, reduces human intervention, and improves the efficiency and reliability of supply chain data management, providing a more robust security guarantee for blockchain data sharing and smart contract execution.

[0111] Example 3

[0112] Please see Figure 1 and Figure 3 The present invention provides a technical solution:

[0113] The methods for obtaining data trustworthiness include:

[0114] Trust characteristic parameters of the collected data;

[0115] The trust feature parameters of the data include data transparency, data consistency, and verification count. The data transparency and data consistency are quantified and rated using a score of 1-10.

[0116] It's important to note that by quantifying data transparency and consistency using a 1-10 rating scale, the trustworthiness calculation is made quantifiable, allowing for a direct measurement of data credibility. Higher data transparency indicates more open data sources and higher credibility; higher data consistency signifies data stability across different sources, also increasing credibility; and increased verification frequency further enhances data reliability and reduces the impact of random factors. Obtaining the data trustworthiness coefficient through weighted calculations allows for a reasonable balance among different data characteristics, resulting in a more scientific and reasonable final assessment.

[0117] The trust coefficient of the data is obtained by analyzing the trust feature parameters. The method is as follows: the data transparency, data consistency and verification number are weighted and calculated to obtain the trust coefficient of the data.

[0118] The data acquisition method, trust coefficient, and historical credit of the data provider are recorded as the initial data.

[0119] The collected initial data is input into a pre-built first machine learning model, which outputs the data confidence level.

[0120] The method for constructing the first machine learning model includes:

[0121] The first machine learning model structure is initialized. The first machine learning model structure adopts a multi-layer feedforward network structure of MLP type, with three input layers, two hidden layers, and one output layer. The three input layers correspond to the data acquisition method, trust coefficient, and historical credit of the data provider, respectively. The hidden layers include a first hidden layer and a second hidden layer. The first hidden layer has 128 nodes and uses the ReLU function as the activation function. The second hidden layer has 64 nodes and uses the ReLU function as the activation function. The output layer corresponds to the trust coefficient of the output data.

[0122] After initializing the first machine learning model structure, the first machine learning model is trained using the initial data and the corresponding data trust scores. The initial data and the corresponding data trust scores are obtained from the database. The database records the acquisition method, trust score coefficient, and historical credit of the data provider for each data point, as well as the corresponding data trust score. This database is used to optimize the model. The optimizer is Adam, the loss function is the MSE loss function, the batch size is set to 32, and the number of iterations is 200 rounds. Training ends when the loss function converges, indicating that training is complete.

[0123] It should be noted that the Adam optimizer is one of the five major optimizers commonly used in machine learning, and its full name is Adaptive Moment Estimation.

[0124] The training method for the first machine learning model includes:

[0125] The initial data is converted into a corresponding set of feature vectors.

[0126] The initial data is used as input to the machine learning model, which outputs the data confidence level corresponding to each set of initial data, uses the actual data confidence level corresponding to each set of initial data as the prediction target, and minimizes the loss function value of the machine learning model as the training objective; training stops when the loss function value of the machine learning model is less than or equal to the preset target loss value.

[0127] The loss function value of the first machine learning model is the mean squared error;

[0128] Mean squared error is one of the commonly used loss functions. This is achieved by using the loss function formula... By training the model with minimization as the objective, the machine learning model can better fit the data, thereby improving the model's performance and accuracy.

[0129] In the loss function, MSE is the loss function value of the first machine learning model, x is the feature vector group number; e is the number of feature vector groups; y x Let x be the data confidence level of the first machine learning model output corresponding to the x-th feature vector. This represents the actual data confidence level corresponding to the x-th feature vector group.

[0130] It is particularly important to note that, since this is strategic data training, the initial data trust level corresponding to the initial sample's optimal decision control group is obtained by human experimentation, where the optimal decision is made and recorded. At present, the initial stage of decision-making training data all requires human decision-making. Machine learning is based on human decision-making, logical judgment, classification, and summarization.

[0131] The initial training data is manually labeled to ensure the reliability of initial decisions and provide high-quality training samples for the machine learning model. This method effectively guarantees the accuracy of data trust assessment, enabling subsequent models to make automatic judgments based on learned features and improving the system's autonomous decision-making ability. As the model training is continuously optimized, its prediction accuracy of data trust will also continue to improve, thus maintaining efficient and accurate trust assessment capabilities even in complex supply chain data environments.

[0132] In this embodiment, a machine learning model is constructed to further optimize data trust assessment, enabling the system to automate trust calculation in large-scale data environments. A multi-layer feedforward neural network (MLP) is employed, with model inputs including data acquisition method, trust coefficient, and the data provider's historical credit history, effectively capturing the impact of data source differences on trust. The first and second hidden layers employ 128 and 64 neurons respectively, using the ReLU activation function, which effectively enhances the model's learning ability and non-linear fitting capability, improving the accuracy of data trust prediction. The Adam optimizer is selected, combined with mean squared error (MSE) as the loss function, allowing the model to converge quickly during training, reducing overfitting risk and improving generalization ability. Simultaneously, 200 rounds of iterative training are used, with a batch size of 32, ensuring a good balance between computational efficiency and accuracy during model training.

[0133] In this embodiment, by quantifying trust characteristics in multiple dimensions, the calculation of data trust level becomes more scientific and reasonable. Combined with machine learning optimization models, the automation level of the system is improved, human intervention is reduced, and the credibility of supply chain data is enhanced, providing a solid trust guarantee for blockchain-based data sharing, access control, and smart contract execution.

[0134] Example 4

[0135] Please see Figure 4 This invention provides a technical solution: an encrypted data secure synchronization and sharing transmission system for implementing the aforementioned encrypted data secure synchronization and sharing method. The system includes:

[0136] The data acquisition module acquires various types of data from supply chain participants, records them as supply chain data, and performs standardized processing on the supply chain data.

[0137] The data upload module processes the standardized supply chain data using encryption algorithms and then uploads it to the blockchain network.

[0138] The data analysis module assesses the trustworthiness and security of supply chain data.

[0139] The shared configuration module designs blockchain smart contracts based on data trust and data security, defines data sharing access control, and uses blockchain smart contracts to realize data sharing access control.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for secure synchronous sharing of encrypted data, characterized in that, include: Step S01: Obtain various types of data from supply chain participants, record them as supply chain data, and standardize the supply chain data. Step S02: After the standardized supply chain data is processed by encryption algorithm, it is uploaded to the blockchain network; Step S03: Obtain the data trustworthiness and data security of the supply chain data; Step S04: Design a blockchain smart contract based on data trust and data security, define data sharing access control, and use the blockchain smart contract to realize data sharing access control.

2. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The method for uploading standardized supply chain data to a blockchain network after processing it with an encryption algorithm includes: Select the hash algorithm as the encryption algorithm, input supply chain data to generate hash values; Upload the complete supply chain data to an external storage system and obtain the storage address; Save the hash value and store it together with the storage address in the blockchain to ensure the integrity of verifiable data.

3. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The standardization process includes unifying the storage format of supply chain data to ensure compatibility of data from different sources, setting unified data fields, and adopting a standardized data model. The storage format includes JSON, XML, CSV, or a database table structure.

4. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The supply chain data includes production information data, logistics information data, quality inspection report data, warehousing data, raw material data, production plan data, and supplier information data.

5. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The methods for obtaining data trustworthiness include: Trust characteristic parameters of the collected data; The trust feature parameters of the data include data transparency, data consistency, and verification count. The data transparency and data consistency are quantified and rated using a score of 1-10. The trust coefficient of the data is obtained by analyzing the aforementioned trust feature parameters; The acquisition method, trust coefficient, and historical credit of the data provider are recorded as the initial data. The collected initial data is input into a pre-built first machine learning model, which outputs the data confidence level.

6. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The methods for obtaining the data security level include: S3.1 Obtain the number of participants in the data verification, the number of verification attempts, and the number of verification failures and successes, and establish a verification dataset; Validate the dataset: YH p YZC represents the p-th verification participant. p T represents the number of verifications associated with the p-th verification participant. p This represents the success rate of the p-th verification participant; S3.2, Obtain data access permissions, and generate quantization coefficient one based on the access permissions; S3.3, Obtain the data storage method, and generate quantization coefficient two based on the storage method; S3.4, the number of data verification participants, the success rate of verification participants, the number of verifications, quantization coefficient one and quantization coefficient two are weighted and summed to obtain the data security level.

7. The encrypted data secure synchronization and sharing method according to claim 6, characterized in that, The method for generating a quantization coefficient based on access permissions includes: establishing a relationship between access permissions and access security levels, i.e., access control method → ​​description → security level assignment; obtaining the corresponding access security level assignment based on the established relationship between access permissions and security levels; and calculating a weighted average value as the access security level assignment when data has multiple access permissions. The method for generating quantization coefficient 2 based on storage method includes: establishing a relationship between storage method and storage security level assignment, i.e., storage method → ​​description → storage security level assignment; obtaining the corresponding storage security level assignment based on the established relationship between storage method and storage security level assignment; and when data has different storage mechanisms, calculating the weighted average as the storage security level assignment.

8. The encrypted data secure synchronization and sharing method according to claim 5, characterized in that, The method for obtaining the trust coefficient of data based on the trust feature parameters includes: performing a weighted calculation on the data transparency, data consistency, and number of verifications to obtain the trust coefficient of the data.

9. The encrypted data secure synchronization and sharing method according to claim 1, characterized in that, The method for defining data sharing access control in blockchain smart contracts based on data trust and data security includes: Preset data trust threshold F and data security threshold H; When the data trust level is greater than or equal to the data trust level threshold F, and the data security level is greater than or equal to the data security level threshold H, automatic sharing is allowed. When the data trust level is greater than or equal to the data trust level threshold F and the data security level is less than the data security level threshold H, or when the data trust level is less than the data trust level threshold F and the data security level is greater than or equal to the data security level threshold H, data generation requires additional verification before sharing. When the data trust level is less than the data trust level threshold F and the data security level is less than the data security level threshold H, sharing is prohibited.

10. A secure, synchronous, and shared encrypted data transmission system, characterized in that, A system for implementing the encrypted data secure synchronization and sharing method according to any one of claims 1-9, comprising: The data acquisition module acquires various types of data from supply chain participants, records them as supply chain data, and performs standardized processing on the supply chain data. The data upload module processes the standardized supply chain data using encryption algorithms and then uploads it to the blockchain network. The data analysis module assesses the trustworthiness and security of supply chain data. The shared configuration module designs blockchain smart contracts based on data trust and data security, defines data sharing access control, and uses blockchain smart contracts to realize data sharing access control.

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