Method for guaranteeing data integrity and traceability of heterogeneous model

Abstract

The invention discloses a method for guaranteeing data integrity and traceability of a heterogeneous model. The method aims at solving the problems that multi-source heterogeneous data integration is poor in safety, whole-process traceability is difficult, and automation adaptation is insufficient. Comprising the following steps: S1, collecting multi-source data from different models, systems or platforms; s2, performing format standardization processing on the multi-source data to realize data compatibility and integration; s3, generating a security data unit through a Hash algorithm and a digital signature; s4, verifying a hash value and a signature in each link, and automatically detecting a tampering risk; s5, writing the operation record and the hash value into a block chain or a distributed account book, and constructing a chain type traceability structure; s6, identifying and generating an adaptation rule by using an automatic rule engine; and S7, automatically distributing data integrity protection and traceability strategies, and applying the data integrity protection and traceability strategies to each data processing module. According to the method, full-life-cycle safety management of heterogeneous data can be realized, and data integrity, traceability and system expansibility are improved.

Description

Technical Field

[0001] This invention relates to the fields of data security and industrial data management technology, and in particular to a method for ensuring the integrity and traceability of heterogeneous model data. It is applicable to application scenarios such as multi-source data acquisition, standardized and compatible processing, data integrity verification, full-process traceability of operations, and secure collaborative management in various heterogeneous model or system environments. Background Technology

[0002] With the rapid development of intelligent manufacturing, the Industrial Internet, and big data technologies, enterprises are gradually introducing various heterogeneous models and systems into their production, management, and decision-making processes. Heterogeneous models refer to data processing and analysis tools based on different architectures, algorithms, platforms, or data formats, widely used in the generation, processing, storage, and application of data. In practical applications, enterprises often need to integrate and analyze multi-source data from production line sensors, quality inspection systems, supply chain management platforms, and intelligent analysis models to improve production efficiency and product quality. However, this data is distributed across different system platforms, using different data formats and communication protocols, with varying structures and management standards, posing significant challenges to data integration and collaborative processing.

[0003] In existing technologies, data integration of heterogeneous systems typically employs methods such as data platforms, ETL (Extract-Transform-Load), and data warehouses. These methods achieve data compatibility and interaction between different systems through format conversion, protocol adaptation, and interface development. However, these solutions primarily focus on data format standardization and transmission efficiency, lacking security guarantees throughout the entire data lifecycle. In environments with multi-system collaboration and frequent data transfer, data is susceptible to abnormal operations or tampering during collection, transformation, transmission, and storage, making it difficult to guarantee data integrity. Although some systems employ data verification mechanisms such as checksums or hash checks, there is still a risk of bypassing or attacking them in complex heterogeneous data flows and multi-stage operation chains. Furthermore, existing log auditing and operation traceability mainly rely on the internal records of each system, making it difficult to achieve unified traceability across platforms and models. In the event of data anomalies or security incidents, it is difficult to quickly locate the problem and the responsible party, impacting security compliance and accountability efficiency.

[0004] Furthermore, in scenarios involving the dynamic expansion and rapid integration of heterogeneous models, existing data security solutions exhibit low adaptability and automation. Integrating new models often requires manual rule customization and security policy configuration, increasing system maintenance complexity and increasing the risk of configuration oversights. For industrial and enterprise applications requiring high scalability and flexible deployment, existing technologies struggle to fully meet the data integrity and traceability requirements across multiple models and platforms.

[0005] In summary, existing technologies still have significant shortcomings in ensuring the integrity of heterogeneous model data, achieving full-process traceability of operations, and supporting automated adaptation of multiple models. There is an urgent need for an innovative method that can achieve standardization of multi-source heterogeneous data, full-process integrity verification, operation traceability based on tamper-proof technologies such as blockchain, and automated adaptation and secure collaboration, so as to improve the level of data security management and the intelligent collaborative capabilities of the system. Summary of the Invention

[0006] One objective of this invention is to propose a method for ensuring the integrity and traceability of data in heterogeneous models. This invention fully integrates technologies such as multi-source data standardization, integrity verification through hashing and digital signatures, blockchain-based operation traceability, and automated adaptation. It details the process of achieving secure data lifecycle management in multi-model, multi-platform environments, possessing advantages such as strong data security, high traceability, high automation, and strong adaptability.

[0007] A method for ensuring the integrity and traceability of heterogeneous model data according to an embodiment of the present invention includes the following steps:

[0008] S1. Collect multi-source data from different models, systems, or platforms;

[0009] S2. Standardize the format of multi-source data and achieve compatibility and integration of heterogeneous data through a unified data structure and communication protocol standard;

[0010] S3. Generate a unique identifier for each data unit in the standardized dataset using a hash algorithm and digitally sign it to form a secure data unit;

[0011] S4. Verify hash values ​​and digital signatures at each stage of data processing, automatically detect data tampering risks and trigger an anomaly response mechanism;

[0012] S5. Write the operation record and hash value of each secure data unit into the blockchain or distributed ledger to build a chain-based traceability structure.

[0013] S6. For newly added heterogeneous models or data sources, use the automated rule engine to automatically identify their characteristics and dynamically generate corresponding adaptation rules.

[0014] S7. Automatically distribute data integrity protection and traceability policies according to the generated adaptation rules and apply them to relevant data processing modules.

[0015] Optionally, step S1 further includes:

[0016] S11. Collect multi-source data according to system functional requirements. The multi-source data includes the collection time interval, data source identifier, data type, and collection priority.

[0017] S12. Through the interface module, the raw data units are automatically extracted from data sources of different models, systems or platforms according to the collection time interval, and each data unit is assigned a data source identifier and a collection timestamp.

[0018] The interface module refers to the software and hardware module used to realize data interaction, protocol adaptation and data extraction between this method and data sources of different models, systems or platforms. It can support multiple data acquisition methods such as API calls, database connections, file reading and message subscription, depending on the data source type.

[0019] S13. Add metadata tags to each raw data unit. The metadata tags include data source identifier, collection time, data type, and collection priority.

[0020] S14. Perform integrity verification on the collected raw data units, the verification including detecting whether the data units are missing, duplicate, or have abnormal format;

[0021] S15. The data units that have passed the integrity verification and their metadata tags are summarized to form an input dataset with standardized format.

[0022] Optionally, step S2 further includes:

[0023] S21. Set a unified data structure and communication protocol standard for the input dataset;

[0024] S22. Analyze the original data structure and communication protocol of each data unit in the input dataset, and convert it into a unified data structure and communication protocol standard according to the preset mapping rules. The process is as follows:

[0025]

[0026] in, For the converted standardized data units, For raw data units, Map() is a function that maps data structures and protocols;

[0027] S23. During the format conversion process, retain and improve the metadata tags of the data units to ensure the integrity and traceability of the source information;

[0028] S24. Perform structural consistency verification on the standardized data units to determine whether they conform to the set data structure and communication protocol standards.

[0029] S25. Summarize the standardized data units that have passed the consistency check and their metadata tags to form a standardized dataset.

[0030] Optionally, step S3 further includes:

[0031] S31. Generate a unique identifier for each data unit in the standardized dataset and its metadata label using a hash algorithm, with the following formula:

[0032] ID i =Hash(Data) i ),

[0033] Among them, ID i Data is a unique identifier for the i-th data unit. i Let represent the content of the i-th data unit, and Hash() be the cryptographic hash algorithm;

[0034] S32. Perform a hash operation on each data unit to generate a corresponding hash value;

[0035] S33. Sign the hash value using a private key signature algorithm to generate a digital signature information for the data unit, as shown in the formula:

[0036] Sign i =Sign priv (ID i ),

[0037] Among them, Sign i Sign is the digital signature for the i-th data unit. priv () represents the private key signature algorithm;

[0038] S34. Attach the unique identifier, hash value and digital signature information to the standardized data unit to form a secure data unit;

[0039] Hash operations ensure the uniqueness and immutability of each data unit, laying a technical foundation for data integrity; private key signature algorithms provide authentication and non-repudiation capabilities, enabling secure traceability and accountability for data operations; the combination of the two provides a solid underlying security guarantee for the integrity and traceability of heterogeneous model data.

[0040] Optionally, step S4 further includes:

[0041] S41. In each stage of data processing, the current data content and metadata of each secure data unit are extracted in real time, the hash value is recalculated and compared with the original stored hash value, specifically:

[0042]

[0043] Among them, Data′ i The ID represents the content of the data unit actually received. i For the original unique identifier, This indicates "equality judgment," which means determining whether the calculation result on the left is equal to the value on the right, used to verify whether the data content has been tampered with; the data processing steps include data generation, transmission, storage, and access.

[0044] S42. If the currently calculated hash value is consistent with the original hash value, it is determined that the data content has not been tampered with; otherwise, it is determined that the data integrity verification fails and the abnormal recording and alarm process is triggered.

[0045] S43. Perform public key verification on the digital signature. If both the hash verification and the signature pass, the data unit is allowed to flow normally; otherwise, the data unit is marked as abnormal data and an abnormal response mechanism is automatically triggered. The public key verification process is as follows:

[0046] Verify pub (Sign i ID i ),

[0047] Among them, Verify pub () represents the public-key verification algorithm, Sign i For digital signatures, ID i It is a unique identifier.

[0048] Optionally, step S5 further includes:

[0049] S51. Assign an operation sequence number to each secure data unit and record the operation type, operation subject, and operation timestamp each time data is processed.

[0050] S52. Construct an operation record entry, the entry including a unique identifier of the data unit, an operation sequence number, an operation type, an operation subject, an operation timestamp, a hash value, and digital signature information;

[0051] S53. For each operation record, calculate the hash value of the operation record using a hash algorithm, specifically as follows:

[0052] H op =Hash(Record),

[0053] Among them, H op `Record` is the hash value of the operation record, and `Record` is the content of the operation record.

[0054] S54. Write the operation records and their hash values ​​sequentially into the blockchain or distributed ledger in chronological order, and associate the hash value of the previous operation record with the current operation record in the blockchain structure to form a chain-like traceability structure, specifically:

[0055] Block n =Hash(Block) n-1 ||Recordn ),

[0056] Among them, Block n The hash value of the nth block, Block n-1 Record is the hash of the previous block. n This is the current operation record; || indicates a concatenation operation.

[0057] Optionally, step S6 further includes:

[0058] S61. Automatically identify new heterogeneous models or data sources through an automated rule engine and obtain their characteristic parameters, including data structure, communication protocol and security requirements.

[0059] S62. Based on the acquired characteristic parameters, automatically generate adaptation rules suitable for the heterogeneous model or data source. The generation process is as follows:

[0060]

[0061] in, To adapt to the rule set, f is the automated rule generation function, and the structure parameters, protocol parameters, and security parameters represent the data structure type, communication protocol type, and security requirement type, respectively.

[0062] S63. Apply the generated adaptation rules to the entire data processing process to achieve automatic adaptation and integration of new heterogeneous models or data sources in each data processing stage.

[0063] Optionally, step S7 further includes:

[0064] S71. Automatically distribute data integrity protection strategies and traceability strategies based on the adaptation rules generated by the automated rule engine;

[0065] S72. Apply the data integrity protection strategy and traceability strategy to the entire data processing process;

[0066] S73. Monitor the entire data processing process. If an anomaly is detected, automatically adjust the strategy and trigger an alarm mechanism to ensure data integrity and traceability in a multi-model environment.

[0067] Optionally, it also includes automatically identifying and matching the data structure and security requirements of newly accessed models or data sources, achieving automatic matching of data standardization and security policies without manual intervention.

[0068] Optionally, it also includes the ability to dynamically adjust adaptation rules based on real-time business needs and data characteristics during model operation or data interaction, thereby achieving adaptive optimization of data management in a multi-model environment.

[0069] Beneficial effects

[0070] This invention employs an innovative solution combining a layered security mechanism with automated intelligent adaptation, significantly improving the security and traceability of multi-source heterogeneous data at all stages of acquisition, processing, transfer, and storage. Compared with existing technologies, this invention not only achieves integrity assurance and accountability throughout the entire data lifecycle but also possesses high scalability and adaptability without human intervention, greatly reducing the complexity of system maintenance and security management, and has broad application value.

[0071] First, this invention enables unified and standardized processing of data from different systems, platforms, and models, significantly improving the compatibility and integration efficiency of multi-source data and laying a solid foundation for data collaboration and subsequent analysis in complex environments. Second, by introducing hash algorithms and digital signature mechanisms, integrity verification is performed on all lifecycle stages of data collection, transmission, and storage, effectively preventing data from being tampered with, lost, or forged during the flow of data, greatly enhancing data security and reliability. Furthermore, by employing immutable distributed ledger technologies such as blockchain, key information of data operations is recorded in a chain-like traceability system, achieving unified traceability and accountability across platforms and models, significantly improving the ability to quickly locate abnormal events and conduct post-event accountability.

[0072] In summary, this invention achieves rapid adaptation to new heterogeneous models and automatic distribution of security policies through an automated rule engine, improving system scalability and operational efficiency, reducing security risks caused by manual configuration, and meeting the high standards of data integrity and traceability required by industrial and enterprise applications. It has broad application prospects and significant practical value. Attached Figure Description

[0073] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0074] Figure 1 This is a schematic diagram of the overall process of a method for ensuring the integrity and traceability of heterogeneous model data proposed in this invention;

[0075] Figure 2 This is a schematic diagram illustrating the key technologies for ensuring the integrity and traceability of heterogeneous model data in this invention;

[0076] Figure 3 This is a schematic diagram of the data traceability process based on metadata tags in this invention. Detailed Implementation

[0077] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0078] refer to Figure 1-3 A method for ensuring the integrity and traceability of heterogeneous model data includes:

[0079] S1. Collect multi-source data from different models, systems, or platforms;

[0080] S2. Standardize the format of multi-source data and achieve compatibility and integration of heterogeneous data through a unified data structure and communication protocol standard;

[0081] S3. Generate a unique identifier for each data unit in the standardized dataset using a hash algorithm and digitally sign it to form a secure data unit;

[0082] S4. Verify hash values ​​and digital signatures at each stage of data processing, automatically detect data tampering risks and trigger an anomaly response mechanism;

[0083] S5. Write the operation record and hash value of each secure data unit into the blockchain or distributed ledger to build a chain-based traceability structure.

[0084] S6. For newly added heterogeneous models or data sources, use the automated rule engine to automatically identify their characteristics and dynamically generate corresponding adaptation rules.

[0085] S7. Automatically distribute data integrity protection and traceability policies according to the generated adaptation rules and apply them to relevant data processing modules.

[0086] In this embodiment, step S1 further includes:

[0087] S11. Collect multi-source data according to system functional requirements. The multi-source data includes the collection time interval, data source identifier, data type, and collection priority.

[0088] S12. Through the interface module, the raw data units are automatically extracted from data sources of different models, systems or platforms according to the collection time interval, and each data unit is assigned a data source identifier and a collection timestamp.

[0089] The interface module refers to the software and hardware module used to realize data interaction, protocol adaptation and data extraction between this method and data sources of different models, systems or platforms. It can support multiple data acquisition methods such as API calls, database connections, file reading and message subscription, depending on the data source type.

[0090] S13. Add metadata tags to each raw data unit. The metadata tags include data source identifier, collection time, data type, and collection priority.

[0091] S14. Perform integrity verification on the collected raw data units, the verification including detecting whether the data units are missing, duplicate, or have abnormal format;

[0092] S15. The data units that have passed the integrity verification and their metadata tags are summarized to form an input dataset with standardized format.

[0093] In this embodiment, step S2 further includes:

[0094] S21. Set a unified data structure and communication protocol standard for the input dataset;

[0095] S22. Analyze the original data structure and communication protocol of each data unit in the input dataset, and convert it into a unified data structure and communication protocol standard according to the preset mapping rules. The process is as follows:

[0096]

[0097] in, For the converted standardized data units, For raw data units, Map() is a function that maps data structures and protocols;

[0098] S23. During the format conversion process, retain and improve the metadata tags of the data units to ensure the integrity and traceability of the source information;

[0099] S24. Perform structural consistency verification on the standardized data units to determine whether they conform to the set data structure and communication protocol standards.

[0100] S25. Summarize the standardized data units that have passed the consistency check and their metadata tags to form a standardized dataset.

[0101] In this embodiment, step S3 further includes:

[0102] S31. Generate a unique identifier for each data unit in the standardized dataset and its metadata label using a hash algorithm, with the following formula:

[0103] ID i =Hash(Data) i ),

[0104] Among them, ID i Data is a unique identifier for the i-th data unit. iLet represent the content of the i-th data unit, and Hash() be the cryptographic hash algorithm;

[0105] S32. Perform a hash operation on each data unit to generate a corresponding hash value;

[0106] S33. Sign the hash value using a private key signature algorithm to generate a digital signature information for the data unit, as shown in the formula:

[0107] Sign i =Sign priv (ID i ),

[0108] Among them, Sign i Sign is the digital signature for the i-th data unit. priv () represents the private key signature algorithm;

[0109] S34. Attach the unique identifier, hash value and digital signature information to the standardized data unit to form a secure data unit;

[0110] Hash operations ensure the uniqueness and immutability of each data unit, laying a technical foundation for data integrity; private key signature algorithms provide authentication and non-repudiation capabilities, enabling secure traceability and accountability for data operations; the combination of the two provides a solid underlying security guarantee for the integrity and traceability of heterogeneous model data.

[0111] In this embodiment, step S4 further includes:

[0112] S41. In each stage of data processing, the current data content and metadata of each secure data unit are extracted in real time, the hash value is recalculated and compared with the original stored hash value, specifically:

[0113]

[0114] Among them, Data′ i The ID represents the content of the data unit actually received. i For the original unique identifier, This indicates "equality judgment," which means determining whether the calculation result on the left is equal to the value on the right, used to verify whether the data content has been tampered with; the data processing steps include data generation, transmission, storage, and access.

[0115] S42. If the currently calculated hash value is consistent with the original hash value, it is determined that the data content has not been tampered with; otherwise, it is determined that the data integrity verification fails and the abnormal recording and alarm process is triggered.

[0116] S43. Perform public key verification on the digital signature. If both the hash verification and the signature pass, the data unit is allowed to flow normally; otherwise, the data unit is marked as abnormal data and an abnormal response mechanism is automatically triggered. The public key verification process is as follows:

[0117] Verify pub (Sign i ID i ),

[0118] Among them, Verify pub () represents the public-key verification algorithm, Sign i For digital signatures, ID i It is a unique identifier.

[0119] In this embodiment, step S5 further includes:

[0120] S51. Assign an operation sequence number to each secure data unit and record the operation type, operation subject, and operation timestamp each time data is processed.

[0121] S52. Construct an operation record entry, the entry including a unique identifier of the data unit, an operation sequence number, an operation type, an operation subject, an operation timestamp, a hash value, and digital signature information;

[0122] S53. For each operation record, calculate the hash value of the operation record using a hash algorithm, specifically as follows:

[0123] H op =Hash(Record),

[0124] Among them, H op `Record` is the hash value of the operation record, and `Record` is the content of the operation record.

[0125] S54. Write the operation records and their hash values ​​sequentially into the blockchain or distributed ledger in chronological order, and associate the hash value of the previous operation record with the current operation record in the blockchain structure to form a chain-like traceability structure, specifically:

[0126] Block n =Hash(Block) n-1 ||Record n ),

[0127] Among them, Block n The hash value of the nth block, Block n-1 Record is the hash of the previous block. n This is the current operation record; || indicates a concatenation operation.

[0128] In this embodiment, step S6 further includes:

[0129] S61. Automatically identify new heterogeneous models or data sources through an automated rule engine and obtain their characteristic parameters, including data structure, communication protocol and security requirements.

[0130] S62. Based on the acquired characteristic parameters, automatically generate adaptation rules suitable for the heterogeneous model or data source. The generation process is as follows:

[0131]

[0132] in, To adapt to the rule set, f is the automated rule generation function, and the structure parameters, protocol parameters, and security parameters represent the data structure type, communication protocol type, and security requirement type, respectively.

[0133] S63. Apply the generated adaptation rules to the entire data processing process to achieve automatic adaptation and integration of new heterogeneous models or data sources in each data processing stage.

[0134] In this embodiment, step S7 further includes:

[0135] S71. Based on the adaptation rules generated by the automated rule engine, automatically distribute data integrity protection strategies and traceability strategies;

[0136] S72. Apply the data integrity protection strategy and traceability strategy to the entire data processing process;

[0137] S73. Monitor the entire data processing process. If an anomaly is detected, automatically adjust the strategy and trigger an alarm mechanism to ensure data integrity and traceability in a multi-model environment.

[0138] In this embodiment, the system also includes automatically identifying and matching the data structure and security requirements of the newly accessed model or data source, thereby achieving automatic matching of data standardization and security policies without manual intervention.

[0139] This embodiment also includes the ability to dynamically adjust adaptation rules based on real-time business needs and data characteristics during model operation or data interaction, thereby achieving adaptive optimization of data management in a multi-model environment.

[0140] Example 1:

[0141] To verify the feasibility of this invention in practice, it was applied to the intelligent manufacturing data governance platform of a large automobile manufacturing enterprise. This enterprise has four production workshops, each equipped with automated control systems for various brands of production lines, quality inspection equipment, warehousing and logistics management platforms, and multiple sets of AI-based defect detection models and energy consumption optimization models. For a long time, due to differences in data structures, communication protocols, and security strategies among the various systems, the enterprise has faced numerous challenges in multi-source data acquisition, standardized integration, security verification, and traceability management. These challenges mainly manifest as incomplete data acquisition, inconsistent formats, high risks of data tampering, difficulty in operational traceability, and slow system expansion, severely impacting the intelligent collaboration of production lines and the scientific nature of group decision-making.

[0142] The company integrates this invention into its data governance platform as a core data acquisition and security management module. The platform needs to uniformly collect and merge data from 15 production lines across four workshops, over 1000 sensors, three independent warehousing systems, two AI defect identification models, and one energy consumption prediction model. This data covers a wide range of complex data, including production process parameters, equipment operating status, quality inspection, logistics scheduling, and AI analysis results. All relevant data is automatically retrieved by the interface module at different collection intervals. The system can identify the source, structure, and protocol type of each data entry and assign a unique data source identifier and collection timestamp to each data unit. For example, on May 10, 2024, the platform collected approximately 32 million raw data entries from the four workshops, including real-time status of over 1000 sensors, equipment alarms, and quality inspection results.

[0143] The collected heterogeneous raw data undergoes standardized format processing by the platform. Data from different vendors and using different protocols is converted into a standard structure defined by the enterprise, and each data entry is tagged with complete metadata. After standardization, the system generates a unique identifier for each data unit using the SHA-256 hash algorithm and digitally signs it with the enterprise's private key, forming a secure data unit. During data transmission and conversion, in case of network packet loss, format anomalies, or other issues, the system can promptly detect and isolate abnormal data by verifying the hash value and digital signature in real time, ensuring data security and integrity. For example, on May 10, 2024, the platform detected seven data anomalies caused by network jitter, all of which were automatically isolated without affecting business operations.

[0144] The platform utilizes a blockchain distributed ledger to record the generation, modification, transmission, and access of all secure data units in a chain-like traceability system. Every operation on each piece of data is immutably recorded in the blockchain, allowing managers to query the data's source, flow path, operating entity, and operation time at any time, achieving unified traceability and accountability across platforms and models. For example, when a customer questioned the quality data of a batch of parts in workshop three, managers could identify the entire process of data collection, transmission, and analysis in just 0.6 seconds, quickly pinpointing the responsible link. This invention also integrates an automated rule engine, greatly simplifying the access and expansion of new equipment and models. On May 10, 2024, the company added two production lines and one AI analysis model. The system automatically identified the data structure and protocol of the new equipment, completing the entire process of data collection, standardization, integrity verification, blockchain traceability, and automatic distribution of security policies within two minutes. Compared to the previous manual configuration that took half a day or even longer, this significantly improved expansion efficiency.

[0145] The operational data of the enterprise platform before and after implementing this invention are shown in Table 1.

[0146] Table 1: Statistical Table of Implementation Effects and Operational Data of the Invention

[0147]

[0148] The data above shows that after the implementation of this invention, the enterprise platform's data collection volume has steadily increased, and the data format error rate has dropped to one in ten thousand, far lower than the 0.2% before the transformation. The integrity and traceability of secure data units have been significantly improved. All critical data operations have chain-like traceability records, and the platform's traceability query speed has been improved to within 0.6 seconds, effectively supporting product quality traceability and security compliance requirements. Abnormal data can be quickly isolated and processed, greatly reducing business risks. During the system expansion phase, the automatic adaptation and deployment time of new devices and models has been significantly shortened, and operational efficiency and system resilience have been significantly improved.

[0149] Furthermore, feedback from enterprise management indicates that the practical application of this invention effectively solves real-world problems such as difficulty in data fusion in heterogeneous environments, high risk of data tampering, slow traceability, and difficulty in expansion. The platform supports multiple business innovations, including intelligent manufacturing, quality traceability, and energy consumption optimization. As business scale continues to expand, the method of this invention demonstrates excellent scalability and application prospects, providing a solid data security and management foundation for the high-quality development of intelligent manufacturing in enterprises.

[0150] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for ensuring the integrity and traceability of heterogeneous model data, characterized in that, Includes the following steps: S1. Collect multi-source data from different models, systems, or platforms; S2. Standardize the format of multi-source data and achieve compatibility and integration of heterogeneous data through a unified data structure and communication protocol standard; S3. Generate a unique identifier for each data unit in the standardized dataset using a hash algorithm and digitally sign it to form a secure data unit; S4. Verify hash values ​​and digital signatures at each stage of data processing, automatically detect data tampering risks and trigger an anomaly response mechanism; S5. Write the operation record and hash value of each secure data unit into the blockchain or distributed ledger to build a chain-based traceability structure. S6. For newly added heterogeneous models or data sources, use the automated rule engine to automatically identify their characteristics and dynamically generate corresponding adaptation rules. S7. Automatically distribute data integrity protection and traceability policies according to the generated adaptation rules and apply them to relevant data processing modules.

2. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S1 further includes: S11. Collect multi-source data according to system functional requirements. The multi-source data includes the collection time interval, data source identifier, data type, and collection priority. S12. Through the interface module, the raw data units are automatically extracted from data sources of different models, systems or platforms according to the collection time interval, and each data unit is assigned a data source identifier and a collection timestamp. S13. Add metadata tags to each raw data unit. The metadata tags include data source identifier, collection time, data type, and collection priority. S14. Perform integrity verification on the collected raw data units, the verification including detecting whether the data units are missing, duplicate, or have abnormal format; S15. The data units that have passed the integrity verification and their metadata tags are summarized to form an input dataset with standardized format.

3. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S2 further includes: S21. Set a unified data structure and communication protocol standard for the input dataset; S22. Analyze the original data structure and communication protocol of each data unit in the input dataset, and convert it into a unified data structure and communication protocol standard according to the preset mapping rules; S23. During the format conversion process, retain and improve the metadata tags of the data units to ensure the integrity and traceability of the source information; S24. Perform structural consistency verification on the standardized data units to determine whether they conform to the set data structure and communication protocol standards. S25. Summarize the standardized data units that have passed the consistency check and their metadata tags to form a standardized dataset.

4. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S3 further includes: S31. Generate a unique identifier for the content and metadata label of each data unit in the standardized dataset using a hash algorithm; S32. Perform a hash operation on each data unit to generate a corresponding hash value; S33. Sign the hash value using digital signature technology to generate digital signature information for the data unit; S34. Attach the unique identifier, hash value, and digital signature information to the standardized data unit to form a secure data unit.

5. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S4 further includes: S41. In each stage of data processing, the current data content and metadata of each secure data unit are extracted in real time, the hash value is recalculated and compared with the original stored hash value. The data processing stage includes data generation, transmission, storage and access. S42. If the currently calculated hash value is consistent with the original hash value, it is determined that the data content has not been tampered with; otherwise, it is determined that the data integrity verification fails and the abnormal recording and alarm process is triggered. S43. Perform public key verification on the digital signature. If both the hash verification and the signature pass, the data unit is allowed to flow normally. Otherwise, the data unit is marked as abnormal data and an abnormal response mechanism is automatically triggered. The abnormal response mechanism includes recording abnormal information, isolating the data unit, and sending an alarm signal.

6. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S5 further includes: S51. Assign an operation sequence number to each secure data unit and record the operation type, operation subject, and operation timestamp each time data is processed. S52. Construct an operation record entry, the entry including a unique identifier of the data unit, operation sequence number, operation type, operation subject, operation timestamp, hash value and digital signature information; S53. Calculate the hash value of each operation record using a hash algorithm; S54. Write the operation records and their hash values ​​into the blockchain or distributed ledger in chronological order, and associate the hash value of the previous operation record with the current operation record in the blockchain structure to form a chain-like traceability structure.

7. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S6 further includes: S61. Automatically identify new heterogeneous models or data sources through an automated rule engine and obtain their characteristic parameters, including data structure, communication protocol and security requirements. S62. Based on the acquired characteristic parameters, automatically generate adaptation rules suitable for the heterogeneous model or data source, so that the entire data processing process can be compatible and integrated with the heterogeneous model or data source. The entire data processing process includes data acquisition, format standardization processing, secure data unit generation, integrity verification and anomaly response, and blockchain traceability recording. S63. Apply the generated adaptation rules to the entire data processing process to achieve automatic adaptation and integration of new heterogeneous models or data sources in each data processing stage.

8. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, Step S7 further includes: S71. Based on the adaptation rules generated by the automated rule engine, automatically distribute data integrity protection strategies and traceability strategies; S72. Apply the data integrity protection strategy and traceability strategy to the entire data processing process; S73. Monitor the entire data processing process. If an anomaly is detected, automatically adjust the strategy and trigger an alarm mechanism to ensure data integrity and traceability in a multi-model environment.

9. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, It also includes automatically identifying and matching the data structure and security requirements of newly accessed models or data sources, achieving automatic matching of data standardization and security policies without manual intervention.

10. The method for ensuring the integrity and traceability of heterogeneous model data according to claim 1, characterized in that, It also includes the ability to dynamically adjust adaptation rules based on real-time business needs and data characteristics during model operation or data interaction, thereby achieving adaptive optimization of data management in a multi-model environment.

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