Data traceability tracking method and device, electronic equipment and storage medium

Abstract

The invention discloses a data source tracing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring original data of a supply chain financial system from a block chain; marking the original data to obtain identification data; extracting data features from the identification data by utilizing machine learning; and according to the data features, performing source tracing on the original data on the supply chain financial system to obtain supply chain data corresponding to the original data. According to the method, data labels are marked on the data, machine learning is used for learning the data labels, data features are obtained, data tracing is achieved through the data features, and therefore the operation condition of the whole supply chain is analyzed through the data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data tracing method and device, electronic equipment, and storage medium. Background Art

[0002] At present, due to policy requirements, efforts need to be made to improve the supply resilience and security level of the industrial chain, so there is an urgent need to explore ways to improve the resilience of the supply chain, so as to build a reliable supply chain financial system and thus ensure economic security. Among them, data tracking and data tracing can improve the transparency and traceability of supply chain financial transactions.

[0003] Therefore, in existing technologies, data traceability is mainly achieved through annotation, that is, some important information of the original data, such as background, author, time, source, etc., is recorded in the form of annotations, and the annotations are transmitted together with the data, so that the data can be traced based on the annotations.

[0004] Since storing annotation information requires additional space overhead and increases costs, and the annotation method is suitable for small systems, it will cause great storage pressure for large systems. Summary of the invention

[0005] Based on the above-mentioned deficiencies of the prior art, the present application provides a data traceability method and device, an electronic device, and a storage medium to solve the problem of increased cost caused by the prior art requiring additional space overhead.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The first aspect of the present application provides a data source tracing method, comprising:

[0008] Obtain the original data of the supply chain finance system from the blockchain;

[0009] Marking the original data to obtain identification data;

[0010] extracting data features from the identification data using machine learning;

[0011] According to the data features, the original data is traced on the supply chain finance system to obtain supply chain data corresponding to the original data.

[0012] Optionally, in the above-mentioned data tracing method, before obtaining the original data of the supply chain finance system from the blockchain, it also includes:

[0013] Obtain original data from the database of the supply chain finance system;

[0014] Use the hash function to convert the original data to obtain the data hash value;

[0015] Creating a blockchain transaction and storing the data hash value in the blockchain transaction;

[0016] Send the stored blockchain transaction to the blockchain for storage.

[0017] Optionally, in the above-mentioned data traceability method, tracing the original data on the supply chain finance system according to the data characteristics to obtain the supply chain data corresponding to the original data includes:

[0018] Using a hash function to convert the data feature to obtain a feature hash value;

[0019] According to the characteristic hash value, the supply chain data of the original data is traced back from the supply chain finance system.

[0020] Optionally, in the above data tracing method, the method further includes:

[0021] When the original data is transmitted in the supply chain finance system, the hash value of the original data is recorded, and the target hash value of the last recorded original data is obtained;

[0022] Determine whether the hash value of the original data is consistent with the target hash value;

[0023] If the hash value of the original data is consistent with the target hash value, it is determined that the original data has not been tampered with;

[0024] If the hash value of the original data is inconsistent with the target hash value, it is determined that the original data has been tampered with.

[0025] Optionally, in the above-mentioned data tracing method, before obtaining the original data of the supply chain finance system from the blockchain, it also includes:

[0026] Encrypting the original data using a convergent encryption algorithm to obtain encrypted original data;

[0027] Determining whether the encrypted original data exists in the supply chain finance system;

[0028] If the encrypted original data exists in the supply chain finance system, deleting the encrypted original data;

[0029] If the encrypted original data does not exist in the supply chain finance system, the encrypted original data is stored in the supply chain finance system.

[0030] The second aspect of the present application provides a data source tracing method and device, including:

[0031] A data acquisition unit, used to obtain the original data of the supply chain finance system from the blockchain;

[0032] A marking unit, used for marking the original data to obtain identification data;

[0033] An extraction unit, configured to extract data features from the identification data using machine learning;

[0034] The traceability tracking unit is used to trace the original data on the supply chain financial system according to the data characteristics to obtain the supply chain data corresponding to the original data.

[0035] Optionally, the above-mentioned data tracing device further includes:

[0036] An acquisition unit, used to acquire original data from the database of the supply chain finance system;

[0037] A first conversion unit, used to convert the original data using a hash function to obtain a data hash value;

[0038] A creation unit, used to create a blockchain transaction and store the data hash value in the blockchain transaction;

[0039] A storage unit is used to send the stored blockchain transactions to the blockchain for storage.

[0040] Optionally, in the above-mentioned data tracing device, the tracing unit includes:

[0041] A second conversion unit, used to convert the data feature using a hash function to obtain a feature hash value;

[0042] A tracking unit is used to trace the supply chain data of the original data from the supply chain financial system according to the characteristic hash value.

[0043] Optionally, the above-mentioned data tracing device further includes:

[0044] A recording unit, used to record the hash value of the original data when the original data is transmitted in the supply chain financial system, and obtain the target hash value of the original data recorded last time;

[0045] A first judging unit, configured to judge whether the hash value of the original data is consistent with the target hash value;

[0046] A first determining unit, configured to determine that the original data has not been tampered with if the hash value of the original data is consistent with the target hash value;

[0047] The second determining unit is configured to determine that the original data has been tampered with if the hash value of the original data is inconsistent with the target hash value.

[0048] Optionally, the above-mentioned data tracing device further includes:

[0049] An encryption unit, used to encrypt the original data using a convergent encryption algorithm to obtain encrypted original data;

[0050] A second determination unit, used to determine whether the encrypted original data exists in the supply chain finance system;

[0051] a deleting unit, configured to delete the encrypted original data if the encrypted original data exists in the supply chain finance system;

[0052] A data storage unit is used to store the encrypted original data in the supply chain financial system if the encrypted original data does not exist in the supply chain financial system.

[0053] A third aspect of the present application provides an electronic device, including:

[0054] Memory and processor;

[0055] Wherein, the memory is used to store programs;

[0056] The processor is used to execute the program, and when the program is executed, it is specifically used to implement a data tracing method as described in any one of the above items.

[0057] A fourth aspect of the present application provides a computer storage medium for storing a computer program. When the computer program is executed, it is specifically used to implement a data traceability tracking method as described in any one of the above items.

[0058] The present application provides a data tracing method, which obtains the original data of the supply chain finance system from the blockchain, then labels the original data to obtain identification data, and then uses machine learning to extract data features from the identification data. Finally, based on the data features, the original data is traced on the supply chain finance system to obtain the supply chain data corresponding to the original data. Thus, by labeling the data with data labels and using machine learning to learn the data labels to obtain data features, the data features can be used to trace the data, thereby realizing the operation of the entire supply chain through data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0060] Figure 1 A flowchart of a data source tracing method provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of a flow chart of a method for storing raw data provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a flow chart of a method for deduplication of raw data provided in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of the structure of a dynamic allocation device according to another embodiment of the present application;

[0064] Figure 5 A flowchart of another method for tracing the source of raw data provided by another embodiment of the present application;

[0065] Figure 6 A schematic diagram of the structure of a data source tracing device provided in another embodiment of the present application;

[0066] Figure 7 A schematic diagram of the structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0069] The embodiment of the present application provides a data traceability tracking method to solve the problem of the prior art that additional space overhead is required, resulting in increased costs.

[0070] Optionally, a data tracing method provided in an embodiment of the present application can be applied to a supply chain finance system. Therefore, in order to implement a data tracing method provided in an embodiment of the present application, optionally, an embodiment of the present application provides a supply chain finance system, including: a blockchain data tamper-proof module, a blockchain data tracing module, and a CP-ABE data tracking module.

[0071] The blockchain anti-data tampering module is the underlying module of the entire supply chain financial system. It can prevent any user or operator from tampering with data during the data circulation process. It has two important meanings. First, it ensures the reliability of data. Second, it prevents important data from losing its importance after being tampered with during circulation. It is an important guarantee for the reliability of the supply chain financial system.

[0072] The blockchain data traceability module can analyze the source of data, allowing analysts to know the location information of the data in the supply chain, thereby giving the data meaning in the supply chain. It is an important foundation for the establishment of a supply chain finance system.

[0073] The CP-ABE data tracking module is mainly used to label data. The labeled data can be used in machine language to understand the background or emotions contained in the data and obtain data features, thereby achieving fast and efficient data traceability based on data features.

[0074] Based on the supply chain finance system provided above, the present application embodiment provides a data traceability tracking method, such as Figure 1 As shown, the specific steps include:

[0075] S101. Obtain the original data of the supply chain finance system from the blockchain.

[0076] It should be noted that in the embodiment of the present application, the data of the entire supply chain finance system is stored in the blockchain, and no user can modify the historical data independently, thereby ensuring the reliability of the data of the supply chain finance system and eliminating the need for unnecessary additional space overhead.

[0077] Optionally, before executing step S101, the original data of the supply chain finance system needs to be stored in the blockchain to ensure the security of the data. Therefore, in another embodiment of the present application, a method for storing original data is also provided, such as Figure 2 As shown, the following steps are included:

[0078] S201. Obtain original data from the database of the supply chain finance system.

[0079] Specifically, in order to prevent the original data in the supply chain finance system from being tampered with, the original data needs to be stored in the blockchain, so the original data needs to be obtained from the database of the supply chain finance system in advance.

[0080] S202: Use a hash function to convert the original data to obtain a data hash value.

[0081] Specifically, using hash functions to convert data can achieve secure storage and verification of original data, so that hash values ​​can be compared later to ensure that no data leakage occurs during transmission or storage.

[0082] S203: Create a blockchain transaction and store the data hash value in the blockchain transaction.

[0083] It should be noted that in the blockchain, data is usually stored through transactions, so it is necessary to create a new transaction, namely a blockchain transaction, and store the data hash value in the blockchain transaction as part of the transaction for subsequent storage in the blockchain.

[0084] S204: Send the stored blockchain transaction to the blockchain for storage.

[0085] Specifically, the blockchain transaction is stored in the blockchain, that is, the original data is stored in the blockchain, and the original data can be obtained later by specifying the blockchain address, data hash value, contract address and other information.

[0086] Optionally, before obtaining the original data of the supply chain finance system, the original data needs to be stored in the supply chain finance system. However, in order to prevent duplicate original data from existing in the supply chain finance system, the original data will be processed to find out whether there is duplicate original data in the supply chain finance system. Therefore, before executing step S101, another embodiment of the present application also provides a method for deduplicating original data, such as Figure 3 As shown, the specific steps include:

[0087] S301. Encrypt original data using a convergent encryption algorithm to obtain encrypted original data.

[0088] It should be noted that in the embodiment of the present application, in order to address the confidentiality issue of data, a secure deduplication solution for outbound data is established with a trusted execution environment as a trusted computing platform, that is, the original data is encrypting using convergent encryption to reduce the storage of duplicate data.

[0089] Specifically, first you need to generate a pair of public and private keys required for an asymmetric encryption algorithm. The public key is used to encrypt data, and the private key is used to decrypt data. Then use a symmetric encryption algorithm (such as AES) and the generated random key to encrypt the original data. Symmetric encryption algorithms have relatively fast encryption speeds, so they are suitable for encrypting large amounts of data. This random key is also called a session key. The generated session key is then asymmetrically encrypted using the public key. Since asymmetric encryption algorithms have high security, it can be ensured that only those with the corresponding private key can decrypt the session key. Finally, the encrypted original data and the encrypted session key are stored together.

[0090] S302. Determine whether there is encrypted original data in the supply chain finance system.

[0091] It should be emphasized that the same data will be encrypted the same, so the storage of duplicate data can be reduced by simply determining whether the encrypted original data exists in the supply chain finance system. Therefore, if the encrypted original data exists in the supply chain finance system, it means that duplicate data exists in the supply chain finance system, so step S303 is executed. If the encrypted original data does not exist in the supply chain finance system, it means that duplicate data does not exist in the supply chain finance system, so step S304 is executed.

[0092] S303: Delete the encrypted original data.

[0093] Specifically, when there is encrypted original data in the supply chain finance system, it means that the original data has been stored in the database of the supply chain finance system. Therefore, for the confidentiality and uniqueness of the data, the encrypted original data needs to be deleted.

[0094] S304. Store the encrypted original data in the supply chain finance system.

[0095] Specifically, when the encrypted original data does not exist in the supply chain finance system, the encrypted original data will be stored in the database of the supply chain finance system for backup.

[0096] S102: Mark the original data to obtain identification data.

[0097] It is understandable that first, it is necessary to determine the set of labels used to mark the data. Labels can be in the form of text, numbers, categories, etc., depending on the application scenario and requirements. For example, if you want to label movies, the label set can include "comedy", "action", "love", etc. Then, each data in the original data is checked one by one, and the corresponding labels are assigned according to their content, attributes or characteristics. This can be done through manual annotation, machine learning classification algorithms or other automated methods. The specific method depends on the data type and scale. Finally, the identification data is stored in association with the original data, and a database, file system or other suitable data storage method can be used. The association method can be to add a label field, create an index or other methods so that the identification data can be easily retrieved and queried in the future.

[0098] S103. Extract data features from the identification data using machine learning.

[0099] It should be noted that, during the extraction process, the identification data needs to be first input into the feature extraction model, so that the data features corresponding to the identification data are output through the feature extraction model.

[0100] S104. According to the data characteristics, the original data is traced on the supply chain finance system to obtain the supply chain data corresponding to the original data.

[0101] It should be emphasized that in the process of tracing the source, it is first necessary to clarify the characteristics of the original data or to track it in the supply chain. For example, it can be information related to the supply chain link such as product serial number, batch number, production date, etc. Secondly, determine the source of the original data, that is, which supply chain links generated the original data. For example, it involves multiple links such as suppliers, production lines, and logistics transportation. Then, through the supply chain financial system, collect supply chain data related to the original data. These data may include supplier information, transportation records, quality inspection reports, warehousing information, etc. Then link the original data with the collected supply chain data to establish a relationship between data nodes, so that a data chain can be formed to trace and track the flow and changes of data in the supply chain. Finally, through the analysis and query functions provided by the supply chain financial system, you can trace the source based on the original data and obtain the supply chain data associated with it.

[0102] Optionally, in another embodiment of the present application, a specific implementation of step S104 is as follows: Figure 4 As shown, the following steps are included:

[0103] S401: Use a hash function to convert data features to obtain feature hash values.

[0104] It should be noted that since the hash value is irreversible, the content of the original data cannot be deduced from the hash value. This can protect the privacy of sensitive data and reduce the risk of information leakage. Therefore, in the embodiment of the present application, the hash value will be used as an index to query the supply chain data, which can avoid directly exposing the original data. Therefore, it is necessary to use the hash function to convert the data features in advance to obtain the feature hash value.

[0105] S402. According to the characteristic hash value, the supply chain data of the original data is traced back from the supply chain finance system.

[0106] Specifically, in the supply chain finance system, the feature hash value is used as an index to query the corresponding supply chain data, and finally, based on the query result, the supply chain data associated with the feature hash value is obtained.

[0107] It is understandable that hash values ​​can be used to establish links between data nodes, forming a data chain to achieve traceability and tracing of supply chain data. Therefore, by tracing the characteristic hash value of the original data, we can understand the flow, changes, and participants of the data in the supply chain, so as to better manage risks, optimize supply chain processes, etc.

[0108] Optionally, in order to know whether the original data has been tampered with during the process of tracing the original data in the supply chain finance system, a method for verifying the original data is provided in another embodiment of the present application, such as Figure 5 As shown, the specific steps include:

[0109] S501. When the original data is transmitted in the supply chain finance system, the hash value of the original data is recorded, and the target hash value of the last recorded original data is obtained.

[0110] It should be noted that in order to prevent data from being tampered with during transmission in the supply chain finance system, it is necessary to record the hash value of the current original data and obtain the hash value recorded when the original data was last transmitted, so as to subsequently determine through comparison whether data tampering has occurred in the data circulation process.

[0111] S502: Determine whether the hash value of the original data is consistent with the target hash value.

[0112] Specifically, due to the hash value, the integrity and consistency of the original data can be ensured. The hash value is a unique identifier of a fixed length calculated based on the data content, and any changes made to the data will cause the hash value to change. Therefore, by determining whether the hash value of the original data is consistent with the target hash value, it is possible to verify whether the original data has been tampered with, thereby enhancing the credibility of the data. Therefore, if the hash value of the original data is consistent with the target hash value, it means that the original data has not been tampered with in the data circulation link, so step S503 is executed. If the hash value of the original data is inconsistent with the target hash value, it means that the original data has been tampered with in the data circulation link, so step S504 is executed.

[0113] S503: Determine that the original data has not been tampered with.

[0114] S504: Determine whether the original data has been tampered with.

[0115] Optionally, when the hash value of the original data is inconsistent with the target hash value, it means that the original data has been leaked, and timely feedback needs to be given to the front end.

[0116] The present application provides a data tracing method, which obtains the original data of the supply chain finance system from the blockchain, then labels the original data to obtain identification data, and then uses machine learning to extract data features from the identification data. Finally, based on the data features, the original data is traced on the supply chain finance system to obtain the supply chain data corresponding to the original data. Thus, by labeling the data with data labels and using machine learning to learn the data labels to obtain data features, the data features can be used to trace the data, thereby realizing the operation of the entire supply chain through data analysis.

[0117] Another embodiment of the present application provides a data source tracing method and device, such as Figure 6 As shown, it includes the following units:

[0118] The data acquisition unit 601 is used to acquire the original data of the supply chain finance system from the blockchain.

[0119] The marking unit 602 is used to mark the original data to obtain identification data.

[0120] The extraction unit 603 is used to extract data features from the identification data using machine learning.

[0121] The traceability tracking unit 604 is used to trace the original data on the supply chain financial system according to the data characteristics to obtain the supply chain data corresponding to the original data.

[0122] It should be noted that the specific working process of the above-mentioned units in the embodiment of the present application can refer to steps S101 to S104 in the above-mentioned method embodiment, and will not be repeated here.

[0123] Optionally, a data source tracing method and device provided in another embodiment of the present application further includes:

[0124] The acquisition unit is used to obtain original data from the database of the supply chain finance system.

[0125] The first conversion unit is used to convert the original data using a hash function to obtain a data hash value.

[0126] The creation unit is used to create a blockchain transaction and store the data hash value in the blockchain transaction.

[0127] A storage unit is used to send the stored blockchain transactions to the blockchain for storage.

[0128] Optionally, in a data source tracing method and device provided in another embodiment of the present application, the source tracing unit 604 includes:

[0129] The second conversion unit is used to convert the data feature by using a hash function to obtain a feature hash value.

[0130] The tracking unit is used to trace the supply chain data of the original data from the supply chain finance system based on the characteristic hash value.

[0131] Optionally, a data source tracing method and device provided in another embodiment of the present application further includes:

[0132] The recording unit is used to record the hash value of the original data when the original data is transmitted in the supply chain financial system, and obtain the target hash value of the last recorded original data.

[0133] The first judging unit is used to judge whether the hash value of the original data is consistent with the target hash value.

[0134] The first determination unit is used to determine that the original data has not been tampered with if the hash value of the original data is consistent with the target hash value.

[0135] The second determining unit is used to determine that the original data has been tampered with if the hash value of the original data is inconsistent with the target hash value.

[0136] Optionally, a data source tracing method and device provided in another embodiment of the present application further includes:

[0137] The encryption unit is used to encrypt the original data using a convergent encryption algorithm to obtain encrypted original data.

[0138] The second judgment unit is used to judge whether there is encrypted original data in the supply chain finance system.

[0139] The deletion unit is used to delete the encrypted original data if there is any in the supply chain finance system.

[0140] The data storage unit is used to store the encrypted original data in the supply chain financial system if the encrypted original data does not exist in the supply chain financial system.

[0141] It should be noted that the specific working process of each unit provided in the above embodiments of the present application can refer to the corresponding steps in the above method embodiments, and will not be repeated here.

[0142] Another embodiment of the present application provides an electronic device, such as Figure 7 As shown, including:

[0143] Memory 701 and processor 702 .

[0144] The memory 701 is used to store programs.

[0145] The processor 702 is used to execute a program. When the program is executed, it is specifically used to implement a data tracing method provided by any one of the above embodiments.

[0146] Another embodiment of the present application provides a computer storage medium for storing a computer program. When the computer program is executed, it is used to implement a data traceability method provided in any one of the above embodiments.

[0147] Computer storage media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0148] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0149] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data tracing method, characterized in that: include: Obtain the original data of the supply chain finance system from the blockchain; Marking the original data to obtain identification data; extracting data features from the identification data using machine learning; According to the data features, the original data is traced on the supply chain finance system to obtain supply chain data corresponding to the original data.

2. The method according to claim 1, characterized in that Before obtaining the original data of the supply chain finance system from the blockchain, it also includes: Obtain original data from the database of the supply chain finance system; Use the hash function to convert the original data to obtain the data hash value; Creating a blockchain transaction and storing the data hash value in the blockchain transaction; Send the stored blockchain transaction to the blockchain for storage.

3. The method according to claim 1, characterized in that According to the data features, tracing the original data on the supply chain finance system to obtain supply chain data corresponding to the original data includes: Using a hash function to convert the data feature to obtain a feature hash value; According to the characteristic hash value, the supply chain data of the original data is traced back from the supply chain finance system.

4. The method according to claim 1, characterized in that Also includes: When the original data is transmitted in the supply chain finance system, the hash value of the original data is recorded, and the target hash value of the last recorded original data is obtained; Determine whether the hash value of the original data is consistent with the target hash value; If the hash value of the original data is consistent with the target hash value, it is determined that the original data has not been tampered with; If the hash value of the original data is inconsistent with the target hash value, it is determined that the original data has been tampered with.

5. The method according to claim 1, characterized in that Before obtaining the original data of the supply chain finance system from the blockchain, it also includes: Encrypting the original data using a convergent encryption algorithm to obtain encrypted original data; Determining whether the encrypted original data exists in the supply chain finance system; If the encrypted original data exists in the supply chain finance system, deleting the encrypted original data; If the encrypted original data does not exist in the supply chain finance system, the encrypted original data is stored in the supply chain finance system.

6. A data tracing device, characterized in that: include: A data acquisition unit, used to obtain the original data of the supply chain finance system from the blockchain; A marking unit, used for marking the original data to obtain identification data; An extraction unit, configured to extract data features from the identification data using machine learning; The traceability tracking unit is used to trace the original data on the supply chain financial system according to the data characteristics to obtain the supply chain data corresponding to the original data.

7. The device according to claim 6, characterized in that Also includes: An acquisition unit, used to acquire original data from the database of the supply chain finance system; A first conversion unit, used to convert the original data using a hash function to obtain a data hash value; A creation unit, used to create a blockchain transaction and store the data hash value in the blockchain transaction; A storage unit is used to send the stored blockchain transactions to the blockchain for storage.

8. The device according to claim 6, characterized in that The traceability unit includes: A second conversion unit, used to convert the data feature using a hash function to obtain a feature hash value; A tracking unit is used to trace the supply chain data of the original data from the supply chain financial system according to the characteristic hash value.

9. An electronic device, characterized in that: include: Memory and processor; Wherein, the memory is used to store programs; The processor is used to execute the program, and when the program is executed, it is specifically used to implement a data traceability method as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that: Used to store a computer program, which, when executed, is used to implement a data traceability method as described in any one of claims 1 to 5.

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