Blockchain-based Textile Traceability Method and System

Abstract

The present invention belongs to the technical field of textile traceability, and specifically relates to a textile traceability method and system based on blockchain. The method includes analyzing the integrity of textile data based on a blockchain network to determine whether the textile data is complete, analyzing the confidence level of textile traceability access to determine whether the textile traceability access behavior is credible, analyzing the textile traceability query results to compare and obtain textile traceability data, and analyzing the status of textile traceability data to compare and obtain risk points of textile traceability data. The present invention solves the problems that the relevant information of traditional textile data traceability may have inaccurate and missing data, and the process of judging the access verification status of textile data traceability is not comprehensive enough, and can more accurately restore the entire production resume of the product, which is beneficial to discovering and positioning risk points in the supply chain and providing a basis for improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of textile traceability, and specifically relates to a textile traceability method and system based on blockchain. Background Art

[0002] Textile traceability refers to tracking the entire process of textiles from raw material collection, production, processing to final sales. The purpose of textile traceability is to ensure product quality, safety and compliance, while improving the transparency and efficiency of the supply chain. The textile industry usually involves a relatively complex supply chain, making it difficult to track the entire life cycle of products. Blockchain technology provides a method of data recording that cannot be tampered with and is easy to verify, which can securely record and share information. Through blockchain, the production and distribution steps of textiles can be transparently recorded, thus realizing true data traceability, not only increasing the transparency of the supply chain, but also improving the efficiency, safety and traceability of operations.

[0003] However, in the process of combining blockchain technology with textile traceability, there are still some problems. For example, the traceability-related information may have problems such as inaccurate and missing data, and the process of judging the access verification status of textile data traceability is not comprehensive enough, which may lead to the inability to comprehensively and accurately master the entire production resume of products during the traceability process, and the inability to timely and accurately identify the authenticity, integrity and credibility of data, affecting the reliability of the traceability results. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a textile traceability method and system based on blockchain, which can effectively record and verify the integrity of textile data and enhance consumers' trust in the traceability results.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0006] Based on the blockchain network, analyze the integrity of textile data to determine whether the textile data is complete;

[0007] Based on the complete textile data, analyze the confidence level of textile traceability access to determine whether the textile traceability access behavior is credible;

[0008] Based on the credible textile traceability access behavior, analyze the textile traceability query results to compare and obtain textile traceability data;

[0009] Based on the textile traceability data obtained by comparison, analyze the status of the textile traceability data to obtain a traceability data status evaluation value;

[0010] Based on the traceability data status evaluation value, compare and obtain the risk points of the textile traceability data.

[0011] Preferably, the process of analyzing the integrity of textile data and determining whether the textile data is complete is as follows:

[0012] Obtain a textile data integrity dataset. Based on the obtained textile data integrity dataset, comprehensively analyze to obtain a data integrity evaluation value, and use the data integrity evaluation value as the analysis basis for determining whether the textile data is complete;

[0013] Compare the data integrity evaluation value with the data integrity definition evaluation value stored in the database;

[0014] If the data integrity evaluation value is equal to the data integrity definition evaluation value, the textile data corresponding to the data integrity evaluation value is complete;

[0015] If the data integrity evaluation value is not equal to the data integrity definition evaluation value, the textile data corresponding to the data integrity evaluation value is incomplete, and a prompt is issued for the incomplete textile data.

[0016] Preferably, the textile data integrity dataset includes the number of textile data transaction records, the actual number of textile data transactions, and the number of textile data transaction record verifications.

[0017] Preferably, the process of analyzing the confidence level of textile traceability access and determining whether the textile traceability access behavior is credible is as follows:

[0018] Obtain a textile traceability access confidence dataset, which includes the total number of single-time textile traceability access verifications, the total duration of single-time textile traceability access verifications, and the highest byte coincidence ratio between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database;

[0019] Based on the obtained textile traceability access confidence dataset, comprehensively analyze to obtain a traceability access confidence value, and use the traceability access confidence value as the analysis basis for determining whether the textile traceability access behavior is credible;

[0020] Compare the traceability access confidence value with the traceability access definition confidence value stored in the database;

[0021] If the traceability access confidence value is higher than or equal to the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is credible;

[0022] If the traceability access confidence value is lower than the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is not credible, and a prompt is issued for the non-credible textile traceability access behavior.

[0023] Preferably, the method for obtaining the traceability access confidence value is:

[0024] ;

[0025] wherein, is the traceability access confidence value, dn is the total number of single - access verifications for textile traceability, dt is the total duration of single - access verifications for textile traceability, cb is the highest proportion of byte coincidence between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor for the set dn, is the compensation factor for the set dt, is the compensation factor for the set cb, and e is the natural constant.

[0026] Preferably, the process of analyzing the textile traceability query results and comparing to obtain textile traceability data is as follows:

[0027] Obtain the textile traceability query result data set, which includes the total number of bytes of the textile traceability query result, the total number of fields of the textile traceability query result, and the update frequency of the textile traceability query record;

[0028] Based on the obtained textile traceability query result data set, comprehensively analyze to obtain the traceability query result eigenvalue, and the traceability query result eigenvalue is used as the analysis basis for comparing to obtain textile traceability data;

[0029] Compare the traceability query result eigenvalue with the textile traceability data corresponding to each traceability query result eigenvalue stored in the database to obtain the textile traceability data corresponding to the traceability query result eigenvalue.

[0030] Preferably, the obtaining method of the traceability query result eigenvalue is:

[0031] ;

[0032] wherein, is the traceability query result eigenvalue, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability query record, is the compensation factor for the set zj, is the compensation factor for the set zd, is the compensation factor for the set pl.

[0033] Preferably, the process of analyzing the status of textile traceability data and obtaining the traceability data status evaluation value is:

[0034] Obtain a dataset of the status of textile traceability data, where the dataset of the status of textile traceability data includes the missing ratio of textile traceability data product information, the missing ratio of textile traceability data production information, the missing ratio of textile traceability data supply chain information, and the absolute value of the difference between the textile traceability data update frequency and the reference update frequency;

[0035] Based on the obtained dataset of the status of textile traceability data, comprehensively analyze to obtain an evaluation value of the traceability data status, and the evaluation value of the traceability data status is used as the analysis basis for comparing and obtaining the risk points of textile traceability data;

[0036] The method for obtaining the evaluation value of the traceability data status is as follows:

[0037] ;

[0038] In the formula, is the evaluation value of the traceability data status, cq is the missing ratio of textile traceability data product information, sq is the missing ratio of textile traceability data production information, gl is the missing ratio of textile traceability data supply chain information, gx is the absolute value of the difference between the textile traceability data update frequency and the reference update frequency, is the textile traceability data update frequency, is the textile traceability data reference update frequency, is the compensation factor of the set cq, is the compensation factor of the set sq, is the compensation factor of the set gl, is the compensation factor of the set gx.

[0039] Preferably, the process of comparing and obtaining the risk points of textile traceability data based on the evaluation value of the traceability data status is as follows:

[0040] Compare the evaluation value of the traceability data status with the textile traceability data risk points corresponding to each evaluation value of the traceability data status stored in the database to obtain the textile traceability data risk points corresponding to this evaluation value of the traceability data status.

[0041] A blockchain-based textile traceability system for implementing the above-mentioned blockchain-based textile traceability method, including a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status evaluation value acquisition module, and a textile traceability data risk point comparison module, where:

[0042] The textile data integrity judgment module is used to analyze the integrity of textile data based on the blockchain network and judge whether the textile data is complete;

[0043] An access behavior confidence analysis module, which is used to analyze the confidence of textile traceability access based on complete textile data and determine whether the textile traceability access behavior is credible;

[0044] A textile traceability data comparison module, which is used to analyze the textile traceability query results based on credible textile traceability access behaviors and obtain textile traceability data through comparison;

[0045] A traceability data status evaluation value acquisition module, which is used to analyze the status of textile traceability data based on the compared textile traceability data and obtain a traceability data status evaluation value;

[0046] A textile traceability data risk point comparison module, which is used to obtain textile traceability data risk points through comparison based on the traceability data status evaluation value.

[0047] The present invention has the following beneficial effects:

[0048] Through the distributed ledger feature of the blockchain, the present invention can effectively record and verify the integrity of textile data, enhance consumers' trust in traceability results, conduct credibility analysis on traceability access behaviors, help identify and investigate malicious access behaviors, improve the security and stability of the traceability system, and can more accurately restore the entire production history of the product based on complete and credible traceability data, which is beneficial to discovering and locating risk points in the supply chain and providing a basis for improvement.

[0049] By analyzing the integrity of textile data to determine whether the textile data is complete, the present invention can effectively prove the quality and source of the product, timely discover potential risks, and avoid quality problems caused by information loss; only complete data can provide sufficient information support for decision-making. Analyzing data integrity can help management make more accurate decisions, optimize supply chain management, ensure data integrity, and enable enterprises to improve operational efficiency and reduce resource waste and time delays caused by data errors or omissions.

[0050] By obtaining textile traceability data risk points through comparison based on the traceability data status evaluation value, evaluating the traceability data status can timely discover abnormal situations in the data, help enterprises identify potential risks early, prevent the expansion of problems, and the evaluation value provides quantitative data support, enabling decision-makers to make more precise decisions based on specific risk points, optimize supply chain management and product quality control. Timely identifying and handling risk points can help enterprises improve production and supply processes targeted; based on regular evaluations and risk point identifications, enterprises can continuously optimize their production processes and quality management systems, forming a virtuous cycle of continuous improvement. Description of the Drawings

[0051] Figure 1Schematic diagram of the method steps of the present invention;

[0052] Figure 2 Schematic diagram of the connection of system modules of the present invention;

[0053] Figure 3 Is the traceability access confidence value Image showing the change of the highest proportion cb of byte coincidence between the device IP address used for textile traceability access and each device IP address stored in the database;

[0054] Figure 4 Is the characteristic value of the traceability query result Image showing the change with the total number of bytes zj of the textile traceability query result. Specific implementation manner

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0056] Example 1: As Figure 1 shown, a blockchain-based textile traceability method includes: analyzing the integrity of textile data based on a blockchain network to determine whether the textile data is complete.

[0057] The specific analysis process is as follows: Obtain a textile data integrity data set. Based on the obtained textile data integrity data set, comprehensively analyze to obtain a data integrity evaluation value. The data integrity evaluation value is used as the analysis basis for determining whether the textile data is complete; compare the data integrity evaluation value with the data integrity definition evaluation value stored in the database; if the data integrity evaluation value is equal to the data integrity definition evaluation value, the textile data corresponding to the data integrity evaluation value is complete; if the data integrity evaluation value is not equal to the data integrity definition evaluation value, the textile data corresponding to the data integrity evaluation value is incomplete, and a prompt is issued for the incomplete textile data. The data integrity definition evaluation value is taken as 2.

[0058] Timely issuing a prompt for incomplete data allows relevant personnel to quickly identify and correct data errors, avoiding further problems caused by incomplete data; the prompt mechanism encourages data maintenance personnel to pay attention to the accuracy and integrity of data, thereby continuously improving data quality and ensuring the reliability of subsequent analysis and decision-making; providing a prompt for data integrity can increase the transparency of the data processing process, helping all parties understand the authenticity and availability of data, thereby enhancing trust; by issuing a prompt, it can prompt data processing personnel to process missing or incorrect data in a timely manner, improve the efficiency of the entire operation process, and reduce subsequent additional workload.

[0059] The textile data integrity dataset includes the number of textile data transaction records, the actual number of textile data transactions, and the number of textile data transaction record verifications. The number of textile data transaction records represents the total number of records related to all transactions in the system or platform. The records include not only successful transactions but also failed or cancelled transactions, which are obtained based on the blockchain network. The actual number of textile data transactions refers to the actual number of transactions, usually meaning that the transactions have been confirmed, including successful transactions, as well as failed or cancelled transactions. It is a specific calculation of the number of transaction records and reflects the real business activities. It is obtained based on the transaction management system. The actual number of textile data transactions reflects the transaction situation of textiles in the entire supply chain, from raw material procurement to production, processing, sales, and other aspects. By recording all transactions (including failed and cancelled transactions), it is possible to comprehensively display the transaction information at each link, ensuring that the transaction history of the product is traceable. At each link in the production and sales of the product, there may be multiple transaction records. The statistics of these transaction records can help detect whether there are data omissions, supply chain management problems, etc., and take timely measures to ensure product quality. The number of textile data transaction record verifications represents the number of times the transaction records are verified, which is obtained based on the data verification and audit system. The number of verifications reflects the degree of verification of each transaction record. A high frequency of transaction record verifications means that the system has conducted strict checks on each transaction to ensure that there are no omissions or errors in the transaction information. This is the key to ensuring the accuracy of the traceability system, especially for possible errors during data input and transmission.

[0060] Calculating the data integrity assessment value based on the number of textile data transaction records, the actual number of textile data transactions, and the number of textile data transaction record verifications is to verify whether the transaction data throughout the textile life cycle is completely recorded, and is used to judge whether the recorded transaction process data is complete and credible before traceability.

[0061] During the textile production process, the data generated at each link can be traced through blockchain technology. Blockchain can generate immutable records for each production link, thus ensuring the transparency and traceability of products. In blockchain, each "operation" generated between links can be regarded as a transaction because it records events that occur at a specific time and place, and once these operations are on the chain, they cannot be tampered with. Through these on-chain transactions, the whole process of each textile from raw material procurement to the hands of the final consumer can be traced.

[0062] Specifically, each "operation" generated between links can be regarded as a "transaction". The following are examples of several key links:

[0063] From raw material procurement to the production link:

[0064] When the raw material procurement is completed and it is ready to enter the production stage, the system records a transaction to confirm that the batch of raw materials has arrived and passed the incoming inspection. At this time, the transaction record from the raw material procurement link to the production link is generated and written into the blockchain.

[0065] Production link to quality inspection link:

[0066] Once the production link is completed (for example, a batch of fabric is produced), the system generates a transaction to mark that the batch of products has been produced and transferred for quality inspection. This is the transaction between the production link and the quality inspection link. The quality inspection link will generate a new transaction to confirm whether the products meet the standards by inspecting the products.

[0067] Quality inspection link to logistics link:

[0068] When the products pass the quality inspection, the data generates a transaction, marking that the products are qualified and enter the logistics link. This means that the products are ready to be delivered to distributors or retailers, and the data handover between the quality inspection link and the logistics link is completed.

[0069] Logistics link to sales link:

[0070] After the products are transported to the sales points (such as stores or warehouses), the system generates transaction records indicating that the products have been successfully transported and reached the sales link. This is the transaction between the logistics link and the sales link.

[0071] Sales link to consumers:

[0072] When consumers purchase textiles, the system records a transaction to confirm that the products have been successfully sold. This is the transaction record from the sales link to consumers.

[0073] The data generated in each link of textile production - whether it is the parameters in the production process or the logistics information - can be regarded as "transactions" in the blockchain. Each transaction represents the execution result of a link and ensures the immutability and full traceability of the data through blockchain technology.

[0074] The number of textile data transaction records refers to the total number of data transactions in all links of textile production, inspection, transportation, etc. recorded in the blockchain, including the total number of successful transactions and failed transactions. For example, during the quality inspection process, if there are two unqualified cases, then there are two failed transactions. Whenever a link (such as raw material procurement, production, quality inspection, transportation, etc.) is completed, the relevant transaction data will be written into the blockchain, and each record of these operations will increase the transaction count.

[0075] Example: Suppose a batch of textiles goes through multiple processes such as raw material procurement, production, quality inspection, and transportation. Each process generates a blockchain transaction record, and the sum of all these records is the "number of transaction records". If a product goes through 5 processes and each process generates one record, then the number of transaction records for this batch of products is 5, and this number is the number of transaction records in the blockchain.

[0076] The actual number of data transactions for textile data refers to the number of actual data transactions that occur, that is, the "number of transactions" generated in the real situation, which may be different from the records on the blockchain.

[0077] The number of times of verifying textile data transaction records refers to the number of times of verifying transaction records in the blockchain network. In the blockchain, each transaction goes through a consensus verification process by network nodes to confirm the validity and accuracy of the transaction. The number of verification times may be the same as the number of transaction records, but in some systems, the verification process may occur multiple times, especially in some complex production or logistics processes.

[0078] Example: Suppose a batch of textiles goes through multiple processes, and each process generates a transaction record. These transaction records need to be verified by nodes in the blockchain to ensure their legality. If each transaction in each process is verified by one node, then the number of verification times is the number of transaction records multiplied by the number of verification nodes. If there are 5 processes generating transactions and each transaction is verified by 1 node, then the number of verification times is 5.

[0079] The way to obtain the data integrity evaluation value is:

[0080] ;

[0081] In the formula, is the data integrity evaluation value, sj is the number of textile data transaction records, jl is the actual number of textile data transactions, and tz is the number of times of verifying textile data transaction records. In the calculations of each formula in this embodiment, data normalization processing can be performed as needed.

[0082] By the number of times of verifying transaction records, the authenticity and accuracy of transaction data can be ensured, thereby reducing data errors and improving the overall data quality; by comparing the number of records with the actual number of transactions, potential missing or incorrect data can be identified and then corrected; the actual number of transactions provides the situation of real transactions, which helps to analyze market demand and supply, supports more scientific data-driven decision-making, helps to enhance the decision-making support ability of enterprises, promotes the optimization of data management processes, and reduces risks.

[0083] Based on the complete textile data, analyze the confidence level of textile traceability access to determine whether the textile traceability access behavior is credible.

[0084] The specific analysis process is as follows: Obtain the textile traceability access confidence dataset, which includes the total number of times of single - access verification for textile traceability, the total duration of single - access verification for textile traceability, and the highest byte coincidence ratio between the IP address of the device used for textile traceability access and each device IP address stored in the database.

[0085] The total number of times of single - access verification for textile traceability refers to the number of times of verifying the same textile traceability information during one access, which is recorded and counted through the Web server and the application server; the total duration of single - access verification for textile traceability refers to the total time consumed by the user from the start of the request to the final completion of the verification of textile traceability information during one access, which is recorded by the Web server and the application monitoring system; the highest byte coincidence ratio between the IP address of the device used for textile traceability access and each device IP address stored in the database is used to measure the similarity between the IP address of the device used by the user during access and all device IP addresses stored in the database; the ratio of byte coincidence represents the degree of identity of two IP addresses in some bits. For example, if two IPv4 addresses are 192.168.1.1 and 192.168.1.2, then their first three bytes (192.168.1) coincide, and the user's IP address is obtained through the network monitoring device and the log recording system.

[0086] Based on the obtained textile traceability access confidence dataset, comprehensively analyze to obtain the traceability access confidence value, which is used as the analysis basis for judging whether the textile traceability access behavior is credible; compare the traceability access confidence value with the defined confidence value of traceability access stored in the database; if the traceability access confidence value is higher than or equal to the defined confidence value of traceability access, the textile traceability access behavior corresponding to this traceability access confidence value is credible; if the traceability access confidence value is lower than the defined confidence value of traceability access, the textile traceability access behavior corresponding to this traceability access confidence value is not credible, and a prompt is issued for the non - credible textile traceability access behavior.

[0087] If the user makes multiple visits during the verification process (such as frequently requesting more information or viewing different textile data), the total duration will increase. A high number of verification attempts usually may also indicate that the user has encountered some difficulties in obtaining the required information, thus increasing the duration of their visit. This metric is related to user identity tracking and access credibility. If a high proportion of bytes of an IP address coincide with those of multiple stored addresses in the database, it indicates that the user may be accessing using the same device repeatedly, showing the coherence of the access behavior. If the user's behavior (total number of times and total duration) is abnormal (such as frequent requests within a short period) and the IP address coincidence rate in the blocking channel is relatively high, the system may need to further review the user's behavior to ensure its security.

[0088] Based on identifying and handling untrusted traceability behaviors, enterprises can effectively manage market risks and reputation risks. By distinguishing between trusted and untrusted behaviors, enterprises can rationally allocate resources and focus on trusted access behaviors, thereby improving overall operational efficiency. By analyzing and judging the credibility of traceability access behaviors, enterprises can ensure that every link in the supply chain is transparent, thus enhancing the reliability and efficiency of the supply chain. By statistically analyzing traceability confidence values, enterprises can make more informed business decisions in a data-driven manner, such as selecting suitable suppliers and identifying market opportunities.

[0089] The way to obtain the traceability access confidence value is as follows:

[0090] ;

[0091] In the formula, is the traceability access confidence value, dn is the total number of times of single - visit verification for textile traceability, dt is the total duration of single - visit verification for textile traceability, cb is the highest proportion of byte coincidence between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor for the set dn, is the compensation factor for the set dt, is the compensation factor for the set cb, and e is the natural constant.

[0092] The traceability access confidence value The graph of the change of the highest proportion of byte coincidence cb between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database is as Figure 3 shown. By analyzing the total number of access verification times and the duration, it can be ensured that each traceability access is fully verified, thereby improving the accuracy of the data. By detecting the coincidence rate of IP addresses, the credibility of the access source can be confirmed, false or duplicate access records can be avoided, and the consistency of the records can be improved. By monitoring the IP addresses of different devices and their access patterns, potential abnormal or risk behaviors can be identified and responded to more quickly.

[0093] The set compensation factors of dn, dt, and cb are obtained from the database. Based on historical data, a mapping set of the total number of single - access validations for textile traceability, the total duration of single - access validations for textile traceability, the highest ratio of byte coincidence between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, and the compensation factors of dn, dt, and cb is established to obtain the compensation factors corresponding to the current dn, dt, and cb. The other compensation factors in this embodiment are obtained in the same way.

[0094] Based on the trusted textile traceability access behavior, analyze the textile traceability query results, compare to obtain the textile traceability data, and complete the traceability of textile data based on the output traceability results (i.e., the compared textile traceability data).

[0095] The specific analysis process is as follows: Obtain the textile traceability query result dataset, which includes the total number of bytes of the textile traceability query result, the total number of fields in the textile traceability query result, and the update frequency of the textile traceability query records. The total number of bytes refers to the total storage space occupied by a certain textile traceability query result, in bytes, including the sum of the bytes of all data (including field names, field contents, and other relevant information) in the query result, and the total number of bytes of the query result can be obtained through an SQL query; the total number of fields refers to the total number of fields (or data columns) included in a specific traceability query result, and the total number of fields can be obtained through the metadata of the table (such as the table structure definition). SQL statements such as SHOW COLUMNS FROM table_name or DESCRIBE table_name usually return the number of fields in the table; the update frequency of the query records is the number of times the traceability query records are updated within a specific time, and the update operation of the records can be monitored through a log table or an audit tracking function.

[0096] Based on the obtained textile traceability query result dataset, comprehensively analyze to obtain the traceability query result eigenvalue, which is used as the analysis basis for comparing and obtaining the textile traceability data; compare the traceability query result eigenvalue with the textile traceability data corresponding to each traceability query result eigenvalue stored in the database to obtain the textile traceability data corresponding to this traceability query result eigenvalue.

[0097] The total number of bytes is usually proportional to the total number of fields. Each field occupies a certain amount of storage space, such as the storage of field names, field types, and field values. The more fields there are, the more the total number of bytes usually increases. Traceability records with a larger total number of fields may contain more information. In theory, if the data structure is well-designed, more fields can provide more specific context, which can be more specific during updates. If some information in these fields is updated frequently, it will lead to an increase in the record update frequency. A higher total number of bytes may mean more complex or diverse records, but this may also lead to a decrease in query and update performance, affecting the effectiveness and frequency of updates. The time and resources required to update large-scale data increase, which may lead to a decrease in the update frequency.

[0098] The total number of bytes of the textile traceability query result, the total number of fields of the textile traceability query result, and the update frequency of the textile traceability record are characteristic parameters in textile data traceability, which helps to confirm the credibility of the textile traceability data obtained by comparison. By comparing the characteristic values of the traceability query result with the textile traceability data corresponding to each characteristic value of the traceability query result stored in the database, the data in the query result characteristic value and the database are compared to obtain the textile traceability data corresponding to the traceability query result characteristic value, and the specific traceability result of the data can be confirmed, reflecting the real supply chain information. Automated data comparison can greatly reduce the time and labor of manual verification, making the query process faster and more efficient.

[0099] The way to obtain the characteristic value of the traceability query result is as follows:

[0100] ;

[0101] In the formula, is the characteristic value of the traceability query result, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability record, is the compensation factor of the set zj, is the compensation factor of the set zd, is the compensation factor of the set pl.

[0102] The characteristic value of the traceability query result The image of the change of the characteristic value of the textile traceability query result with the total number of bytes zj is as Figure 4 shown. Through the analysis of the total number of fields, it can ensure that the query result covers key information, improving the integrity and accuracy of the data. The analysis of the total number of fields and the total number of bytes can help the system provide more intuitive and rich query results. Combining the analysis of the update frequency of the query record, the high-quality traceability query result obtained can provide accurate data support for the management level to help it refer to and analyze during decision-making.

[0103] Based on the textile traceability data obtained through comparison, analyze the status of the textile traceability data to obtain an evaluation value of the traceability data status. The evaluation value of the traceability data status is used to determine whether there are risk points where the output traceability result (i.e., the compared textile traceability data) is recorded but incomplete or not updated in a timely manner. If there are risk points, although the output traceability result (i.e., the compared textile traceability data) is obtained, the output traceability result (i.e., the compared textile traceability data) is not completely reliable.

[0104] The specific analysis process is as follows: Obtain the dataset of the textile traceability data status, which includes the missing ratio of textile product information in the traceability data, the missing ratio of textile production information in the traceability data, the missing ratio of textile supply chain information in the traceability data, and the absolute value of the difference between the update frequency of the textile traceability data and the reference update frequency. The missing ratio of textile product information in the traceability data refers to the ratio of the missing textile-related product information (such as product name, model, composition, etc.). A high ratio means that the product information is incomplete and is obtained based on the database management system; the missing ratio of textile production information in the traceability data reflects the ratio of the missing information related to the production process (such as production date, manufacturer, production process, etc.). If this ratio is relatively high, it indicates insufficient transparency of the product production process and is obtained based on the production management system; the missing ratio of textile supply chain information in the traceability data involves the ratio of the missing information in each link of the supply chain (such as supplier information, transportation information, inventory status, etc.). A high missing ratio may lead to poor supply chain management and increase uncertainty, and is obtained based on the supply chain management software; the absolute value of the difference between the update frequency of the textile traceability data and the reference update frequency reflects the gap between the actual data update frequency and the preset reference update frequency. The larger the absolute value, the more the data lags behind the expectation, which may affect the timeliness and real-time nature of the information and is obtained based on the data management system; based on the obtained dataset of the textile traceability data status, comprehensively analyze to obtain the evaluation value of the traceability data status, and the evaluation value of the traceability data status is used as the analysis basis for the risk points of the compared textile traceability data;

[0105] The way to obtain the evaluation value of the traceability data status is:

[0106] ;

[0107] In the formula, is the evaluation value of the traceability data status, cq is the missing ratio of textile product information in the traceability data, sq is the missing ratio of textile production information in the traceability data, gl is the missing ratio of textile supply chain information in the traceability data, gx is the absolute value of the difference between the update frequency of the textile traceability data and the reference update frequency, is the update frequency of the textile traceability data, is the reference update frequency of the textile traceability data, is the compensation factor for the set cq, is the compensation factor for the set sq, is the compensation factor for the set gl, is the compensation factor for the set gx.

[0108] By comprehensively considering the missing ratio and update frequency of different types of information, the traceability data status evaluation value can more comprehensively reflect the quality and reliability of textile traceability data, ensuring that the evaluation covers all key aspects of the dataset, thus providing a more accurate risk assessment; by specifically analyzing the missing ratio of different information categories, enterprises can accurately locate the types and severity of data defects, which helps to prioritize the resolution of issues that have the greatest impact on traceability integrity and accuracy. Incorporating changes in the data update frequency into the evaluation can help enterprises promptly detect delays or anomalies in data updates, thereby quickly responding to maintain the timeliness and accuracy of supply chain data, and further optimizing data management strategies.

[0109] The above product information, production information, and supply chain information are different parts that constitute a complete traceability system. If a certain dimension of information is lacking, it may affect other dimensions. A high missing ratio of product information may affect the information records of production and the supply chain. A low data update frequency often means outdated information, which may lead to an increase in the missing ratio. If supply chain information is not updated in a timely manner, relevant production information and product information may also be missing due to the lack of the latest data. The greater the absolute value of the difference between the update frequency and the reference update frequency, the more it means that the information update is lagging, which may directly affect the missing ratio. If the real-time update frequency of production information is much lower than the reference update frequency, it may cause the missing of relevant data, increasing the risks faced by enterprises.

[0110] Through the quantitative analysis of the proportions of various missing information (such as product, production, and supply chain information), enterprises can clearly understand the integrity of the data, identify which aspects have a high missing rate, effectively improve data transparency, enabling managers to quickly locate problems. The calculation of the traceability data status evaluation value can help identify specific factors related to risks. If the missing ratio of production information for certain products is relatively high, it may mean that the quality of these products cannot be effectively guaranteed, thus forming risk points. The differences in various data missing situations and update frequencies can reveal potential risk areas. Evaluating the traceability data status evaluation value can help enterprises establish a mechanism for continuous improvement. Enterprises can adjust internal processes and optimize data management strategies based on the evaluation results to enhance the quality and efficiency of the textile traceability system. By comparing the changes in different evaluation periods, quantitative effect analysis can be carried out to verify the effectiveness of improvement measures. The analysis of the difference between the missing ratio and update frequency of supply chain information in traceability data can directly help enterprises understand the vulnerable links in their supply chains, optimize supply chain management, and thus improve overall efficiency and reliability.

[0111] Based on the evaluation value of the traceability data status, the risk points of the textile traceability data are obtained by comparison.

[0112] The specific analysis process is as follows: The evaluation value of the traceability data status is compared with the risk points of the textile traceability data corresponding to each evaluation value of the traceability data status stored in the database to obtain the risk points of the textile traceability data corresponding to the evaluation value of the traceability data status.

[0113] By updating and comparing the evaluation value of the traceability data status in real time, the enterprise can continuously monitor the status of the entire supply chain, respond to any anomalies in a timely manner, ensure the stable operation of the supply chain, systematically analyze the risk points, the enterprise can optimize the supply chain management strategy, improve efficiency and effectiveness, systematically analyze the risk points, the enterprise can optimize the supply chain management strategy, improve efficiency and effectiveness, and the early warning system helps the enterprise to take preventive measures to avoid or mitigate potential losses.

[0114] Example 2: As Figure 2 shown, a textile traceability system based on blockchain is used to implement the method in Example 1, including a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status evaluation value acquisition module, and a textile traceability data risk point comparison module, where:

[0115] The textile data integrity judgment module is used to analyze the integrity of the textile data based on the blockchain network and judge whether the textile data is complete.

[0116] The access behavior confidence analysis module is used to analyze the confidence of the textile traceability access based on the complete textile data and judge whether the textile traceability access behavior is credible.

[0117] The textile traceability data comparison module is used to analyze the textile traceability query results based on the credible textile traceability access behavior and obtain the textile traceability data by comparison.

[0118] The traceability data status evaluation value acquisition module is used to analyze the traceability data status based on the compared textile traceability data and obtain the traceability data status evaluation value.

[0119] The textile traceability data risk point comparison module is used to obtain the textile traceability data risk points by comparison based on the traceability data status evaluation value.

Claims

1. A blockchain-based textile traceability method, characterized in that: The following steps are involved: Based on the blockchain network, the integrity of textile data is analyzed to determine whether the textile data is complete; Based on the complete textile data, the confidence of textile traceability access is analyzed to determine whether the textile traceability access behavior is credible. The process is as follows: Obtain a textile traceability access confidence data set, which includes the total number of textile traceability single access verifications, the total duration of textile traceability single access verifications, and the highest percentage of overlap between the IP address of the device used for textile traceability access and the IP address bytes of each device stored in the database; Based on the obtained textile traceability access confidence data set, a comprehensive analysis is conducted to obtain the traceability access confidence value, which is used as the analysis basis for judging whether the textile traceability access behavior is credible. Comparing the traceability access confidence value with the traceability access bounded confidence value stored in the database; If the traceability access confidence value is higher than or equal to the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is credible; If the traceability access confidence value is lower than the traceability access definition confidence value, the textile traceability access behavior corresponding to the traceability access confidence value is untrustworthy, and a prompt is issued for the untrustworthy textile traceability access behavior; Based on the credible textile traceability access behavior, the textile traceability query results are analyzed and compared to obtain the textile traceability data; Based on the textile traceability data obtained by comparison, the textile traceability data status is analyzed to obtain the traceability data status evaluation value; Based on the traceability data status assessment value, the risk points of textile traceability data are obtained by comparison.

2. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the integrity of textile data and determining whether the textile data is complete is as follows: Acquire a textile data integrity data set, and obtain a data integrity assessment value based on a comprehensive analysis of the acquired textile data integrity data set, wherein the data integrity assessment value serves as an analysis basis for determining whether the textile data is complete; comparing the data integrity assessment value with a data integrity delimitation assessment value stored in a database; If the data integrity assessment value is equal to the data integrity delimitation assessment value, the textile data corresponding to the data integrity assessment value is complete; If the data integrity assessment value is not equal to the data integrity definition assessment value, the textile data corresponding to the data integrity assessment value is incomplete, and a prompt is issued for the incomplete textile data.

3. The blockchain-based textile traceability method according to claim 2 is characterized in that: The textile data integrity data set includes the number of textile data transaction records, the number of actual textile data transactions, and the number of textile data transaction record verifications.

4. The blockchain-based textile traceability method according to claim 1, characterized in that: The traceability access confidence value is obtained in the following way: ； In the formula, is the confidence value of traceability access, dn is the total number of single access verifications for textile traceability, dt is the total duration of single access verification for textile traceability, cb is the maximum proportion of bytes of overlap between the IP address of the device used for textile traceability access and the IP addresses of each device stored in the database, is the compensation factor of the set dn, is the compensation factor of the set dt, is the compensation factor of the set cb, and e is a natural constant.

5. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the textile traceability query results and comparing the textile traceability data is as follows: Obtain a textile traceability query result data set, the textile traceability query result data set including the total number of textile traceability query result bytes, the total number of textile traceability query result fields, and the update frequency of textile traceability query records; Based on the obtained textile traceability query result data set, a comprehensive analysis is performed to obtain the traceability query result characteristic value, and the traceability query result characteristic value is used as the analysis basis for comparing the textile traceability data; The traceability query result characteristic value is compared with the textile traceability data corresponding to each traceability query result characteristic value stored in the database to obtain the textile traceability data corresponding to the traceability query result characteristic value.

6. The blockchain-based textile traceability method according to claim 5 is characterized in that: The method for obtaining the characteristic value of the traceability query result is: ； In the formula, is the characteristic value of the traceability query result, zj is the total number of bytes of the textile traceability query result, zd is the total number of fields of the textile traceability query result, pl is the update frequency of the textile traceability query record, is the compensation factor of the set zj, is the compensation factor of the set zd, is the compensation factor of the set pl.

7. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of analyzing the textile traceability data status to obtain the traceability data status evaluation value is as follows: Obtain a textile traceability data status data set, which includes the proportion of missing product information in textile traceability data, the proportion of missing production information in textile traceability data, the proportion of missing supply chain information in textile traceability data, and the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency; Based on the obtained textile traceability data status data set, a comprehensive analysis is performed to obtain the traceability data status assessment value, which is used as the analysis basis for comparing the risk points of textile traceability data; The method for obtaining the traceability data status evaluation value is as follows: ； In the formula, is the traceability data status assessment value, cq is the proportion of missing product information in textile traceability data, sq is the proportion of missing production information in textile traceability data, gl is the proportion of missing supply chain information in textile traceability data, gx is the absolute value of the difference between the update frequency of textile traceability data and the reference update frequency, The update frequency of textile traceability data, Reference update frequency for textile traceability data, is the compensation factor of the set cq, is the compensation factor of the set sq, is the compensation factor of the set gl, is the compensation factor of the set gx.

8. The blockchain-based textile traceability method according to claim 1, characterized in that: The process of obtaining the risk points of textile traceability data by comparing the traceability data status evaluation value is as follows: The traceability data status evaluation value is compared with the textile traceability data risk points corresponding to each traceability data status evaluation value stored in the database to obtain the textile traceability data risk points corresponding to the traceability data status evaluation value.

9. The blockchain-based textile traceability system is characterized by: The blockchain-based textile traceability method for implementing any one of claims 1 to 8 comprises a textile data integrity judgment module, an access behavior confidence analysis module, a textile traceability data comparison module, a traceability data status evaluation value acquisition module, and a textile traceability data risk point comparison module, wherein: The textile data integrity judgment module is used to analyze the integrity of textile data based on the blockchain network and determine whether the textile data is complete; The access behavior confidence analysis module is used to analyze the confidence of textile traceability access based on complete textile data to determine whether the textile traceability access behavior is credible; The textile traceability data comparison module is used to analyze the textile traceability query results based on the credible textile traceability access behavior and compare the textile traceability data; A traceability data status evaluation value acquisition module is used to analyze the textile traceability data status based on the textile traceability data obtained by comparison to obtain the traceability data status evaluation value; The textile traceability data risk point comparison module is used to obtain the textile traceability data risk points based on the traceability data status assessment value.

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