Block chain evaluation system for cross-border supply chain

Abstract

The invention relates to the technical field of data information transmission, in particular to a block chain evaluation system for a cross-border supply chain, and the system comprises a multi-source data collection module which collects multi-source data; the block chain generation module is used for constructing the digital ID and generating a block chain based on the digital ID; the credibility labeling module is used for calculating the ID credit score of the digital ID, carrying out credibility labeling on the digital ID, carrying out stable adjustment on the calculation process of the ID credit score, and carrying out sanitary calibration on the stable adjustment process; and the block chain node information verification module verifies the node information of the block chain according to the violation condition, and revises the calculation process of the ID credit score according to the violation condition. According to the system, the purpose of quickly generating the traceable block chain with high credibility is achieved through credit-violation-region combination.

Description

Technical Field

[0001] This invention relates to the field of data information transmission technology, and in particular to a blockchain evaluation system for cross-border supply chains. Background Technology

[0002] Existing cross-border blockchain traceability systems mostly adopt static credit node lists and fixed threshold models. Credit scores remain unchanged, there is no regional risk linkage, no dynamic threshold adjustment, and no calibration of stability coefficients such as public health, seasonality, and macroeconomics. This results in slow blockchain generation speed, lack of three-level linkage between credit, violations, and regions, and low credibility of blockchain information, which cannot meet the needs of real-time, accurate, and adjustable cross-border transaction supervision.

[0003] Chinese patent CN111932280A discloses a method for verifying the consistency of agricultural product traceability based on blockchain smart contracts. The method includes: a verification node acquiring agricultural product information to be verified, which is circulation information generated based on timestamps and encryption algorithms, and has a defined data type and format; the verification node sending traceability requests to other nodes on the chain besides the current verification node; other nodes sending traceability packages back to the verification node, which include all content that the other node deems should be included in the traceability package; the verification node checking whether the received traceability package comes from a legitimate trusted node in the trusted node list, storing it if it does, and discarding it otherwise; finally, determining a list of recognized agricultural product information based on the stored traceability packages; and generating final traceability consistency information and reaching consensus on the traceability consistency information if each piece of agricultural product information in the list receives recognition from at least more than a set threshold of trusted nodes. However, this scheme still suffers from slow blockchain generation speed, lack of three-level linkage between credit, violation, and region, and low credibility of blockchain information. Summary of the Invention

[0004] To address these issues, this invention provides a blockchain evaluation system for cross-border supply chains, which overcomes the problems of slow blockchain generation speed, lack of three-level linkage between credit, violation, and region, and low credibility of blockchain information in existing technologies.

[0005] To achieve the above objectives, the present invention provides a blockchain evaluation system for cross-border supply chains, comprising: The multi-source data acquisition module is used to acquire data from multiple sources. The blockchain generation module is used to construct digital IDs based on multi-source data and generate blockchains based on these digital IDs. The credibility labeling module is used to calculate the ID credit score of digital IDs and to label the credibility of digital IDs based on the ID credit score. It is also used to calculate the stability coefficient and to make stability adjustments to the ID credit score calculation process based on the stability coefficient. Furthermore, it is used to obtain the public health coefficient and to perform health calibration on the stability adjustment process based on the public health coefficient. The blockchain node information verification module is used to calculate the violation coefficient, judge the violation based on the violation coefficient, and verify the node information of the blockchain based on the judgment result. It is also used to revise the calculation process of ID credit score based on the violation, obtain the risk level of the product, and perform risk correction on the calculation process of the violation coefficient based on the risk level of the product. Furthermore, it is used to obtain the list of risk areas and update the risk correction process based on the list of risk areas.

[0006] Furthermore, the blockchain generation module constructs digital IDs based on multi-source data, specifically including: The production batch number of the goods and the supplier's factory inspection certificate number corresponding to this transaction in the multi-source data are used to calculate a fixed-length hash using SHA3-256, which is then used as the physical anchor code. The physical anchor code, product type, site parameters, and supplier's unified social credit code are concatenated and then hashed again to generate a digital ID.

[0007] Furthermore, the blockchain generation module generates the blockchain based on a digital ID, specifically including: When obtaining the hash coefficient based on the product type and site parameters corresponding to the digital ID and generating the site block based on the hash coefficient, the product type and site parameters in the valid contract are input into the hash coefficient model to obtain the site hash coefficient Ni output by the hash coefficient model. The site hash coefficient Ni is compared with the preset hash coefficient N0, and the necessity of on-chain is judged based on the comparison result. The site block is generated based on the judgment result, wherein: When Ni < N0, the necessity of adding to the chain is determined to be unnecessary, and no site block is generated; When Ni≥N0, the necessity of uploading to the chain is determined to be necessary. A site block is generated, and the site parameters and product information are uploaded to the corresponding site block to obtain the site block. All site blocks and transaction blocks are then linked to obtain the blockchain.

[0008] Furthermore, the credibility labeling module calculates the ID credit score of the digital ID and labels the credibility of the digital ID based on the ID credit score, specifically including: The normalization coefficient A1, timeliness coefficient A2, and accuracy coefficient A3 are obtained. Based on these coefficients, the ID credit score P is calculated using the following formulas: normalization coefficient A1, timeliness coefficient A2, accuracy coefficient A3, first credit weight α1, second credit weight α2, and third credit weight α3. The formula is P = A1 × α1 + A2 × α2 + A3 × α3. The ID credit score P is compared with a preset ID credit score P0. The creditworthiness is judged based on the comparison result, and the credibility of the digital ID is labeled according to the judgment result. When P≥P0, the credibility labeling module determines the credit status as high credit, labels the credibility of the digital ID as high credibility, arranges all traceability information in the blockchain associated with the digital ID in ascending order of timestamp, and displays it at the top of the consumer interface with a high credibility green label. When P < P0, the credibility labeling module determines the credit status as low, labels the credibility of the digital ID as low, collapses and displays all traceability information packages in the blockchain associated with the digital ID, and pops up a low credibility warning pop-up, prompting consumers that the traceability information of this entity is abnormal and they should verify with the merchant.

[0009] Furthermore, the credibility labeling module calculates a stability coefficient and adjusts the ID credit score calculation process based on the stability coefficient, specifically including: The market volatility index B1, seasonal stress index B2, and macroeconomic prosperity index B3 are obtained. The stability coefficient K is calculated based on these indices, along with the first stability weight β1, the second stability weight β2, and the third stability weight β3. The stability coefficient K is then compared with the preset stability coefficient K0. The stability is assessed based on the comparison results, and the ID credit score calculation process is adjusted accordingly. When K≥K0, the credibility labeling module determines the stability to be normal and does not make any stability adjustments to the ID credit score calculation process; When K < K0, the credibility labeling module determines the stability status as abnormal and performs a stability adjustment on the ID credit score calculation process. The stability adjustment includes: increasing the value of the second credit weight α2 by 0.05-0.15 and decreasing the value of the third credit weight α3 by 0.05-0.15 to obtain the adjusted second credit weight α4 and the adjusted third credit weight α5, and recalculating the ID credit score based on the first credit weight α1, the adjusted second credit weight α4, and the adjusted third credit weight α5.

[0010] Furthermore, the credibility labeling module acquires the public health coefficient and performs health calibration on the stability adjustment process based on the public health coefficient, specifically including: The system captures WHO epidemic risk levels and National Health Commission emergency response levels for public health emergencies in real time. These levels are quantified and used as a public health coefficient C. This coefficient C is compared to a preset public health coefficient C0. Based on the comparison results, the public health situation is assessed, and the stabilization adjustment process is calibrated accordingly. Specifically: When C≤C0, the confidence labeling module determines that the public health situation is normal and does not perform health calibration on the stable adjustment process; When C > C0, the credibility labeling module determines that the public health situation is abnormal and performs health calibration on the stability adjustment process. The health calibration includes: increasing the value of the seasonal stress index B2 in the stability coefficient K calculation process by 20% to obtain the calibrated seasonal stress index B4, and recalculating the stability coefficient K based on the calibrated seasonal stress index B4.

[0011] Furthermore, the blockchain node information verification module calculates a violation coefficient, judges the violation based on the violation coefficient, and verifies the blockchain node information based on the judgment result, specifically including: The embargo matching degree D1, value deviation degree D2, and quality violation coefficient D3 are obtained. The violation coefficient Q is calculated based on these factors, along with the first violation weight γ1, the second violation weight γ2, and the third violation weight γ3, with the formula Q = D1 × γ1 + D2 × γ2 + D3 × γ3. The violation coefficient Q is then compared with the first preset violation coefficient Q1 and the second preset violation coefficient Q2. Based on the comparison result, the violation is judged, and the blockchain node information is verified according to the judgment result. When Q < Q1, the blockchain node information verification module determines that there is no violation and does not verify the node information of the blockchain. When Q1≤Q<Q2, the blockchain node information verification module determines the violation to be a general violation, verifies the blockchain node information, and places the blockchain node information corresponding to the digital ID into the sampling pool for random inspection, with the sampling rate set at 30%. When Q≥Q2, the blockchain node information verification module determines the violation to be a serious violation, verifies the blockchain node information, and pushes the blockchain node information corresponding to the digital ID to the supervisory node workbench for manual review.

[0012] Furthermore, the blockchain node information verification module revises the ID credit score calculation process based on the violation details, specifically including: The blockchain node information verification module revises the ID credit score calculation process based on the violation details, specifically including: When the violation is deemed not to be a violation, the calculation process for the ID credit score will not be revised. When the violation is classified as a minor violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the first attenuation coefficient m1, where m1 = e -Penalty×（Q-Q1） Penalty is an empirical coefficient. The first revised ID credit score P1 is obtained. P1 is set to m1×P. The credibility of the digital ID is marked according to the first revised ID credit score P1. When the violation is classified as a serious violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the second attenuation coefficient m2, where m2 = e -Penalty×（Q-Q1）×L L is the empirical amplification factor, and the first revised ID credit score P2 is obtained. P2 is set to m2×P, and the credibility of the digital ID is marked according to the first revised ID credit score P2.

[0013] Furthermore, the blockchain node information verification module acquires the risk level of the product and performs risk correction on the calculation process of the violation coefficient based on the product risk level, specifically including: The commodity HS code corresponding to the digital ID is mapped to the General Administration of Customs' commodity risk level table to obtain the commodity risk level F. The commodity risk level F is compared with the preset commodity risk level F0. Based on the comparison result, the commodity risk situation is judged. Based on the judgment result, the calculation process of the violation coefficient is adjusted for risk correction. When F≤F0, the blockchain node information verification module determines that the risk of the product is low and does not perform risk correction in the calculation process of the violation coefficient; When F > F0, the blockchain node information verification module determines that the risk of the product is high and performs risk correction on the calculation process of the violation coefficient. The risk correction includes: increasing the value of the third violation weight γ3 by 0.2, decreasing the value of the first violation weight γ1 by 0.05-0.15, and decreasing the value of the second violation weight γ2 by 0.05-0.15 to obtain the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5. The violation coefficient Q is then recalculated based on the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5.

[0014] Furthermore, the blockchain node information verification module acquires the list of risk areas and updates the risk correction process based on the list of risk areas, specifically including: Obtain the credit scores P of all IDs in region Y within the past 30 days. IDs with credit scores P below 0.5 are classified as having extremely low credit scores and counted to obtain the number n of extremely low credit IDs in the region. Based on the number n and the total number N of IDs in region Y, calculate the proportion R of extremely low credit scores, setting R = n / N. Compare the proportion R of extremely low credit scores with a preset proportion R0. Based on the comparison results, obtain a list of high-risk regions. When R≥R0, the blockchain node information verification module will include this region as a risk region in the risk region list; When R < R0, the blockchain node information verification module will not include this region as a risk region in the list of risk regions; The region corresponding to the digital ID is compared with the risk regions in the risk region list, and the risk is updated based on the comparison results during the risk correction process. When the region corresponding to the digital ID matches a risk region in the risk region list, the risk correction process is updated. The risk update includes: canceling the risk correction, reducing the values ​​of the first preset violation coefficient Q1 and the second preset violation coefficient Q2 by 0.05-0.15 to obtain the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g, and then re-comparing the violation coefficient Q with the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: the system collects multi-source data in real time through a multi-source data acquisition module, providing basic data support for subsequent processes and ensuring accurate and complete initial information throughout the process. The system generates digital IDs and blockchains through a blockchain generation module, enabling the creation of individual traceable blockchains for merchants, allowing customers to query product information. Furthermore, the system calculates the ID credit score of the digital ID through a credibility labeling module and labels the digital ID's credibility based on the ID credit score, enabling the rapid generation of highly credible blockchains. Simultaneously, the calculation process for the ID credit score is stabilized and adjusted to further improve its accuracy, thereby enhancing the credibility of the blockchain. The stabilization and adjustment process is also adjusted based on public health factors. The system performs hygiene calibration to maximize the credibility of the blockchain. It also calculates the violation coefficient through a blockchain node information verification module, judges violations based on the coefficient, and verifies the node information based on the judgment results. This improves the authenticity of node information in the blockchain and prevents the inflow of false information. Simultaneously, it revises the ID credit score calculation process based on the violation situation, further enhancing the credibility of the blockchain information. The system performs risk correction on the violation coefficient calculation process using product risk levels and updates the risk correction process based on a list of risky regions, improving the accuracy of the violation coefficient calculation and thus enhancing the credibility of the blockchain information. By combining credit, violation, and region information, the system aims to quickly generate a highly credible and traceable blockchain. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the blockchain evaluation system used in this embodiment for cross-border supply chains. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0020] Please see Figure 1 As shown, this is a schematic diagram of the structure of a blockchain evaluation system for cross-border supply chains in this embodiment. The system includes: The multi-source data acquisition module is used to acquire data from multiple sources. A blockchain generation module is used to construct digital IDs based on multi-source data and generate a blockchain based on the digital IDs. The blockchain generation module is connected to the multi-source data acquisition module. The credibility labeling module is used to calculate the ID credit score of the digital ID and to label the credibility of the digital ID based on the ID credit score. It is also used to calculate the stability coefficient and to make stability adjustments to the ID credit score calculation process based on the stability coefficient. Furthermore, it is used to obtain the public health coefficient and to make health calibrations to the stability adjustment process based on the public health coefficient. The credibility labeling module is connected to the blockchain generation module. The blockchain node information verification module is used to calculate the violation coefficient, judge the violation based on the violation coefficient, and verify the node information of the blockchain based on the judgment result. It is also used to revise the calculation process of ID credit score based on the violation, obtain the risk level of the product, and perform risk correction on the calculation process of the violation coefficient based on the risk level of the product. Furthermore, it is used to obtain the list of risk areas and update the risk correction process based on the list of risk areas. The blockchain node information verification module is connected to the credibility labeling module.

[0021] Specifically, the blockchain assessment system for cross-border supply chains is applied in cross-border product transaction terminals. This system replaces existing static thresholds and isolated credit models with a credit-violation-region linkage mechanism, enabling real-time generation, three-level linkage verification, and adaptive adjustment of cross-border transaction blockchains. This significantly improves blockchain generation speed, linkage completeness, and information credibility. The system uses a multi-source data acquisition module to collect multi-source data in real time, providing basic data support for subsequent processes and ensuring accurate and complete initial information. The system uses a blockchain generation module to generate digital IDs and blockchains, creating separate traceable blockchains for merchants so customers can query product information. The system also uses a credibility labeling module to calculate the ID credit score of digital IDs and label their credibility based on the score, enabling the rapid generation of highly credible blockchains. Simultaneously, the calculation process for the ID credit score... The system performs stability adjustments to further improve the accuracy of ID credit scores, thereby enhancing the credibility of the blockchain. It also performs hygiene calibration on the stability adjustment process based on public health coefficients to maximize blockchain credibility. Furthermore, the system calculates violation coefficients through a blockchain node information verification module, judges violations based on these coefficients, and verifies the blockchain node information based on the judgment results, improving the authenticity of node information in the blockchain and preventing the influx of false information. Simultaneously, the system revises the ID credit score calculation process based on violation cases, further enhancing the credibility of the blockchain information. It performs risk correction on the violation coefficient calculation process using commodity risk levels and updates the risk correction process based on a list of risky regions, improving the accuracy of violation coefficient calculations and thus enhancing the credibility of the blockchain information. Through the combination of credit, violation, and region, the system aims to rapidly generate a highly credible and traceable blockchain.

[0022] Specifically, the multi-source data includes product production batch number, supplier factory inspection certificate number, product type, and site parameters. The multi-source data acquisition module obtains the product production batch number through the supplier's ERP system API interface, obtains the supplier's factory inspection certificate number through the supplier's quality inspection system PDF export interface, obtains the product type through the "product name" field of the customs declaration PDF, and obtains the site parameters through the logistics provider's IoT gateway MQTT subscription. The product production batch number refers to the input segment of the physical anchor code, the supplier's factory inspection certificate number refers to another input segment of the physical anchor code, the product type refers to the text category name mapped by the HS6-bit code in the customs declaration, and the site parameters refer to the fields of the logistics unit identity NFT, including IoT data such as temperature, humidity, GPS, and seal number.

[0023] Specifically, the multi-source data acquisition module is also used to perform zero-knowledge proof privacy protection on sensitive supplier information, including: When the multi-source data acquisition module captures the supplier's unified social credit code, transaction amount, and factory quality inspection certificate number, it does not directly upload plaintext, but generates verifiable credentials through the zk-SNARKs zero-knowledge proof circuit.

[0024] Specifically, the zk-SNARKs zero-knowledge proof circuit refers to a non-interactive zero-knowledge proof logic circuit customized to meet the "verifiable but not visible" requirement for sensitive supplier information (such as unified social credit code, transaction amount, and quality inspection certificate number) in cross-border supply chain traceability. The verifiable certificate refers to: using the SHA3-256 hash value of the supplier's unified social credit code, the Pedersen commitment of the transaction amount, and the Merkle root of the quality inspection certificate number as public inputs, and the original plaintext as a private witness, a proof π is generated through a preset circuit. The proof content includes "the supplier's qualifications are valid", "the transaction amount is within a reasonable range", and "the quality inspection certificate number exists in a certain Merkle tree", but the original plaintext is never disclosed.

[0025] Specifically, the blockchain generation module constructs digital IDs based on multi-source data, including: The production batch number of the goods and the supplier's factory inspection certificate number corresponding to this transaction in the multi-source data are used to calculate a fixed-length hash using SHA3-256, which is then used as the physical anchor code. The physical anchor code, product type, site parameters, and supplier's unified social credit code are concatenated and then hashed again to generate a digital ID.

[0026] Specifically, SHA3-256 refers to the 256-bit cryptographic hash function in the Keccak algorithm family officially released by NIST.

[0027] Specifically, the blockchain generation module generates digital IDs and blockchain data to create a separate, traceable blockchain for merchants, enabling customers to query product information.

[0028] Specifically, the blockchain generation module generates the blockchain based on a digital ID, including: When obtaining the hash coefficient based on the product type and site parameters corresponding to the digital ID and generating the site block based on the hash coefficient, the product type and site parameters in the valid contract are input into the hash coefficient model to obtain the site hash coefficient Ni output by the hash coefficient model. The site hash coefficient Ni is compared with the preset hash coefficient N0, and the necessity of on-chain is judged based on the comparison result. The site block is generated based on the judgment result, wherein: When Ni < N0, the necessity of adding to the chain is determined to be unnecessary, and no site block is generated; When Ni≥N0, the necessity of uploading to the chain is determined to be necessary. A site block is generated, and the site parameters and product information are uploaded to the corresponding site block to obtain the site block. All site blocks and transaction blocks are then linked to obtain the blockchain.

[0029] Specifically, the hash coefficient model refers to a recurrent neural network (RNN) model that takes product type and site parameters as input and outputs a site hash coefficient. This embodiment does not limit the specific construction method of the hash coefficient model. Those skilled in the art can divide the hash dataset into training, validation, and test sets, select a RNN model as the neural network architecture for the hash coefficient model, select the Adam optimizer and cross-entropy loss function to train the RNN model, load the training set into the RNN model, perform forward propagation through the RNN model, calculate the output value of the RNN model, calculate the loss function value based on the output value and the true value, calculate the gradient through backpropagation, and update the weights and biases of the RNN model. This process of forward propagation, loss calculation, and backpropagation is repeated until a preset number of training rounds are reached. The performance of the RNN model is verified on the validation set, and the RNN model that meets the performance requirements is output as the hash coefficient model. The hash dataset includes 14071 sets of historically acquired product types, site parameters, and corresponding site hash coefficients. The preset hash coefficient refers to a preset value used to determine the necessity of uploading to the blockchain. This embodiment does not limit the specific value of the preset hash coefficient; those skilled in the art can set it according to actual conditions, such as setting N0 to 0.7. The necessity of uploading to the blockchain refers to whether the site information needs to be uploaded to the block, determined by the site hash coefficient and the preset hash coefficient. The site information refers to the site parameters of the logistics site. The necessity of uploading to the blockchain includes unnecessary and necessary. Linking all site blocks and transaction blocks to obtain the blockchain means using on-chain smart contracts to link the transaction corresponding to this transaction. The Easy Block is concatenated with all site blocks of Ni≥N0 in ascending order of timestamps to form a complete traceability chain from "transaction occurrence → logistics sites → consumer", ensuring a one-to-one correspondence between physical flow and data flow, resulting in an immutable and complete blockchain. The transaction block refers to the data fingerprint calculated by the multi-source data acquisition module, which captures the purchase contract, digital ID, transaction amount, and logistics status completion timestamp. The fingerprint, digital ID, transaction amount, and logistics status are then written into the transaction block as the parent hash anchor for subsequent site blocks. The value range is the full amount of transaction data, and the timestamp is written into the on-chain metadata.

[0030] Specifically, the credibility labeling module calculates the ID credit score of the digital ID and labels the credibility of the digital ID based on the ID credit score, including: The normalization coefficient A1, timeliness coefficient A2, and accuracy coefficient A3 are obtained. Based on these coefficients, the ID credit score P is calculated using the following formulas: normalization coefficient A1, timeliness coefficient A2, accuracy coefficient A3, first credit weight α1, second credit weight α2, and third credit weight α3. The formula is P = A1 × α1 + A2 × α2 + A3 × α3. The ID credit score P is compared with a preset ID credit score P0. The creditworthiness is judged based on the comparison result, and the credibility of the digital ID is labeled according to the judgment result. When P≥P0, the credibility labeling module determines the credit status as high credit, labels the credibility of the digital ID as high credibility, arranges all traceability information in the blockchain associated with the digital ID in ascending order of timestamp, and displays it at the top of the consumer interface with a high credibility green label. When P < P0, the credibility labeling module determines the credit status as low, labels the credibility of the digital ID as low, collapses and displays all traceability information packages in the blockchain associated with the digital ID, and pops up a low credibility warning pop-up, prompting consumers that the traceability information of this entity is abnormal and they should verify with the merchant.

[0031] Specifically, the preset ID credit score refers to a preset value used to judge credit status. The standardization coefficient is a numerical representation of the compliance of mapping digital IDs, obtained and calculated through the following path: the multi-source data acquisition module extracts all traceability information packages within the past 180 days from the identity anchoring log, performs ISO 8000 format compliance checks on each package, and divides the number of compliant packages by the total number of packages to obtain the compliance rate. The standardization coefficient A1 = compliance rate, with a value range of 0-1. The timeliness coefficient is a numerical representation of the timeliness of mapping digital IDs, obtained and calculated through the following path: the multi-source data acquisition module reads the on-chain timestamp of each traceability information package and the corresponding physical event completion timestamp, calculates the difference between the two to obtain the delay time, and records packages with a delay time ≤ 2 hours as timely packages. The timeliness coefficient A2 = number of timely packages ÷ total number of packages, with a value range of 0-1. The accuracy coefficient is a numerical representation of the accuracy of mapping digital IDs, obtained and calculated through the following path: the multi-source data acquisition module compares the data fingerprints in the information package with the physical entities... The hash is compared byte by byte. If the comparison is consistent, it is recorded as accurate. The accuracy coefficient A3 = number of accurate packets ÷ total number of packets, with a value range of 0-1. The first credit weight refers to the weight ratio of the corresponding normality coefficient in the ID credit score. The second credit weight refers to the weight ratio of the corresponding timeliness coefficient in the ID credit score. The third credit weight refers to the weight ratio of the corresponding accuracy coefficient in the ID credit score. Since the normality ratio is greater than the timeliness and accuracy ratio in the ID credit score, the system in this embodiment sets α1=0.4, α2=0.3, and α3=0.3. The credit status refers to the credit level determined by the ID credit score and the preset ID credit score. The credit status includes high credit and low credit.

[0032] Specifically, the credibility labeling module calculates a stability coefficient and adjusts the ID credit score calculation process based on the stability coefficient, including: The market volatility index B1, seasonal stress index B2, and macroeconomic prosperity index B3 are obtained. The stability coefficient K is calculated based on these indices, along with the first stability weight β1, the second stability weight β2, and the third stability weight β3. The stability coefficient K is then compared with the preset stability coefficient K0. The stability is assessed based on the comparison results, and the ID credit score calculation process is adjusted accordingly. When K≥K0, the credibility labeling module determines the stability to be normal and does not make any stability adjustments to the ID credit score calculation process; When K < K0, the credibility labeling module determines the stability status as abnormal and performs a stability adjustment on the ID credit score calculation process. The stability adjustment includes: increasing the value of the second credit weight α2 by 0.05-0.15 and decreasing the value of the third credit weight α3 by 0.05-0.15 to obtain the adjusted second credit weight α4 and the adjusted third credit weight α5, and recalculating the ID credit score based on the first credit weight α1, the adjusted second credit weight α4, and the adjusted third credit weight α5.

[0033] Specifically, the preset stability coefficient K0 is a preset value used to judge the stability status. Since the main body does not need to adjust the weight when it is above 0.70, the system load is minimized; only when it is below 0.70 will the weight be adjusted, reducing large-scale weight drift and lowering computation and on-chain gas costs. Therefore, K0 is set to 0.7. The market volatility index is a numerical representation of the short-term price instability of the market environment in which the digital ID is located, obtained and calculated through the following path: the multi-source data acquisition module captures the customs export average price sequence of commodity categories in real time over the past 30 days, and calculates the ratio of its standard deviation to the mean. The specific formula is: Market volatility index B1 = standard deviation ÷ mean, where the standard deviation refers to the sample of the daily export average price over the past 30 days. The standard deviation, the mean refers to the arithmetic mean of the daily average export prices over the past 30 days, and the seasonal stress index is a numerical representation of the seasonal load pressure of the logistics system to which the digital ID is located, obtained and calculated through the following path: the multi-source data acquisition module captures the average logistics order volume of the same period over the past 5 years and the current order volume. The specific formula is: Seasonal stress index B2 = (Current order volume - Average of the same period) ÷ Standard deviation of the same period, where the current order volume refers to the total logistics order volume in the past 30 days, the average of the same period refers to the average total logistics order volume in the same 30-day window over the past 5 years, and the standard deviation of the same period refers to the standard deviation of the total logistics order volume in the same 30-day window over the past 5 years. The macroeconomic prosperity index is obtained through the following path. The numerical representation of the macroeconomic environment in which the digital ID is located is calculated: the multi-source data acquisition module captures the monthly PMI, quarterly GDP growth rate, and RMB exchange rate volatility released by the National Bureau of Statistics. The specific formula is: Macroeconomic Prosperity Index B3 = 0.4 × PMI + 0.3 × GDP Growth Rate + 0.3 × (1 - Exchange Rate Volatility). Here, PMI refers to the Purchasing Managers' Index (percentage form, range 0-100), GDP growth rate refers to the quarterly year-on-year GDP growth rate (percentage form), and exchange rate volatility refers to the average daily volatility of the RMB / USD exchange rate over the past 30 days (percentage form). The first stability weight is used in the calculation formula of the stability coefficient K to weight market volatility. The coefficient of the price fluctuation index B1, with a system preset β1 = 0.3, reflects the relative impact weight of price fluctuations on overall stability. The second stability weight refers to the coefficient used in the calculation formula of the stability coefficient K to weight the seasonal stress index B2, with a system preset β2 = 0.2, reflecting the relative impact weight of seasonal loads on overall stability. The third stability weight refers to the coefficient used in the calculation formula of the stability coefficient K to weight the macroeconomic prosperity index B3, with a system preset β3 = 0.2, reflecting the relative impact weight of macroeconomic prosperity on overall stability. The stability status refers to whether the stability is normal based on the stability coefficient and the preset stability coefficient. The stability status includes normal stability and abnormal stability.

[0034] Specifically, the credibility labeling module acquires the public health coefficient and performs health calibration on the stability adjustment process based on the public health coefficient, including: The system captures WHO epidemic risk levels and National Health Commission emergency response levels for public health emergencies in real time. These levels are quantified and used as a public health coefficient C. This coefficient C is compared to a preset public health coefficient C0. Based on the comparison results, the public health situation is assessed, and the stabilization adjustment process is calibrated accordingly. Specifically: When C≤C0, the confidence labeling module determines that the public health situation is normal and does not perform health calibration on the stable adjustment process; When C > C0, the credibility labeling module determines that the public health situation is abnormal and performs health calibration on the stability adjustment process. The health calibration includes: increasing the value of the seasonal stress index B2 in the stability coefficient K calculation process by 20% to obtain the calibrated seasonal stress index B4, and recalculating the stability coefficient K based on the calibrated seasonal stress index B4.

[0035] Specifically, this embodiment maps WHO epidemic risk levels to numerical values, with level 1 corresponding to 1, level 2 to 2, level 3 to 3, and level 4 to 4. It also maps the National Health Commission's emergency response levels for public health emergencies to numerical values, with level IV response to 1, level III response to 2, level II response to 3, and level I response to 4. Simultaneously, it acquires the numerical values ​​of both the WHO epidemic risk level and the National Health Commission's emergency response level for public health emergencies, and outputs the larger of the two values ​​as the public health coefficient. This allows for real-time acquisition of the WHO epidemic risk level and the National Health Commission's emergency response level for public health emergencies, and the numerical conversion of the levels into a public health coefficient. The preset public health coefficient refers to a preset value used to assess the public health situation. Pilot data shows that when the WHO epidemic risk level is ≥3 or the National Health Commission's emergency response level for public health emergencies is Level III, the average logistics delay is >2 hours, the B2 index jumps >1.5, the fluctuation within the 0-2 range is gradual, and 2 becomes a significant inflection point. Therefore, C0=2 is set. The public health situation refers to the situation of whether public health is normal based on the public health coefficient and the preset public health coefficient. The public health situation includes normal public health and abnormal public health. Among them, the pilot data statistics show that when the WHO is ≥3 or the response is Level III, the standard deviation of logistics order volume increases by 18.7%-21.3% compared with the normal situation. Therefore, the seasonal stress index B2 increases by 20%, which is the rounded value that is closest to the median of actual fluctuation.

[0036] Specifically, the credibility labeling module calculates the ID credit score of the digital ID and labels the digital ID with credibility based on the ID credit score in order to quickly generate a blockchain with high credibility. At the same time, the calculation process of the ID credit score is stabilized and adjusted to further improve the accuracy of the ID credit score, thereby improving the credibility of the blockchain. The stabilization and adjustment process is also calibrated according to the public health coefficient to maximize the credibility of the blockchain.

[0037] Specifically, the blockchain node information verification module calculates a violation coefficient, judges the violation based on the violation coefficient, and verifies the blockchain node information based on the judgment result, including: The embargo matching degree D1, value deviation degree D2, and quality violation coefficient D3 are obtained. The violation coefficient Q is calculated based on these factors, along with the first violation weight γ1, the second violation weight γ2, and the third violation weight γ3, with the formula Q = D1 × γ1 + D2 × γ2 + D3 × γ3. The violation coefficient Q is then compared with the first preset violation coefficient Q1 and the second preset violation coefficient Q2. Based on the comparison result, the violation is judged, and the blockchain node information is verified according to the judgment result. When Q < Q1, the blockchain node information verification module determines that there is no violation and does not verify the node information of the blockchain. When Q1≤Q<Q2, the blockchain node information verification module determines the violation to be a general violation, verifies the blockchain node information, and places the blockchain node information corresponding to the digital ID into the sampling pool for random inspection, with the sampling rate set at 30%. When Q≥Q2, the blockchain node information verification module determines the violation to be a serious violation, verifies the blockchain node information, and pushes the blockchain node information corresponding to the digital ID to the supervisory node workbench for manual review.

[0038] Specifically, the sampling pool refers to a queue of nodes to be sampled maintained by an on-chain smart contract, used for probabilistic verification of generally non-compliant digital IDs. The regulatory node workbench refers to a web-based workbench interface jointly maintained by the on-chain accounts of the local customs, market supervision, taxation, and foreign exchange departments. This dedicated interface receives, assigns, and processes abnormal alarms, performs manual review tasks, and updates regional risks. It has functions such as viewing abnormal digital ID markers, signing off on sampling tasks, reviewing violation coefficients, and adjusting risk region thresholds. All operation records are written to the on-chain regulatory operation log metadata for auditing and accountability. The embargo matching degree refers to a numerical representation obtained and calculated through the following path, used to map the degree of overlap between digital ID-associated goods and the embargo list. The multi-source data acquisition module captures the latest embargo lists (including WCO, Customs, and Ministry of Commerce lists) on the blockchain in real time. It performs character-level fuzzy matching between the product's HS code and the list entries. The embargo matching degree = number of matched characters ÷ total number of characters in the HS code, ranging from 0 to 1. A higher value indicates a closer match to embargoed goods. The value deviation degree refers to the numerical representation obtained and calculated through the following path, used to map the deviation between the declared value and the insured amount of goods associated with digital IDs: The multi-source data acquisition module reads the insured amount and declared value of the same batch during the same period. Value deviation degree = |declared value - insured amount| ÷ insured amount, ranging from 0 to 1. A higher value indicates a greater deviation in declared value. The quality violation coefficient refers to the value obtained and calculated through the following path... The numerical representation of the degree of quality non-compliance of goods associated with the mapped digital ID is as follows: The multi-source data acquisition module captures the "Measured Value" and "Standard Limit" fields from the quality inspection report PDF associated with the digital ID in real time, and calculates them according to the following formula: Quality Violation Coefficient = min(Measured Value ÷ Standard Limit, 1), where the measured value refers to the measured value of the inspected item indicated in the quality inspection report, and the standard limit refers to the maximum allowable value of the item specified by national or industry mandatory standards. The first violation weight γ1 refers to the weight coefficient corresponding to the embargo matching degree D1 in the violation coefficient Q calculation formula. The system presets γ1 = 0.40 to quantify the relative impact of embargo violations on the overall violation degree. The second violation weight γ2 refers to the weight coefficient corresponding to the value deviation in the violation coefficient Q calculation formula. The weighting coefficient of deviation D2, preset by the system as γ2 = 0.25, is used to quantify the relative impact of the deviation in declared value on the overall degree of violation. The third violation weight γ3 refers to the weighting coefficient corresponding to the quality violation coefficient D3 in the calculation formula of the violation coefficient Q, preset by the system as γ3 = 0.35, used to quantify the relative impact of quality non-compliance on the overall degree of violation. The first preset violation coefficient Q1 and the second preset violation coefficient Q2 are preset values ​​used to judge the violation situation. In this embodiment, Q1 is set to 0.6 and Q2 is set to 0.85, which is based on 70% medium-risk entities as the dividing line, so that 30% random inspection covers most potential violations, and 5% manual review is used as a backup. This is in line with the allocation of customs supervision resources and is in line with the AEO early warning threshold, and can be dynamically ±0.05. The "violation situation" refers to the situation where a violation has occurred, determined based on a violation coefficient compared to a first preset violation coefficient and a second preset violation coefficient. The violation situation includes no violation, minor violation, and serious violation.

[0039] Specifically, the blockchain node information verification module revises the ID credit score calculation process based on the violation, including: When the violation is deemed not to be a violation, the calculation process for the ID credit score will not be revised. When the violation is classified as a minor violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the first attenuation coefficient m1, where m1 = e -Penalty×（Q-Q1） Penalty is an empirical coefficient. The first revised ID credit score P1 is obtained. P1 is set to m1×P. The credibility of the digital ID is marked according to the first revised ID credit score P1. When the violation is classified as a serious violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the second attenuation coefficient m2, where m2 = e -Penalty×（Q-Q1）×L L is the empirical amplification factor, and the first revised ID credit score P2 is obtained. P2 is set to m2×P, and the credibility of the digital ID is marked according to the first revised ID credit score P2.

[0040] Specifically, Penalty refers to the empirical coefficient used to control the rate of decline in ID credit score in the violation coefficient revision formula. The system default is 3. L refers to the empirical amplification coefficient, which is used to increase the penalty when the violation coefficient is ≥0.85, making the credit score decline more significant. The system sets L=1.5. e refers to the base of the natural logarithm.

[0041] Specifically, the blockchain node information verification module acquires the risk level of the product and performs risk correction on the calculation process of the violation coefficient based on the product risk level, including: The commodity HS code corresponding to the digital ID is mapped to the General Administration of Customs' commodity risk level table to obtain the commodity risk level F. The commodity risk level F is compared with the preset commodity risk level F0. Based on the comparison result, the commodity risk situation is judged. Based on the judgment result, the calculation process of the violation coefficient is adjusted for risk correction. When F≤F0, the blockchain node information verification module determines that the risk of the product is low and does not perform risk correction in the calculation process of the violation coefficient; When F > F0, the blockchain node information verification module determines that the risk of the product is high and performs risk correction on the calculation process of the violation coefficient. The risk correction includes: increasing the value of the third violation weight γ3 by 0.2, decreasing the value of the first violation weight γ1 by 0.05-0.15, and decreasing the value of the second violation weight γ2 by 0.05-0.15 to obtain the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5. The violation coefficient Q is then recalculated based on the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5.

[0042] Specifically, the commodity HS code refers to the "commodity number" field extracted by the multi-source data acquisition module from the customs declaration PDF associated with the digital ID. The first 6 digits are extracted as the HS 6-digit code, which is then matched character-level with the latest version of the "Customs Tariff of the People's Republic of China" issued by the General Administration of Customs to obtain a complete 10-digit HS code. The value range is 0101-999999, which is used for subsequent risk level mapping. The commodity risk level table of the General Administration of Customs refers to the commodity risk level table established by the General Administration of Customs. The preset commodity risk level refers to the preset value used to judge the risk status of commodities. The system presets F0=3. When the commodity risk level F>3, risk correction is triggered. When F≤3, it is not triggered. The commodity risk status refers to the level of risk of the commodity as judged based on the commodity risk level F and the preset commodity risk level. The commodity risk status includes high risk and low risk.

[0043] Specifically, the blockchain node information verification module obtains the list of risk areas and updates the risk correction process based on the list of risk areas, including: Obtain the credit scores P of all IDs in region Y within the past 30 days. IDs with credit scores P below 0.5 are classified as having extremely low credit scores and counted to obtain the number n of extremely low credit IDs in the region. Based on the number n and the total number N of IDs in region Y, calculate the proportion R of extremely low credit scores, setting R = n / N. Compare the proportion R of extremely low credit scores with a preset proportion R0. Based on the comparison results, obtain a list of high-risk regions. When R≥R0, the blockchain node information verification module will include this region as a risk region in the risk region list; When R < R0, the blockchain node information verification module will not include this region as a risk region in the list of risk regions; The region corresponding to the digital ID is compared with the risk regions in the risk region list, and the risk is updated based on the comparison results during the risk correction process. When the region corresponding to the digital ID matches a risk region in the risk region list, the risk correction process is updated. The risk update includes: canceling the risk correction, reducing the values ​​of the first preset violation coefficient Q1 and the second preset violation coefficient Q2 by 0.05-0.15 to obtain the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g, and then re-comparing the violation coefficient Q with the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g.

[0044] Specifically, the preset ultra-low credit ratio refers to a preset threshold used to determine whether a region is included in the risk region list. The system initially sets R0 = 30% (i.e., 0.3). When the proportion of ultra-low credit IDs in a region is ≥30% within 30 days, the region is automatically included in the risk region list. This preset ultra-low credit ratio can be dynamically adjusted by ±5% every quarter based on the historical regional misjudgment rate to maintain the system's sensitivity and adjustability to regional risks. The region corresponding to the digital ID being consistent with the risk region in the risk region list means that the region corresponding to the digital ID has the same name as the risk region in the risk region list. The region corresponding to the digital ID being inconsistent with the risk region in the risk region list means that the region corresponding to the digital ID has a different name than the risk region in the risk region list.

[0045] Specifically, the blockchain node information verification module calculates the violation coefficient, judges the violation based on the violation coefficient, and verifies the node information of the blockchain based on the judgment result, thereby improving the authenticity of the node information in the blockchain and preventing the inflow of false information. At the same time, it revises the calculation process of ID credit score according to the violation, further improving the information credibility of the blockchain. It also performs risk correction on the calculation process of violation coefficient through commodity risk level and updates the risk correction process according to the risk area list, thereby improving the calculation accuracy of violation coefficient and thus improving the information credibility of the blockchain.

[0046] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A blockchain-based evaluation system for cross-border supply chains, characterized in that: include: The multi-source data acquisition module is used to acquire data from multiple sources. The blockchain generation module is used to construct digital IDs based on multi-source data and generate blockchains based on these digital IDs. The credibility labeling module is used to calculate the ID credit score of digital IDs and to label the credibility of digital IDs based on the ID credit score. It is also used to calculate the stability coefficient and to make stability adjustments to the ID credit score calculation process based on the stability coefficient. Furthermore, it is used to obtain the public health coefficient and to perform health calibration on the stability adjustment process based on the public health coefficient. The blockchain node information verification module is used to calculate the violation coefficient, judge the violation based on the violation coefficient, and verify the node information of the blockchain based on the judgment result. It is also used to revise the calculation process of ID credit score based on the violation, obtain the risk level of the product, and perform risk correction on the calculation process of the violation coefficient based on the risk level of the product. Furthermore, it is used to obtain the list of risk areas and update the risk correction process based on the list of risk areas.

2. The blockchain evaluation system for cross-border supply chains according to claim 1, characterized in that, The blockchain generation module constructs digital IDs based on multi-source data, specifically including: The production batch number of the goods and the supplier's factory inspection certificate number corresponding to this transaction in the multi-source data are used to calculate a fixed-length hash using SHA3-256, which is then used as the physical anchor code. The physical anchor code, product type, site parameters, and supplier's unified social credit code are concatenated and then hashed again to generate a digital ID.

3. The blockchain evaluation system for cross-border supply chains according to claim 2, characterized in that, The blockchain generation module generates the blockchain based on a digital ID, specifically including: When obtaining the hash coefficient based on the product type and site parameters corresponding to the digital ID and generating the site block based on the hash coefficient, the product type and site parameters in the valid contract are input into the hash coefficient model to obtain the site hash coefficient Ni output by the hash coefficient model. The site hash coefficient Ni is compared with the preset hash coefficient N0, and the necessity of on-chain is judged based on the comparison result. The site block is generated based on the judgment result, wherein: When Ni < N0, the necessity of adding to the chain is determined to be unnecessary, and no site block is generated; When Ni≥N0, the necessity of uploading to the chain is determined to be necessary. A site block is generated, and the site parameters and product information are uploaded to the corresponding site block to obtain the site block. All site blocks and transaction blocks are then linked to obtain the blockchain.

4. The blockchain evaluation system for cross-border supply chains according to claim 3, characterized in that, The credibility labeling module calculates the ID credit score of the digital ID and labels the credibility of the digital ID based on the ID credit score, specifically including: The normalization coefficient A1, timeliness coefficient A2, and accuracy coefficient A3 are obtained. Based on these coefficients, the ID credit score P is calculated using the following formulas: normalization coefficient A1, timeliness coefficient A2, accuracy coefficient A3, first credit weight α1, second credit weight α2, and third credit weight α3. The formula is P = A1 × α1 + A2 × α2 + A3 × α3. The ID credit score P is compared with a preset ID credit score P0. The creditworthiness is judged based on the comparison result, and the credibility of the digital ID is labeled according to the judgment result. When P≥P0, the credibility labeling module determines the credit status as high credit, labels the credibility of the digital ID as high credibility, arranges all traceability information in the blockchain associated with the digital ID in ascending order of timestamp, and displays it at the top of the consumer interface with a high credibility green label. When P < P0, the credibility labeling module determines the credit status as low, labels the credibility of the digital ID as low, collapses and displays all traceability information packages in the blockchain associated with the digital ID, and pops up a low credibility warning pop-up, prompting consumers that the traceability information of this entity is abnormal and they should verify with the merchant.

5. The blockchain evaluation system for cross-border supply chains according to claim 4, characterized in that, The credibility labeling module calculates a stability coefficient and adjusts the ID credit score calculation process based on this coefficient, specifically including: The market volatility index B1, seasonal stress index B2, and macroeconomic prosperity index B3 are obtained. The stability coefficient K is calculated based on these indices, along with the first stability weight β1, the second stability weight β2, and the third stability weight β3. The stability coefficient K is then compared with the preset stability coefficient K0. The stability is assessed based on the comparison results, and the ID credit score calculation process is adjusted accordingly. When K≥K0, the credibility labeling module determines the stability to be normal and does not make any stability adjustments to the ID credit score calculation process; When K < K0, the credibility labeling module determines the stability status as abnormal and performs a stability adjustment on the ID credit score calculation process. The stability adjustment includes: increasing the value of the second credit weight α2 by 0.05-0.15 and decreasing the value of the third credit weight α3 by 0.05-0.15 to obtain the adjusted second credit weight α4 and the adjusted third credit weight α5, and recalculating the ID credit score based on the first credit weight α1, the adjusted second credit weight α4, and the adjusted third credit weight α5.

6. The blockchain evaluation system for cross-border supply chains according to claim 5, characterized in that, The credibility labeling module acquires the public health coefficient and performs health calibration on the stability adjustment process based on the public health coefficient, specifically including: The system captures WHO epidemic risk levels and National Health Commission emergency response levels for public health emergencies in real time. These levels are quantified and used as a public health coefficient C. This coefficient C is compared to a preset public health coefficient C0. Based on the comparison results, the public health situation is assessed, and the stabilization adjustment process is calibrated accordingly. Specifically: When C≤C0, the confidence labeling module determines that the public health situation is normal and does not perform health calibration on the stable adjustment process; When C > C0, the credibility labeling module determines that the public health situation is abnormal and performs health calibration on the stability adjustment process. The health calibration includes: increasing the value of the seasonal stress index B2 in the stability coefficient K calculation process by 20% to obtain the calibrated seasonal stress index B4, and recalculating the stability coefficient K based on the calibrated seasonal stress index B4.

7. The blockchain evaluation system for cross-border supply chains according to claim 6, characterized in that, The blockchain node information verification module calculates a violation coefficient, judges the violation based on the violation coefficient, and verifies the blockchain node information based on the judgment result. Specifically, this includes: The embargo matching degree D1, value deviation degree D2, and quality violation coefficient D3 are obtained. The violation coefficient Q is calculated based on these factors, along with the first violation weight γ1, the second violation weight γ2, and the third violation weight γ3, with the formula Q = D1 × γ1 + D2 × γ2 + D3 × γ3. The violation coefficient Q is then compared with the first preset violation coefficient Q1 and the second preset violation coefficient Q2. Based on the comparison result, the violation is judged, and the blockchain node information is verified according to the judgment result. When Q < Q1, the blockchain node information verification module determines that there is no violation and does not verify the node information of the blockchain. When Q1≤Q<Q2, the blockchain node information verification module determines the violation to be a general violation, verifies the blockchain node information, and places the blockchain node information corresponding to the digital ID into the sampling pool for random inspection, with the sampling rate set at 30%. When Q≥Q2, the blockchain node information verification module determines the violation to be a serious violation, verifies the blockchain node information, and pushes the blockchain node information corresponding to the digital ID to the supervisory node workbench for manual review.

8. The blockchain evaluation system for cross-border supply chains according to claim 7, characterized in that, The blockchain node information verification module revises the ID credit score calculation process based on the violation details, specifically including: When the violation is deemed not to be a violation, the calculation process for the ID credit score will not be revised. When the violation is classified as a minor violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the first attenuation coefficient m1, where m1 = e -Penalty×（Q-Q1） Penalty is an empirical coefficient. The first revised ID credit score P1 is obtained. P1 is set to m1×P. The credibility of the digital ID is marked according to the first revised ID credit score P1. When the violation is classified as a serious violation, the calculation process for the ID credit score is revised. The ID credit score P is revised based on the second attenuation coefficient m2, where m2 = e -Penalty×（Q-Q1）×L L is the empirical amplification factor, and the first revised ID credit score P2 is obtained. P2 is set to m2×P, and the credibility of the digital ID is marked according to the first revised ID credit score P2.

9. The blockchain evaluation system for cross-border supply chains according to claim 8, characterized in that, The blockchain node information verification module acquires the risk level of the product and performs risk correction on the calculation process of the violation coefficient based on the product risk level, specifically including: The commodity HS code corresponding to the digital ID is mapped to the General Administration of Customs' commodity risk level table to obtain the commodity risk level F. The commodity risk level F is compared with the preset commodity risk level F0. Based on the comparison result, the commodity risk situation is judged. Based on the judgment result, the calculation process of the violation coefficient is adjusted for risk correction. When F≤F0, the blockchain node information verification module determines that the risk of the product is low and does not perform risk correction in the calculation process of the violation coefficient; When F > F0, the blockchain node information verification module determines that the risk of the product is high and performs risk correction on the calculation process of the violation coefficient. The risk correction includes: increasing the value of the third violation weight γ3 by 0.2, decreasing the value of the first violation weight γ1 by 0.05-0.15, and decreasing the value of the second violation weight γ2 by 0.05-0.15 to obtain the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5. The violation coefficient Q is then recalculated based on the corrected first violation weight γ4, corrected second violation weight γ6, and corrected third violation weight γ5.

10. The blockchain evaluation system for cross-border supply chains according to claim 9, characterized in that, The blockchain node information verification module obtains the list of risk areas and updates the risk correction process based on the list of risk areas, specifically including: Obtain the credit scores P of all IDs in region Y within the past 30 days. IDs with credit scores P below 0.5 are classified as having extremely low credit scores and counted to obtain the number n of extremely low credit IDs in the region. Based on the number n and the total number N of IDs in region Y, calculate the proportion R of extremely low credit scores, setting R = n / N. Compare the proportion R of extremely low credit scores with a preset proportion R0. Based on the comparison results, obtain a list of high-risk regions. When R≥R0, the blockchain node information verification module will include this region as a risk region in the risk region list; When R < R0, the blockchain node information verification module will not include this region as a risk region in the list of risk regions; The region corresponding to the digital ID is compared with the risk regions in the risk region list, and the risk is updated based on the comparison results during the risk correction process. When the region corresponding to the digital ID matches a risk region in the risk region list, the risk correction process is updated. The risk update includes: canceling the risk correction, reducing the values ​​of the first preset violation coefficient Q1 and the second preset violation coefficient Q2 by 0.05-0.15 to obtain the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g, and then re-comparing the violation coefficient Q with the updated preset violation coefficient Q1g and the updated second preset violation coefficient Q2g.

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