A method for secure verification of commodity supply chain data based on blockchain technology

Abstract

This application relates to the field of data security verification technology, specifically to a method for verifying the security of commodity supply chain data based on blockchain technology. The method includes: determining a comprehensive reputation value based on the difference in data deviation between successful and failed block verifications of each authoritative node within a rotation cycle, the number of successful block verifications, and an initial reputation score; determining a reputation value by analyzing the confidence levels of various supply chain data between each applicant node and any authoritative node at the current moment, and combining this with the comprehensive reputation value; and rotating the authoritative nodes in the current blockchain network based on the comprehensive reputation value and the reputation value. This application solves the problem of data tampering or incorrect on-chain uploads caused by fixed nodes, improving the transparency and trustworthiness of data security verification throughout the entire commercial supply chain.

Description

Technical Field

[0001] This application relates to the field of data security verification technology, specifically to a method for verifying the security of commodity supply chain data based on blockchain technology. Background Technology

[0002] In the commodity supply chain, data security and authenticity verification are crucial. Traditional supply chain data management relies on centralized databases, which are susceptible to single points of failure, data tampering risks, and low efficiency in cross-stage data collaboration. Blockchain technology, with its decentralized, immutable, and traceable characteristics, has been introduced into supply chain scenarios to improve data credibility. The Proof-of-Authority (PoA) consensus mechanism, due to its high throughput and low energy consumption, has become a commonly used solution for supply chain blockchains.

[0003] In Proof-of-Authority (PoA) consensus mechanisms, the selection of authority nodes is crucial, directly impacting the network's decentralization, security, performance, and reliability. Traditional algorithms typically employ fixed and pre-defined authority nodes, composed of trusted entities in the supply chain such as core manufacturers, logistics companies, and regulatory bodies. However, if authority nodes remain fixed for an extended period, it can lead to a monopoly of power, contradicting the decentralized nature of blockchain. Furthermore, fixed nodes become targets for attacks; if a node is compromised or its private key is leaked, the entire network's security is threatened. Additionally, some nodes may experience performance degradation due to hardware aging, network latency, or inadequate maintenance, increasing the risk of data tampering or incorrect on-chain data, and reducing the transparency and trust in data security verification throughout the commercial supply chain. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a blockchain-based method for verifying the security of commodity supply chain data, thereby resolving the existing problems.

[0005] The blockchain-based method for verifying the security of commodity supply chain data adopts the following technical solution:

[0006] One embodiment of this application provides a method for secure verification of commodity supply chain data based on blockchain technology, the method comprising the following steps:

[0007] In the current blockchain network for commodity supply, the number of types of qualification documents that have been successfully verified among all qualification documents submitted by each node when joining the blockchain is counted. Combined with the average distribution of the number of transactions at all times during the TPS test of each node, and the average distribution of the time from submission to confirmation of all transactions by the blockchain network, the initial reputation score of each node is determined to screen out the initial authoritative nodes. And combined with the preset value, authoritative nodes are selected from all the initial authoritative nodes.

[0008] Real-time acquisition of various supply chain data for each node within the preset rotation period prior to the current moment; within the rotation period prior to the current moment, analysis of the overlap of the value ranges of various supply chain data between each authoritative node's block verification process and the rotation period, as well as the differences in various supply chain data, to determine the data deviation of each authoritative node during each block verification.

[0009] Based on the difference in data deviation between block verification successes and failures within the rotation cycle, and the number of block verification successes, the true verification value of each authoritative node at the current moment is determined, and combined with the initial reputation score, the comprehensive reputation value of each authoritative node at the current moment is determined.

[0010] All nodes that have newly applied to join the blockchain network at the current moment are denoted as application nodes. All types of supply chain data of each application node are obtained. By analyzing the confidence of various types of supply chain data between each application node and any authoritative node at the current moment, the correlation value between each application node and any authoritative node is determined. Combined with the comprehensive reputation value, the reputation value of each application node at the current moment is determined.

[0011] The authoritative nodes in the current blockchain network are rotated based on the combined reputation value of all authoritative nodes and the reputation value of all applicant nodes.

[0012] Preferably, the expression for the initial reputation score of each node is: In the formula, This represents the initial reputation score of node i; This represents the preset initial score for node i; This represents the total number of types of qualification documents submitted by node i when it joins the blockchain network; This represents the number of qualification documents that successfully passed verification among all qualification documents submitted by node i when it joined the blockchain; it also represents the average number of transactions at all times during node i's TPS test. This represents the average time from submission to confirmation by the blockchain network for all transactions during the TPS test of node i.

[0013] Preferably, the process of selecting the initial authoritative node includes:

[0014] Cluster all nodes in the current blockchain network, where the distance metric is set to the absolute value of the initial reputation score difference between nodes, resulting in two clusters. Calculate the mean of the initial reputation score of all nodes in each cluster, and select all nodes in the cluster with the largest mean as the initial authoritative node.

[0015] Preferably, the step of selecting authoritative nodes from all initial authoritative nodes includes:

[0016] If the number of initial authoritative nodes in the current blockchain network is greater than a preset value, then the performance index factors of all initial authoritative nodes are sorted in descending order, and the initial authoritative nodes corresponding to the first preset value of performance index factors in the sorting result are taken as authoritative nodes; otherwise, all initial authoritative nodes are taken as authoritative nodes.

[0017] Preferably, the method for determining the data deviation of each authoritative node during each block verification is as follows:

[0018] Within the rotation period prior to the current moment, calculate the length of the intersection between the value range of various types of supply chain data during each block verification by each authoritative node and the value range of the corresponding type of supply chain data throughout the entire rotation period;

[0019] Calculate the mean difference between all data in each type of supply chain data and the mode of the corresponding type of supply chain data in the rotation cycle when verifying each block for each authoritative node;

[0020] The result of dividing the mean of the difference by the length of the interval is recorded as the deviation index of various supply chain data at each authoritative node during each block verification.

[0021] The sum of the deviation indices of all types of supply chain data during each block verification by each authoritative node is used as the data deviation degree during each block verification by each authoritative node.

[0022] Preferably, the method for determining the verification true value of each authoritative node at the current moment is as follows:

[0023] Calculate the sum of data deviations for all successful sub-block verifications of each authoritative node within the previous rotation period, and the sum of data deviations for all failed sub-block verifications, and record them as the first sum and the second sum, respectively.

[0024] Calculate the result of dividing the first sum by the second sum, and multiply the result by the number of successful verifications of all blocks. This product is used as the true verification value of each authoritative node at the current moment.

[0025] Preferably, the comprehensive reputation value of each authoritative node at the current moment is the product of the initial reputation score and the verified true value of each authoritative node at the current moment.

[0026] Preferably, the method for determining the association value between each application node and any authoritative node is as follows:

[0027] An association rule mining algorithm is used to obtain the maximum confidence score between each type of supply chain data of each applicant node and all types of supply chain data of any authoritative node at the current time. The sum of the maximum confidence scores of all types of supply chain data of each applicant node is used as the association value between each applicant node and any authoritative node at the current time.

[0028] Preferably, the method for determining the reputation value of each requesting node at the current moment is as follows:

[0029] Calculate the product of the correlation value between each applicant node and any authoritative node at the current time and the initial reputation score of the corresponding authoritative node, and record it as the correlation product. The mean of the correlation products between each applicant node and all authoritative nodes is recorded as the reputation adjustment factor of each applicant node at the current time.

[0030] For each application node, the initial reputation score of each application node is obtained according to the method for obtaining the initial reputation score of the authoritative node. The sum of the initial reputation score of each application node and the reputation adjustment factor is used as the reputation value of each application node at the current time.

[0031] Preferably, the rotation of authoritative nodes in the current blockchain network includes:

[0032] The combined reputation values ​​of all authoritative nodes in the current blockchain network and the reputation values ​​of all applicant nodes are sorted in descending order. The first preset number of nodes in the sorted results are selected as the new authoritative nodes in the current blockchain network.

[0033] This application has at least the following beneficial effects:

[0034] This application selects authoritative nodes from all initial authoritative nodes by statistically analyzing the number of successfully verified qualification documents submitted by each node when joining the blockchain, combined with the average distribution of transaction counts at all times during each node's TPS test, and the average distribution of the time from submission to blockchain network confirmation. This effectively avoids the problem of a few pre-selected nodes monopolizing block generation and verification power for a long time. Furthermore, this application analyzes the confidence level of various supply chain data between each applicant node and any authoritative node at the current moment to determine the correlation value between each applicant node and any authoritative node, and combines it with the comprehensive reputation value to determine the reputation value of each applicant node at the current moment. This allows qualified new entities to dynamically join the authoritative node pool, continuously injecting new nodes, preventing the network from forming a closed circle, increasing the difficulty and cost of identifying data tampering, and effectively reducing the risk of erroneous or malicious data being successfully uploaded to the chain. This application effectively solves the core problems that traditional Proof of Authority (PoA) may have in supply chain scenarios, such as power concentration, static fragility, performance bottlenecks, and insufficient data verification credibility, by constructing a dynamic, multi-dimensional, data-driven reputation management system and an intelligently triggered node rotation mechanism. Its ultimate effect is to create a more decentralized, more secure, more robust, higher-performance, and more accurate and reliable commodity supply chain data verification blockchain network, which significantly reduces the risk of data being tampered with or incorrectly uploaded to the chain, and improves the transparency and trust of the entire supply chain. Attached Figure Description

[0035] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating the steps of a blockchain-based commodity supply chain data security verification method, as provided in one embodiment of this application;

[0037] Figure 2 This is a schematic diagram illustrating the comprehensive reputation value extraction process provided in one embodiment of this application. Detailed Implementation

[0038] To further illustrate the technical means and effects adopted by this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a blockchain-based commodity supply chain data security verification method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0040] The following description, in conjunction with the accompanying drawings, details a specific scheme for a blockchain-based commodity supply chain data security verification method provided in this application.

[0041] This application provides an embodiment of a blockchain-based method for verifying the security of commodity supply chain data. Specifically, it provides the following method for verifying the security of commodity supply chain data based on blockchain technology. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0042] Step S1: In the current commodity supply blockchain network, count the number of types of qualification documents that have been successfully verified in all qualification documents submitted by each node when joining the blockchain, and combine the average distribution of the number of transactions at all times during the TPS test of each node, as well as the average distribution of the time from submission to confirmation of all transactions by the blockchain network, to determine the initial reputation score of each node, so as to screen out the initial authoritative nodes, and combine the preset value to screen out the authoritative nodes from all the initial authoritative nodes.

[0043] Proof-of-Authority (PoA) is a high-efficiency, low-energy-consumption blockchain consensus mechanism suitable for scenarios requiring controllability and high throughput. Its core idea is to pre-select authoritative nodes responsible for block generation and verification. In the authoritative authentication algorithm, the selection of authoritative nodes is a crucial factor, directly impacting the network's decentralization, security, performance, and reliability. The dynamic rotation mechanism is a core strategy in PoA consensus used to improve decentralization and network robustness, replacing authoritative nodes through conditional triggers to prevent long-term concentration of power.

[0044] Encrypting and transmitting commodity supply chain data based on a dynamic rotation mechanism involves a node election process when using blockchain technology to securely verify the data. This process determines which nodes are authorized as "verification nodes" or "authoritative nodes." In traditional algorithms, authoritative nodes are typically pre-defined and consist of trusted entities in the supply chain, such as manufacturers, logistics companies, and regulatory agencies.

[0045] However, if authoritative nodes remain fixed for a long time, it may lead to a monopoly of power, which violates the original intention of blockchain decentralization. At the same time, fixed nodes may become targets of attacks. If a node is hacked or its private key is leaked, the security of the entire network will be threatened. Furthermore, some nodes may experience performance degradation due to hardware aging, network latency, or insufficient maintenance.

[0046] Therefore, this embodiment determines the initial reputation score of each node by statistically analyzing the number of successfully verified qualification documents submitted by each node when joining the blockchain network, combined with the average distribution of the number of transactions at all times during the TPS test of each node, and the average distribution of the time from submission to confirmation of all transactions by the blockchain network. This is used to screen out initial authoritative nodes. Furthermore, based on preset values, authoritative nodes are selected from all initial authoritative nodes, and the reputation values ​​of the nodes are adjusted to achieve dynamic rotation of authoritative nodes. Specifically:

[0047] (1) In this embodiment, the initial reputation score of each node is determined by counting the number of types of qualification documents that have been successfully verified among all qualification documents submitted by each node when joining the blockchain network, and combining the average distribution of the number of transactions at all times during the TPS test of each node, as well as the average distribution of the time from submission to confirmation by the blockchain network for all transactions. Specifically:

[0048] First, the number of all types of qualification documents submitted by each node when joining the blockchain is determined. The qualification documents are verified using a zero-knowledge proof algorithm. Based on the verification results, the number of qualification document types that have successfully passed verification is obtained. Next, the number of transactions at all moments in the TPS test process of each node within the current blockchain network is obtained in real time, along with the time from submission to confirmation by the blockchain network for each transaction. The zero-knowledge proof algorithm and the acquisition of all data are well-known technologies. The process of verifying the success or failure of qualification documents using the zero-knowledge proof algorithm and the specific acquisition process of all data are not detailed here.

[0049] Furthermore, as one implementation method, in this embodiment, the initial reputation score of node i... The expression is: In the formula, This represents the preset initial score for node i; This represents the total number of types of qualification documents submitted by node i when it joins the blockchain network; This represents the number of qualification documents that successfully passed verification among all qualification documents submitted when node i joins the blockchain. This represents the average number of transactions at all points in time during the TPS test of node i. This represents the average time from submission to confirmation by the blockchain network for all transactions during the TPS test of node i.

[0050] It should be noted that the preset initial score is set as follows: Since each node may correspond to a company, institution or device, for companies and institutions, their initial score can be obtained by the company credit score obtained after evaluation by a third-party evaluation agency, which is used as the preset initial score value. The preset initial score for devices can be obtained from the corresponding "Equipment Operation Manual". If it does not exist, the preset initial score is 0.

[0051] The TPS testing process and the acquisition of transaction counts are both well-known technologies, and their specific principles and acquisition processes will not be elaborated here.

[0052] Based on each node's initial reputation score, it can be understood that a higher initial score indicates that the enterprise, institution, or device represented by the node had a higher level of credibility, reliability, and qualifications before joining the blockchain network, providing a foundation of trust for the node's entry into the blockchain network. Therefore, the corresponding initial reputation score will also be higher. Simultaneously, if a node submits a greater total number of qualification documents when joining the blockchain network, and if a greater number of all qualification documents successfully pass verification, it indicates that the node has a greater number of valid documents that have passed verification, signifying higher authenticity and validity of the node's qualifications, and a higher initial reputation score. Furthermore, if the average number of transactions at all times during the node's TPS test is higher, and the average time from submission to confirmation of all transactions during the TPS test is lower, it means that the node can handle more transactions under simulated high load pressure, and the transaction speed from submission to confirmation is faster. This indicates that the node has stronger processing capabilities and lower latency, enabling it to participate more efficiently in the consensus and data verification process of the blockchain network, resulting in a higher initial reputation score.

[0053] Conversely, a lower initial score for a node indicates that the enterprise, institution, or device it represents had lower credibility, reliability, and qualification levels before joining the blockchain network. This means the node lacked sufficient basic trust before joining the network and may pose a higher risk; therefore, the corresponding initial reputation score L will also be lower. Simultaneously, if a node submits a small total number of qualification documents when joining the blockchain network, or if it submits multiple types of documents but only a small number of successfully verified documents, it indicates that the node has few valid and verified documents among its submitted qualification documents. This indicates that the node's qualifications and validity are questionable, or that it has limited compliance information to disclose, resulting in a lower initial reputation score. Furthermore, if the average number of transactions (S) at all times during the node's TPS test is small, and the average time (t) from transaction submission to blockchain network confirmation is large, it means the node has a weak ability to process transactions under simulated high load pressure, and the transaction confirmation speed is slow. This suggests that the node has insufficient hardware performance, high network latency, or low software efficiency, making it difficult to efficiently handle network tasks and potentially becoming a network bottleneck or having poor reliability; the corresponding initial reputation score will also be lower.

[0054] (2) Further, in this embodiment, based on the initial reputation score of each node, initial authoritative nodes are selected, and authoritative nodes are selected from all initial authoritative nodes in combination with preset values, specifically as follows:

[0055] All nodes in the current blockchain network are clustered. In this embodiment, the number of clusters is set to 2, and the metric distance is set to the absolute value of the difference in the initial reputation score between nodes, resulting in two clusters. The mean of the initial reputation score of all nodes in each cluster is calculated, and all nodes in the cluster with the largest mean are taken as the initial authoritative nodes.

[0056] Furthermore, since an excessive number of authoritative nodes may affect network scalability, complicate the consensus process, and consequently impact data verification efficiency, this embodiment further filters the initial authoritative nodes, specifically as follows:

[0057] If the number of initial authoritative nodes in the current blockchain network is greater than a preset value, then the performance index factors of all initial authoritative nodes are sorted in descending order, and the initial authoritative nodes corresponding to the first preset value of performance index factors in the sorting result are taken as authoritative nodes; otherwise, all initial authoritative nodes are taken as authoritative nodes.

[0058] It should be noted that there are many commonly used clustering algorithms. In this embodiment, the k-means clustering algorithm is used to cluster the initial authority nodes. In practical applications, as other implementation methods, implementers may also use other clustering methods such as the DBSCAN clustering algorithm according to specific circumstances. This embodiment does not impose any special restrictions on the selection of clustering algorithms.

[0059] Among them, the k-means clustering algorithm is a well-known technique, and its specific principles will not be elaborated here.

[0060] It should be noted that the preset value is set manually. In this embodiment, the preset value is 10. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0061] Thus, this embodiment achieves more dynamic and reliable security verification than the traditional fixed mode by comprehensively evaluating the types and authenticity of the qualifications submitted by the nodes, their transaction processing capabilities and latency, setting an initial reputation score, and using clustering and performance screening to determine authoritative nodes, thereby improving the efficiency and credibility of supply chain data verification.

[0062] Step S2: Real-time acquisition of various supply chain data of each node within the preset rotation period before the current time; within the rotation period before the current time, analyze the degree of overlap of the value range of various supply chain data between each authoritative node's block verification process and the rotation period, as well as the differences of various supply chain data, and determine the data deviation of each authoritative node during each block verification.

[0063] As commodity supply chain data is continuously verified, new blocks are constantly being generated. During this process, authoritative nodes may become fixed for a long time, leading to a monopoly of power. Fixed nodes may also become targets of attacks. If a node is compromised or its private key is leaked, the security of the entire network will be threatened. Furthermore, some nodes may experience performance degradation due to hardware aging, network latency, or insufficient maintenance.

[0064] Therefore, this embodiment acquires various supply chain data of each node within a preset rotation period prior to the current time in real time; within the rotation period prior to the current time, it analyzes the overlap of the value ranges of various supply chain data between each authoritative node's block verification process and the rotation period, as well as the differences in various supply chain data, to determine the data deviation of each authoritative node during each block verification, specifically:

[0065] For each authoritative node, it acquires various supply chain data from each node within a preset rotation period prior to the current moment, such as temperature data during goods transportation and goods production time. Furthermore, within the rotation period prior to the current moment, it analyzes the overlap and differences in the value ranges of various supply chain data between each authoritative node's block verification process and the rotation period, determining the data deviation at each authoritative node's block verification. Specifically:

[0066] As one implementation method, in this embodiment, within the rotation period before the current time, the length of the intersection of the value range of various types of supply chain data when each authoritative node verifies a block and the value range of the corresponding type of supply chain data within the entire rotation period is calculated.

[0067] It should be noted that the preset rotation period is set manually. In this embodiment, the preset rotation period is 3 months. In actual application, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0068] Furthermore, the mean difference between all data in each type of supply chain data and the mode of the corresponding type of supply chain data in the rotation cycle is calculated when each authoritative node verifies a block.

[0069] It should be noted that there are many methods to measure the differences between data. In this embodiment, the absolute value of the difference between all data in each type of supply chain data and the mode of the corresponding type of supply chain data in the rotation cycle during each block verification by each authoritative node is used as the difference between all data in each type of supply chain data and the mode of the corresponding type of supply chain data in the rotation cycle during each block verification by each authoritative node. In practical applications, as other implementation methods, implementers may also choose other methods to measure the differences between data, such as the square or ratio of the difference, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods to measure the differences between data.

[0070] Furthermore, the mean of the difference divided by the length of the interval is recorded as the deviation index of various supply chain data during each block verification by each authoritative node;

[0071] Furthermore, the sum of the deviation indices of all types of supply chain data during each block verification by each authoritative node is used as the data deviation degree during each block verification by each authoritative node.

[0072] Based on the data deviation during each block verification by authoritative nodes, it can be understood that the larger the length of the intersection between the value range of the supply chain data at the current authoritative node's block verification and the value range of the corresponding type of supply chain data throughout the entire rotation cycle, the more it indicates that the corresponding type of supply chain data at the current verification is within its historical normal fluctuation range and there are no obvious anomalies. Therefore, the corresponding data deviation is smaller. At the same time, the smaller the difference between all data in the supply chain data at the current authoritative node's block verification and the mode of the corresponding type of supply chain data in the rotation cycle, the more stable the supply chain data at the current authoritative node's block verification is, and the corresponding data deviation is smaller.

[0073] Conversely, if the length of the intersection between the value range of the supply chain data at the current authoritative node block verification and the value range of the corresponding supply chain data throughout the entire rotation cycle is smaller, it indicates that the corresponding supply chain data at the current verification may have exceeded its historical normal fluctuation range, showing obvious anomalies. Therefore, the corresponding data deviation is greater. At the same time, if the difference between all data in the supply chain data at the current authoritative node block verification and the mode of the corresponding supply chain data in the rotation cycle is greater, it indicates that the supply chain data at the current authoritative node block verification is volatile and unstable, and the corresponding data deviation is greater.

[0074] Thus, this embodiment analyzes the overlap and differences between the verification data and historical data of authoritative nodes during the rotation cycle, calculates the data deviation, and dynamically evaluates the reliability and data stability of node verification behavior. This provides a basis for subsequent adjustment of node reputation and implementation of dynamic rotation, and enhances the network's ability to identify abnormal behavior.

[0075] Step S3: Based on the difference in data deviation between successful and failed block verifications of each authoritative node within the rotation cycle, and the number of successful block verifications, determine the true verification value of each authoritative node at the current moment, and combine it with the initial reputation score to determine the comprehensive reputation value of each authoritative node at the current moment. According to Step S2, which constructed the data deviation of various supply chain data when authoritative nodes perform verification, this embodiment further determines the true verification value of each authoritative node at the current moment based on the difference in data deviation between successful and failed block verifications of each authoritative node within the rotation cycle, and the number of successful block verifications, and combines it with the initial reputation score to determine the comprehensive reputation value of each authoritative node at the current moment. Specifically:

[0076] As one implementation method, in this embodiment, the sum of data deviation when all sub-blocks of each authoritative node are successfully verified within the rotation period before the current time, and the sum of data deviation when all sub-blocks are unverified are calculated respectively, and are denoted as the first sum and the second sum.

[0077] Furthermore, the result of dividing the first sum by the second sum is calculated, and the product of this division with the number of successful verifications of all blocks is used as the verification true value of each authoritative node at the current moment. The larger the verification true value, the greater the deviation of the data observed by the authoritative node when verification fails compared to the deviation observed when verification succeeds, indicating that the verification logic of the authoritative node is accurate and reliable, and the authority node has high reliability. Conversely, the smaller the verification true value, the less the deviation of the data observed by the authoritative node when verification fails compared to the deviation observed when verification succeeds, indicating that the verification logic of the authoritative node is flawed, and the authority node has low reliability.

[0078] Furthermore, the product of the initial reputation score of each authoritative node at the current moment and the verified true value is used as the comprehensive reputation value of each authoritative node at the current moment.

[0079] Preferably, the schematic diagram of the comprehensive reputation value extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0080] Based on the overall reputation scores of each authoritative node at the current moment, it can be understood that the higher the initial reputation score of an authoritative node, the better the initial conditions of the node. If the authoritative node corresponds to a company, it means that the company has high credibility and complete qualifications. Therefore, the higher the overall reputation score, the higher the reliability of the authoritative node. At the same time, the higher the verification authenticity of an authoritative node, it means that when the authoritative node successfully verifies, the supply chain data it processes is usually within the normal historical fluctuation range. The verification behavior of the authoritative node is reliable and trustworthy. Therefore, its reputation score will be improved, that is, the higher the overall reputation score.

[0081] Conversely, if the initial reputation score of an authoritative node is low, it indicates that the initial conditions of the node are relatively poor. If the authoritative node corresponds to a company, it means that the company's reputation or qualifications may be insufficient. Therefore, the corresponding comprehensive reputation value is relatively low, indicating that the reliability of the authoritative node is low. At the same time, if the verification authenticity of an authoritative node is low, it means that when the authoritative node successfully verifies, the supply chain data it processes may deviate from the normal historical fluctuation range, or the verification behavior is not reliable and trustworthy enough. Therefore, its reputation value may be negatively affected, that is, the comprehensive reputation value is relatively low.

[0082] Thus, this embodiment calculates the true verification value by comparing the difference in data deviation between successful and failed verifications and the number of successful verifications, and combines it with the initial reputation score to dynamically update the comprehensive reputation value of the authoritative node. This effectively evaluates the reliability of the node's verification behavior, enabling the reputation value to reflect the actual performance of the node and providing a more accurate basis for subsequent node rotation.

[0083] Step S4: Record all nodes that have newly applied to join the blockchain network at the current moment as application nodes, obtain all types of supply chain data of each application node, determine the correlation value between each application node and any authoritative node by analyzing the confidence of various types of supply chain data between each application node and any authoritative node at the current moment, and combine the comprehensive reputation value to determine the reputation value of each application node at the current moment.

[0084] During the rotation of authoritative nodes, new nodes will also apply to join the blockchain network. These are referred to as applicant nodes. When an applicant node joins the blockchain network, it typically requires the approval of an authoritative node. Therefore, this embodiment obtains all types of supply chain data for each applicant node, analyzes the confidence level of various types of supply chain data between each applicant node and any authoritative node at the current moment, determines the correlation value between each applicant node and any authoritative node, and combines this with the comprehensive reputation value to determine the reputation value of each applicant node at the current moment. Specifically:

[0085] In this embodiment, an association rule mining algorithm is used to obtain the maximum confidence score between each type of supply chain data of each applicant node and all types of supply chain data of any authoritative node at the current moment. The sum of the maximum confidence scores of all types of supply chain data of each applicant node is used as the association value between each applicant node and any authoritative node at the current moment. The larger the association value, the stronger the similarity between the supply chain data category of the applicant node and the supply chain data category verified by the authoritative node. This reflects that the business scope and data type of the applicant node are highly consistent with the supply chain links or data types covered by the authoritative nodes in the current blockchain network. The applicant node is valuable for maintaining and expanding the data integrity and coverage of the blockchain network.

[0086] It should be noted that there are many commonly used association rule algorithms. In this embodiment, the Apriori algorithm is used to calculate the confidence of various supply chain data between the requesting node and the authoritative node. In actual applications, as other implementation methods, implementers may also use other association rule algorithms according to specific circumstances. This embodiment does not impose any special restrictions.

[0087] The Apriori algorithm is a well-known technique, and the principle and process of using it to calculate the confidence between two variables will not be elaborated here.

[0088] Furthermore, the product of the correlation value between each application node and any authoritative node at the current time and the initial reputation score of the corresponding authoritative node is calculated and recorded as the correlation product. The mean of the correlation products between each application node and all authoritative nodes is recorded as the reputation adjustment factor of each application node at the current time.

[0089] For each application node, the initial reputation score of each application node is obtained according to the method for obtaining the initial reputation score of the authoritative node. The sum of the initial reputation score of each application node and the reputation adjustment factor is used as the reputation value of each application node at the current time.

[0090] Based on the reputation scores of each applicant node at the current moment, it can be understood that the reputation score reflects the comprehensive potential of the applicant node to be accepted as a new authoritative node by the blockchain network. If the reputation adjustment factor of the current applicant node is larger, it means that the data category of the applicant node is highly correlated with the historical data of the authoritative node, which means that the applicant node can provide the data types that the authoritative node is interested in or needs. This indicates that the applicant node can better integrate into the current network's business processes and improve the comprehensiveness of data verification. At the same time, if the initial reputation score of the authoritative node is higher, it means that its recognition of the applicant node will bring a greater bonus. That is, the applicant node recommended by a reputable authoritative node will carry more weight than the recommendation of a less reputable authoritative node.

[0091] Conversely, a smaller reputation adjustment factor for the current applicant node indicates a lower correlation between the applicant node's data category and the authoritative node's historical data. This means the data type provided by the applicant node may not be what the authoritative node is currently primarily focused on or urgently needs, indicating greater difficulty for the applicant node to integrate into the current core network business processes, and its direct contribution to improving the comprehensiveness of data verification is relatively limited. At the same time, a lower initial reputation score for the authoritative node indicates lower credibility and reliability. Therefore, the added effect of its "approval" or vote for the applicant node will be small, meaning its recommendation or vote carries less weight. This implies that even if an authoritative node with average credibility supports an applicant node, its influence is relatively limited, making it difficult to significantly improve the applicant node's overall reputation value.

[0092] Thus, this embodiment analyzes the correlation between the data of the applicant node and the data of the authoritative node, and calculates the reputation value of the applicant node by combining it with the current reputation of the authoritative node, thereby assessing its potential to become a new authoritative node. This ensures that the new node not only meets its own requirements, but its data type is also highly compatible with network needs, thereby improving the comprehensiveness of network data coverage and the efficiency of verification.

[0093] Step S5: Based on the combined reputation value of all authoritative nodes in the current blockchain network and the reputation value of all applicant nodes, rotate the authoritative nodes in the current blockchain network.

[0094] The reputation value of the application node has been obtained through the above steps. Therefore, further, this embodiment rotates the authoritative nodes in the current blockchain network based on the comprehensive reputation value of all authoritative nodes and the reputation value of all application nodes. Specifically, the comprehensive reputation value of all authoritative nodes and the reputation value of all application nodes in the current blockchain network are arranged in descending order, and the first preset number of nodes in the arrangement are selected as the new authoritative nodes in the current blockchain network. This completes the adaptive dynamic rotation of authoritative nodes in the Proof-of-Authority (PoA) consensus mechanism in blockchain technology, improves the efficiency and accuracy of data security verification in the commodity supply chain, and reduces the risk of data tampering.

[0095] It should be noted that the preset number is set manually. In this embodiment, the preset number is 10. In actual application, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0096] Thus, this embodiment comprehensively evaluates node qualifications, transaction processing capabilities, verification behavior stability, and data correlation, dynamically calculates node reputation values, and rotates authoritative nodes accordingly. This effectively avoids node monopolies, improves the rationality of new node integration, and enhances the comprehensiveness, security, and overall network performance of supply chain data verification.

[0097] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0099] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A blockchain-based method for verifying data security of a commodity supply chain, characterized in that, The method comprises the following steps: The blockchain network of the current commodity supply is recorded as a current blockchain network, wherein the number of types of qualification files successfully passed verification among all qualification files submitted by each node when joining the blockchain is counted, and the average distribution of the number of transactions at all times during the TPS test of each node is combined with the average distribution of the time length of all transactions from submission to confirmation of the current blockchain network to determine the initial reputation score of each node, so as to screen out initial authoritative nodes, and the initial authoritative nodes are combined with a preset value to screen out authoritative nodes from all initial authoritative nodes; Real-time acquisition of various types of supply chain data of each node within a preset rotation period before the current time; within the rotation period before the current time, the overlap degree of the value range of each type of supply chain data between each block verification process of each authoritative node and the rotation period and the difference of each type of supply chain data are analyzed to determine the data deviation of each block verification of each authoritative node; Based on the difference of the data deviation of each authoritative node between block verification success and block verification failure within the rotation period before the current time and the number of times of block verification success, the verification true value of each authoritative node at the current time is determined, and the initial reputation score is combined to determine the comprehensive reputation value of each authoritative node at the current time; All nodes newly applying to join the current blockchain network at the current time are recorded as application nodes, all types of supply chain data of each application node are acquired, the correlation value between each application node and any authoritative node is determined by analyzing the confidence of each type of supply chain data between each application node and any authoritative node at the current time, and the comprehensive reputation value is combined to determine the reputation value of each application node at the current time; Based on the comprehensive reputation value of all authoritative nodes in the current blockchain network and the reputation value of all application nodes, the authoritative nodes in the current blockchain network are rotated. 2.The method of claim 1, wherein The expression of the initial reputation score of each node is: ; wherein, represents the initial reputation score of node i; represents the preset initial score of node i; represents the total number of types of qualification files submitted by node i when joining the current blockchain network; represents the number of types of qualification files successfully verified among all types of qualification files submitted by node i when joining the blockchain; represents the average number of transactions at all times during the TPS test process of node i; represents the average duration from submission to confirmation in the current blockchain network of all transactions during the TPS test process of node i. 3.The method of claim 1, wherein, The screening of the initial authoritative nodes comprises: Clustering all nodes in the current blockchain network, wherein the absolute value of the initial reputation score difference between nodes is set as the metric distance, two clustering clusters are obtained, the mean value of the initial reputation score of all nodes in each clustering cluster is calculated, and all nodes in the clustering cluster corresponding to the maximum mean value are taken as the initial authoritative nodes. 4.The method of claim 1, wherein, The screening of the initial authoritative nodes comprises: If the number of initial authoritative nodes in the current blockchain network is greater than the preset value, the performance index factors of all initial authoritative nodes are arranged in descending order, the initial authoritative nodes corresponding to the first preset number of performance index factors in the arrangement result are taken as the authoritative nodes, and otherwise, all initial authoritative nodes are taken as the authoritative nodes. 5.The method of claim 1, wherein, The data deviation of each block verification of each authoritative node is determined by: Within the rotation period before the current time, the interval length of the intersection of the value range of each type of supply chain data at each block verification of each authoritative node and the value range of the corresponding type of supply chain data within the entire rotation period is calculated; The mean value of the difference between all data in each type of supply chain data at each block verification of each authoritative node and the mode of the corresponding type of supply chain data in the rotation period is calculated; The result of dividing the mean of the difference by the length of the interval is recorded as the deviation index of each type of supply chain data of each authoritative node at each block verification; The cumulative sum of the deviation indexes of all types of supply chain data of each authoritative node at each block verification is taken as the data deviation degree of each authoritative node at each block verification. 6.The method of claim 1, wherein, The determination method of the verification true value of each authoritative node at the current moment is as follows: The sum of the data deviation degrees of each authoritative node at all successful block verifications in the rotation period before the current moment and the sum of the data deviation degrees of each authoritative node at all failed block verifications are calculated respectively, and are recorded as a first sum and a second sum respectively; The result of dividing the first sum by the second sum is calculated, and the product of the division result and the number of successful block verifications is taken as the verification true value of each authoritative node at the current moment. 7.The method of claim 1, wherein, The comprehensive reputation value of each authoritative node at the current moment is the product of the initial reputation score of each authoritative node at the current moment and the verification true value.

8. The method for secure verification of commodity supply chain data based on blockchain technology as described in claim 1, characterized in that, The determination method of the association value between each application node and any authoritative node is as follows: The maximum value in the confidence degree between each type of supply chain data of each application node and all types of supply chain data of any authoritative node at the current moment is obtained by using an association rule mining algorithm, and the cumulative sum of the maximum values of all types of supply chain data of each application node is taken as the association value between each application node and any authoritative node at the current moment.

9. A method for secure verification of commodity supply chain data based on blockchain technology as described in claim 1, characterized in that, The determination method of the reputation value of each application node at the current moment is as follows: The product of the association value between each application node and any authoritative node at the current moment and the initial reputation score of the corresponding authoritative node is calculated, and is recorded as an association product. The mean of the association products between each application node and all authoritative nodes is recorded as a reputation adjustment factor of each application node at the current moment. For each application node, the initial reputation score of each application node is obtained according to the method for obtaining the initial reputation score of the authoritative node, and the sum of the initial reputation score of each application node and the reputation adjustment factor is taken as the reputation value of each application node at the current moment.

10. A method for secure verification of commodity supply chain data based on blockchain technology as described in claim 1, characterized in that, The rotation of the authoritative nodes in the current blockchain network includes: The comprehensive reputation values of all authoritative nodes in the current blockchain network and the reputation values of all application nodes are arranged in descending order, and the first pre-set number of nodes in the arrangement result is taken as the new authoritative nodes in the current blockchain network.

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