Anti-counterfeiting Verification and Tracking Method and System Based on RFID Technology

Abstract

The present invention discloses an anti-counterfeiting verification and tracking method and system based on RFID technology, which relates to the field of anti-counterfeiting tracking technology. All unexpected nodes in the second sequence are identified, and after analyzing the historical data of the unexpected nodes, the confidence score of each unexpected node is obtained. According to the correlation index, the influence weight of each unexpected node is obtained, and after calculating the confidence scores and influence weights of all unexpected nodes through a fusion algorithm, a verification coefficient is generated for the product. According to the comparison result between the verification coefficient and the verification threshold, corresponding management strategies are generated for the product. After regularly obtaining the number of identified unexpected nodes of all products in the same series, it is analyzed whether it is necessary to manage the products in the same series in advance. The tracking system can identify all unexpected nodes through sequence comparison and analyze their historical data, effectively discovering abnormal paths, thereby enhancing the system's ability to identify abnormal circulation and improving the tracking accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-counterfeiting tracking, and particularly relates to an anti-counterfeiting verification and tracking method and system based on RFID technology. Background Art

[0002] The trend of globalization in modern supply chains has exacerbated the emergence of counterfeit and shoddy goods. Especially in the fields of medicine, food, electronic products, and high-end luxury goods, the harm of counterfeit products is particularly prominent. It not only damages the interests of consumers but also leads to damage to the reputation of brand owners. Tracking systems aim to use radio frequency identification (RFID) technology to achieve the identification of the identity of goods, authenticity verification, and full-process tracking, prevent the circulation of counterfeit and shoddy goods, and improve the transparency and efficiency of supply chain management.

[0003] The prior art has the following defects:

[0004] Traditional tracking systems mainly compare based on predefined standard paths to identify whether a product deviates from the normal circulation path. However, tracking systems can often only detect whether a product has reached an unexpected node, lacking in-depth path analysis, unable to identify the true risks of these unexpected nodes, and also difficult to determine which nodes may represent serious supply chain anomalies or forgery risks. This rough path anomaly detection mechanism may lead to frequent false alarms or failure to issue warnings in actual risk situations.

[0005] Based on this, the present invention proposes an anti-counterfeiting verification and tracking method and system based on RFID technology. By sequence comparison, all unexpected nodes are identified, and historical data analysis is performed on them, which can effectively detect path anomalies, thereby enhancing the system's ability to identify abnormal circulation and improving tracking accuracy. Summary of the Invention

[0006] The object of the present invention is to provide an anti-counterfeiting verification and tracking method and system based on RFID technology to solve the deficiencies in the background art.

[0007] To achieve the above object, the present invention provides the following technical solution: An anti-counterfeiting verification and tracking method based on RFID technology, the tracking method comprising the following steps:

[0008] The tracking system scans the RFID tag of the product to be tracked, marks the standard path of the product as the first sequence, obtains the actual circulation path of the product, marks the actual circulation path as the second sequence, performs sequence comparison on the first sequence and the second sequence, and identifies all unexpected nodes in the second sequence;

[0009] After analyzing the historical data of unanticipated nodes, obtain the confidence score for each unanticipated node. If the confidence score of any unanticipated node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of the product; otherwise, the tracking system obtains the influence weight of each unanticipated node based on the correlation index;

[0010] After calculating the confidence scores and influence weights of all unanticipated nodes through a fusion algorithm, generate a verification coefficient for the product. Based on the comparison result between the verification coefficient and the verification threshold, generate corresponding management strategies for the product. After regularly obtaining the number of unanticipated node identifications of all products in the same series, analyze whether it is necessary to manage the products in the same series in advance, and send the analysis result to the administrator.

[0011] In a preferred embodiment, after analyzing the historical data of unanticipated nodes, obtain the confidence score for each unanticipated node, including the following steps:

[0012] Obtain the historical data of unanticipated nodes, where the historical data includes the historical occurrence frequency, normal marking ratio, abnormal marking ratio, and position deviation index of unanticipated nodes;

[0013] Comprehensively calculate the occurrence frequency, normal marking ratio, abnormal marking ratio, and position deviation index to obtain the confidence score of the unanticipated node. The expression is: , where in the formula, is the confidence score, is the occurrence frequency, is the normal marking ratio, is the abnormal marking ratio, is the position deviation index, is the adjustment coefficient, , are the weights of the normal marking ratio and abnormal marking ratio respectively, and .

[0014] In a preferred embodiment, after calculating the confidence scores and influence weights of all unanticipated nodes through a fusion algorithm, generate a verification coefficient for the product, including the following steps:

[0015] After calculating the confidence scores and influence weights of all unanticipated nodes through a fusion algorithm, generate a verification coefficient for the product. The expression is: , where in the formula, is the verification coefficient, is the influence weight of the th unanticipated node, is the th confidence score of the unanticipated node, is the number of unanticipated nodes in the product material path;

[0016] Compare the obtained verification coefficient with a preset verification threshold. The verification threshold is used to determine whether there is a risk of forgery for the product. If the verification coefficient is less than or equal to the verification threshold, it is determined that the product has no forgery risk. If the verification coefficient is greater than the verification threshold, it is determined that the product has a forgery risk.

[0017] In a preferred embodiment, after regularly obtaining the number of unanticipated node identifications for all products in the same series, analyze whether it is necessary to perform early management on the products in the same series, including the following steps:

[0018] Establish a product set for the products in the same series, obtain the number of unanticipated nodes identified by each product's RFID tag in the product set, and calculate the mean value and standard deviation of the quantity based on the number of unanticipated nodes identified by all products. The expressions are: , where is the mean value of the quantity, is the standard deviation of the quantity, is the number of products in the same series, is the th number of unanticipated nodes identified by the product. Obtain the management value by dividing the mean value of the quantity by the standard deviation of the quantity;

[0019] Compare the management value of the products in the same series with a preset management threshold. The management threshold is used to determine whether it is necessary to perform early management on the products in the same series. If the management value is greater than the management threshold, it is determined that early management is required for the products in the same series. If the management value is less than or equal to the management threshold, it is determined that early management is not required for the products in the same series.

[0020] In a preferred embodiment, the tracking system obtains the influence weight of each unanticipated node based on the correlation index, including the following steps:

[0021] The acquisition logic of the correlation index is: obtain the number of subsequent nodes of the unanticipated node, and use the number of subsequent nodes of the unanticipated node as the correlation index. The greater the correlation index of the unanticipated node, the greater the relative importance of the current unanticipated node in the logistics path. Obtain the total correlation index by summing the correlation indices of all unanticipated nodes, and divide the correlation index of the unanticipated node by the total correlation index to obtain the influence weight of the unanticipated node.

[0022] In a preferred embodiment, if the confidence score of any unanticipated node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of the product, including the following steps:

[0023] Compare the confidence scores of all unexpected nodes in the product with a preset score threshold, which is used to determine whether there is an anomaly in the unexpected nodes. If the confidence score of an unexpected node is less than the score threshold, it is determined that the unexpected node is abnormal, and a warning sign is marked on the RFID tag of the product. If the confidence score of the unexpected node is greater than or equal to the score threshold, it is determined that the unexpected node is not abnormal, and the tracking system obtains the influence weight of each unexpected node according to the correlation index.

[0024] In a preferred embodiment, the calculation logic of the position deviation index is as follows: Obtain the geographical coordinates of the unexpected node, the geographical coordinates of the previous node, and the coordinates of the subsequent node, and calculate the Euclidean distance between the unexpected node and the previous node. The expression is: , where is the Euclidean distance between the unexpected node and the previous node, is the geographical coordinates of the unexpected node, is the geographical coordinates of the previous node, calculate the Euclidean distance between the unexpected node and the subsequent node. The expression is: , where is the Euclidean distance between the unexpected node and the subsequent node, is the geographical coordinates of the unexpected node, is the geographical coordinates of the subsequent node, then the position deviation index .

[0025] In a preferred embodiment, the calculation logic of the normal marking ratio is as follows: Obtain the historical normal marking times and abnormal marking times of the unexpected node, sum the normal marking times and abnormal marking times to obtain the total marking times, and divide the normal marking times by the total marking times to obtain the normal marking ratio;

[0026] The calculation logic of the abnormal marking ratio is as follows: Obtain the historical normal marking times and abnormal marking times of the unexpected node, sum the normal marking times and abnormal marking times to obtain the total marking times, and divide the abnormal marking times by the total marking times to obtain the abnormal marking ratio;

[0027] The calculation logic of the occurrence frequency is as follows: Obtain the total number of times the unexpected node appears in the historical data, obtain the total number of times all nodes appear in the historical data, and divide the total number of times the unexpected node appears in the historical data by the total number of times all nodes appear in the historical data to obtain the occurrence frequency.

[0028] In a preferred embodiment, perform sequence alignment on the first sequence and the second sequence to identify all unexpected nodes in the second sequence, including the following steps:

[0029] Select the dynamic programming algorithm and initialize the alignment matrix. Starting from the starting nodes of the standard path and the actual path, compare each pair of nodes one by one. If the nodes in the two paths match, continue to compare the next pair of nodes. If they do not match, mark the node as an unexpected node;

[0030] For all unmatched nodes in the alignment, record these nodes as unexpected nodes. An unexpected node represents a node that does not appear in the standard path in the actual path, that is, an abnormal point in the circulation path;

[0031] Determine the position of each unexpected node in the second sequence and record it in the alignment result. The position information is used for subsequent risk analysis and path adjustment, and generate a sequence alignment result report, which includes a list of all unexpected nodes and their positions in the actual path.

[0032] An anti-counterfeiting verification and tracking system based on RFID technology includes a marking module, a node identification module, a calculation module, and a management module;

[0033] Marking module: Scan the RFID tag of the product to be tracked, mark the standard path of the product as the first sequence, obtain the actual circulation path of the product, and mark the actual circulation path as the second sequence;

[0034] Node identification module: Perform sequence alignment on the first sequence and the second sequence, identify all unexpected nodes in the second sequence, and obtain the confidence score of each unexpected node after analyzing the historical data of the unexpected nodes;

[0035] Calculation module: If the confidence score of any unexpected node of the product is lower than the score threshold, put a warning mark on the RFID tag of the product. Otherwise, obtain the influence weight of each unexpected node according to the correlation index, and calculate the confidence score and influence weight of all unexpected nodes through a fusion algorithm to generate a verification coefficient for the product;

[0036] Management module: Generate corresponding management strategies for the product based on the comparison result of the verification coefficient and the verification threshold. After regularly obtaining the number of identified unexpected nodes of all products in the same series, analyze whether it is necessary to manage the products in the same series in advance.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention:

[0038] 1. The present invention performs sequence alignment on the first sequence and the second sequence, identifies all unexpected nodes in the second sequence, analyzes the historical data of the unexpected nodes to obtain the confidence score of each unexpected node, obtains the influence weight of each unexpected node according to the correlation index, and calculates the confidence scores and influence weights of all unexpected nodes through a fusion algorithm to generate a verification coefficient for the product. According to the comparison result between the verification coefficient and the verification threshold, corresponding management strategies are generated for the product. After regularly obtaining the number of identified unexpected nodes of all products in the same series, it is analyzed whether it is necessary to perform advance management on the products in the same series. The tracking system can effectively detect path anomalies by identifying all unexpected nodes through sequence alignment and analyzing their historical data, thereby enhancing the system's ability to identify abnormal circulation and improving the tracking accuracy;

[0039] 2. The present invention establishes a product set for products in the same series, obtains the number of unexpected nodes identified by each product's RFID tag in the product set (regardless of whether the confidence score of the unexpected node is greater than or equal to the score threshold or less than the score threshold), calculates the quantity mean and quantity standard deviation based on the number of unexpected nodes identified by all products (reflecting the fluctuation degree of the number of unexpected nodes identified between different products), obtains a management value by dividing the quantity mean by the quantity standard deviation. The larger the management value, the more unexpected nodes are identified in the overall of this series of products. According to the comparison result between the management value and the preset management threshold, it is judged whether it is necessary to perform advance management on the products in the same series, effectively improving the enterprise's management efficiency for products in the same series. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1: Please refer to Figure 1As shown, for the anti-counterfeiting verification and tracking method based on RFID technology in this embodiment, the tracking method includes the following steps:

[0044] The tracking system scans the RFID tag of the product to be tracked, marks the standard path of the product as the first sequence, obtains the actual circulation path of the product, marks the actual circulation path as the second sequence, performs sequence alignment on the first sequence and the second sequence, identifies all unexpected nodes in the second sequence, analyzes the historical data of the unexpected nodes, obtains the confidence score of each unexpected node, if the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of the product, otherwise, the tracking system obtains the influence weight of each unexpected node according to the correlation index, and calculates the confidence scores and influence weights of all unexpected nodes through a fusion algorithm to generate a verification coefficient for the product, generates corresponding management strategies for the product according to the comparison result between the verification coefficient and the verification threshold, regularly obtains the number of identified unexpected nodes of all products in the same series, and analyzes whether it is necessary to manage the products in the same series in advance.

[0045] In this application, by performing sequence alignment on the first sequence and the second sequence, identifying all unexpected nodes in the second sequence, analyzing the historical data of the unexpected nodes, obtaining the confidence score of each unexpected node, obtaining the influence weight of each unexpected node according to the correlation index, and calculating the confidence scores and influence weights of all unexpected nodes through a fusion algorithm to generate a verification coefficient for the product, generating corresponding management strategies for the product according to the comparison result between the verification coefficient and the verification threshold, regularly obtaining the number of identified unexpected nodes of all products in the same series, and analyzing whether it is necessary to manage the products in the same series in advance. The tracking system can effectively detect path anomalies by identifying all unexpected nodes through sequence alignment and analyzing their historical data, thereby enhancing the system's ability to identify abnormal circulation and improving the tracking accuracy.

[0046] Embodiment 2: The tracking system scans the RFID tag of the product to be tracked, marks the standard path of the product as the first sequence, obtains the actual circulation path of the product, and marks the actual circulation path as the second sequence, including the following steps:

[0047] The tracking system scans the RFID tag of the product to be tracked, reads the standard path of the product, and marks it as the first sequence. The standard path is an ideal circulation path preset by the system, usually including all expected logistics nodes (such as factories, warehouses, distribution centers, retail stores, etc.).

[0048] Actual path collection: The tracking system also obtains the actual circulation path of the product through continuous scanning and marks the actual circulation path as the second sequence. The actual path is the real path that the product experiences during the logistics process, including all time and location data.

[0049] Suppose a high-end watch of a certain brand is affixed with a unique RFID tag at the time of factory production, and the system presets the standard circulation path of this watch. This path starts from the manufacturing factory, passes through the regional warehouse, brand retail store, and finally reaches the hands of consumers. This standard path is marked as the first sequence:

[0050] First sequence (standard path): Manufacturing factory → Regional warehouse → Brand retail store.

[0051] In actual circulation, the tracking system obtains the circulation path of this watch through RFID scanning and records it as the second sequence:

[0052] Second sequence (actual path): Manufacturing factory → Regional warehouse → Temporary storage center → Brand retail store.

[0053] Perform a sequence comparison on the first sequence and the second sequence to identify all unexpected nodes in the second sequence, including the following steps:

[0054] Select the dynamic programming algorithm and initialize the comparison matrix to compare each node in the first sequence (standard path) and the second sequence (actual path).

[0055] Starting from the starting nodes of the standard path and the actual path, compare each pair of nodes one by one. If the nodes in the two paths match, continue to compare the next pair of nodes; if they do not match, mark this node as a potential unexpected node.

[0056] For all unmatched nodes in the comparison, record these nodes as "unexpected nodes". These nodes represent the nodes that do not appear in the standard path in the actual path, that is, the abnormal points in the circulation path.

[0057] Determine the position of each unexpected node in the second sequence and record it in the comparison result. The position information is used for subsequent risk analysis and path adjustment.

[0058] Generate a sequence comparison result report, including a list of all unexpected nodes and their positions in the actual path. This report provides a basis for further risk analysis, which is used to determine the anti-counterfeiting status of the product or for circulation management.

[0059] Suppose the high-end watch of this brand is affixed with a unique RFID tag at the time of factory production, and the standard circulation path (first sequence) of this watch is:

[0060] First sequence (standard path):

[0061] Manufacturing factory;

[0062] Regional warehouse;

[0063] Brand retail store;

[0064] Consumer;

[0065] In the actual circulation process, the tracking system records the actual path (second sequence) of the watch as follows:

[0066] Second sequence (actual path):

[0067] Manufacturing factory;

[0068] Regional warehouse;

[0069] Temporary storage center;

[0070] Brand retail store;

[0071] Consumer;

[0072] Initialize the comparison algorithm: The system selects the dynamic programming algorithm as the comparison tool and establishes a comparison matrix to gradually compare each node of the first sequence and the second sequence.

[0073] Gradually compare nodes:

[0074] Compare the first node: Manufacturing factory, match.

[0075] Compare the second node: Regional warehouse, match.

[0076] Compare the third node: The node in the first sequence is a brand retail store, while the node in the second sequence is a temporary storage center, there is a mismatch.

[0077] Identify the unexpected node: The temporary storage center is identified as an unexpected node because it is not in the standard path.

[0078] Locate the position of the unexpected node: The system records the position of the temporary storage center in the second sequence, which is the 3rd node.

[0079] Output the comparison result: The comparison result indicates that the unexpected node is the temporary storage center, located at the 3rd position of the actual path.

[0080] Result: Through comparison, the system discovers that the watch passed through an unexpected node, the temporary storage center, during the circulation process. This anomaly may indicate the existence of a non-standard storage process. The tracking system will further analyze the historical data of this node to evaluate the risk and, if necessary, mark a warning label on the RFID tag to remind the subsequent processes to pay attention to the circulation status of this watch.

[0081] After analyzing the historical data of unanticipated nodes, obtain the confidence score for each unanticipated node, including the following steps:

[0082] Obtain the historical data of unanticipated nodes, where the historical data includes the occurrence frequency, normal marking ratio, abnormal marking ratio, and position deviation index of the unanticipated node history;

[0083] Comprehensively calculate the occurrence frequency, normal marking ratio, abnormal marking ratio, and position deviation index to obtain the confidence score of the unanticipated node. The expression is: , where in the formula, is the confidence score, is the occurrence frequency, is the normal marking ratio, is the abnormal marking ratio, is the position deviation index, is the adjustment coefficient, which is used to adjust the proportion of the influence of the position deviation index on the confidence score, , are the weights of the normal marking ratio and abnormal marking ratio respectively, and .

[0084] The larger the confidence score of the unanticipated node, the more normal the unanticipated node is in the current logistics path. If the unanticipated node appears for the first time, the confidence score is 0, indicating that the unanticipated node is in an untrustworthy state.

[0085] The calculation logic of the occurrence frequency is: obtain the total number of times the unanticipated node appears in the historical data, obtain the total number of times all nodes appear in the historical data, and divide the total number of times the unanticipated node appears in the historical data by the total number of times all nodes appear in the historical data to obtain the occurrence frequency. The higher the occurrence frequency, the more frequently the unanticipated node appears in the logistics path of the product.

[0086] The occurrence frequency of the unanticipated node is used to correct the normal marking ratio or abnormal marking ratio. When the calculated value is positive, it indicates that the normal marking ratio is greater than the abnormal marking ratio. At this time, the higher the occurrence frequency of the unanticipated node, the greater the confidence score. When the calculated value is negative, it indicates that the normal marking ratio is less than the abnormal marking ratio. At this time, the higher the occurrence frequency of the unanticipated node, the smaller the confidence score. And, there may be a situation where the normal marking ratio and abnormal marking ratio of the unanticipated node are equal. Generally speaking, in order to improve the anti-counterfeiting verification of the product, the weight of the abnormal marking ratio is greater than the weight of the normal marking ratio, that is, when the normal marking ratio and abnormal marking ratio are equal, the calculated value is negative.

[0087] The calculation logic of the normal marking ratio is as follows: Obtain the historical normal marking times and abnormal marking times of the unexpected node, sum the normal marking times and abnormal marking times to obtain the total marking times, divide the normal marking times by the total marking times to obtain the normal marking ratio. The larger the normal marking ratio, the more legal the unexpected node is considered to be historically.

[0088] The calculation logic of the abnormal marking ratio is as follows: Obtain the historical normal marking times and abnormal marking times of the unexpected node, sum the normal marking times and abnormal marking times to obtain the total marking times, divide the abnormal marking times by the total marking times to obtain the abnormal marking ratio. The larger the abnormal marking ratio, the greater the probability that the unexpected node is considered abnormal historically.

[0089] The calculation logic of the position deviation index is as follows: Obtain the geographical coordinates of the unexpected node, the geographical coordinates of the previous node, and the coordinates of the subsequent node (the previous node is the node before the unexpected node, that is, the product is transported to the unexpected node through the previous node, and the subsequent node is the node after the unexpected node, that is, the product is transported from the unexpected node to the subsequent node), calculate the Euclidean distance between the unexpected node and the previous node, and the expression is: , where is the Euclidean distance between the unexpected node and the previous node, is the geographical coordinates of the unexpected node, is the geographical coordinates of the previous node, calculate the Euclidean distance between the unexpected node and the subsequent node, and the expression is: , where is the Euclidean distance between the unexpected node and the subsequent node, is the geographical coordinates of the unexpected node, is the geographical coordinates of the subsequent node, then the position deviation index .

[0090] Normal marking ratio:

[0091] High ratio (large value): A high normal marking ratio indicates that the node has been considered normal historically, with relatively high credibility and thus a higher confidence score.

[0092] Low ratio (small value): A low normal marking ratio means that the appearance of the node is often regarded as abnormal, thus reducing the confidence score.

[0093] Relationship: The higher the normal marking ratio, the higher the confidence score, which can be expressed as a positive proportional relationship.

[0094] Abnormal marking ratio:

[0095] High ratio (large value): A high anomaly marking ratio indicates that this node has often been marked as an anomaly in historical data, which means this node has a higher risk, thus reducing the confidence score.

[0096] Low ratio (small value): A low anomaly marking ratio indicates that this node is rarely regarded as an anomaly. It may be a deviation of a legitimate node, so the confidence score is relatively high.

[0097] Relationship: The higher the anomaly marking ratio, the lower the confidence score, which can be expressed as an inverse relationship.

[0098] Position deviation index:

[0099] High deviation index (large value): When the position deviation index is relatively high, it means that the unexpected node is far from both the preceding node and the succeeding node. This may indicate that the position of this node is abnormal, or the movement of this node exceeds the normal path range. Therefore, the confidence score is usually low, indicating a greater anomaly of this node.

[0100] Low deviation index (small value): When the position deviation index is relatively low, it means that the unexpected node is close to both the preceding node and the succeeding node, indicating that this node is near the expected path. It may be a deviation due to temporary reasons, and the confidence score is high, indicating that this node is relatively reliable.

[0101] If the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of this product. Otherwise, the tracking system obtains the influence weight of each unexpected node according to the association index, including the following steps:

[0102] Compare the confidence scores of all unexpected nodes in the product with the preset score threshold. The score threshold is used to judge whether there is an anomaly in the unexpected node. If the confidence score of the unexpected node is less than the score threshold, it is judged that the unexpected node has an anomaly, and a warning mark is placed on the RFID tag of this product. If the confidence score of the unexpected node is greater than or equal to the score threshold, it is judged that the unexpected node has no anomaly, and the tracking system obtains the influence weight of each unexpected node according to the association index.

[0103] The tracking system obtains the influence weight of each unexpected node according to the association index, including the following steps:

[0104] The acquisition logic of the association index is as follows: Obtain the number of succeeding nodes of the unexpected node, and use the number of succeeding nodes of the unexpected node as the association index. The larger the association index of the unexpected node, the greater the relative importance of the current unexpected node in the logistics path. Obtain the total association index by summing up the association indexes of all unexpected nodes, and divide the association index of the unexpected node by the total association index to obtain the influence weight of this unexpected node.

[0105] After calculating the confidence scores of all unexpected nodes and the impact weights through a fusion algorithm, a verification coefficient is generated for the product. According to the comparison result between the verification coefficient and the verification threshold, corresponding management strategies are generated for the product, including the following steps:

[0106] After calculating the confidence scores of all unexpected nodes and the impact weights through a fusion algorithm, a verification coefficient is generated for the product, and the expression is: , where is the verification coefficient, is the impact weight of the th unexpected node, is the confidence score of the th unexpected node, is the number of unexpected nodes in the product material path. The larger the verification coefficient, the greater the risk of product forgery.

[0107] Compare the obtained verification coefficient with the preset verification threshold. The verification threshold is used to judge whether the product has a forgery risk. If the verification coefficient is less than or equal to the verification threshold, it is judged that the product has no forgery risk. If the verification coefficient is greater than the verification threshold, it is judged that the product has a forgery risk.

[0108] 1) Management strategies when it is judged that the product has no forgery risk:

[0109] Release strategy: Allow the product to continue to circulate in the supply chain, and update the tracking information and node status in the system.

[0110] Path optimization: Record the current node of the product in the path information as a normal path node to provide a reference for the path optimization of subsequent similar products.

[0111] Trustworthiness update: Improve the trustworthiness of the product and its current circulation node, and mark it as "normal circulation" in the historical record.

[0112] Regular inspection and adjustment: Set a longer inspection period for the product in the system, reduce the frequency of repeated inspections, and reduce the system burden.

[0113] 2) Management strategies when it is judged that the product has a forgery risk:

[0114] Risk marking and isolation: Mark the "forgery risk" status for the product in the system, and isolate or lock the product to stop its circulation in the supply chain.

[0115] Further investigation: Initiate a detailed manual or automated investigation process to analyze the abnormal nodes in the product circulation path to confirm the reasons for forgery or abnormality.

[0116] Notify relevant parties: Send early warning notifications to key relevant parties in the supply chain, such as warehouses, retailers, or producers, to alert them to the potential risks of this product.

[0117] Trace the source: Combine the information of abnormal nodes for tracing, and determine whether it is necessary to review the upstream links involved to prevent the spread of counterfeit products.

[0118] Customized tracking plan: Generate a high-frequency tracking and verification plan for this product to ensure that any abnormalities can be identified and responded to in a timely manner during further circulation.

[0119] After regularly obtaining the number of unanticipated node identifications for all products in the same series, analyze whether it is necessary to conduct early management of products in the same series, including the following steps:

[0120] Generally speaking, the entire logistics path of a product is usually preset through a product management platform. If the number of unanticipated node identifications for all products in the same series is increasing, it not only indicates that there may be a risk of counterfeiting for the product, but also indicates that the logistics path management of the product may have poor performance. For example:

[0121] Unclear or unstable path planning: The preset of the logistics path is not clear or changes frequently, resulting in the appearance of multiple unanticipated nodes. This may be because the transportation providers and storage warehouses in different regions change frequently, or the circulation processes of the product in different regions are not standardized.

[0122] Incomplete regulatory process: Multiple nodes in the logistics chain are not strictly supervised, and no reasonable inspection mechanism is established, resulting in the inability to detect and respond to abnormal circulation nodes in a timely manner. For example, the lack of real-time upload of node information or the lack of verification processes increases the probability of unanticipated nodes.

[0123] Lagging system updates: The path data of the product management platform is not updated in a timely manner, making the actual logistics nodes inconsistent with the paths set in the system, and unanticipated nodes may occur due to the failure to input the latest path into the system.

[0124] Chaotic multi-channel distribution management: The lack of coordination in multi-channel sales management leads to the possible generation of their own unanticipated nodes in different circulation channels. Especially when the management of non-standard third-party logistics companies or authorized distributors is improper, unauthorized circulation paths are likely to appear.

[0125] Large differences in regional distribution standards: The distribution standards in different regions are not unified, and non-standard transportation or storage operations may be adopted in some regions, resulting in the generation of unplanned transfer nodes or temporary storage nodes, deviating from the preset path.

[0126] Missing or delayed information transmission: The communication between the logistics nodes and the platform is unstable, and the information transmission is not timely, resulting in a lag in the path status information, or the passing information of specific nodes is not uploaded to the management platform in a timely manner, affecting the timely verification of path nodes.

[0127] Therefore, in order to facilitate enterprises to further understand the actual situation of the logistics paths of the same series of products, and to make corresponding management in response to the actual situation of the logistics paths, improve management efficiency, and avoid waste of resources, we propose the following solutions:

[0128] Establish a product set for the same series of products, and obtain the number of unanticipated nodes identified by each product's RFID tag in the product set (recorded regardless of whether the confidence score of the unanticipated node is greater than or equal to the score threshold or less than the score threshold). Calculate the mean value and standard deviation of the number based on the number of unanticipated nodes identified by all products (reflecting the degree of fluctuation in the number of unanticipated nodes identified between different products). The expression is: , where is the mean value of the number, is the standard deviation of the number, is the number of products in the same series, is the th product's identified number of unanticipated nodes;

[0129] Obtain the management value by dividing the mean value of the number by the standard deviation of the number. The larger the management value, the more unanticipated nodes are identified overall for this series of products;

[0130] Compare the management value of the same series of products with a preset management threshold. The management threshold is used to determine whether it is necessary to conduct early management on the same series of products. If the management value is greater than the management threshold, it is determined that early management is required for the same series of products. If the management value is less than or equal to the management threshold, it is determined that early management is not required for the same series of products.

[0131] In this application, by establishing a product set for the same series of products, obtaining the number of unanticipated nodes identified by each product's RFID tag in the product set (recorded regardless of whether the confidence score of the unanticipated node is greater than or equal to the score threshold or less than the score threshold), calculating the mean value and standard deviation of the number based on the number of unanticipated nodes identified by all products (reflecting the degree of fluctuation in the number of unanticipated nodes identified between different products), obtaining the management value by dividing the mean value of the number by the standard deviation of the number, where the larger the management value, the more unanticipated nodes are identified overall for this series of products, and based on the comparison result of the management value with the preset management threshold, determining whether it is necessary to conduct early management on the same series of products, it effectively improves the management efficiency of enterprises for the same series of products.

[0132] Example 3: Please refer to Figure 1As shown, the anti-counterfeiting verification and tracking system based on RFID technology in this embodiment includes a marking module, a node identification module, a calculation module, and a management module;

[0133] Marking module: Scan the RFID tag of the product to be tracked, mark the standard path of the product as the first sequence, obtain the actual circulation path of the product, mark the actual circulation path as the second sequence, and send the first sequence and the second sequence information to the node identification module;

[0134] Node identification module: Compare the first sequence and the second sequence, identify all unexpected nodes in the second sequence, analyze the historical data of the unexpected nodes, obtain the confidence score of each unexpected node, and send the confidence score to the calculation module;

[0135] Calculation module: If the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of the product. Otherwise, according to the correlation index, obtain the influence weight of each unexpected node, and calculate the confidence scores and influence weights of all unexpected nodes through a fusion algorithm to generate a verification coefficient for the product, and send the verification coefficient to the management module;

[0136] Management module: Generate corresponding management strategies for the product based on the comparison result of the verification coefficient and the verification threshold. After regularly obtaining the number of identified unexpected nodes of all products in the same series, analyze whether it is necessary to manage the products in the same series in advance.

[0137] The above formulas are all calculated by taking the numerical values after dimensionless. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0138] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0139] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. Anti-counterfeiting verification and tracking method based on RFID technology, characterized by: The tracking method comprises the following steps: The tracking system scans the RFID tag of the tracked product, marks the standard path of the product as a first sequence, obtains the actual circulation path of the product, marks the actual circulation path as a second sequence, performs sequence comparison on the first sequence and the second sequence, and identifies all unexpected nodes in the second sequence; After analyzing the historical data of unexpected nodes, the confidence score of each unexpected node is obtained. If the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is marked on the RFID tag of the product. Otherwise, the tracking system obtains the impact weight of each unexpected node based on the correlation index; The logic of obtaining the correlation index is as follows: obtain the number of subsequent nodes of the unexpected node, use the number of subsequent nodes of the unexpected node as the correlation index, obtain the total correlation index by summing the correlation indexes of all unexpected nodes, and obtain the influence weight of the unexpected node by dividing the correlation index of the unexpected node by the total correlation index; After calculating the confidence scores and impact weights of all unexpected nodes through the fusion algorithm, a verification coefficient is generated for the product. Based on the comparison result between the verification coefficient and the verification threshold, a corresponding management strategy is generated for the product. After regularly obtaining the number of unexpected node identifications for all products in the same series, it is analyzed whether the same series of products needs to be managed in advance, and the analysis results are sent to the administrator; After analyzing the historical data of unexpected nodes, the confidence score of each unexpected node is obtained, including the following steps: Obtain historical data of unexpected nodes, including the occurrence frequency, normal marking ratio, abnormal marking ratio and position deviation index of the unexpected nodes; The confidence score of the unexpected node is obtained by comprehensively calculating the occurrence frequency, normal mark ratio, abnormal mark ratio and position deviation index. The expression is: , where is the confidence score, is the frequency of occurrence, is the normal marking ratio, is the abnormal labeling ratio, is the position deviation index, is the adjustment coefficient, , are the weights of normal label ratio and abnormal label ratio respectively, and .

2. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 1 is characterized in that: After calculating the confidence scores and impact weights of all unexpected nodes through a fusion algorithm, a verification coefficient is generated for the product, including the following steps: After calculating the confidence scores and impact weights of all unexpected nodes through the fusion algorithm, a verification coefficient is generated for the product. The expression is: , where is the verification coefficient, For the The influence weight of unexpected nodes, For the The confidence scores of the unexpected nodes, is the number of unexpected nodes in the product material path; The obtained verification coefficient is compared with the preset verification threshold. The verification threshold is used to determine whether the product has a risk of counterfeiting. If the verification coefficient is less than or equal to the verification threshold, it is determined that the product has no risk of counterfeiting. If the verification coefficient is greater than the verification threshold, it is determined that the product has a risk of counterfeiting.

3. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 2 is characterized in that: After regularly obtaining the number of unexpected node identifications for all products in the same series, analyze whether it is necessary to manage the same series of products in advance, including the following steps: Create a product set for the same series of products, obtain the expected number of nodes that each product RFID tag identifies in the product set, and calculate the mean and standard deviation of the number of unexpected nodes identified by all product RFID tags. The expression is: , where is the quantity mean, is the quantity standard deviation, is the number of products in the same series, For the Each product identifies the unexpected number of nodes, and the management value is obtained by dividing the mean of the number by the standard deviation of the number; The management value of the same series of products is compared with the preset management threshold. The management threshold is used to determine whether it is necessary to manage the same series of products in advance. If the management value is greater than the management threshold, it is determined that the same series of products needs to be managed in advance. If the management value is less than or equal to the management threshold, it is determined that it is not necessary to manage the same series of products in advance.

4. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 3 is characterized in that: If the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is marked on the RFID tag of the product, including the following steps: The confidence scores of all unexpected nodes in the product are compared with the preset score threshold. The score threshold is used to determine whether there is an abnormality in the unexpected node. If the confidence score of the unexpected node is less than the score threshold, it is determined that the unexpected node is abnormal, and a warning mark is placed on the RFID tag of the product. If the confidence score of the unexpected node is greater than or equal to the score threshold, it is determined that there is no abnormality in the unexpected node. The tracking system obtains the influence weight of each unexpected node based on the correlation index.

5. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 4 is characterized in that: The calculation logic of the position deviation index is: obtain the geographical coordinates of the unexpected node, the geographical coordinates of the preceding node, and the coordinates of the succeeding node, and calculate the Euclidean distance between the unexpected node and the preceding node. The expression is: , where is the Euclidean distance between the unexpected node and the preceding node, are the geographic coordinates of the unexpected nodes, is the geographic coordinate of the preceding node, and the Euclidean distance between the unexpected node and the succeeding node is calculated. The expression is: , where is the Euclidean distance between the unexpected node and the subsequent node, are the geographic coordinates of the unexpected nodes, is the geographic coordinates of the post-node, then the position deviation index .

6. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 5 is characterized in that: The calculation logic of the normal marking ratio is as follows: obtaining the historical normal marking times and abnormal marking times of unexpected nodes, summing the normal marking times and the abnormal marking times to obtain the total marking times, and dividing the normal marking times by the total marking times to obtain the normal marking ratio; The calculation logic of the abnormal marking ratio is as follows: obtaining the historical normal marking times and abnormal marking times of unexpected nodes, summing the normal marking times and the abnormal marking times to obtain the total marking times, and dividing the abnormal marking times by the total marking times to obtain the abnormal marking ratio; The calculation logic of the occurrence frequency is: obtain the total number of times unexpected nodes appear in historical data, obtain the total number of times all nodes appear in historical data, and divide the total number of times unexpected nodes appear in historical data by the total number of times all nodes appear in historical data to obtain the occurrence frequency.

7. The anti-counterfeiting verification and tracking method based on RFID technology according to claim 6 is characterized in that: Performing a sequence alignment on the first sequence and the second sequence to identify all unexpected nodes in the second sequence includes the following steps: Select the dynamic programming algorithm and initialize the comparison matrix. Start from the starting nodes of the standard path and the actual path, and compare each pair of nodes one by one. If the nodes in the two paths match, continue to compare the next pair of nodes. If not, mark the node as an unexpected node. For all the unmatched nodes that appear in the comparison, these nodes are recorded as unexpected nodes. Unexpected nodes refer to nodes in the actual path that do not appear in the standard path, that is, abnormal points in the circulation path; The position of each unexpected node in the second sequence is determined and recorded in the comparison result. The position information is used for subsequent risk analysis and path adjustment. A sequence comparison result report is generated, which contains a list of all unexpected nodes and their positions in the actual path.

8. An anti-counterfeiting verification and tracking system based on RFID technology, used to solve the tracking method described in any one of claims 1 to 7, characterized in that: It includes a marking module, a node identification module, a calculation module, and a management module; Marking module: scans the RFID tag of the tracked product, marks the standard path of the product as the first sequence, obtains the actual circulation path of the product, and marks the actual circulation path as the second sequence; Node identification module: perform sequence comparison on the first sequence and the second sequence, identify all unexpected nodes in the second sequence, analyze the historical data of the unexpected nodes, and obtain the confidence score of each unexpected node; Calculation module: If the confidence score of any unexpected node of the product is lower than the score threshold, a warning mark is placed on the RFID tag of the product. Otherwise, the influence weight of each unexpected node is obtained according to the correlation index, and the confidence scores and influence weights of all unexpected nodes are calculated through a fusion algorithm to generate a verification coefficient for the product. Management module: Generates corresponding management strategies for products based on the comparison results of verification coefficient and verification threshold, regularly obtains the number of unexpected node identifications for all products in the same series, and analyzes whether it is necessary to manage the same series of products in advance.

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