Product intelligent tracing method and system based on radio frequency tag

Abstract

The invention relates to the technical field of data processing, and provides an intelligent product tracing method and system based on a radio frequency tag, and the method comprises the steps: obtaining the collection node information, production circulation node information and node detection information of all products; determining a node detection information sequence, obtaining product information consistency of the nodes, obtaining cumulative change consistency of the nodes, and further obtaining range influence degrees of the nodes; the method comprises the following steps: obtaining the passing product quantity of an association path, obtaining the association degree of nodes, obtaining the local consistency of information, obtaining the adjustment necessity of the distance between the nodes, obtaining a distance adjustment value between the two nodes, and updating the distance adjustment value according to the distance adjustment value; and obtaining information credibility of the nodes according to the distance adjustment value between the nodes, and realizing intelligent product tracing based on the radio frequency tag according to the information credibility of the nodes. According to the invention, the problem that the product tracing result is unreliable due to the multi-correspondence between the circulation nodes is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for intelligent product traceability based on radio frequency tags. Background Art

[0002] Intelligent product traceability based on radio frequency tags means collecting, recording, storing, and querying information on the entire process of product production, circulation, and sales through radio frequency identification technology. During the process of product traceability, the product involves the circulation and transportation of multiple links from production and manufacturing to transportation and sales. Different links correspond to different circulation nodes, and there are multiple corresponding relationships between the circulation nodes. At the same time, the transportation mode and route of the product are restricted by the product itself. Therefore, the data of the production and circulation processes corresponding to different products may have great similarities.

[0003] When it is necessary to trace the product to obtain the nodes where problems occur, due to the multiple corresponding relationships between the circulation nodes, the possibilities of different nodes in the entire production, circulation, and transportation process of the product being the nodes where problems occur are different. Therefore, generally, the traceability information corresponding to the product is determined according to the possibility of the node being the node where problems occur to achieve intelligent product traceability. However, the multiple corresponding relationships existing between the circulation nodes often lead to inaccurate evaluation of the possibility of the nodes, making the product traceability result unreliable. Summary of the Invention

[0004] The present invention provides a method and system for intelligent product traceability based on radio frequency tags to solve the problem that the multiple corresponding relationships between the circulation nodes make the product traceability result unreliable. The specific technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for intelligent product traceability based on radio frequency tags, and the method includes the following steps: Obtain the set node information and production and circulation node information of all products, and obtain node detection information according to the set node information; Obtain the node position sequence of the product, determine the node detection information sequence, obtain the product information consistency of the node, obtain the cumulative change consistency of the node according to the node detection information sequence and the product information consistency of the node, and obtain the range influence degree of the node according to the cumulative change consistency of the node; Define the node to be analyzed and the predecessor node of the node to be analyzed, determine the association path between the predecessor node and the node to be analyzed, obtain the number of passing products of the association path, obtain the association degree of the node according to the range influence degree of the node and the number of passing products of the association path, obtain the information local consistency of two different nodes in the same node position sequence, obtain the necessity of distance adjustment between nodes according to the association degree of the node and the information local consistency of different nodes, obtain the distance adjustment value between two nodes according to the necessity of distance adjustment between two nodes in the same node position sequence and the measurement distance between nodes, and update the distance adjustment value according to the distance adjustment value; Obtain the information credibility of the node according to the distance adjustment value between nodes, and realize the intelligent traceability of products based on radio frequency tags according to the information credibility of the node.

[0005] Furthermore, the specific method for obtaining the cumulative change consistency of the node is as follows: Take each node in the node position sequence as the first node respectively, and denote the nodes before the first node in the node position sequence as the second nodes; Denote the mean value of the product information consistency of all nodes before and including the first node in the node position sequence as the first product information consistency of the first node; Denote the absolute value of the difference between the product information consistency of the second node and the first product information consistency of the first node as the information consistency difference of the second node; Denote the mean value of the information consistency differences of all second nodes as the first mean value of the first node; Denote the power with the natural constant as the base and the opposite number of the first mean value of the first node as the exponent as the cumulative change consistency of the first node.

[0006] Furthermore, the specific method for obtaining the range influence degree of the node according to the cumulative change consistency of the node includes: Denote the absolute value of the difference between the cumulative change consistency of the second node and the cumulative change consistency of the previous node of the second node as the first difference of the second node; Denote the absolute value of the difference between the cumulative change consistency of the first node and the cumulative change consistency of the previous node of the first node as the first difference of the first node; Denote the mean value of the first difference of the first node and the first differences of all second nodes as the range influence degree of the first node.

[0007] Furthermore, the specific method for defining the node to be analyzed and the predecessor node of the node to be analyzed and determining the association path between the predecessor node and the node to be analyzed includes: Denote the first node as the node to be analyzed and the second node as the predecessor node of the node to be analyzed; The path from the predecessor node of the node to be analyzed to the node to be analyzed is denoted as the associated path between the predecessor node and the node to be analyzed.

[0008] Furthermore, the specific formula for obtaining the association degree of the node is: where, represents the association degree of the th node; represents the range influence degree of the th node; represents the range influence degree of the th node before the th node in the node position sequence; represents the linear normalization function; represents the th order of the th node in the node position sequence of the product; represents the number of products passed by the associated path between the th node and the represents the exponential function with the natural constant as the base.

[0009] Furthermore, the specific method for obtaining the necessity of distance adjustment between the nodes is: Denote the product of the absolute value of the difference between the association degrees of two nodes and the local information consistency of the two nodes as the first factor of the two nodes; Denote the normalized value of the reciprocal of the sum of the first factor of the two nodes and the adjustment parameter as the necessity of distance adjustment between the two nodes.

[0010] Furthermore, the specific method for obtaining the distance adjustment value between the two nodes is: Denote the average value of the measured distances between all different nodes in the same node position sequence as the second average value of the node position sequence; Denote the ratio of the necessity of distance adjustment between two nodes in the same node position sequence to the second average value of the node position sequence where the two nodes are located as the first ratio; Denote the product of the first ratio and the measured distance between the two nodes as the distance adjustment value between the two nodes.

[0011] Furthermore, the specific method for updating the distance adjustment value according to the distance adjustment value includes: When the necessity of distance adjustment between two nodes in the same node position sequence is greater than or equal to the adjustment necessity threshold, assign the distance metric between the two nodes to the distance adjustment value between the two nodes; otherwise, the distance metric between the two nodes remains unchanged.

[0012] Furthermore, obtaining the information credibility of nodes according to the distance adjustment value between nodes and realizing product intelligent traceability based on radio frequency tags includes the following specific methods: Taking the distance adjustment value between nodes as the distance between nodes, clustering all nodes in the same node position sequence to obtain the clustering clusters of nodes, and taking the average value of the range influence degrees of all nodes included in the same clustering cluster as the information credibility of all nodes in the clustering cluster; During the traceability process of defective products, taking the node with the highest information credibility as the defective node to realize product intelligent traceability based on radio frequency tags.

[0013] In a second aspect, an embodiment of the present invention further provides a product intelligent traceability system based on radio frequency tags, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0014] The beneficial effects of the present invention are as follows: Based on the set node information and node detection information extracted from the radio frequency tags of products, the present invention analyzes the structural characteristics of node distribution, judges the importance of nodes in the entire production and circulation process of products, and then obtains the range influence degree of nodes. From the perspective of the node distribution structure, it measures the possibility that a node is a defective node, and analyzes from the path distribution characteristics of products during production and transportation to improve the accuracy of determining defective nodes during product traceability; then, considering that when a node has a problem, before the defective node, the difference in node detection information of the products of adjacent nodes is still caused by different arrangements in the production and circulation links of the products. However, after the defective node, due to the influence of the defective node, the node detection information of the products corresponding to all subsequent nodes will change or be abnormal, resulting in a relatively large range influence degree for multiple nodes during the product traceability process, and it is impossible to accurately locate the exact position of the abnormal node. Therefore, the hierarchical relationship of nodes of different products in the node position sequence is vertically adjusted according to the range influence degree of nodes to obtain the distance adjustment value between nodes. According to the distance adjustment value between nodes, the information credibility of nodes is obtained, and product intelligent traceability based on radio frequency tags is realized according to the information credibility of nodes, improving the accuracy of product traceability results and solving the problem that the multiple correspondence relationships between the circulation nodes of products make the product traceability results unreliable. Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of a product intelligent traceability method based on radio frequency tags provided by an embodiment of the present invention; Figure 2 It is a flowchart for obtaining the influence degree of the range; Figure 3 It is a flowchart for obtaining the correlation degree. Specific embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0018] Please refer to Figure 1 , which shows a flowchart of a product intelligent traceability method based on radio frequency tags provided by an embodiment of the present invention. The method includes the following steps: Step S001, obtain the set node information and production and circulation node information of all products, and obtain the node detection information.

[0019] Use an RFID scanner to scan the radio frequency tags of each product respectively, and extract the set node information of the product. The set node information of the product includes the node information of all nodes passed through before the product reaches its current location. The node information of each node includes: arrival node timestamp, departure node timestamp, handler information, and node detection information.

[0020] The node detection information includes the storage conditions of the product, storage temperature, product name, serial number, specification, humidity, vibration, and appearance inspection records.

[0021] According to the layout of product production and circulation, obtain all nodes and the association relationships between the nodes. Record all nodes and the association relationships between the nodes as production and circulation node information.

[0022] So far, obtain the set node information, production and circulation node information, and node detection information of all products.

[0023] Step S002: Determine the node detection information sequence, obtain the product information consistency of the node, obtain the cumulative change consistency of the node, and then obtain the range influence degree of the node.

[0024] During the process of product circulation and transportation, the radio frequency tags of each product not only relate to the production, transportation, sales and other stages of the product during the information update node, but also relate to the circulation paths of each stage. Therefore, when tracing the product due to problems occurring at different positions on different transmission chains, it is necessary to extract nodes according to the distribution structure of the multi-product traceability paths at the product circulation and transportation nodes, and combine the traceability of different nodes to accurately and efficiently obtain the product traceability result.

[0025] First of all, during the production process of the same type of product, the production process is consistent. After the product is produced and enters the circulation stage, due to the limitations of the transportation requirements of different product characteristics, the node information of different products will be different during the circulation stage. At the same time, due to the limitations of the transportation route planning, different products may also have the same node information. Therefore, when tracing the product, a single node in the product production and circulation may correspond to multiple other nodes, resulting in insufficient credibility in judging the circulation status of the products on the node during the tracing process. Therefore, in order to ensure the accuracy of the tracing result, it is necessary to analyze the node distribution structure characteristics according to the production and circulation node information to facilitate the subsequent judgment of information errors during the tracing process.

[0026] According to the set node information of the product, arrange all the nodes passed by the product in the order of arrival time to obtain the node position sequence of the product.

[0027] Take the node detection information of each node in the node position sequence of the product as the data corresponding to the node, and arrange the data corresponding to each node in the node position sequence of the product in the order of the nodes in the node position sequence to obtain the node detection information sequence.

[0028] Use the Doc2Vec model to obtain the vectors of the node detection information sequences of all products passing through the same node, and take the mean value of the cosine similarities between all the vectors of this node as the product information consistency of the node.

[0029] According to the node detection information sequence and the product information consistency of the node, obtain the cumulative change consistency of the node.

[0030] In the formula, represents the cumulative change consistency of the th node in the node position sequence; represents that in the node position sequence, the th node before the Product information consistency of nodes; In the node position sequence representing the product, the mean value of the product information consistency of all nodes before the th node and the th node; Indicates the order of the th node in the node position sequence of the product;

[0031] When the order of the node in the node position sequence of the product is more forward, and the difference in the product information consistency of the node and all nodes before it is smaller, the possibility of a problem occurring at this node is smaller, and this node is more important in the entire production and circulation process of the product. At this time, the cumulative change consistency of the node is greater.

[0032] According to the positional relationship of the cumulative change consistency of the nodes in the node position sequence, continue to analyze the cumulative change consistency. Specifically, according to the cumulative change consistency of the nodes, obtain the range influence degree of the nodes.

[0033] Among them, Indicates the range influence degree of the th node; Indicates the cumulative change consistency of the th node in the node position sequence; Indicates the cumulative change consistency of the th node in the node position sequence; Indicates the order of the th node in the node position sequence of the product; Indicates the linear normalization function.

[0034] When the difference between the range influence degrees of adjacent nodes is larger, the difference in the node detection information of the products of adjacent nodes is larger. At this time, the range influence degree of the node is larger, and it is more likely to correspond to the node with problems.

[0035] The range influence degree of the node measures the possibility of the node being a node with problems from the node distribution structure characteristics of the product production process.

[0036] So far, obtain the range influence degree of the nodes. The flow chart for obtaining the range influence degree is as Figure 2 shown.

[0037] Step S003: Obtain the number of products passing through the associated path, obtain the association degree of the node, obtain the local information consistency of two different nodes in the same node position sequence, obtain the necessity of distance adjustment between nodes, and then obtain the distance adjustment value between two nodes, and update the distance adjustment value according to the distance adjustment value.

[0038] When a problem occurs in a certain node, before the node where the problem occurs, the difference in the node detection information of the products of the adjacent nodes is still caused by the different arrangements in the production and circulation links of the products. However, after the node where the problem occurs, due to the influence of the node where the problem occurs, the node detection information of the products corresponding to all subsequent nodes will change or be abnormal, resulting in a large range of influence degrees of multiple nodes during the product traceability process, and it is impossible to accurately locate the exact position of the abnormal node. Therefore, longitudinally adjust the hierarchical relationship of the nodes of different products in the node position sequence according to the range influence degree of the nodes to improve the accuracy of the product traceability result.

[0039] Take each node in the node position sequence as the node to be analyzed respectively, and record all the nodes before the node to be analyzed in the node position sequence as the pre-order nodes of the node to be analyzed.

[0040] The greater the difference between the range influence degree of the node to be analyzed and the range influence degree of the pre-order node of the node to be analyzed, the greater the possible influence of the pre-order node of the node to be analyzed on the node to be analyzed.

[0041] Obtain the path from the pre-order node of the node to be analyzed to the node to be analyzed, and record the path from the pre-order node of the node to be analyzed to the node to be analyzed as the associated path between the pre-order node and the node to be analyzed.

[0042] According to the node position sequences of all products, obtain the number of products corresponding to the node position sequence containing the associated path between the pre-order node and the node to be analyzed, and record the obtained number of products as the number of products passing through the associated path.

[0043] The greater the number of products passing through the associated path, the more products pass through this associated path. When problem products appear among these products, the greater the possibility that the node causing the problem is the pre-order node and the node to be analyzed corresponding to the associated path. That is: when determining the problem node based on the problem product, the more the number of products passing through the associated path, the greater the possibility that the pre-order node and the node to be analyzed corresponding to the associated path are the problem nodes.

[0044] Obtain the association degree of the node according to the range influence degree of the node and the number of products passing through the associated path.

[0045] Among them, represents the The correlation degree of a node; Indicates the range influence degree of the th node; in the node position sequence, indicates the range influence degree of the th node before the th node; indicates the linear normalization function; Indicates the order of the th node in the node position sequence of the product; Indicates the number of products passing through the correlation path between the

[0046] th node and the th node. Among them, the th node is the node to be analyzed, and the

[0047] th node is the previous node of the node to be analyzed. When the difference between the range influence degree of the node to be analyzed and the range influence degree of its previous node is smaller, and the number of products passing through the correlation path between the previous node and the node to be analyzed is larger, the correlation degree of the node is larger. At this time, the possibility that the previous node and the node to be analyzed corresponding to the correlation path are problem nodes is greater, and the possibility that the node corresponding to the correlation degree is a node with problems is greater.

[0048] The flowchart for obtaining the correlation degree is as Figure 3 shown.

[0049] In the node position sequence, with the node to be analyzed as the center, establish a window with a length of the first preset threshold, arrange the node detection information of each node in the window in the order of the nodes in the node position sequence, and obtain the adjacent sequence of the node to be analyzed.

[0050] Use the Doc2Vec model to obtain the vectors of the adjacent sequences of two different nodes in the same node position sequence, and take the cosine similarity between the vectors of the adjacent sequences of these two different nodes as the information local consistency of these two different nodes.

[0051] Based on the correlation degree of the node, combined with the information local consistency of different nodes, analyze the information response degree of the product at other nodes after the current node undergoes product information changes, and adjust the hierarchical relationship between nodes during the product traceability process according to the information response degree.

[0052] According to the correlation degree of the node and the information local consistency of different nodes, obtain the necessity of distance adjustment between nodes.

[0053] Among them, Indicates the necessity of distance adjustment between node a and node b in the same node position sequence; Indicates the degree of association of node a; Indicates the degree of association of node b; Indicates the local information consistency between node a and node b; Indicates the linear normalization function; Indicates the adjustment parameter, which is used to prevent the denominator from being zero. In this embodiment, the value is 0.01.

[0054] When the local information consistency between two nodes in the same node position sequence is greater and the difference in the degree of association between the two nodes is greater, the necessity of distance adjustment between the two nodes is smaller. In the process of intelligent traceability of the product corresponding to the node position sequence, the evaluation of the distance between two nodes is more accurate, and the necessity of adjusting the distance between the two nodes is smaller.

[0055] When the necessity of distance adjustment between two nodes in the same node position sequence is greater than or equal to the adjustment necessity threshold, it is considered that the distance measurement between the two nodes is inaccurate and needs to be adjusted.

[0056] Taking the arrangement order of nodes in the node position sequence as the abscissa and the range influence degree of the nodes as the ordinate, a two-dimensional coordinate system of the node position sequence is established, and the Euclidean distance between two different nodes in the two-dimensional coordinate system of the node position sequence is recorded as the measurement distance between the nodes.

[0057] According to the necessity of distance adjustment between two nodes in the same node position sequence and the measurement distance between the nodes, the distance adjustment value between the two nodes is obtained.

[0058] Among them, Indicates the distance adjustment value between node a and node b in the same node position sequence; Indicates the measurement distance between node a and node b; Indicates the necessity of distance adjustment between node a and node b in the same node position sequence; Indicates the mean value of the measurement distances between all different nodes in the node position sequence where node a and node b are located.

[0059] When the distance measurement between two nodes is inaccurate and needs to be adjusted, the distance measurement between the two nodes is assigned the distance adjustment value between the two nodes.

[0060] So far, the distance adjustment value between nodes is obtained.

[0061] Step S004: Obtain the information credibility of nodes according to the distance adjustment value between nodes, and realize the intelligent traceability of products based on radio frequency tags according to the information credibility of nodes.

[0062] Use the distance adjustment value between nodes as the distance between nodes, and process all nodes in the same node position sequence using the hierarchical clustering algorithm to obtain the clustering clusters of nodes.

[0063] Take the mean value of the range influence degrees of all nodes included in the same clustering cluster as the information credibility of all nodes within the clustering cluster.

[0064] During the process of intelligent traceability of products, when multiple nodes that may correspond to problem nodes are obtained during the traceability process of problem products, take the node with the highest information credibility as the problem node to achieve efficient and rapid positioning of the problem node.

[0065] Thus, the intelligent traceability of products based on radio frequency tags is realized.

[0066] Based on the same inventive concept as the above method, an embodiment of the present invention also provides an intelligent product traceability system based on radio frequency tags, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for the intelligent product traceability method based on radio frequency tags.

[0067] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The product intelligent tracing method based on radio frequency tags is characterized by: The method comprises the following steps: Obtain the collection node information and production and circulation node information of all products, and obtain the node detection information based on the collection node information; Obtain the node position sequence of the product, determine the node detection information sequence, obtain the consistency of the node's product information, obtain the cumulative change consistency of the node based on the node detection information sequence and the node's product information consistency, and obtain the scope impact degree of the node based on the cumulative change consistency of the node; Define the node to be analyzed and the predecessor node of the node to be analyzed, determine the association path between the predecessor node and the node to be analyzed, obtain the number of passing products of the association path, obtain the association degree of the node according to the range influence degree of the node and the number of passing products of the association path, obtain the local consistency of information of two different nodes in the same node position sequence, obtain the necessity of distance adjustment between nodes according to the association degree of the node and the local consistency of information of different nodes, obtain the necessity of distance adjustment between two nodes in the same node position sequence and the measured distance between the nodes, obtain the distance adjustment value between the two nodes, and update the distance adjustment value according to the distance adjustment value; According to the distance adjustment value between nodes, the information credibility of the nodes is obtained, and the intelligent product traceability based on radio frequency tags is realized according to the information credibility of the nodes.

2. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The specific method for obtaining the cumulative change consistency of the node is as follows: Each node in the node position sequence is regarded as the first node, and the node before the first node in the node position sequence is regarded as the second node; The average of the product information consistency of the first node and all nodes before the first node in the node position sequence is recorded as the first product information consistency of the first node; The absolute value of the difference between the product information consistency of the second node and the first product information consistency of the first node is recorded as the information consistency difference of the second node; The mean of the information consistency differences of all second nodes is recorded as the first mean of the first node; The power with the natural constant as the base and the opposite of the first mean of the first node as the exponent is recorded as the cumulative change consistency of the first node.

3. The product intelligent tracing method based on radio frequency tags according to claim 2 is characterized in that: The specific method of obtaining the range influence degree of the node according to the cumulative change consistency of the node includes: The absolute value of the difference between the cumulative change consistency of the second node and the cumulative change consistency of the node before the second node is recorded as the first difference of the second node; The absolute value of the difference between the cumulative change consistency of the first node and the cumulative change consistency of the node before the first node is recorded as the first difference of the first node; The average of the first difference of the first node and the first differences of all the second nodes is recorded as the range influence degree of the first node.

4. The product intelligent tracing method based on radio frequency tags according to claim 2 is characterized in that: The specific method of defining the node to be analyzed and the predecessor node of the node to be analyzed, and determining the association path between the predecessor node and the node to be analyzed is as follows: The first node is recorded as the node to be analyzed, and the second node is recorded as the predecessor node of the node to be analyzed; The path from the predecessor node of the node to be analyzed to the node to be analyzed is recorded as the associated path between the predecessor node and the node to be analyzed.

5. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The specific formula for obtaining the relevance of the node is: in, Indicates The relevance of nodes; Indicates The extent of the impact of the range of nodes; Indicates that in the node position sequence, The node before The extent of the impact of the range of nodes; represents the linear normalization function; Indicates The order of the nodes in the product's node position sequence; Indicates Nodes and The number of products that pass through the associated path of each node; Represents an exponential function with a natural constant as base.

6. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The necessity of adjusting the distance between the nodes is obtained by: The product of the absolute value of the difference between the association degrees of two nodes and the local consistency of the information of the two nodes is recorded as the first factor of the two nodes; The normalized value of the reciprocal of the sum of the first factor of the two nodes and the adjustment parameter is recorded as the necessity of distance adjustment between the two nodes.

7. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The specific method of obtaining the distance adjustment value between the two nodes is as follows: The mean of the measured distances between all different nodes in the same node position sequence is recorded as the second mean of the node position sequence; The ratio of the necessity of adjusting the distance between two nodes in the same node position sequence to the second mean of the node position sequence where the two nodes are located is recorded as the first ratio; The product of the first ratio and the measured distance between the two nodes is recorded as the distance adjustment value between the two nodes.

8. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The specific method of updating the distance adjustment value according to the distance adjustment value is as follows: When the necessity of distance adjustment between two nodes in the same node position sequence is greater than or equal to the necessity threshold, the distance metric between the two nodes is assigned to the distance adjustment value between the two nodes, otherwise, the distance metric between the two nodes remains unchanged.

9. The product intelligent tracing method based on radio frequency tags according to claim 1 is characterized in that: The specific method of obtaining the information credibility of the node according to the distance adjustment value between the nodes and realizing the intelligent product traceability based on the radio frequency tag according to the information credibility of the node includes: The distance adjustment value between nodes is used as the distance between nodes, and all nodes in the same node position sequence are clustered to obtain the node clusters. The mean of the range influence degree of all nodes included in the same cluster is used as the information credibility of all nodes in the cluster. In the process of tracing the source of problematic products, the node with the highest information credibility is used as the problematic node to achieve intelligent product traceability based on radio frequency tags.

10. A product intelligent tracing system based on radio frequency tags, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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