A method and system for tracking the recycling of renewable resources based on the Internet of Things

Abstract

This application relates to the field of data traceability technology for recycled resource recovery, specifically to a method and system for tracking recycled resource recovery based on the Internet of Things (IoT). The method includes: collecting the recycling code sequences of each category of recycled resource recovery data from each recycling entity and the total number of times each entity has conducted resource recovery; obtaining the importance and detail of each piece of recycled resource recovery data from each recycling entity; calculating the coding similarity and effective non-redundancy factor of each piece of recycled resource recovery data from each recycling entity; obtaining the effective feature factors of each recycling entity; obtaining the directed edge weights of any two nodes in a directed graph; obtaining the effective transaction sharing factors for each node in the traceability directed graph; and obtaining the traceability reliability of each node in the traceability directed graph, thereby tracking recycled resource recovery via the IoT. This application reduces the tracking cost of recycled resource recovery.

Description

Technical Field

[0001] This application relates to the field of data traceability technology for recycled resource recovery, specifically to a method and system for tracking recycled resource recovery based on the Internet of Things. Background Technology

[0002] As global resources are gradually depleted, traditional resource extraction has brought enormous challenges and pressures to the natural environment. Overexploitation of natural resources not only leads to resource depletion but also causes severe ecological damage and environmental burden. Recycling can significantly reduce the demand for primary resources, thereby mitigating environmental damage and the overconsumption of natural resources. Recycling tracking, as a crucial topic in modern environmental protection and resource management, utilizes IoT technology to acquire data throughout the entire recycling process, providing essential support for optimizing resource allocation and achieving sustainable development.

[0003] Existing data tracing methods mainly include data annotation, reverse lookup, bidirectional pointer tracing, and graph theory. Data tracing methods based on graph theory can intuitively and structurally express data relationships and provide efficient data tracing paths. However, in the process of tracking the recycling of renewable resources, due to the diverse and complex statistical channels for recycling and the possibility of multi-level recycling transactions among various recycling entities, the phenomenon of duplicate data entry for recycling of renewable resources is serious. This increases the complexity of selecting recycling tracing paths. Inappropriate tracing paths not only make it impossible to accurately monitor the flow of recycled resources but also significantly increase tracing costs. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for tracking the recycling of renewable resources based on the Internet of Things (IoT). The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for tracking the recycling of renewable resources based on the Internet of Things, the method comprising the following steps:

[0006] Collect the recycling code sequence of each category of recycled resource recycling data from each recycling entity and the total number of times each recycling entity has carried out resource recycling;

[0007] Based on the frequency of the types of recycled resources and the total number of times the recycling entities have carried out resource recycling in the recycled resource recycling data, we can obtain the important details of each case of recycled resource recycling data for each recycling entity.

[0008] Based on the correlation between the recycling codes of the same category of recycled resource recycling data in any two cases of recycled resource recycling data, the degree of coding similarity of each case of recycled resource recycling data of each recycling entity is obtained;

[0009] Based on the importance of detail, coding similarity, and recycling coding sequence of recycled resource recovery data, the effective non-redundant factor of each recycling data point of each recycling entity is obtained;

[0010] Effective characteristic factors of each recycling entity are obtained based on effective non-redundant factors;

[0011] The directed edge weights between any two nodes in a directed graph are obtained based on effective feature factors.

[0012] Based on directed edge weights and effective non-redundant factors, the transaction-shared effective factors for each node in the traceability directed graph are obtained.

[0013] The traceability reliability of each node in the traceability directed graph is obtained based on the effective factor of transaction sharing.

[0014] Tracking of renewable resource recycling via the Internet of Things is based on traceability reliability.

[0015] Furthermore, the method for obtaining the aforementioned important details is as follows:

[0016] The categories of recycled resource data include the name of the recycled resource delivery person, the type of recycled resource, the status of the recycled resource material, the start time of recycled resource processing, the end time of recycled resource processing, the weight of recycled resource, and the utilization rate of recycled resource.

[0017] For each recycling entity, the frequency of the recycled resource type appearing in the total number of times the recycling entity conducts resource recycling is calculated in each case of recycled resource recycling data of the recycling entity. The product of this frequency and the total number of times the recycling entity conducts resource recycling is used as the important detail of each case of recycled resource recycling data of each recycling entity.

[0018] Furthermore, the formula for calculating the encoding similarity is as follows: In the formula, β i The encoding similarity of the i-th example of recycled resource data for each recycling entity; I represents the total number of times each recycling entity conducts resource recycling, J represents the total number of categories of recycled resource types in all resource recycling processes for each recycling entity, and c represents the total number of categories of recycled resource types. i,j c k,j , respectively, are the recycling code sequences corresponding to the j-th category data in the i-th and k-th examples of recycled resource recycling data of each recycling entity, and Ed() is the ED edit distance.

[0019] Furthermore, the formula for calculating the effective non-redundancy factor is as follows: In the formula, A i a is the effective non-redundant factor for the i-th example of recycled resource recovery data in each recycling entity; i To ensure the importance and detail of the recycling data in the i-th column of each recycling entity, βi m represents the degree of similarity in the encoding of the i-th example of recycled resource recovery data from each recycling entity. i ε is the sum of the most significant bits of the binary codes of all categories of recycling code sequences in the i-th example of recycled resource recycling data of each recycling entity, and ε is a preset adjustment parameter.

[0020] Furthermore, the method for obtaining the effective feature factors is as follows:

[0021] The effective non-redundant factors of all cases of recycled resource recovery data in each recycling entity are arranged in the forward order of resource recovery and recorded as the effective information sequence of each recycling entity. The sum of all effective non-redundant factors in the effective information sequence is used as the effective feature factor of each recycling entity.

[0022] Furthermore, the method for obtaining the directed edge weights is as follows:

[0023] Construct a directed graph by treating all recycling entities as nodes in the directed graph. When there is a recycling transaction or data sharing between one recycling entity and another recycling entity, a directed edge is formed, pointing from the seller to the buyer.

[0024] For any two nodes in a directed graph that have a directed edge, the sum of the effective feature factors of the two nodes is calculated as the directed edge weight of the directed edge between the two nodes in the directed graph.

[0025] Furthermore, the method for obtaining the transaction sharing effective factor is as follows:

[0026] Reverse the direction of all directed edges in the directed graph to construct a source-tracing directed graph. Obtain the degree of each node in the source-tracing directed graph. Take the previous neighbor of each node in the source-tracing directed graph as the predecessor node of each node. Take the weight of the directed edge between each node and its predecessor node as the predecessor directed weight of each node. Calculate the predecessor source path fit degree of each node by summing the predecessor directed weights of each node and all its predecessor nodes.

[0027] Each node in the source tracing directed graph is taken as its next neighbor node. The sum of the JS divergence between each node and the effective sequence of information of the recycling subject corresponding to all its next nodes is calculated as the source tracing path fit degree of each node.

[0028] For each node in the traceability directed graph, the ratio of the result of the exponential function with the natural constant as the base and the previous traceability path fitness degree as the exponent is calculated to the subsequent traceability path fitness degree. The product of the ratio and the degree of each node in the traceability directed graph is used as the transaction sharing effective factor of each node in the traceability directed graph.

[0029] Furthermore, the method for obtaining the reliability of the traceability is as follows:

[0030] The PageRank algorithm is used to obtain the PR value of each node in the source-tracing directed graph, which is then used as the PR value of the corresponding reclaiming entity for each node.

[0031] For each node in the traceability directed graph, the product of the PR value of the corresponding recycling entity of each node and the transaction sharing effective factor of each node is used as the traceability reliability of each node in the traceability directed graph.

[0032] Furthermore, the tracking of renewable resource recycling based on traceability reliability in the Internet of Things includes:

[0033] The absolute value of the difference between the source tracing reliability of two nodes with a source tracing path is used as the weight of the directed edge between the two nodes to construct a reliable source tracing directed graph. The Bellman-Ford algorithm is used to obtain the shortest source tracing path from any node in the reliable source tracing directed graph to all other nodes.

[0034] For each recycling data in the Internet of Things, the shortest traceability path for each recycling data is obtained based on a reliable traceability directed graph, and the recycling of recycling resources is tracked.

[0035] Secondly, embodiments of this application also provide an Internet of Things-based recycling and tracking system for recyclable resources, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0036] This application has at least the following beneficial effects:

[0037] This application obtains an effective non-redundancy factor by leveraging the effective non-redundancy characteristics of recycling data from recycling entities. It comprehensively analyzes the important and detailed conditions of recycling data within each entity and the possibility of duplicate data entry, thus more accurately assessing the effective non-redundancy of each instance of recycling data within the entity. Based on the effective non-redundancy factor and the robustness of the recycling entity's traceability under multi-level recycling transactions and data sharing, it obtains traceability reliability. Considering the traceability connections between the recycling entity and other recycling entities during the data traceability process, it incorporates an analysis enhancing the effective non-redundancy information of the traceability object, more accurately reflecting the reliability of the recycling entity's corresponding node in the recycling process. By filtering recycling entities in the recycling process through traceability reliability and obtaining the traceability path accordingly, it avoids the drawbacks of duplicate data entry caused by multi-level recycling transactions between recycling entities, improves the effective traceability selection of recycling paths, reduces traceability costs, and realizes an IoT-based recycling tracking method and system. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the steps of an IoT-based recycling and tracking method for recyclable resources, provided as an embodiment of this application;

[0040] Figure 2 This is a schematic diagram of a directed graph for recycling transactions of renewable resources provided in one embodiment of this application;

[0041] Figure 3 This is a schematic diagram of a source-tracing directed graph provided in one embodiment of this application. Detailed Implementation

[0042] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an Internet of Things-based recycling and tracking method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0044] The following description, in conjunction with the accompanying drawings, details the specific scheme of the Internet of Things-based recycling tracking method and system provided in this application.

[0045] Please see Figure 1 The diagram illustrates a flowchart of a method for tracking and recycling renewable resources based on the Internet of Things, according to an embodiment of this application. The method includes the following steps:

[0046] Step S1: Collect the recycling code sequence of each category of recycled resource recycling data in each case of recycled resource recycling data of each recycling entity and the total number of times each recycling entity has carried out resource recycling.

[0047] The recycling data of various recycling entities during the recycling process is obtained through an Internet of Things (IoT) platform. In this embodiment, the recycling entities include individuals, enterprises, and township / street transfer stations / recycling points. The categories of recycling data include the name of the recycling contributor, the type of recycled resource, the status of the recycled material, the start time of recycling processing, the end time of recycling processing, the weight of the recycled resource, and the utilization rate of the recycled resource. In this embodiment, the types of recycled resources include: plastics, paper, metals, and electronic waste. For each recycling entity, all recycling data from each recycling operation is taken as each recycling data instance for that entity, and the total number of recycling operations performed by each entity is obtained.

[0048] To prevent the different categories of recycled resource data from affecting subsequent analysis, this application uses UTF-8 encoding technology to convert all recycled resource data into a unified data code. The maximum code length for all categories of recycled resource data across all recycling entities is denoted as V. The code for each category of recycled resource data in all recycling entities is padded with leading zeros according to V, aligning the lengths of the code sequences for all categories of recycled resource data across all recycling entities. The code sequence for the i-th category of recycled resource data in any recycling entity is denoted as the recycling code sequence for the i-th category of that recycling entity. When the i-th category of recycled resource data cannot be collected, all data elements in the recycling code sequence for the i-th category of recycled resource data in that recycling entity are set to 0. Since UTF-8 encoding technology is a known technology, the specific acquisition process will not be elaborated further.

[0049] Thus, the recycling code sequence of each category of recycled resource recycling data in each case of recycled resource recycling data of each recycling entity can be obtained through the above method.

[0050] Step S2: Based on the frequency of the recyclable resource type in the recycling data and the total number of times the recycling entity conducts resource recycling, obtain the importance and detail of each case of recycling data for each recycling entity; based on the correlation between the recycling code data of the same category of recycling data in any two cases of recycling data, obtain the coding similarity of each case of recycling data for each recycling entity; based on the importance and detail, coding similarity, and recycling code sequence of the recycling data, obtain the effective non-redundant factor of each case of recycling data for each recycling entity.

[0051] Recycling is a massive undertaking involving multiple recycling entities and a vast amount of data related to recycled resources. Typically, these entities use different systems to process and record information, and there are also transactions between them. When these entities' data storage systems lack effective integration or data sharing, the same batch of recycled resource data may be recorded by multiple entities during the recycling tracking process. Furthermore, the Internet of Things (IoT) can also lead to duplicate data recording when inputting large amounts of the same type of recycled resource data from different entities. This increases data redundancy and creates incomplete data tracking paths during the data tracing process, affecting the accuracy and reliability of data tracing.

[0052] Specifically, in the recycling data of each recycling entity, the higher the proportion of a certain type of recycled resource, the higher the recycling degree of that type of recycled resource by the recycling entity, and the stronger the possibility of the recycling entity's transaction behavior regarding that type of recycled resource, i.e., the more likely it is to be recorded by other recycling entities; the more recycling resource data categories recorded in each recycling data instance of the recycling entity, the more detailed the recycling data of that instance, and the lower the possibility of being repeatedly entered; the higher the similarity between the recycling code sequences of each category in the recycling data of different instances of recycled resources in the recycling entity, the stronger the possibility of the data being repeatedly entered.

[0053] Based on the above analysis, for each case of recycled resource recycling data of each recycling entity, the importance and detail of each case of recycled resource recycling data is obtained, reflecting the importance and detail of each case of recycled resource recycling data of each recycling entity. The method is as follows: for each recycling entity, calculate the frequency of the recycled resource type appearing in the total number of times the recycling entity has carried out resource recycling in each case of recycled resource recycling data of the recycling entity, and multiply the frequency by the total number of times the recycling entity has carried out resource recycling as the importance and detail of each case of recycled resource recycling data of each recycling entity.

[0054] Furthermore, the coding similarity of each recycling entity's data for every instance of recycled resources is calculated using the following formula: In the formula, β i The encoding similarity of the i-th example of recycled resource data for each recycling entity; I represents the total number of times each recycling entity conducts resource recycling, J represents the total number of categories of recycled resource types in all resource recycling processes for each recycling entity, and c represents the total number of categories of recycled resource types. i,j c k,j , respectively, are the recycling code sequences corresponding to the j-th category data in the i-th and k-th examples of recycled resource recycling data of each recycling entity, and Ed() is the ED edit distance.

[0055] It should be noted that the weaker the coding consistency of the same category of recycling data among different examples of recycling data in the recycling entity, the smaller the similarity between the coding sequences of all categories of recycling data of the i-th example of recycling data in the recycling entity and the coding sequences of the same category of recycling data of all other examples of recycling data, that is, the coding similarity β. i The smaller the value of β, the higher the encoding similarity. i The larger the value, the better.

[0056] Furthermore, to reflect the likelihood that each instance of recycled resource recovery data from each recycling entity will be recycled by other recycling entities, an effective non-redundancy factor for each instance of recycled resource recovery data from each recycling entity is obtained based on importance and detail, coding similarity, and the recycling coding sequence of the recycled resource recovery data. The calculation formula is as follows: In the formula, A i a is the effective non-redundant factor for the i-th example of recycled resource recovery data in each recycling entity; i To ensure the importance and detail of the recycling data in the i-th column of each recycling entity, β i m represents the degree of similarity in the encoding of the i-th example of recycled resource recovery data from each recycling entity. i The sum of the most significant bits of the binary codes of all categories of recycling codes in the i-th example of recycled resource recycling data from each recycling entity is given. ε is a preset adjustment parameter, which is set to 1 in this embodiment to prevent m from being... i A value of 0 has no impact on the calculation.

[0057] It should be noted that the higher the degree of recycling of any given instance of recycled resource recovery data, the more detailed the recorded recycled resource recovery data, the greater the frequency of the corresponding recycling type among all recycling types, and the larger the total number of recycled resource data categories entered in the recycled resource recovery data, the greater the value of importance and detail obtained. Simultaneously, when the recycling entity contains a large amount of data, resulting in longer binary codes for each category of recycled resource recovery data, the higher the number of most significant bits (m) in the binary code sequence of all categories of recycled resource recovery data. i The larger the value of β, the weaker the coding consistency of the same category of data among different examples of recycled resource recycling data in the recycling entity, and the smaller the similarity between the recycling coding sequences of all categories of the i-th example of recycled resource recycling data and the recycling coding sequences of the same category of the other examples of recycled resource recycling data, i.e., β i The smaller the value, the less likely it is that the data for any single instance of recycled resource recovery from a particular recycling entity will be recorded by different recycling entities, thus preventing duplicate data entry. This results in a higher effective non-redundancy factor A. i The larger the value, the better; conversely, the smaller the effective non-redundancy factor A is. iThe smaller the value, the better.

[0058] Step S3: Obtain the effective feature factors of each recycling entity based on the effective non-redundant factors; obtain the directed edge weights of the directed edges between any two nodes in the directed graph based on the effective feature factors; obtain the transaction-shared effective factors for each node in the traceability directed graph based on the directed edge weights and the effective non-redundant factors; obtain the traceability reliability of each node in the traceability directed graph based on the transaction-shared effective factors.

[0059] Furthermore, the effective non-redundant factors of all examples of recycled resource recovery data in each recycling entity are arranged in the forward order of resource recovery and recorded as the effective information sequence of each recycling entity. The sum of all effective non-redundant factors in the effective information sequence is used as the effective feature factor of each recycling entity.

[0060] Furthermore, by treating all recycling entities as nodes in a directed graph, a directed graph is constructed. When there is a recycling transaction or data sharing between two recycling entities, a directed edge is formed, pointing from the seller to the buyer. The weight of the directed edge is calculated using the formula: γ p,q =d p +d q In the formula, γ p,q This represents the weight of the directed edge between nodes p and q; d p d q These represent the effective feature factors of the buyer and seller corresponding recycling entity nodes on the directed edge, respectively.

[0061] It should be noted that when the weight of a directed edge is larger, it indicates that in the directed graph corresponding to the recycling transaction relationship, d p When the recycling entity at the corresponding node acts as the buyer, from d q The more detailed and non-redundant the recycling data obtained from the seller recycling entity at the corresponding node, the more likely the recycling entities corresponding to nodes p and q should be included as part of the traceability path during the data tracing process. Furthermore, the more likely the path between the recycling entities corresponding to nodes p and q should be selected as the traceability path.

[0062] A schematic diagram of a directed graph for recycling and trading of renewable resources is shown below. Figure 2 As shown, hollow circles represent various recycling entities, and directed edges indicate recycling transactions or data sharing activities, pointing from the seller to the buyer or from the data transmitter to the receiver. d p d q d r These are the effective feature factors of the recycling entities corresponding to nodes p, q, and r, respectively, and γ. p,q γ p,r Let γ represent the directed edge weights of directed edge qp and directed edge rp, respectively.p,q Less than γ p,r The more detailed and non-redundant the recycling data obtained by the recycling entity corresponding to node p from the recycling entity corresponding to node r is, the more significant the directed edge between the recycling entities corresponding to node r and node q should be used as the tracing path.

[0063] Tracking the recycling of recyclable resources is essentially a process of acquiring and querying data tracking paths, that is, tracing from the buyer to the seller. When there are multiple levels of recycling transactions between recycling entities, the validity and duplication of recycling information between different recycling entities will vary greatly, which has a significant impact on the redundancy and completeness of the traceability path.

[0064] Specifically, when there are multi-level recycling transactions between different recycling entities, multiple sellers may point to the same buyer during the traceability process. The higher the accuracy of data traceability, the more important it is to select the applicant entity with obvious effective non-redundant characteristics on the traceability path as the starting and ending points of the traceability path. That is, the traceability path with the larger the weight of the directed edge should be selected. However, the weight of a single directed edge cannot accurately reflect the reliability of the traceability path.

[0065] Furthermore, the directions of all directed edges in the directed graph are reversed to construct a source-tracing directed graph and the degree of each node in the source-tracing directed graph. Each node's previous neighbor in the source-tracing directed graph is taken as its predecessor node, and the weight of the directed edge between each node and its predecessor node is taken as the predecessor directed weight of each node. The sum of the predecessor directed weights of each node and all its predecessor nodes is calculated as the source-tracing path fit degree of each node. Specifically, the more suitable a node is as the next neighbor of its predecessor node in the source-tracing path, the greater its source-tracing path fit degree; conversely, the less suitable, the smaller its source-tracing path fit degree. The method for obtaining the degree of each node in the directed graph is a well-known technique and will not be elaborated upon in this embodiment.

[0066] Each node in the source tracing directed graph is considered as its successor node. The sum of the JS divergences between each node and the effective information sequences of the recycling subjects corresponding to all its successors is calculated as the successor source tracing path fit of each node. Specifically, the more suitable a node is as the predecessor node of its successor node on the source tracing path, the greater the successor source tracing path fit between each node and its successors; conversely, the less suitable a node is as the predecessor node of its successors, the smaller the successor source tracing path fit between each node and its successors.

[0067] Furthermore, to reflect the degree to which each node in the traceability directed graph is suitable as a node in the traceability path, the transaction sharing effectiveness factor of each node in the traceability directed graph is obtained based on the adaptability of the preceding traceability path, the adaptability of the following traceability path, and the degree of the node. The calculation formula is as follows: In the formula, f x To trace the effective factor of transactions shared by the x-th node in the directed graph; g x To trace the degree of the x-th node in a directed graph, δ x h represents the fitness of the preceding tracing path to the x-th node in the tracing directed graph. x The fit of the subsequent tracing path for the x-th node in the tracing directed graph.

[0068] It should be noted that the less severe the data duplication caused by multi-level recycling transactions during the global traceability process, the more significant the multi-level recycling transactions or data sharing within the recycling entity, and the greater the degree of the node. In this case, the obtained g... x The larger the value, the more significant the increase in the effective characteristic factor of the traceability object becomes, as the recycling entity is part of the traceability path. That is, δ x The larger the h value, the stronger the consistency of recycled resource recycling data between the recycling entity and the traceability object, and the smaller the difference between a node and all valid information sequences corresponding to the next node with a traceability path, i.e., h x The smaller the value, the larger the value of the transaction sharing effective factor obtained; conversely, the larger the value, the smaller the value of the transaction sharing effective factor obtained.

[0069] The PageRank algorithm is used to obtain the PageRank (PR) value of each node in the traceability directed graph, which serves as the PR value for the corresponding recycling entity. It should be noted that a higher PR value indicates that the recycling entity corresponding to the node contains more effective and non-redundant data related to recycled resource transactions, and that it is more likely to be able to trace the next traceable object (i.e., the node pointed to by the directed edge) through that recycling entity. The damping factor is set to 0.85, and the initial node PR value is the reciprocal of the total number of recycling entities. The PageRank algorithm is a well-known technique and will not be elaborated upon in this embodiment. A schematic diagram of the traceability directed graph is shown below. Figure 3 As shown.

[0070] Furthermore, the reliability of tracing the source of each node in the directed graph is calculated using the following formula: D x =PR x ×f x In the formula, D x To determine the source tracing reliability of the x-th node in the directed graph; PR x To trace the PR value of the recycling entity corresponding to the x-th node in the directed graph; f xTo trace the effective factor of transactions shared by the x-th node in the directed graph.

[0071] It should be noted that when a recycling entity is more likely to have stronger connections with other recycling entities during the global directed tracing process, then PR (Public Recycling) is more appropriate. x The larger the value of the transaction sharing effective factor, and the more suitable each node is as a node in the traceability path, the greater the value of the transaction sharing effective factor. At this time, the more important the recycling data of the recycling entity corresponding to the node is, the more extensive the effective connection with other recycling entities is, the stronger the traceability reliability with the node as a link in the process of establishing the data traceability path, and the greater the value of the traceability reliability obtained; conversely, the smaller the value of the traceability reliability obtained.

[0072] At this point, the source tracing reliability of the x-th node in the source tracing directed graph is obtained.

[0073] Step S4: Track the recycling of renewable resources in the Internet of Things based on traceability reliability.

[0074] The absolute value of the difference between the source tracing reliability of two nodes with a source tracing path is used as the weight of the directed edge between the two nodes to construct a reliable source tracing directed graph. The Bellman-Ford algorithm is used to obtain the shortest source tracing path from any node in the reliable source tracing directed graph to all other nodes.

[0075] If there is no shortest tracing path between two nodes, it means that there is no recycling transaction or data sharing between the recycling entities corresponding to the two nodes. In other words, the two recycling entities are independent of each other and there is no tracing relationship. The Bellman-Ford algorithm is a well-known technology, and its specific acquisition process will not be described in detail here.

[0076] For implementers, based on the obtained recycling data and the nodes corresponding to the recycling entities in the reliable traceability directed graph, the shortest traceability path obtained above can be used to obtain all the flow processes of the recycling data, that is, to obtain all the tracking paths of the recycling data. In other words, for each recycling data in the Internet of Things, the shortest traceability path of each recycling data is obtained according to the reliable traceability directed graph, thus completing the tracking of recycling.

[0077] Based on the same inventive concept as the above methods, this application also provides an Internet of Things-based recycling and tracking system, 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-described Internet of Things-based recycling and tracking methods.

[0078] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0079] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0080] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for tracking and recycling recyclable resources based on the Internet of Things, characterized in that, The method includes the following steps: Collect the recycling code sequence of each category of recycled resource recycling data from each recycling entity and the total number of times each recycling entity has carried out resource recycling; Based on the frequency of the types of recycled resources and the total number of times the recycling entities have carried out resource recycling in the recycled resource recycling data, we can obtain the important details of each case of recycled resource recycling data for each recycling entity. Based on the correlation between the recycling codes of the same category of recycled resource recycling data in any two cases of recycled resource recycling data, the degree of coding similarity of each case of recycled resource recycling data of each recycling entity is obtained; Based on the importance of detail, coding similarity, and recycling coding sequence of recycled resource recovery data, the effective non-redundant factor of each recycling data point of each recycling entity is obtained; Effective characteristic factors of each recycling entity are obtained based on effective non-redundant factors; The directed edge weights between any two nodes in a directed graph are obtained based on effective feature factors. Based on directed edge weights and effective non-redundant factors, the transaction-shared effective factors for each node in the traceability directed graph are obtained. The traceability reliability of each node in the traceability directed graph is obtained based on the effective factor of transaction sharing. Tracking of renewable resource recycling via the Internet of Things based on traceability reliability; The method for obtaining the aforementioned important details is as follows: The categories of data for recycling of renewable resources include the name of the recycling contributor, the type of recycled resource, the status of the recycled resource material, the start time of recycling processing, the end time of recycling processing, the weight of recycled resource, and the utilization rate of recycled resource. For each recycling entity, the frequency of the recycled resource type appearing in the total number of times the recycling entity conducts resource recycling in each case of recycled resource recycling data of the recycling entity is calculated, and the product of the frequency and the total number of times the recycling entity conducts resource recycling is used as the important detail of each case of recycled resource recycling data of each recycling entity. The formula for calculating the encoding similarity is: In the formula, β i The encoding similarity of the i-th example of recycled resource data for each recycling entity; I represents the total number of times each recycling entity conducts resource recycling, J represents the total number of categories of recycled resource types in all resource recycling processes for each recycling entity, and c represents the total number of categories of recycled resource types. i,j c k,j These are the recycling code sequences corresponding to the j-th category data in the i-th and k-th examples of recycled resource recycling data of each recycling entity, respectively, and Ed() is the ED edit distance; The formula for calculating the effective non-redundancy factor is as follows: In the formula, A i a is the effective non-redundant factor for the i-th example of recycled resource recovery data in each recycling entity; i To ensure the importance and detail of the recycling data in the i-th column of each recycling entity, β i m represents the degree of similarity in the encoding of the i-th example of recycled resource recovery data from each recycling entity. i ε is the sum of the most significant bits of the binary codes of all categories of recycling code sequences in the i-th example of recycled resource recycling data in each recycling entity, where ε is a preset adjustment parameter; The method for obtaining the effective feature factors is as follows: The effective non-redundant factors of all cases of recycled resource recovery data in each recycling entity are arranged in the forward order of resource recovery and recorded as the effective information sequence of each recycling entity. The sum of all effective non-redundant factors in the effective information sequence is used as the effective feature factor of each recycling entity. The method for obtaining the directed edge weights is as follows: Construct a directed graph by treating all recycling entities as nodes in the directed graph. When there is a recycling transaction or data sharing between one recycling entity and another recycling entity, a directed edge is formed, pointing from the seller to the buyer. For any two nodes in a directed graph that have a directed edge, calculate the sum of the effective feature factors of the two nodes as the directed edge weight of the directed edge between the two nodes in the directed graph. The method for obtaining the effective factor for transaction sharing is as follows: Reverse the direction of all directed edges in the directed graph to construct a source-tracing directed graph. Obtain the degree of each node in the source-tracing directed graph. Take the previous neighbor of each node in the source-tracing directed graph as the predecessor node of each node. Take the weight of the directed edge between each node and its predecessor node as the predecessor directed weight of each node. Calculate the predecessor source path fit degree of each node by summing the predecessor directed weights of each node and all its predecessor nodes. Each node in the source tracing directed graph is taken as its next neighbor node. The sum of the JS divergence between each node and the effective sequence of information of the recycling subject corresponding to all its next nodes is calculated as the source tracing path fit degree of each node. For each node in the traceability directed graph, calculate the ratio of the result of the exponential function with the natural constant as the base and the previous traceability path fitness degree as the exponent to the subsequent traceability path fitness degree. Multiply the ratio by the degree of each node in the traceability directed graph as the transaction sharing effective factor of each node in the traceability directed graph. The method for obtaining the reliability of the traceability is as follows: The PageRank algorithm is used to obtain the PR value of each node in the source-tracing directed graph, which is then used as the PR value of the corresponding reclaiming entity for each node. For each node in the traceability directed graph, the product of the PR value of the corresponding recycling entity of each node and the transaction sharing effective factor of each node is used as the traceability reliability of each node in the traceability directed graph.

2. The method for tracking and recycling renewable resources based on the Internet of Things as described in claim 1, characterized in that, The method of tracking the recycling of renewable resources based on traceability reliability in the Internet of Things includes: The absolute value of the difference between the source tracing reliability of two nodes with a source tracing path is used as the weight of the directed edge between the two nodes to construct a reliable source tracing directed graph. The Bellman-Ford algorithm is used to obtain the shortest source tracing path from any node in the reliable source tracing directed graph to all other nodes. For each recycling data in the Internet of Things, the shortest traceability path for each recycling data is obtained based on a reliable traceability directed graph, and the recycling of recycling resources is tracked.

3. A recycling and tracking system for recyclable resources based on the Internet of Things, 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, it implements the steps of the Internet of Things-based recycling and tracking method as described in any one of claims 1-2.

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