Renewable resource recovery tracking method and system based on Internet of Things

Abstract

The invention relates to the technical field of renewable resource recovery data tracing, in particular to a renewable resource recovery tracing method and system based on the Internet of Things. The method comprises the following steps: collecting a recovery coding sequence of each type of renewable resource recovery data in each case of renewable resource recovery data of each recovery main body and the total number of times of resource recovery of each recovery main body; obtaining important details of each case of renewable resource recovery data of each recovery subject; calculating a coding similarity degree and an effective non-redundancy factor of each case of renewable resource recovery data of each recovery main body; obtaining an effective characteristic factor of each recovery main body; obtaining a directed edge weight of a directed edge between any two nodes in the directed graph; obtaining transaction sharing effective factors of each node in the traceability directed graph; and obtaining the traceability reliability of each node in the traceability directed graph, and tracking renewable resource recovery of the Internet of Things. According to the invention, the tracking cost of renewable resource recovery is reduced.

Description

Technical Field

[0001] This application relates to the technical field of recycling resource recovery data traceability, and specifically relates to a recycling resource recovery tracking method and system based on the Internet of Things. Background Art

[0002] With the gradual consumption of global resources, traditional resource extraction has brought huge challenges and pressures to the natural environment. The overexploitation of natural resources will not only lead to the depletion of resources, but also cause serious ecological damage and environmental burden; the recycling of renewable resources can greatly reduce the demand for primary resources, thereby reducing environmental damage and overconsumption of natural resources. As an important topic in the field of modern environmental protection and resource management, recycling resource recovery tracking obtains the whole-process data of recycling resource recovery through Internet of Things technology, providing an important guarantee for optimizing resource allocation and achieving sustainable development.

[0003] Existing data traceability and tracking methods mainly include data annotation method, reverse query method, two-way pointer tracking method, graph theory idea, etc. The data traceability method based on graph theory idea can intuitively and structurally express data relationships and provide an efficient data tracking path; in the process of recycling resource recovery tracking, due to the diverse and complex statistical channels of recycling resource recovery, there may be multi-level recycling transactions among multiple recycling entities, resulting in serious duplicate data entry of recycling resource recovery data, which further increases the complexity of the selection of recycling resource recovery tracking paths. An inappropriate tracking path will not only make it impossible to accurately monitor the flow of recycling resources, but also greatly increase the tracking cost. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a recycling resource recovery tracking method and system based on the Internet of Things, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, the embodiment of this application provides a recycling resource recovery tracking method based on the Internet of Things, and the method includes the following steps:

[0006] Collect the recycling code sequences of various types of recycling resource recovery data in each case of recycling resource recovery data of each recycling entity and the total number of times each recycling entity conducts resource recovery.

[0007] Based on the frequency of the appearance of the recycled resource type in the recycling resource recovery data and the total number of times the recycling entity conducts resource recovery, obtain the importance and detail of each case of recycling resource recovery data of each recycling entity.

[0008] Based on the correlation between the recycling code data of the same type of recycling resource recovery data in any two cases of recycling resource recovery data, obtain the coding similarity degree of each case of recycling resource recovery data of each recycling entity.

[0009] Based on the importance details, coding similarity degree, and recycling resource recovery data, obtain the effective non-redundant factor of each recycling entity's recycling resource recovery data for each case;

[0010] Based on the effective non-redundant factor, obtain the effective characteristic factor of each recycling entity;

[0011] Based on the effective characteristic factor, obtain the directed edge weight of the directed edge between any two nodes in the directed graph;

[0012] Based on the directed edge weight and the effective non-redundant factor, obtain the transaction sharing effective factor for each node in the traceability directed graph;

[0013] Based on the transaction sharing effective factor, obtain the traceability reliability of each node in the traceability directed graph;

[0014] Based on the traceability reliability, track the recycling of renewable resources in the Internet of Things.

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

[0016] The categories of recycling resource recovery data include the name of the recycling resource deliverer, the type of recycled resource, the material state of the recycled resource, the start time of recycled resource processing, the end time of recycled resource processing, the weight of the recycled resource, and the utilization rate of the recycled resource;

[0017] For each recycling entity, calculate the product of the frequency of the recycled resource type appearing in the total number of resource recycling times of the recycling entity in each case of the recycling entity's recycling resource recovery data and the total number of resource recycling times of the recycling entity as the importance details of each case of the recycling entity's recycling resource recovery data.

[0018] Furthermore, the calculation formula for the coding similarity degree is: In the formula, β i is the coding similarity degree of the i-th case of the recycling resource recovery data of each recycling entity; I is the total number of resource recycling times of each recycling entity, J is the total number of categories of recycled resource types in all resource recycling processes of each recycling entity, c i,j and c k,j are the recycling coding sequences corresponding to the j-th category data in the i-th and k-th cases of the recycling resource recovery data of each recycling entity respectively, and Ed() is the ED edit distance.

[0019] Furthermore, the calculation formula for the effective non-redundant factor is: In the formula, A i is the effective non-redundant factor of the i-th case of the recycling resource recovery data in each recycling entity; a i is the importance details of the i-th column of the recycling resource recovery data in each recycling entity, βi is the coding similarity degree of the i-th recycled resource recovery data of each recycling entity, m i is the cumulative result of the most significant bit of the binary coding of the recycling coding sequences of all categories in the i-th recycled resource recovery data of each recycling entity, and ε is a preset adjustment parameter.

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

[0021] The sequence formed by arranging the effective non-redundant factors of the recycled resource recovery data of all examples of each recycling entity in the positive order during resource recycling is denoted as the information effective sequence of each recycling entity, and the cumulative result of all the effective non-redundant factors in the information effective sequence is used as the effective feature factor of each recycling entity.

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

[0023] Construct a directed graph with all recycling entities as each node in the directed graph. When there is a recycled resource recovery transaction or data sharing between one recycling entity and another recycling entity, a directed edge is formed, and the directed edge points from the seller to the buyer;

[0024] For any two nodes with a directed edge in the directed graph, calculate the sum value 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.

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

[0026] Reverse the directions of all the directed edges in the directed graph to construct a traceability directed graph, obtain the degrees of each node in the traceability directed graph, take each previous adjacent node of each node in the traceability directed graph as the pre-node of each node, take the directed edge weight between each node and its pre-node as the pre-directed weight of each node, and calculate the sum value of the pre-directed weights of each node and all its pre-nodes as the pre-traceability path fitness of each node;

[0027] Take each subsequent adjacent node of each node in the traceability directed graph as the post-node of each node, and calculate the summation result of the JS divergences between each node and the information effective sequences of the recycling entities corresponding to all its post-nodes as the post-traceability path fitness of each node;

[0028] For each node in the traceability directed graph, calculate the ratio of the calculation result of the exponential function with the natural constant as the base and the pre-traceability path fitness as the exponent to the post-traceability path fitness, and take the product of the ratio and the degree of each node in the traceability directed graph as the transaction sharing effective factor of each node in the traceability directed graph.

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

[0030] The PageRank web page ranking algorithm is used to obtain the PR value of each node in the traceability directed graph as the PR value of the recycling entity corresponding to each node;

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

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

[0033] Take the absolute value of the difference between the traceability reliabilities corresponding to two nodes with a traceability path as the weight of the directed edge between the two nodes, construct a reliable traceability directed graph, and obtain the shortest traceability path from any node in the reliable traceability directed graph to all other nodes through the Bellman-Ford algorithm;

[0034] For each renewable resource recycling data in the Internet of Things, obtain the shortest traceability path of each renewable resource recycling data according to the reliable traceability directed graph, and track the renewable resource recycling.

[0035] In a second aspect, an embodiment of the present application further provides a renewable resource recycling tracking system based on the Internet of Things, 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.

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

[0037] The present application obtains an effective non-redundant factor through the effective non-redundant features of the renewable resource recycling data of the recycling entity, comprehensively analyzes the important detailed conditions of the renewable resource recycling data in the recycling entity and the possibility of duplicate data entry, and more accurately evaluates the effective non-redundancy of each instance of the renewable resource recycling data in the recycling entity; obtains the traceability reliability according to the effective non-redundant factor and the traceability robust features of the recycling entity under multi-level recycling transactions and data sharing, and adds an analysis of enhancing the effective non-redundant information of the traceability object on the basis of considering the traceability connection between the recycling entity and other recycling entities during the data traceability process, which more accurately reflects the reliability degree in the process of tracking the renewable resource recycling of the node corresponding to the recycling entity; screens the recycling entities in the process of tracking the renewable resource recycling through the traceability reliability, and obtains the tracking path therefrom, avoiding the disadvantages of the impact of duplicate data entry caused by multi-level recycling transactions between recycling entities, improving the effective traceability selection of the renewable resource recycling tracking path, reducing the tracking cost, and realizing a method and system for tracking the renewable resource recycling based on the Internet of Things. Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of the steps of a method for recycling resource recovery tracking based on the Internet of Things provided by an embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of a directed graph of recycling resource recovery transactions provided by an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of a traceability directed graph provided by an embodiment of the present application. Detailed Embodiment

[0042] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following will, in conjunction with the drawings and preferred embodiments, describe in detail the specific implementation manner, structure, features and effects of a method and system for recycling resource recovery tracking based on the Internet of Things proposed by the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the 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 those of ordinary skill in the technical field to which this application belongs.

[0044] The following will specifically describe the specific solutions of a method and system for recycling resource recovery tracking based on the Internet of Things provided by the present application in conjunction with the drawings.

[0045] Please refer to Figure 1 , which shows a flowchart of the steps of a method for recycling resource recovery tracking based on the Internet of Things provided by an embodiment of the present application. The method includes the following steps:

[0046] Step S1, collect the recovery coding sequences of various types of recycled resource recovery data in each piece of recycled resource recovery data of each recovery entity and the total number of times each recovery entity conducts resource recovery.

[0047] By means of the Internet of Things platform, the recycled resource recovery data of each recycling entity in the process of recycled resource recovery is obtained. In this embodiment, the recycling entities include individuals, enterprises, and transfer stations - recycling points in townships and streets. The categories of recycled resource recovery data include the name of the recycled resource recycler, the type of recycled resources, the material state of the recycled resources, the start time of recycled resource processing, the end time of recycled resource processing, the weight of recycled resources, and the utilization rate of recycled resources. In this embodiment, the types of recycled resources include plastics, paper, metals, and electronic waste. For each recycling entity, all the recycled resource recovery data at each time of resource recovery is used as each example of the recycled resource recovery data of the recycling entity, and the total number of times of resource recovery by each recycling entity is obtained.

[0048] To prevent the difference in the categories of recycled resource recovery data from affecting subsequent analysis, the present application uses UTF-8 encoding technology to convert all recycled resource recovery data into a unified data encoding. Among them, the maximum encoding length of all categories of recycled resource recovery data of all recycling entities is denoted as V, and the encoding of each category of recycled resource recovery data of all recycling entities is padded with 0 at the first position according to V to complete the alignment of the encoding sequence lengths of all categories of recycled resource recovery data of all recycling entities. The encoding sequence of the recycled resource data of the i-th category of any recycling entity is denoted as the recycling encoding sequence of the i-th category of any recycling entity. When the recycled resource recovery data of the i-th category of the obtained recycling entity cannot be collected, all the data elements in the recycling encoding sequence of the recycled resource recovery data of the i-th category of the recycling entity are set to 0. Since the UTF-8 encoding technology is a well-known technology, the specific acquisition process will not be elaborated too much.

[0049] Thus far, the recycling encoding sequences of various categories of recycled resource recovery data in each example of the recycled resource recovery data of each recycling entity can be obtained through the above method.

[0050] Step S2: Based on the frequency of the type of recycled resources appearing in the recycled resource recovery data and the total number of times of resource recovery by the recycling entity, obtain the important details of each example of the recycled resource recovery data of each recycling entity; based on the correlation between the recycling encoding data of the same category of recycled resource recovery data in any two examples of recycled resource recovery data, obtain the encoding similarity degree of each example of the recycled resource recovery data of each recycling entity; based on the important details, the encoding similarity degree, and the recycling encoding sequence of the recycled resource recovery data, obtain the effective non-redundant factor of each example of the recycled resource recovery data of each recycling entity.

[0051] Recycled resource recovery is a huge project, involving multiple recovery entities and a large amount of data related to recycled resources. Usually, different systems are used among recovery entities to process and record information, and there are also certain trading behaviors. When there is no effective docking or data sharing processing in the data storage systems of these recovery entities, during the process of tracing recycled resource recovery, it may lead to the same batch of recycled resource recovery data being recorded by multiple recovery entities. In addition, the Internet of Things will also have the phenomenon of duplicate data recording when entering a large amount of recycled resource recovery data of the same type for recovery entities, which increases data redundancy and results in an incomplete data tracing path during data traceability, affecting the tracing accuracy and reliability of data traceability.

[0052] Specifically, among the recycled resource recovery data of each recovery entity, the larger the proportion of a certain type of recycled resource, the higher the recovery degree of the recovery entity for this type of recycled resource, and the stronger the possibility of trading behaviors of the recovery entity regarding this type of recycled resource, that is, the more likely it is to be recorded by other recovery entities; the more categories of recycled resource data recorded in each piece of recycled resource recovery data of the recovery entity, the more detailed this piece of recycled resource recovery data is, and the lower the possibility of being repeatedly entered; when the similarity between the recovery coding sequences of different categories in different pieces of recycled resource recovery and utilization data of the recovery entity is higher, the stronger the possibility of the data being repeatedly entered.

[0053] Based on the above analysis, for each piece of recycled resource recovery data of each recovery entity, obtain the important detail of each piece of recycled resource recovery data, which reflects the importance degree and detailed situation of each piece of recycled resource recovery data of each recovery entity. The obtaining method is: for each recovery entity, calculate the product of the frequency of the recycled resource type appearing in the total number of times of resource recovery by the recovery entity in each piece of recycled resource recovery data of the recovery entity and the total number of times of resource recovery by the recovery entity as the important detail of each piece of recycled resource recovery data of each recovery entity.

[0054] Furthermore, calculate the coding similarity degree of each piece of recycled resource recovery data of each recovery entity. The calculation formula is: In the formula, β i is the coding similarity degree of the i-th piece of recycled resource recovery data of each recovery entity; I is the total number of times of resource recovery by each recovery entity, J is the total number of categories of recycled resource types in all resource recovery processes of each recovery entity, c i,j and c k,j are the recovery coding sequences corresponding to the j-th category data in the i-th and k-th pieces of recycled resource recovery data of each recovery entity respectively, and Ed() is the ED edit distance.

[0055] It should be noted that the weaker the coding consistency of the same-category data among the recycling data of different cases of renewable resources in the recycling entity, the smaller the similarity degree between the recycling coding sequences of all categories of the i-th case of renewable resources recycling data in the recycling entity and the recycling coding sequences of the same categories between the recycling data of all other cases of renewable resources, that is, the coding similarity degree β i is smaller; conversely, the coding similarity degree β i is larger.

[0056] Furthermore, in order to reflect the possibility that each case of renewable resources recycling data of each recycling entity is recycled by other recycling entities, based on the importance and detail, coding similarity degree, and the recycling coding sequence of the renewable resources recycling data, the effective non-redundant factor of each case of renewable resources recycling data of each recycling entity is obtained, and the calculation formula is: In the formula, A i is the effective non-redundant factor of the i-th case of renewable resources recycling data in each recycling entity; a i is the importance and detail of the i-th column of renewable resources recycling data in each recycling entity, β i is the coding similarity degree of the i-th case of renewable resources recycling data in each recycling entity, m i is the cumulative result of the most significant bit of the binary coding of the recycling coding sequences of all categories in the i-th case of renewable resources recycling data in each recycling entity, and ε is a preset adjustment parameter, and the value in this embodiment is 1, and the purpose is to prevent m i from being 0 and affecting the calculation.

[0057] It should be noted that the higher the recycling degree of the renewable resources corresponding to any case of renewable resources recycling data, the more detailed the recorded renewable resources recycling data, the greater the frequency of the recycling type corresponding to the renewable resources recycling data in all recycling types, and the greater the total number of categories of the recycled renewable resources data entered in the renewable resources recycling data. At this time, the obtained value of the importance and detail is larger; at the same time, when the recycling entity has a larger amount of data, resulting in longer binary coding for various categories of renewable resources recycling data, the most significant bit of the binary coding in all categories of recycling coding sequences in the renewable resources recycling data is larger, that is, m i is larger; when the coding consistency of the same-category data among different cases of renewable resources recycling data in the recycling entity is weaker, the similarity degree between the recycling coding sequences of all categories of the i-th case of renewable resources recycling data in the recycling entity and the recycling coding sequences of the same categories between the recycling data of all other cases of renewable resources is smaller, that is, β i is smaller. At this time, it means that it is less likely that any case of renewable resources recycling data corresponding to the recycling entity will be recorded by different recycling entities, resulting in duplicate data entry. The obtained effective non-redundant factor A i is larger; conversely, the effective non-redundant factor A iThe smaller the value.

[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 sharing effective factors of 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 sharing effective factors.

[0059] Furthermore, the sequence formed by arranging the effective non-redundant factors of the recycled resource recovery data of all cases in each recycling entity in ascending order during resource recovery is denoted as the information effective sequence of each recycling entity, and the cumulative result of all the effective non-redundant factors in the information effective sequence is used as the effective feature factor of each recycling entity.

[0060] Furthermore, take all recycling entities as the respective nodes in the directed graph to construct a directed graph. When there is a recycled resource recovery transaction or data sharing between one recycling entity and another recycling entity, a directed edge is formed. The directed edge points from the seller to the buyer, and the calculation formula for the directed edge weight is: γ p,q =d p +d q ; In the formula, γ p,q represents the directed edge weight between node p and node q; d p , d q respectively represent the effective feature factors of the recycling entity nodes corresponding to the buyer and the seller on the directed edge.

[0061] It should be noted that when the directed edge weight is larger, it means that in the directed graph corresponding to the recycled resource recovery transaction relationship, when the recycling entity corresponding to d p is the buyer, the recycled resource recovery data obtained from the seller recycling entity corresponding to d q is more detailed and non-redundant. The recycling entities corresponding to nodes p and q should be regarded as more important parts of the traceability path during the data traceability process. Furthermore, the path existing between the recycling entities corresponding to nodes p and q should be more likely to be selected as the traceability path.

[0062] The schematic diagram of the recycled resource recovery transaction directed graph is as Figure 2 shown. Among them, the hollow circles represent each recycling entity. The directed edges indicate the existence of recycled resource recovery transaction behaviors or data sharing behaviors, pointing from the seller to the buyer or from the recycled resource recovery data transmission party to the receiving party. d p , d q , d r are respectively the effective feature factors of the recycling entities corresponding to nodes p, q, and r. γ p,q , γ p,r respectively represent the directed edge weights of the directed edge qp and the directed edge rp. When γp,q less than γ p,r indicates that the more significant the detailed non-redundant features of the recycled resource recovery data obtained by the recycling entity corresponding to node p from the recycling entity corresponding to node r are, the more the directed edge between the recycling entity corresponding to node r and the recycling entity corresponding to node q should be used as the traceability path.

[0063] Tracking the recycling of recycled resources is essentially a process of obtaining the data tracking path and querying, that is, a process of gradually tracking from the buyer to the seller. When there are multi-level recycled resource recycling transactions between recycling entities, that is, the effectiveness and duplication status of recycled resource recycling information between different recycling entities will vary greatly, which has a greater impact on the redundancy and integrity of the traceability path.

[0064] Specifically, when there are multi-level recycled resource recycling transactions between different recycling entities, there will be multiple sellers pointing to the same buyer during the traceability process. The higher the accuracy of data traceability, the more the application entity with obvious effective non-redundant features on the tracking path should be selected as the starting and ending points of the tracking path, that is, the traceability path with a larger directed edge weight should be selected, but the reliability of the traceability path cannot be accurately reflected by the weight of a single directed edge alone.

[0065] Furthermore, reverse the directions of all directed edges in the directed graph to construct the traceability directed graph and the degrees of each node in the traceability directed graph. Take each previous adjacent node of each node in the traceability directed graph as the pre-node of each node, and take the directed edge weight between each node and its pre-node as the pre-directed weight of each node. Calculate the sum value of the pre-directed weights of each node and all its pre-nodes as the pre-traceability path fitness of each node; specifically, when each node is more suitable as the subsequent adjacent node of its pre-node in the traceability path, the larger the pre-traceability path fitness of each node; on the contrary, when the pre-traceability path fitness of each node is smaller. Among them, the method for obtaining the degree of each node in the directed graph is a well-known technology and will not be elaborated in this embodiment.

[0066] Take each subsequent adjacent node of each node in the traceability directed graph as the post-node of each node, and calculate the summation result of the JS divergences between each node and the information effective sequences of the recycling entities corresponding to all its post-nodes as the post-traceability path fitness of each node; specifically, when each node is more suitable as the pre-node of its post-node in the traceability path, the larger the post-traceability path fitness between each node and its each post-node; on the contrary, the smaller the post-traceability path fitness between each node and its each post-node.

[0067] Further, in order 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 effective factor of each node in the traceability directed graph is obtained based on the front-end traceability path adaptability, the back-end traceability path adaptability, and the degree of the node. The calculation formula is as follows: In the formula, f x is the transaction sharing effective factor of the x-th node in the traceability directed graph; g x is the degree of the x-th node in the traceability directed graph, δ x is the front-end traceability path adaptability of the x-th node in the traceability directed graph, h x is the back-end traceability path adaptability of the x-th node in the traceability directed graph.

[0068] It should be noted that when the phenomenon of duplicate data entry caused by multi-level recycling transaction behaviors of the recycling entity during the global traceability process is less severe, the multi-level recycling transactions or data sharing status of the recycling entity is more significant, and the degree of the node is larger. At this time, the value of g x obtained is larger. At the same time, as a link in the traceability path, the recycling entity has a more significant feature that the effective characteristic factor of the traceability object becomes larger, that is, δ x is larger; the stronger the consistency of the recycled resource recovery data between the recycling entity and the traceability object, the smaller the difference between the node and all the effective sequence of information corresponding to the next node in the traceability path, that is, h x is smaller. At this time, the value of the obtained transaction sharing effective factor is larger; on the contrary, the value of the obtained transaction sharing effective factor is smaller.

[0069] The PageRank web page ranking algorithm is used to obtain the PR value of each node in the traceability directed graph as the PR value of the recycling entity corresponding to each node; it should be noted that when the PR value is higher, it is considered that the recycling entity corresponding to the node contains more obvious effective non-redundant features of the recycled resource recovery transaction data, and it is more likely to trace and query the next traceability object through the recycling entity corresponding to the node, that is, the node pointed to by the directed edge. Among them, 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 web page ranking algorithm is a well-known technology and will not be elaborated in this embodiment. The schematic diagram of the traceability directed graph is as Figure 3 shown.

[0070] Further, for the traceability reliability of each node on the traceability directed graph, the calculation formula is: D x = PR x × f x ; in the formula, D x is the traceability reliability of the x-th node in the traceability directed graph; PR x is the PR value of the recycling entity corresponding to the x-th node in the traceability directed graph; f xIt is the transaction sharing effective factor of the x-th node in the traceability directed graph.

[0071] It should be noted that when the recycling entity is more likely to have a stronger connection with the rest of the recycling entities during the global directed traceability process, at this time, PR x is larger, and when each node is more suitable as a node in the traceability path, the value of the transaction sharing effective factor is larger. At this time, the importance of the recycled resource recovery data of the node corresponding recycling entity is stronger, the effective connection with the rest of the recycling entities is more extensive, the traceability reliability of using the node as a link in the process of establishing the data traceability path is stronger, and the obtained traceability reliability value is larger; on the contrary, the obtained traceability reliability value is smaller.

[0072] Thus, the traceability reliability of the x-th node in the traceability directed graph is obtained.

[0073] Step S4, trace the recycled resources recycling of the Internet of Things based on the traceability reliability.

[0074] Take the absolute value of the difference between the traceability reliabilities corresponding to two nodes with a traceability path as the weight of the directed edge between the two nodes, construct a reliable traceability directed graph, and obtain the shortest traceability paths from any one node in the reliable traceability directed graph to all the other nodes through the Bellman-Ford algorithm.

[0075] If there is no shortest traceability path between two nodes, it means that there is no recycling transaction or data sharing behavior between the recycling entities corresponding to the two nodes, that is, the two recycling entities are independent of each other and there is no traceability relationship. Among them, the Bellman-Ford algorithm is a well-known technology, and the specific obtaining process will not be elaborated too much.

[0076] For the implementer, according to the obtained recycled resource recovery data and the nodes corresponding to the recycling entities in the reliable traceability directed graph, all the transfer processes of the recycled resource recovery data can be obtained through the shortest traceability paths obtained above, that is, all the traceability paths of the recycled resource recovery data are obtained. That is, for each recycled resource recovery data in the Internet of Things, the shortest traceability path of each recycled resource recovery data is obtained according to the reliable traceability directed graph, and the tracing of the recycled resources recycling is completed.

[0077] Based on the same inventive concept as the above method, the embodiment of the present application also provides a recycled resources recycling tracing system based on the Internet of Things, 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-mentioned recycled resources recycling tracing methods based on the Internet of Things.

[0078] It should be noted that: The above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. Also, the above description of specific embodiments of this specification has been made. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments.

[0080] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included within the protection scope of the present application.

Claims

1. A renewable resource recycling tracking method based on the Internet of Things, characterized in that: The method comprises the following steps: Collect the recycling code sequence of each category of recycling data of each recycling entity and the total number of times each recycling entity performs resource recycling; Based on the frequency of occurrence of the types of recycled resources in the recycled resource recycling data and the total number of times the recycling subject performs resource recycling, obtain the important details of each case of recycled resource recycling data of each recycling subject; Based on the correlation between the recycling code data of the same category of recycled resource recycling data in any two cases of recycled resource recycling data, the coding similarity of each case of recycled resource recycling data of each recycling subject is obtained; Obtaining effective non-redundant factors for each recycling data of each recycling subject based on important details, coding similarity and recycling coding sequence of recycling data of renewable resources; Obtain effective characteristic factors of each recycling subject based on effective non-redundant factors; Obtain the directed edge weight of the directed edge between any two nodes in the directed graph based on the effective feature factor; Based on the directed edge weights and effective non-redundant factors, the transaction sharing effective factors of each node in the traceability directed graph are obtained; Obtain the traceability reliability of each node in the traceability directed graph based on the transaction shared validity factor; Track the recycling of renewable resources in the Internet of Things based on traceability reliability.

2. A renewable resource recycling tracking method based on the Internet of Things as claimed in claim 1, characterized in that: The method for obtaining the important details is: The categories of renewable resource recycling data include the name of the renewable resource recycling sender, the type of recycled resources, the material status of the recycled resources, the start time of recycled resource processing, the end time of recycled resource processing, the weight of recycled resources, and the utilization rate of recycled resources; For each recycling entity, calculate the frequency of occurrence of the recycled resource type in each case of recycled resource recycling data of the recycling entity in the total number of times the recycling entity performs resource recycling, and multiply it by the total number of times the recycling entity performs resource recycling as the important details of each case of recycled resource recycling data of each recycling entity.

3. A renewable resource recovery tracking method based on the Internet of Things as claimed in claim 2, characterized in that: The calculation formula of the encoding similarity is: In the formula, β i is the coding similarity of the i-th recycling resource recycling data of each recycling entity; I is the total number of times each recycling entity recycles resources, J is the total number of types of recycled resources in all recycling processes of each recycling entity, and c i,j 、c k,j are the recycling code sequences corresponding to the j-th category data in the i-th and k-th renewable resource recycling data of each recycling entity, and Ed() is the ED edit distance.

4. The method for tracking recycling of renewable resources based on the Internet of Things as claimed in claim 1, characterized in that: The calculation formula of the effective non-redundant factor is: In the formula, A i is the effective non-redundant factor of the i-th renewable resource recycling data in each recycling entity; a i is the importance and details of the recycling data of the i-th column of each recycling entity, β i is the coding similarity of the i-th recycling resource recycling data of each recycling entity, m i It is the cumulative result of the most significant digit of the binary code of all categories of recycling code sequences in the i-th example of recycled resource recycling data in each recycling entity, and ε is the preset adjustment parameter.

5. The method for tracking recycling of renewable resources based on the Internet of Things as claimed in claim 1, characterized in that: The method for obtaining the effective characteristic factor is: The valid non-redundant factors of all recycled resource recycling data of each recycling subject are arranged in the positive order of resource recycling, which is recorded as the information valid sequence of each recycling subject. The cumulative result of all valid non-redundant factors in the information valid sequence is taken as the effective characteristic factor of each recycling subject.

6. A renewable resource recovery tracking method based on the Internet of Things as claimed in claim 5, characterized in that: The method for obtaining the directed edge weight is: A directed graph is constructed 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, and the directed edge points from the seller to the buyer. For any two nodes in a directed graph with a directed edge, the sum of the effective characteristic factors of the two nodes is calculated as the directed edge weight of the directed edge between the two nodes in the directed graph.

7. A renewable resource recovery tracking method based on the Internet of Things as claimed in claim 6, characterized in that: The method for obtaining the transaction sharing effective factor is: Reverse the directions of all directed edges in the directed graph, construct a traceability directed graph, obtain the degree of each node in the traceability directed graph, take the previous adjacent nodes of each node in the traceability directed graph as the predecessor nodes 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 sum of the predecessor directed weights of each node and all its predecessor nodes, and obtain the fitness degree of the predecessor traceability path of each node; Take the next adjacent nodes of each node in the traceability directed graph as the post-nodes of each node, calculate the sum of the JS divergences between the valid sequences of the information of the recycling subject corresponding to each node and all its post-nodes, and use it as the post-tracing path fitness of each node; For each node in the traceability directed graph, the ratio of the calculation result of the exponential function with the natural constant as the base and the fitness of the preceding traceability path as the exponent to the fitness of the subsequent traceability path is calculated, and the product of the ratio and the degree of each node in the traceability directed graph is used as the transaction sharing effectiveness factor of each node in the traceability directed graph.

8. The method for tracking recycling of renewable resources based on the Internet of Things as claimed in claim 7, characterized in that: The method for obtaining the traceability reliability is: The PageRank webpage ranking algorithm is used to obtain the PR value of each node in the traceability directed graph as the PR value of the recycling entity corresponding to 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 effectiveness factor of each node is calculated as the traceability reliability of each node in the traceability directed graph.

9. A renewable resource recovery tracking method based on the Internet of Things as claimed in claim 8, characterized in that: The tracking of recycled resources recycling of the Internet of Things based on traceability reliability includes: The absolute value of the difference between the traceability reliability of two nodes with a traceability path is used as the weight of the directed edge between the two nodes to construct a reliable traceability directed graph. The shortest traceability path from any node in the reliable traceability directed graph to all other nodes is obtained through the Bellman-Ford algorithm. For each renewable resource recycling data in the Internet of Things, the shortest traceability path of each renewable resource recycling data is obtained according to a reliable traceability directed graph to track the recycling of renewable resources.

10. A renewable resource recycling tracking system 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, the steps of a renewable resource recycling tracking method based on the Internet of Things as described in any one of claims 1-9 are implemented.

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