Radio frequency identification network cluster head determination method, apparatus, device, medium, and product

Abstract

The application relates to a radio frequency identification network cluster head determination method, device, equipment, medium and product. In one aspect, the radio frequency identification network cluster head determination method comprises the following steps: acquiring node state information corresponding to each node in a radio frequency identification network, wherein the node state information comprises energy data and centrality data corresponding to each node; performing fuzzy logic operation processing on the energy data and the centrality data to obtain a target opportunity probability; and determining a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability, wherein the target cluster head is used for communication with other clusters. The method can improve the transmission efficiency of the radio frequency identification network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radio frequency identification technology, and in particular, to a radio frequency identification network cluster head determination method, device, equipment, medium and product. BACKGROUND

[0002] In a radio frequency identification network, nodes in the radio frequency identification network can form clusters through clustering methods, and the cluster head of each cluster collects and integrates the collected data of all nodes in the cluster to reduce repeated transmission. Therefore, it is necessary to accurately determine the cluster head of each cluster to ensure the data transmission efficiency of the radio frequency identification network.

[0003] In the traditional technology, the distance between nodes is evaluated based on the received signal strength or transmitted signal power of each node in the radio frequency identification network, and then the node with the shortest distance to other nodes in the cluster is selected as the cluster head.

[0004] However, the cluster head determination method in the traditional technology can cause uneven selection and low network transmission efficiency. SUMMARY

[0005] Therefore, it is necessary to provide a radio frequency identification network cluster head determination method, device, equipment, medium and product capable of improving network transmission efficiency to solve the above technical problems.

[0006] In a first aspect, the present application provides a radio frequency identification network cluster head determination method, which comprises:

[0007] Obtaining node state information corresponding to each node in the radio frequency identification network, the node state information comprising energy data and centrality data corresponding to each node;

[0008] Performing fuzzy logic operation processing on the energy data and the centrality data to obtain a target opportunity probability;

[0009] Determining a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability, the target cluster head being used for communication with other clusters.

[0010] In one embodiment, the fuzzy logic operation processing on the energy data and the centrality data to obtain the target opportunity probability comprises:

[0011] Performing first fuzzy processing on the energy data to obtain an energy opportunity probability;

[0012] Performing second fuzzy processing on the centrality data to obtain a centrality opportunity probability;

[0013] Obtaining the target opportunity probability according to a preset opportunity fuzzy rule, the energy opportunity probability and the centrality opportunity probability.

[0014] In one of the embodiments, the energy data is subjected to a first fuzzy processing to obtain an energy opportunity probability, including:

[0015] Based on the energy data and a preset energy fuzzy set, an energy membership degree is determined;

[0016] According to the energy membership degree and a first fuzzy rule, an opportunity membership degree corresponding to the energy data is obtained;

[0017] The opportunity membership degree is subjected to a defuzzy processing to obtain the energy opportunity probability.

[0018] In one of the embodiments, according to the target opportunity probability, a target cluster head corresponding to each cluster in the radio frequency identification network is determined, including:

[0019] Random attribute data corresponding to each node is obtained;

[0020] A node with random attribute data greater than a scale threshold is taken as an intermediate node;

[0021] According to the target opportunity probability corresponding to the intermediate node, a target cluster head corresponding to each cluster is determined from the intermediate node.

[0022] In one of the embodiments, the scale threshold is a scale ratio of the cluster to the node.

[0023] In one of the embodiments, according to the target opportunity probability corresponding to the intermediate node, a target cluster head corresponding to each cluster is determined from the intermediate node, including:

[0024] A candidate set is obtained;

[0025] For each cluster, an intermediate node is sequentially selected from the cluster, and in a case that the target opportunity probability corresponding to the intermediate node is greater than the target opportunity probability corresponding to all nodes in the candidate set, the intermediate node is taken as the target cluster head of the cluster.

[0026] In a second aspect, the application further provides a radio frequency identification network cluster head determination device, the device including:

[0027] A data acquisition module is configured to acquire node state information corresponding to each node in a radio frequency identification network, the node state information including energy data and centrality data corresponding to each node;

[0028] A fuzzy operation module is configured to perform fuzzy logic operation processing on the energy data and the centrality data to obtain a target opportunity probability;

[0029] A cluster head determination module is configured to determine a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability, the target cluster head being configured to communicate with other clusters.

[0030] In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method of the first aspect when executing the computer program.

[0031] In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the method of the first aspect.

[0032] In a fifth aspect, the present application also provides a computer program product comprising a computer program, the computer program being executed by a processor to implement the steps of the method of the first aspect.

[0033] The above-mentioned radio frequency identification network cluster head determination method, device, equipment, medium and product, wherein the radio frequency identification network cluster head determination method provided in one aspect comprises the following steps: obtaining node state information corresponding to each node in a radio frequency identification network, the node state information comprising energy data and centrality data corresponding to each node; performing fuzzy logic operation processing on the energy data and the centrality data to obtain a target opportunity probability; and determining a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability, the target cluster head being used for communication with other clusters. In the present application, based on the energy and centrality of each node in the network, the probability of cluster head selection is dynamically adjusted by using fuzzy logic, so that the nodes with large energy consumption in each round do not frequently serve as cluster heads, the nodes at the edge of the network are not selected as cluster heads, the energy consumption of the network communication process can be dispersed, the stability of inter-cluster communication is improved, the life of the entire network is prolonged, and the transmission efficiency of the radio frequency identification network is improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 An application environment diagram of the radio frequency identification network cluster head determination method in one embodiment;

[0036] Figure 2 A flowchart of the radio frequency identification network cluster head determination method in one embodiment;

[0037] Figure 3 A flowchart of the radio frequency identification network cluster head determination method in another embodiment;

[0038] Figure 4 A structural block diagram of the radio frequency identification network cluster head determination device in one embodiment;

[0039] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] The radio frequency identification network cluster head determination method provided in this application embodiment can be applied to, for example, Figure 1 The RFID network shown includes multiple nodes 102 and a server 104. Clustering algorithms, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), are used to cluster the nodes 102, resulting in multiple clusters. Figure 1 Cluster 1 and Cluster 2 in the middle.

[0042] Server 104 communicates with each cluster via a network. Specifically, server 104 acquires node status information corresponding to each node in the RFID network, including energy data and centrality data for each node; it performs fuzzy logic operations on the energy data and centrality data to obtain the target probability; based on the target probability, it determines the target cluster head corresponding to each cluster in the RFID network, and the target cluster head is used to communicate with other clusters. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0043] In one exemplary embodiment, such as Figure 2 As shown, a method for determining the cluster head of a radio frequency identification network is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 202 to 206. Wherein:

[0044] Step 202: Obtain the node status information corresponding to each node in the radio frequency identification network.

[0045] The node status information includes energy data and centrality data for each node. The energy data includes the node's current remaining energy and expected remaining energy. The current remaining energy can be obtained by measuring the energy of each node using sensors; the expected remaining energy can be the average remaining energy of all nodes in the RFID network, or a pre-set energy threshold.

[0046] In one possible implementation, the expected residual energy E r may be expressed as:

[0047] E r ≤ E t -E c ,

[0048] wherein E c represents the consumed energy, E t represents the total energy. The consumed energy E c is calculated by the energy consumption E ci of each transmission and the energy consumption E i in the idle state:

[0049] E c = E ci +E i ,

[0050] wherein,

[0051] E ci = E ti (tr),

[0052]

[0053] The current consumption Cur idle of the node in the idle state is:

[0054]

[0055] wherein E ti is the single transmission energy consumption, tr is the transmission time, Vol(t i ) is the transmission voltage, Cur(t i ) is the transmission current, △t is the sampling interval, and w(t i ) represents the idle current at time t i . T idle is the total idle time of the node when it does not participate in any transmission.

[0056] The centrality data C i may represent the relative position or importance of the node in the network, which can be determined by calculating the number of neighbors of the node, the distance to other nodes, etc. The higher the centrality data of the node, the higher the connectivity degree of the node in the network.

[0057] For example, the process of obtaining the centrality data can be expressed as:

[0058]

[0059] wherein L i represents the local index, and x ijdenotes a neighboring node, y i denotes the degree of directly connected neighbor nodes, and denotes the global average degree of node i and node j, respectively. The global indicator G i can be calculated by the following formula:

[0060]

[0061] wherein P ij denotes the distance of the ij node pair, which is calculated by the following formula:

[0062]

[0063] wherein D ij denotes the distance between node i and node j. Similarly,

[0064]

[0065] wherein w0 denotes all isolated nodes in the topology. Another case is:

[0066]

[0067] wherein w i denotes the node isolation in the topology after the node is destroyed. The isolation degree of the node in the topology can be calculated by the following formula:

[0068]

[0069] The centrality data C i of each node can be represented as:

[0070]

[0071] Step 204, fuzzy logic operation processing is performed on the energy data and the centrality data to obtain a target opportunity probability.

[0072] wherein the target opportunity probability is used to measure the suitability of the node as a cluster head, and the higher the target opportunity probability, the more likely the node is to be selected as a cluster head. The value range of the target opportunity probability is [0, 1].

[0073] Step 206, according to the target opportunity probability, a target cluster head corresponding to each cluster in the radio frequency identification network is determined, and the target cluster head is used to communicate with other clusters.

[0074] In a possible implementation, after the node is determined as the target cluster head, the target cluster head broadcasts a join request signal to all nodes connected to the cluster where the target cluster head is located, so that each node establishes communication with the target cluster head based on the join request information. The target cluster head processes the received join request message and sends an acknowledgement message to ensure that the radio frequency communication is not interrupted.

[0075] In a possible implementation, the node does not meet the condition of becoming the target cluster head and is not determined as the target cluster head, and the node broadcasts a failure signal to other nodes to end the cluster head determination process corresponding to the node.

[0076] In a possible implementation, the server 104 periodically acquires state information of each target cluster head; in the case that the state information meets a preset failure condition, a target cluster head failure signal is broadcasted, and the radio frequency identification network cluster head determination method provided in this embodiment is executed until a target cluster head is obtained, so as to realize dynamic updating of the target cluster head and guarantee the transmission efficiency of the radio frequency identification network.

[0077] In the radio frequency identification network cluster head determination method, the node state information corresponding to each node in the radio frequency identification network is acquired, the node state information includes energy data and centrality data corresponding to each node; the energy data and the centrality data are processed by fuzzy logic operation to obtain a target opportunity probability; and the target cluster head corresponding to each cluster in the radio frequency identification network is determined according to the target opportunity probability, and the target cluster head is used for communication with other clusters. In this embodiment, based on the energy and centrality of each node in the network, the probability of cluster head selection is dynamically adjusted by using fuzzy logic, so that the nodes with large energy consumption in each round do not frequently serve as cluster heads, the nodes at the edge of the network are not selected as cluster heads, the energy consumption of the network communication process can be dispersed, the stability of inter-cluster communication is improved, the life of the entire network is prolonged, and the transmission efficiency of the radio frequency identification network is improved.

[0078] In an example embodiment, the target opportunity probability is obtained by processing the energy data and the centrality data by fuzzy logic operation, and the process includes: Figure 2 In the embodiment shown in FIG. 6, the process of obtaining the target opportunity probability by processing the energy data and the centrality data by fuzzy logic operation in the method provided in this embodiment includes: performing first fuzzy processing on the energy data to obtain an energy opportunity probability; performing second fuzzy processing on the centrality data to obtain a centrality opportunity probability; and obtaining the target opportunity probability according to a preset opportunity fuzzy rule, the energy opportunity probability, and the centrality opportunity probability.

[0079] In a possible implementation, the process of obtaining the energy opportunity probability can further include:

[0080] The energy membership degree is determined based on the energy data and a preset energy fuzzy set; the opportunity membership degree corresponding to the energy data is obtained according to the energy membership degree and a first fuzzy rule; and the energy opportunity probability is obtained by defuzzification processing on the opportunity membership degree.

[0081] Exemplarily, in the case that the energy data comprises the current residual energy and the expected residual energy, the energy fuzzy set corresponding to the current residual energy can be defined as: a low energy set, a medium energy set and a high energy set. Among them, the low energy set corresponds to the current residual energy of the node being low, which is not suitable to be a cluster head; the medium energy set corresponds to the current residual energy of the node being medium, which can be a candidate cluster head; and the high energy set corresponds to the current residual energy of the node being sufficient, which is suitable to be a cluster head. The energy fuzzy set corresponding to the expected residual energy and the current residual energy can be consistent.

[0082] The fuzzy set corresponding to the output energy opportunity probability can be defined as: a low cluster head opportunity value, a medium cluster head opportunity value and a high cluster head opportunity value. Among them, the low cluster head opportunity value indicates that the node is not suitable to be a cluster head, the medium cluster head opportunity value indicates that the node has a certain opportunity to be a cluster head, and the high cluster head opportunity value indicates that the node is very suitable to be a cluster head.

[0083] The energy data is fuzzily processed according to the preset membership mapping relationship to obtain an energy membership. Exemplarily, the current residual energy is 60%, and according to the membership mapping relationship, the membership corresponding to the medium energy set can be 0.7, and the membership corresponding to the high energy set can be 0.3.

[0084] Exemplarily, the first fuzzy rule can include but is not limited to: the current residual energy belongs to the high energy set, and the expected residual energy belongs to the high energy set, then the cluster head opportunity value is high; the current residual energy belongs to the high energy set and the expected residual energy belongs to the medium energy set, then the cluster head opportunity value is high; the current residual energy belongs to the medium energy set, and the expected residual energy belongs to the high energy set, then the cluster head opportunity value is medium; the current residual energy belongs to the medium energy set and the expected residual energy belongs to the medium energy set, then the cluster head opportunity value is medium; the current residual energy belongs to the low energy set and the expected residual energy belongs to the low energy set, then the cluster head opportunity value is low.

[0085] Optionally, the opportunity membership is the smaller value or the larger value in the energy membership. Exemplarily, according to the energy membership, the current residual energy belongs to the high energy set, and the corresponding energy membership is 0.3; the expected residual energy belongs to the high energy set, and the corresponding energy membership is 0.8; according to the first fuzzy rule, the cluster head opportunity value is high, and the opportunity membership corresponding to the energy data is min(0.3, 0.8) = 0.3.

[0086] The defuzzification processing can be a defuzzification operation based on the gravity center method to obtain the energy opportunity probability.

[0087] In a possible implementation, the process of obtaining the centrality opportunity probability is similar to the process of obtaining the energy opportunity probability, see the process of obtaining the energy opportunity probability. For example, the centrality fuzzy set corresponding to the centrality data can be defined as: a long-distance set, a middle-distance set, and a near-distance set. The nodes corresponding to the long-distance set are located at the edge of the network or have fewer connections with other nodes; the nodes corresponding to the middle-distance set are located in the middle and have a certain number of neighbors; and the nodes corresponding to the near-distance set are located at the center of the network and have a high degree of connectivity. Optionally, the long-distance set can correspond to the centrality data between 0% and 30%; the middle-distance set can correspond to the centrality data between 31% and 70%; and the near-distance set can correspond to the centrality data between 71% and 100%. The fuzzy set corresponding to the output centrality opportunity probability can be defined as: a low cluster head opportunity value, a medium cluster head opportunity value, and a high cluster head opportunity value. The low cluster head opportunity value indicates that the node is not suitable to become a cluster head, the medium cluster head opportunity value indicates that the node has a certain opportunity to become a cluster head, and the high cluster head opportunity value indicates that the node is very suitable to become a cluster head.

[0088] In a possible implementation, the opportunity fuzzy rule can include: normalizing the energy opportunity probability and the centrality opportunity probability, and summing the normalized results to obtain the target opportunity probability; or the opportunity fuzzy rule can include: performing weighted probability solving on the energy opportunity probability and the centrality opportunity probability to obtain the target opportunity probability.

[0089] In an example embodiment, the target opportunity probability is determined based on Figure 2 In the example embodiment shown, each element in the target cluster head set is a target cluster head corresponding to each cluster; and the process of determining the target cluster head set corresponding to the radio frequency identification network according to the target opportunity probability in the provided method includes: obtaining random attribute data corresponding to each node; regarding a node with random attribute data greater than a scale threshold as an intermediate node; and determining a target cluster head corresponding to each cluster from the intermediate nodes according to the target opportunity probability corresponding to the intermediate nodes.

[0090] The random attribute data can be a random number generated based on a random function, and the value range of the random attribute data is [0, 1]. The scale threshold can be dynamically set according to the node density of the radio frequency identification network, the number of clusters, and other parameters.

[0091] In a possible implementation, the scale threshold is the scale ratio of the cluster to the node. The scale threshold t can be expressed as: t = m / N, where m is the number of clusters, and N is the number of nodes.

[0092] In this embodiment, the randomness is introduced into the cluster head selection process by comparing the random attribute data with the scale threshold, the repeatability of each round of cluster head selection is reduced, all nodes that meet the conditions are avoided from becoming cluster heads, and the randomness of the cluster head distribution is increased.

[0093] Optionally, the intermediate node with the largest target opportunity probability in each cluster can be selected as the target cluster head.

[0094] Optionally, the process of determining the target cluster head can further include: obtaining a candidate set C; for each cluster, sequentially selecting an intermediate node from the cluster, and in the case that the target opportunity probability corresponding to the intermediate node is larger than the target opportunity probabilities corresponding to all nodes in the candidate set C, the intermediate node is selected as the target cluster head of the cluster. Exemplarily, the elements in the candidate set C can be nodes with a current residual energy greater than a preset energy threshold, or nodes with centrality data greater than a preset centrality threshold.

[0095] In this embodiment, the random number judgment and the threshold comparison can avoid all nodes competing for the cluster head under the same condition, and increase the fairness of selection.

[0096] In an exemplary embodiment, as shown in Figure 3 , a radio frequency identification network cluster head determination method is provided. The method is applied to the server 104 in Figure 1 as an example, and includes the following steps 301 to 306. Among them:

[0097] Step 301, obtaining node state information, random attribute data and a candidate set corresponding to each node in the radio frequency identification network.

[0098] Among them, the node state information includes energy data and centrality data corresponding to each node.

[0099] Step 302, performing first fuzzy processing on the energy data to obtain an energy opportunity probability.

[0100] Among them, the energy membership degree is determined based on the energy data and a preset energy fuzzy set; the opportunity membership degree corresponding to the energy data is obtained according to the energy membership degree and a first fuzzy rule; and the energy opportunity probability is obtained by defuzzification processing on the opportunity membership degree.

[0101] Step 303, performing second fuzzy processing on the centrality data to obtain a centrality opportunity probability.

[0102] Step 304, obtaining a target opportunity probability according to a preset opportunity fuzzy rule, the energy opportunity probability and the centrality opportunity probability.

[0103] Step 305, selecting a node with a random attribute data greater than a scale threshold as an intermediate node.

[0104] Among them, the scale threshold is the scale ratio of the cluster and the node.

[0105] Step 306, for each cluster, sequentially selecting an intermediate node from the cluster, and in the case that the target opportunity probability corresponding to the intermediate node is greater than the target opportunity probabilities corresponding to all nodes in the candidate set, taking the intermediate node as the target cluster head of the cluster.

[0106] In one possible implementation, the provided radio frequency identification network cluster head determination method can be expressed in pseudo code as follows:

[0107]

[0108]

[0109]

[0110] Experiments are conducted on the radio frequency identification network cluster head determination method provided in the embodiments of the present application. The initial energy of each node used in the experiments is 0.5 joule, the network can include at most 1000 nodes, the size of the site is selected as 100m x 100m, the transmission range of each tag node is 100m, 10 x 5 iterations are conducted, and the number of cluster heads will increase with the increase of the number of nodes in each iteration. The experiments select five different time periods for multiple simulations, and analyze the performance, including success rate, error rate, accuracy, throughput and delay.

[0111] The experimental results show that in a network with a large number of nodes, the success rate of the algorithm reaches 97.8%; the error rate remains at a low level in multiple experiments, especially under full load, the error rate is 0.22%; the overall accuracy of the network is high, especially in the case of a large number of nodes, the accuracy reaches 97.80%. With the expansion of the number of cluster heads and the size of the network, the throughput and delay are adjusted appropriately, the experiments show that the throughput reaches 2.64%, and the delay is 36.91 seconds.

[0112] These experiments and evaluations fully prove the effectiveness of the radio frequency identification network cluster head determination method in the embodiments of the present application in a large-scale radio frequency identification network, and it has significant advantages in energy management, data transmission accuracy and network stability.

[0113] The radio frequency identification network cluster head determination method provided in the embodiment supports expanding the network scale by increasing the number of clusters, and the network performance does not decrease significantly when the network nodes are increased, thereby ensuring the scalability of the large-scale radio frequency identification network; the communication path inside and outside the network is optimized through effective cluster head selection and management, the information transmission delay between nodes is reduced, and the overall network efficiency is improved; the radio frequency identification network cluster head determination method can effectively adapt to the demand of the large-scale radio frequency identification network, can process a network with up to 1000 nodes, and can still maintain a high success rate and a low error rate at this scale. The simulation results show that the error rate remains at a low level at different time intervals, indicating that the radio frequency identification network cluster head determination method has strong reliability in a large-scale network. The energy consumption calculation model considers the energy usage of the nodes in the transmission and idle states, further improves the accuracy of energy management, and ensures the rationality in the cluster head selection process. The method is not only suitable for radio frequency identification networks, but also can be extended to large-scale network environments such as the Internet of Things, supply chain management, vehicle tracking, and the like, which require efficient and reliable communication.

[0114] It should be understood that, although each step in the flowchart involved in each of the above embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiments of the present application also provide a radio frequency identification network cluster head determination device for implementing the above-mentioned radio frequency identification network cluster head determination method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more radio frequency identification network cluster head determination device embodiments provided below can refer to the limitations of the radio frequency identification network cluster head determination method in the foregoing, which will not be described here.

[0116] In one exemplary embodiment, as shown in Figure 4 a radio frequency identification network cluster head determination device is provided, which includes a data acquisition module 402, a fuzzy operation module 404, and a cluster head determination module 406.

[0117] Among them:

[0118] The data acquisition module 402 is configured to acquire node state information corresponding to each node in the radio frequency identification network, and the node state information comprises energy data and centrality data corresponding to each node.

[0119] The fuzzy operation module 404 is configured to perform fuzzy logic operation processing on the energy data and the centrality data to obtain a target opportunity probability.

[0120] The cluster head determination module 406 is configured to determine a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability, and the target cluster head is used for communication with other clusters.

[0121] In one of the embodiments, the fuzzy operation module 404 is further configured to perform first fuzzy processing on the energy data to obtain an energy opportunity probability, perform second fuzzy processing on the centrality data to obtain a centrality opportunity probability, and obtain the target opportunity probability according to a preset opportunity fuzzy rule, the energy opportunity probability and the centrality opportunity probability.

[0122] In one of the embodiments, the fuzzy operation module 404 is further configured to determine an energy membership degree based on the energy data and a preset energy fuzzy set, obtain an opportunity membership degree corresponding to the energy data according to the energy membership degree and a first fuzzy rule, and perform defuzzification processing on the opportunity membership degree to obtain the energy opportunity probability.

[0123] In one of the embodiments, the cluster head determination module 406 is further configured to acquire random attribute data corresponding to each node, take a node with a random attribute data greater than a scale threshold as an intermediate node, and determine a target cluster head corresponding to each cluster from the intermediate nodes according to a target opportunity probability corresponding to the intermediate node.

[0124] In one of the embodiments, the scale threshold is a scale ratio of the cluster to the node.

[0125] In one of the embodiments, the cluster head determination module 406 is further configured to acquire a candidate set, and for each cluster, sequentially select an intermediate node from the cluster, and take the intermediate node as a target cluster head of the cluster in a case that a target opportunity probability corresponding to the intermediate node is greater than a target opportunity probability corresponding to all nodes in the candidate set.

[0126] The above-mentioned modules in the cluster head determination apparatus for the radio frequency identification network can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned modules can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform operations corresponding to the above-mentioned modules.

[0127] In one exemplary embodiment, a computer device is provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 5As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store node state information. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a radio frequency identification network cluster head determination method.

[0128] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0129] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the above method embodiments.

[0130] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in each of the above method embodiments.

[0131] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in each of the above method embodiments.

[0132] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0134] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0135] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for determining a cluster head of a radio frequency identification network, characterized in that, The method comprises: acquiring node state information and random attribute data corresponding to each node in a radio frequency identification network, the node state information comprising energy data and centrality data corresponding to each node, the energy data comprising current residual energy and expected residual energy of the node; determining energy membership degrees based on the energy data and a preset energy fuzzy set; obtaining opportunity membership degrees corresponding to the energy data according to the energy membership degrees and a first fuzzy rule; performing defuzzification processing on the opportunity membership degrees to obtain energy opportunity probabilities; performing second fuzzy processing on the centrality data to obtain centrality opportunity probabilities; obtaining target opportunity probabilities according to a preset opportunity fuzzy rule, the energy opportunity probabilities and the centrality opportunity probabilities; regarding nodes with random attribute data greater than a scale threshold as intermediate nodes; determining target cluster heads corresponding to each cluster from the intermediate nodes according to target opportunity probabilities corresponding to the intermediate nodes, the target cluster heads being used for communication with other clusters; where the expected residual energy and the centrality data are respectively expressed as: wherein, represents the energy consumed, represents the total energy; represents the local index, represents the global index, represents the degree of isolation of the node in the topology, respectively as: where, denotes the neighboring nodes, denotes the degree of directly connected neighbor nodes, and denotes the global average degree of node i and node j, respectively; denotes the distance of ij node pair, denotes the distance between node and node ; and denotes all isolated nodes in the topology; denotes the node isolation in the topology after node destruction.

2. The method of claim 1, wherein, the scale threshold being a scale ratio of the cluster to the node.

3. The method of claim 1, wherein, The determining of the target cluster heads corresponding to each cluster from the intermediate nodes according to the target opportunity probabilities corresponding to the intermediate nodes comprises: acquiring a candidate set; for each cluster, sequentially selecting an intermediate node from the cluster, and regarding the intermediate node as a target cluster head of the cluster in a case where a target opportunity probability corresponding to the intermediate node is greater than target opportunity probabilities corresponding to all nodes in the candidate set.

4. A radio frequency identification network cluster head determining apparatus, characterized in that, The apparatus comprises: a data acquisition module configured to acquire node state information and random attribute data corresponding to each node in a radio frequency identification network, the node state information comprising energy data and centrality data corresponding to each node, the energy data comprising current residual energy and expected residual energy of the node; a fuzzy operation module configured to determine energy membership degrees based on the energy data and a preset energy fuzzy set, obtain opportunity membership degrees corresponding to the energy data according to the energy membership degrees and a first fuzzy rule, perform defuzzification processing on the opportunity membership degrees to obtain energy opportunity probabilities, perform second fuzzy processing on the centrality data to obtain centrality opportunity probabilities, and obtain target opportunity probabilities according to a preset opportunity fuzzy rule, the energy opportunity probabilities and the centrality opportunity probabilities; a cluster head determination module configured to regard nodes with random attribute data greater than a scale threshold as intermediate nodes, and determine target cluster heads corresponding to each cluster from the intermediate nodes according to target opportunity probabilities corresponding to the intermediate nodes, the target cluster heads being used for communication with other clusters; where the expected residual energy and the centrality data are respectively expressed as: wherein, represents the energy consumed, represents the total energy; represents the local index, represents the global index, represents the degree of isolation of the node in the topology, respectively: wherein, denotes the proximity of a node, denotes the degree of directly connected neighbor nodes, and denotes the global average degree of node i and node j, respectively; denotes the distance of the ij node pair, denotes the distance between node and node ; denotes all isolated nodes in the topology; denotes the node isolation in the topology after node disruption. 5.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-4 when the computer program is executed by the processor. the processor implements the steps of the method of any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 3.

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