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

Abstract

The invention relates to a radio frequency identification network cluster head determination method and device, equipment, a medium and a product. According to the radio frequency identification network cluster head determination method provided by the invention, node state information corresponding to each node in a radio frequency identification network is acquired, 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 central data to obtain a target opportunity probability; and according to the target opportunity probability, determining a target cluster head corresponding to each cluster in the radio frequency identification network, the target cluster head being used for communicating with other clusters. By adopting the method, the transmission efficiency of the radio frequency identification network can be improved.

Description

Technical Field

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

[0002] In the RFID network, each node in the RFID network can form a cluster through the clustering method, 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 RFID network.

[0003] In traditional technology, the distance between nodes is evaluated based on the received signal strength or transmitted signal power of each node in the RFID 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 traditional technology will lead to the problem of unbalanced selection and low network transmission efficiency. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, equipment, medium and product for determining a RFID network cluster head that can improve network transmission efficiency in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for determining a cluster head in a radio frequency identification network, the method comprising:

[0007] Obtain node status information corresponding to each node in the RFID network, the node status information includes energy data and centrality data corresponding to each node;

[0008] Perform fuzzy logic operations on energy data and centrality data to obtain target opportunity probability;

[0009] According to the target opportunity probability, the target cluster head corresponding to each cluster in the RFID network is determined, and the target cluster head is used to communicate with other clusters.

[0010] In one embodiment, fuzzy logic operation is performed on energy data and centrality data to obtain target opportunity probability, including:

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

[0012] Perform the second fuzzy processing on the centrality data to obtain the central opportunity probability;

[0013] According to the preset chance fuzzy rules, energy chance probability and center chance probability, the target chance probability is obtained.

[0014] In one embodiment, a first fuzzy processing is performed on the energy data to obtain the energy opportunity probability, including:

[0015] Determine energy membership based on energy data and a preset energy fuzzy set;

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

[0017] The opportunity membership is defuzzified to obtain the energy opportunity probability.

[0018] In one embodiment, determining a target cluster head corresponding to each cluster in the radio frequency identification network according to the target opportunity probability includes:

[0019] Get the random attribute data corresponding to each node;

[0020] Nodes whose random attribute data is greater than the scale threshold are regarded as intermediate nodes;

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

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

[0023] In one embodiment, determining a target cluster head corresponding to each cluster from the intermediate node according to the target opportunity probability corresponding to the intermediate node includes:

[0024] Get the candidate set;

[0025] For each cluster, the intermediate nodes are selected from the cluster in turn. When 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 used as the target cluster head of the cluster.

[0026] In a second aspect, the present application further provides a device for determining a cluster head in a radio frequency identification network, the device comprising:

[0027] A data acquisition module is used to acquire node status information corresponding to each node in the RFID network, where the node status information includes energy data and centrality data corresponding to each node;

[0028] The fuzzy operation module is used to perform fuzzy logic operations on energy data and centrality data to obtain target opportunity probability;

[0029] The cluster head determination module is used to determine the 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 to communicate with other clusters.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method of the first aspect are implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method of the first aspect when the computer program is executed by a processor.

[0032] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the method of the first aspect when executed by a processor.

[0033] The above-mentioned RFID network cluster head determination method, device, equipment, medium and product, one of which provides a RFID network cluster head determination method by obtaining node status information corresponding to each node in the RFID network, the node status information includes energy data and centrality data corresponding to each node; fuzzy logic operation processing is performed on the energy data and centrality data to obtain the target opportunity probability; according to the target opportunity probability, the target cluster head corresponding to each cluster in the RFID network is determined, and the target cluster head is used to communicate with other clusters. In this application, based on the energy and centrality of each node in the network, fuzzy logic is used to dynamically adjust the probability of cluster head selection, so that nodes with large energy consumption in each round no longer frequently serve as cluster heads, and nodes at the edge of the network will not be selected as cluster heads, which can disperse the energy consumption of the network communication process, improve the stability of inter-cluster communication, thereby extending the life of the entire network and improving the transmission efficiency of the RFID network. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] FIG1 is a diagram showing an application environment of a method for determining a cluster head in a radio frequency identification network according to an embodiment;

[0036] FIG2 is a schematic diagram of a flow chart of a method for determining a cluster head in a radio frequency identification network according to an embodiment;

[0037] FIG3 is a schematic diagram of a flow chart of a method for determining a cluster head in an RFID network in another embodiment;

[0038] FIG4 is a structural block diagram of a device for determining a cluster head in a radio frequency identification network according to an embodiment;

[0039] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] The method for determining the cluster head of the RFID network provided in the embodiment of the present application can be applied to the RFID network shown in FIG1. ​​The RFID network includes multiple nodes 102 and a server 104. Each node 102 is clustered by a clustering algorithm, such as a density-based spatial clustering of applications with noise (DBSCAN), to obtain multiple clusters, such as cluster 1 and cluster 2 in FIG1.

[0042] The server 104 communicates with each cluster through the network. Specifically, the server 104 is used to obtain the node status information corresponding to each node in the RFID network, and the node status information includes the energy data and centrality data corresponding to each node; the energy data and centrality data are processed by fuzzy logic operation to obtain the target opportunity probability; according to the target opportunity probability, the target cluster head corresponding to each cluster in the RFID network is determined, and the target cluster head is used to communicate with other clusters. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0043] In an exemplary embodiment, as shown in FIG2 , a method for determining a cluster head in an RFID network is provided, and the method is described by taking the application of the method to the server 104 in FIG1 as an example, including the following steps 202 to 206. Among them:

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

[0045] The node status information includes the energy data and centrality data corresponding to each node. The energy data includes the current remaining energy and expected remaining energy of the node. The current remaining energy can be obtained by measuring the energy of each node using a sensor; 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 remaining energy It can be expressed as:

[0047] ,

[0048] in, represents the energy consumed, Indicates total energy. Energy consumed The energy consumption per transmission and idle state energy consumption calculate:

[0049] ,

[0050] in, ,

[0051] .

[0052] Current consumption when the node is idle for:

[0053] .

[0054] in, is the energy consumption of a single transmission, tr is the transmission time, is the transmission voltage, is the transmission current, △ is the sampling interval, Indicates time The idle current. The total idle time when the node is not participating in any transmission.

[0055] Centrality data It can represent the relative position or importance of a 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, the more connected the node is in the network.

[0056] Exemplarily, the process of obtaining centrality data can be expressed as:

[0057] ,

[0058] in, represents a local indicator, and Indicates the neighboring nodes. represents the degree of directly connected neighbor nodes, Represents the global average degree. Global index The following formula can be used to calculate:

[0059] ,

[0060] in, Represents the distance between the ij node pairs, calculated using the following formula:

[0061] ,

[0062] in, Representation Node and nodes Similarly,

[0063] ,

[0064] in, Indicates all isolated nodes in the topology. Another case:

[0065] ,

[0066] in, Indicates the isolation of nodes in the topology after the node is destroyed. The degree of isolation of a node in the topology can be calculated by the following formula:

[0067] .

[0068] Centrality data of each node It can be expressed as:

[0071] .

[0072] Step 204, perform fuzzy logic operations on the energy data and centrality data to obtain the target opportunity probability.

[0073] The target opportunity probability is used to measure the suitability of a node as a cluster head. The higher the target opportunity probability, the more likely the node is to be selected as a cluster head. The target opportunity probability ranges from [0,1].

[0074] Step 206: Determine the target cluster head corresponding to each cluster in the RFID network according to the target opportunity probability, and the target cluster head is used to communicate with other clusters.

[0075] In a possible implementation, after a node is determined as a target cluster head, the target cluster head will broadcast a join request signal to all nodes connected to the cluster, so that each node can establish communication with the target cluster head based on the join request information. The target cluster head processes the received join request message and sends a confirmation message to ensure that the radio frequency communication is not interrupted.

[0076] In a possible implementation manner, the node does not meet the conditions for becoming a target cluster head and is not determined as the target cluster head. The node broadcasts a failure signal to other nodes, and ends the cluster head determination process corresponding to the node.

[0077] In a possible implementation, the server 104 periodically obtains the status information of each target cluster head; when the status information meets the preset fault condition, the target cluster head failure signal is broadcast, and the RFID network cluster head determination method provided in this embodiment is executed until the target cluster head is obtained, thereby realizing dynamic update of the target cluster head and ensuring the transmission efficiency of the RFID network.

[0078] In the above-mentioned method for determining the cluster head of the RFID network, the node status information corresponding to each node in the RFID network is obtained, and the node status information includes the energy data and centrality data corresponding to each node; the energy data and the centrality data are processed by fuzzy logic operation to obtain the target opportunity probability; according to the target opportunity probability, the target cluster head corresponding to each cluster in the RFID network is determined, and the target cluster head is used to communicate 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 using fuzzy logic, so that the nodes with large energy consumption in each round no longer frequently serve as cluster heads, and the nodes at the edge of the network will not be selected as cluster heads, which can disperse the energy consumption of the network communication process, improve the stability of inter-cluster communication, thereby extending the life of the entire network and improving the transmission efficiency of the RFID network.

[0079] In an exemplary embodiment, based on the embodiment shown in Figure 2, the method provided performs fuzzy logic operations on energy data and centrality data to obtain a target opportunity probability, including: performing a first fuzzy processing on the energy data to obtain an energy opportunity probability; performing a second fuzzy processing on the centrality data to obtain a center opportunity probability; and obtaining the target opportunity probability based on preset opportunity fuzzy rules, energy opportunity probability and center opportunity probability.

[0080] In a possible implementation, the process of obtaining the energy opportunity probability may further include:

[0081] Based on the energy data and the preset energy fuzzy set, the energy membership is determined; according to the energy membership and the first fuzzy rule, the opportunity membership corresponding to the energy data is obtained; the opportunity membership is defuzzified to obtain the energy opportunity probability.

[0082] Exemplarily, in the case where the energy data includes 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 is low and is not suitable to be a cluster head; the medium energy set corresponds to the current residual energy of the node is medium and can be a candidate cluster head; the high energy set corresponds to the current residual energy of the node is sufficient and suitable to be a cluster head. The energy fuzzy sets corresponding to the expected residual energy and the current residual energy can be consistent.

[0083] The fuzzy set corresponding to the output energy opportunity probability can be defined as: low cluster head opportunity value, medium cluster head opportunity value and high cluster head opportunity value. Among them, low cluster head opportunity value means that the node is not suitable to become a cluster head, medium cluster head opportunity value means that the node has a certain chance to become a cluster head, and high cluster head opportunity value means that the node is very suitable to become a cluster head.

[0084] The energy data is fuzzy processed according to the preset membership mapping relationship to obtain the energy membership. For example, the current remaining energy is 60%. According to the membership mapping relationship, the membership corresponding to the medium energy set may be 0.7, and the membership corresponding to the high energy set may be 0.3.

[0085] Exemplarily, the first fuzzy rule may include but is not limited to: if 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; if 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; if 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; if 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; if 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.

[0086] Optionally, the opportunity membership is a smaller value or a larger value in the energy membership. Exemplarily, according to the energy membership, the current remaining energy belongs to the high energy set, and the corresponding energy membership is 0.3, and the expected remaining 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.

[0087] The defuzzification process can be a defuzzification operation based on the centroid method to obtain the energy opportunity probability.

[0088] In a possible implementation, the process of obtaining the center opportunity probability is similar to the process of obtaining the energy opportunity probability, see the process of obtaining the energy opportunity probability. Exemplarily, the center fuzzy set corresponding to the centrality data can be defined as: a long-distance set, a medium-distance set, and a close-distance set. Among them, 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 medium-distance set are centered and have a certain number of neighbors; the nodes corresponding to the close-distance set are located in the center of the network and have high connectivity. Optionally, the long-distance set can correspond to centrality data between 0% and 30%; the medium-distance set can correspond to centrality data between 31% and 70%; the close-distance set can correspond to centrality data between 71% and 100%. The fuzzy set corresponding to the output center opportunity probability can be defined as: low cluster head opportunity value, medium cluster head opportunity value, and high cluster head opportunity value. Among them, a low cluster head opportunity value indicates that the node is not suitable to be a cluster head, a medium cluster head opportunity value indicates that the node has a certain chance to become a cluster head, and a high cluster head opportunity value indicates that the node is very suitable to be a cluster head.

[0089] In one possible implementation, the opportunity fuzzy rule may include: normalizing the energy opportunity probability and the center opportunity probability, and summing the normalized results to obtain the target opportunity probability; or, the opportunity fuzzy rule may include: performing weighted probability solution on the energy opportunity probability and the center opportunity probability to obtain the target opportunity probability.

[0090] In an exemplary embodiment, based on the embodiment shown in FIG. 2 , each element in the target cluster head set is a target cluster head corresponding to each cluster; 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; taking nodes whose random attribute data is greater than a scale threshold as intermediate nodes; and determining the target cluster head corresponding to each cluster from the intermediate nodes according to the target opportunity probability corresponding to the intermediate nodes.

[0091] The random attribute data may 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 may be dynamically set according to parameters such as the node density and the number of clusters of the RFID network.

[0092] 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.

[0093] In this embodiment, randomness is introduced into the cluster head selection process by comparing random attribute data with the scale threshold, reducing the repeatability of each round of cluster head selection, avoiding all qualified nodes from becoming cluster heads, and increasing the randomness of cluster head distribution.

[0094] Optionally, the intermediate node with the highest target opportunity probability in each cluster can be used as the target cluster head.

[0095] Optionally, the process of determining the target cluster head may further include: obtaining a candidate set C; for each cluster, selecting intermediate nodes from the cluster in turn, and when the target opportunity probability corresponding to the intermediate node is greater than the target opportunity probability corresponding to all nodes in the candidate set C, using the intermediate node as the target cluster head of the cluster. Exemplarily, the elements in the candidate set C may be nodes whose current residual energy is greater than a preset energy threshold, or nodes whose centrality data is greater than a preset centrality threshold.

[0096] In this embodiment, by comparing the random number judgment with the threshold value, it is possible to avoid all nodes competing for the cluster head under the same conditions, thereby increasing the fairness of the selection.

[0097] In an exemplary embodiment, as shown in FIG3 , a method for determining a cluster head in an RFID network is provided, and the method is described by taking the application of the method to the server 104 in FIG1 as an example, and includes the following steps 301 to 306. Among them:

[0098] Step 301, obtaining node status information, random attribute data and candidate sets corresponding to each node in the RFID network.

[0099] The node status information includes the energy data and centrality data corresponding to each node.

[0100] Step 302, perform a first fuzzy processing on the energy data to obtain the energy opportunity probability.

[0101] Among them, based on energy data and a preset energy fuzzy set, the energy membership is determined; according to the energy membership and the first fuzzy rule, the opportunity membership corresponding to the energy data is obtained; the opportunity membership is defuzzified to obtain the energy opportunity probability.

[0102] Step 303, performing a second fuzzy processing on the centrality data to obtain the central opportunity probability.

[0103] Step 304, obtaining the target opportunity probability according to the preset opportunity fuzzy rules, energy opportunity probability and center opportunity probability.

[0104] Step 305: Nodes whose random attribute data is greater than the scale threshold are regarded as intermediate nodes.

[0105] The scale threshold is the ratio of the scale of the cluster to the scale of the node.

[0106] Step 306 , for each cluster, select an intermediate node from the cluster in turn, and when the target opportunity probability corresponding to the intermediate node is greater than the target opportunity probability corresponding to all nodes in the candidate set, use the intermediate node as the target cluster head of the cluster.

[0107] In a possible implementation, the provided RFID network cluster head determination method can be expressed in pseudo code as follows:

[0108]

[0109] The method for determining the cluster head of the RFID network provided in the embodiment of the present application is experimented. The initial energy of each node used in the experiment is 0.5 joules. The network can contain up to 1000 nodes. The size of the site is 100m×100m. The transmission range of each tag node is 100m. 10×5 iterations are performed. The number of cluster heads will increase with the increase in the number of nodes in each iteration. The experiment selects five groups of different time periods for multiple simulations and analyzes their performance, including success rate, error rate, accuracy, throughput and delay.

[0110] Experimental results show that in a network with a large number of nodes, the algorithm success rate reaches 97.8%; the error rate remains low in multiple experiments, especially in the case of 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%. As the number of cluster heads and network size increases, the throughput and delay are appropriately adjusted. Experiments show that the throughput reaches 2.64% and the delay is 36.91 seconds.

[0111] These experiments and evaluations fully demonstrate the effectiveness of the RFID network cluster head determination method in the embodiment of the present application in large-scale RFID networks, especially in terms of energy management, data transmission accuracy and network stability.

[0112] The RFID network cluster head determination method provided in this embodiment supports expanding the network scale by increasing the number of clusters. When adding network nodes, the network performance will not decrease significantly, ensuring the scalability of large-scale RFID networks; through effective cluster head selection and management, the communication paths inside and outside the network are optimized, the information transmission delay between nodes is reduced, and the overall network efficiency is improved; it can effectively adapt to the needs of large-scale RFID networks, can handle networks with up to 1,000 nodes, and can still maintain a high success rate and a low error rate at this scale. At different time intervals, the simulation results show that the error rate remains at a low level, indicating that the RFID network cluster head determination method has strong reliability in large-scale networks. The energy consumption calculation model takes into account the energy usage of nodes in transmission and idle states, further improves the accuracy of energy management, and ensures the rationality of the cluster head selection process. This method is not only applicable to RFID networks, but can also be extended to large-scale network environments that require efficient and reliable communication, such as the Internet of Things, supply chain management, and vehicle tracking.

[0113] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0114] Based on the same inventive concept, the embodiment of the present application also provides a radio frequency identification network cluster head determination device for implementing the radio frequency identification network cluster head determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded 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 above, and will not be repeated here.

[0115] In an exemplary embodiment, as shown in FIG4 , a device for determining a cluster head in a radio frequency identification network is provided, comprising: a data acquisition module 402, a fuzzy operation module 404, and a cluster head determination module 406. Among them:

[0116] The data acquisition module 402 is used to acquire node status information corresponding to each node in the RFID network, where the node status information includes energy data and centrality data corresponding to each node;

[0117] A fuzzy operation module 404 is used to perform fuzzy logic operation on the energy data and the centrality data to obtain the target opportunity probability;

[0118] The cluster head determination module 406 is used to determine the target cluster head corresponding to each cluster in the RFID network according to the target opportunity probability, and the target cluster head is used to communicate with other clusters.

[0119] In one embodiment, the fuzzy operation module 404 is also used to perform a first fuzzy processing on the energy data to obtain the energy opportunity probability; perform a second fuzzy processing on the centrality data to obtain the center opportunity probability; and obtain the target opportunity probability based on the preset opportunity fuzzy rules, energy opportunity probability and center opportunity probability.

[0120] In one embodiment, the fuzzy operation module 404 is also used to determine the energy membership based on the energy data and the preset energy fuzzy set; obtain the opportunity membership corresponding to the energy data according to the energy membership and the first fuzzy rule; and defuzzify the opportunity membership to obtain the energy opportunity probability.

[0121] In one embodiment, the cluster head determination module 406 is further used to obtain random attribute data corresponding to each node; take nodes whose random attribute data is greater than the scale threshold as intermediate nodes; and determine the target cluster head corresponding to each cluster from the intermediate nodes according to the target opportunity probability corresponding to the intermediate nodes.

[0122] In one embodiment, the size threshold is a ratio of the size of the cluster to the size of the node.

[0123] In one of the embodiments, the cluster head determination module 406 is further used to obtain a candidate set; for each cluster, an intermediate node is selected from the cluster in turn, and when 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 used as the target cluster head of the cluster.

[0124] Each module in the above-mentioned RFID network cluster head determination device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0125] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. 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 status information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining a cluster head of a radio frequency identification network is implemented.

[0126] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this 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 memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access 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 and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a cluster head in a radio frequency identification network, characterized in that: The method comprises: Obtaining node status information corresponding to each node in the radio frequency identification network, wherein the node status information includes energy data and centrality data corresponding to each of the nodes; Performing fuzzy logic operation on the energy data and the centrality data to obtain a target opportunity probability; 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.

2. The method according to claim 1, characterized in that The performing fuzzy logic operation on the energy data and the centrality data to obtain the target opportunity probability includes: Performing a first fuzzy processing on the energy data to obtain an energy opportunity probability; Performing a second fuzzy processing on the centrality data to obtain a central opportunity probability; The target opportunity probability is obtained according to the preset opportunity fuzzy rule, the energy opportunity probability and the center opportunity probability.

3. The method according to claim 2, characterized in that The performing a first fuzzy processing on the energy data to obtain an energy opportunity probability includes: Determining energy membership based on the energy data and a preset energy fuzzy set; According to the energy membership and the first fuzzy rule, obtaining the opportunity membership corresponding to the energy data; The opportunity membership is defuzzified to obtain the energy opportunity probability.

4. The method according to claim 1, characterized in that The step of determining the target cluster heads corresponding to each cluster in the radio frequency identification network according to the target opportunity probability includes: Obtaining random attribute data corresponding to each of the nodes; The nodes whose random attribute data are greater than the scale threshold are regarded as intermediate nodes; According to the target opportunity probability corresponding to the intermediate node, the target cluster head corresponding to each of the clusters is determined from the intermediate node.

5. The method according to claim 4, characterized in that The scale threshold is a scale ratio of the cluster to the node.

6. The method according to claim 4, characterized in that The step of determining the target cluster head corresponding to each of the clusters from the intermediate nodes according to the target opportunity probability corresponding to the intermediate nodes includes: Get the candidate set; For each cluster, an intermediate node is selected from the cluster in turn, and when 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 used as the target cluster head of the cluster.

7. A device for determining a cluster head in a radio frequency identification network, characterized in that: The device comprises: A data acquisition module, used to acquire node status information corresponding to each node in the RFID network, wherein the node status information includes energy data and centrality data corresponding to each node; A fuzzy operation module, used for performing fuzzy logic operation on the energy data and the centrality data to obtain a target opportunity probability; The cluster head determination module is used to determine the 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 to communicate with other clusters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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