Networking method and device of Internet of Things equipment, electronic equipment and storage medium

Abstract

The invention discloses a networking method and device of Internet of Things equipment, electronic equipment and a storage medium, and relates to the technical field of Internet of Things networking, and the method comprises the steps: collecting the feature information of each piece of Internet of Things equipment through an edge calculation node; clustering the Internet of Things devices based on the feature information to obtain a networking result, and monitoring the change of the feature information; and under the condition that the variable quantity of the feature information exceeds a preset threshold value, adjusting the networking result based on the changed feature information. According to the method, the feature information of the Internet of Things equipment is acquired by adopting the edge computing node, and the Internet of Things equipment is dynamically clustered, so that the network structure is adjusted according to the real-time change of the feature information of the equipment, the resource utilization efficiency is improved, the response capability of the system is enhanced, and the user experience is improved. And the Internet of Things equipment can be efficiently and flexibly networked in a complex and changeable environment.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things networking, and in particular to a networking method, device, electronic device and storage medium for Internet of Things devices. Background Art

[0002] With the explosive growth in the number of IoT devices, a large number of IoT devices are widely used in scenarios such as smart cities, industrial IoT, and smart homes. Due to the wide geographical distribution and wide variety of IoT devices, how to effectively build an efficient and dynamic network has become a major challenge. Traditional static networking methods are difficult to adapt to changes in device status and environment, resulting in low resource utilization efficiency and poor system responsiveness.

[0003] Therefore, how to solve the problem that the networking method of IoT devices is difficult to adapt to changes in device status and environment is a problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of this application is to provide a networking method, device, electronic device and storage medium for Internet of Things devices, aiming to solve the technical problem that the networking method of Internet of Things devices is difficult to adapt to changes in device status and environment.

[0005] To achieve the above objectives, the present application proposes a networking method for IoT devices, the method comprising: Collect characteristic information of each IoT device through edge computing nodes; Clustering the IoT devices based on the characteristic information to obtain a networking result, and monitoring changes in the characteristic information; When the change amount of the characteristic information exceeds a preset threshold, the networking result is adjusted based on the changed characteristic information.

[0006] In one embodiment, the characteristic information includes location information and status information of each IoT device, and the step of clustering the IoT devices based on the characteristic information to obtain a networking result includes: Calculate the relative distance between the IoT devices based on the location information; For any IoT device, determine the number of IoT devices included in a preset cluster range corresponding to the IoT device based on the relative distances, wherein the preset cluster range is an area centered on the IoT device; When the number of IoT devices included in the preset cluster range of the IoT device is greater than or equal to the preset number, the IoT device is used as a cluster core point for clustering, and the IoT devices included in the preset cluster range of the IoT device are added to the cluster where the cluster core point is located; After traversing the IoT devices, IoT devices that do not belong to the cluster are removed to obtain denoised IoT devices; According to the state information of each IoT device after denoising, the core device and the auxiliary device in each cluster are determined to obtain the networking result.

[0007] In one embodiment, the state information includes the computing power, network bandwidth and point mutual information value of each IoT device after denoising, and the step of determining the core device and the auxiliary device in each cluster according to the state information of each IoT device after denoising includes: Calculating the index score of each IoT device after denoising, wherein the index score is obtained by weighted summation of the computing power, network bandwidth and point mutual information value of the IoT device; For any cluster, the IoT device with the highest indicator score in the cluster is used as the core device in the cluster, and the remaining IoT devices in the cluster except the core device are used as auxiliary devices in the cluster.

[0008] In one embodiment, when the change amount of the characteristic information exceeds a preset threshold, the step of adjusting the networking result based on the changed characteristic information includes: When the change in the location information of any IoT device exceeds a preset distance threshold, returning to execute the step of calculating the relative distance between the IoT devices based on the location information based on the changed location information; When the amount of change in the status information of any IoT device exceeds a preset status threshold, the step of determining the core device and the auxiliary device in each of the clusters is returned based on the changed status information.

[0009] In one embodiment, the networking method of the Internet of Things device further includes: For any one of the networked target IoT devices, monitor the task load assigned to the target IoT device, and calculate the resource allocation amount of the target IoT device according to the task load and the computing capacity of the target IoT device; The task load allocated to the target IoT device is adjusted based on the resource allocation amount.

[0010] In one embodiment, the step of collecting characteristic information of each IoT device through the edge computing node includes: In the edge computing node, the indicator data of each IoT device is sampled at a preset sampling frequency to obtain sampled data, wherein the sampled data includes the location data and status data of each IoT device; The edge computing node processes the location data and the status data to obtain the feature information.

[0011] In one embodiment, the step of processing the location data and the status data by the edge computing node to obtain the feature information includes: In the edge computing node, the location data and the status data are cleaned according to a preset standard data reference range to obtain cleaned sampling data; The edge computing node converts the data format of the cleaned sampled data into a preset standard format to obtain converted sampled data; The converted sampling data is denoised by the edge computing node to obtain position information corresponding to the position data and state information corresponding to the state data, and the position information and the state information are combined into the feature information.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a networking device for an Internet of Things device, the networking device for an Internet of Things device comprising: The collection module is used to collect characteristic information of each IoT device through edge computing nodes; A clustering module, used to cluster the IoT devices based on the feature information, obtain a networking result, and monitor changes in the feature information; The adjustment module is used to adjust the networking result based on the changed characteristic information when the change amount of the characteristic information exceeds a preset threshold.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the networking method of the Internet of Things device as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the networking method of the Internet of Things device as described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the networking method of the Internet of Things device as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: The present application first collects characteristic information of each IoT device through an edge computing node to realize data processing on the edge side of the network, thereby saving bandwidth costs and reducing transmission delays, and improving the responsiveness of the system; clusters the IoT devices based on the characteristic information to obtain a networking result, and monitors changes in the characteristic information to cluster and network IoT devices with adjacent locations; when the change in the characteristic information exceeds a preset threshold, adjusts the networking result based on the changed characteristic information, so as to adapt to the status and environmental changes of the device by adjusting the networking of the device in real time.

[0017] In summary, the present application avoids the problem that traditional static networking methods are difficult to adapt to changes in device status and environment by using edge computing nodes to collect feature information of IoT devices and dynamically clustering IoT devices. It adjusts the network structure according to real-time changes in device feature information, thereby improving resource utilization efficiency, enhancing the system's responsiveness, and ensuring that IoT devices can be efficiently and flexibly networked in complex and changing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of the first embodiment of the networking method of the Internet of Things device of the present application is provided; Figure 2 A flow chart of the second embodiment of the networking method of the Internet of Things device of the present application; Figure 3 A schematic diagram of a simplified process of the networking method of the Internet of Things devices provided in the second embodiment of the present application; Figure 4 This is a schematic diagram of the module structure of the networking device of the Internet of Things device according to the embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the networking method of the Internet of Things devices in the embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of the embodiment of the present application is: collect the characteristic information of each IoT device through the edge computing node; cluster the IoT devices based on the characteristic information to obtain the networking result, and monitor the change of the characteristic information; when the change amount of the characteristic information exceeds the preset threshold, adjust the networking result based on the changed characteristic information.

[0025] Due to the wide geographical distribution and wide variety of IoT devices, how to effectively build an efficient and dynamic network has become a major challenge. Traditional static networking methods are difficult to adapt to changes in device status and environment, resulting in low resource utilization efficiency and poor system responsiveness. Therefore, how to solve the problem that the networking methods of IoT devices are difficult to adapt to changes in device status and environment is an urgent problem that needs to be solved.

[0026] The present application provides a solution, which collects characteristic information of IoT devices by adopting edge computing nodes and dynamically clusters IoT devices, thereby avoiding the problem that traditional static networking methods are difficult to adapt to changes in device status and environment, and realizing the adjustment of network structure according to real-time changes in device characteristic information, thereby improving resource utilization efficiency, enhancing the responsiveness of the system, and ensuring that IoT devices can be efficiently and flexibly networked in complex and changing environments.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions. The following takes an electronic device as an example to illustrate this embodiment and the following embodiments.

[0028] Based on this, the present application embodiment provides a networking method for an Internet of Things device, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the networking method of the Internet of Things devices of the present application.

[0029] In this embodiment, the networking method of the Internet of Things device includes steps S10 to S30: Step S10, collecting characteristic information of each IoT device through the edge computing node; It should be noted that an edge computing node refers to a node located at the edge of the network, close to the IoT device, with data processing and analysis capabilities. The node can perform preliminary processing of information at the source of data generation; feature information refers to various data that can characterize the status and capabilities of IoT devices, such as location information, status information, hardware information, etc.

[0030] It is understandable that due to the wide geographical distribution of IoT devices and dynamic changes in their status, it is necessary to collect and process device information in real time to adapt to environmental changes. Therefore, performing step S10 can avoid problems such as improper network configuration and inefficient resource utilization caused by untimely information collection, thereby improving the flexibility and efficiency of network networking, and further ensuring that IoT devices can dynamically form an efficient network based on real-time conditions. Provides a reliable data foundation.

[0031] Exemplarily, various characteristic information of each IoT device is collected through the edge computing node, including: hardware information (CPU (Central Processing Unit) core number, memory size, storage capacity, etc.), software characteristics (communication protocols and applications supported by the device, etc.), location information (geographic location information, device height, etc.) and status information (current CPU load, memory usage, network latency, energy status, etc.).

[0032] In a feasible implementation, step S10 may include steps S11-S12: Step S11, in the edge computing node, sampling the indicator data of each IoT device at a preset sampling frequency to obtain sampled data, wherein the sampled data includes the location data and status data of each IoT device; It should be noted that indicator data refers to the performance parameters generated by IoT devices during operation, such as geographic location information, current CPU load, memory usage, etc.; sampled data refers to the data points that record indicator data at certain time intervals; location data refers to the original location sampled from IoT devices, which can be GPS (Global Positioning System) coordinates, etc.; state data refers to the original state sampled from IoT devices, which can be current CPU load, memory usage, network latency, energy status, etc.

[0033] It can be understood that since adapting to the status and environmental changes of IoT devices requires being able to capture real-time changes in device performance, performing step S11 can avoid data analysis delays caused by untimely data sampling, ensure the accuracy and real-time nature of the acquired data, and thus make network management more efficient.

[0034] Step S12: Process the location data and the status data through the edge computing node to obtain the feature information.

[0035] It is understandable that since the sampled data is incomplete, abnormal, or has inconsistent formats, performing step S12 can avoid the accuracy of device networking optimization being affected by data quality issues, thereby achieving an accurate description of the status of IoT devices.

[0036] In this implementation, by sampling and processing the indicator data of the IoT devices in the edge computing node, the problem of reduced accuracy of device networking optimization due to poor data continuity and data quality issues is avoided, and the real-time capture of device characteristics, ensuring data accuracy and real-time, and accurately describing the device status are achieved, thereby improving the efficiency of network management.

[0037] In a feasible implementation, step S12 may include steps S121 to S123: Step S121, in the edge computing node, performing data cleaning on the location data and the status data according to a preset standard data reference range to obtain cleaned sampling data; Exemplarily, the edge computing node is used to remove the data in the sampling data whose position data accuracy is lower than a preset lower limit of accuracy, or whose device status change rate is greater than a preset change rate, to obtain cleaned sampling data.

[0038] Step S122, converting the data format of the cleaned sampled data into a preset standard format through the edge computing node to obtain converted sampled data; Exemplarily, the data formats of different IoT devices in the sampled data are converted into a unified preset standard format through the edge computing node, such as unifying the network delay data of different devices into the same time unit.

[0039] Step S123, obtaining the position information corresponding to the position data and the state information corresponding to the state data, and combining the position information and the state information into the feature information.

[0040] Exemplarily, algorithms such as Kalman filtering are used in edge computing nodes to remove noise from data and obtain location information and status information of each IoT device, thereby ensuring the accuracy of device feature information.

[0041] In this implementation, by performing data cleaning, data format conversion and denoising in the edge computing node, the influence of outliers, inconsistent formats and noise contained in the sampled data on the analysis results is avoided, the standardization and purification of the sampled data is achieved, and the accuracy and reliability of the obtained feature information are ensured, thereby improving the accuracy of IoT device status collection and the effectiveness of network management.

[0042] Step S20, clustering the IoT devices based on the characteristic information to obtain a networking result, and monitoring changes in the characteristic information; It is understandable that due to the diversity of IoT devices, effective network organization needs to be performed according to the characteristics of different devices. Therefore, performing step S20 can avoid the difficulty of organizing devices with different characteristics into a suitable network, thereby achieving a more suitable network organization for a large number of IoT devices.

[0043] In a feasible implementation manner, the characteristic information includes at least the location information and status information of each IoT device, and the step of clustering the IoT devices based on the characteristic information to obtain a networking result in step S20 may include steps S21 to S25: Step S21, calculating the relative distance between the IoT devices based on the location information; It should be noted that location information refers to the geographical location or spatial location of an IoT device in physical space, and relative distance refers to the geographical interval or spatial interval between different IoT devices calculated based on location information.

[0044] Exemplarily, the relative distance d(p) between IoT devices is calculated by the following Euclidean distance metric: i ,p j ):

[0045] Among them, p i represents the coordinates of IoT device i (x i ,y i ), p j represents the coordinates of IoT device j (x j ,y j ).

[0046] Step S22: for any IoT device, determine the number of IoT devices included in a preset cluster range corresponding to the IoT device based on the relative distances, wherein the preset cluster range is an area centered on the IoT device; It is understandable that since the network structure needs to be initially determined based on the geographical location of the IoT device, performing step S22 can avoid delays caused by long communication distances between devices and ensure an efficient communication range between devices in the network.

[0047] Step S23, when the number of IoT devices included in the preset cluster range of the IoT device is greater than or equal to the preset number, the IoT device is used as a cluster core point for clustering, and the IoT devices included in the preset cluster range of the IoT device are added to the cluster where the cluster core point is located; It should be noted that the cluster core point refers to a key device in a specific area whose number of surrounding devices reaches a preset threshold; the cluster where the cluster core point is located refers to a collection of surrounding devices centered on the cluster core point.

[0048] It can be understood that, since an efficient network unit requires that devices with close geographical locations be organized together, performing step S23 can avoid the problem of uneven distribution of devices in the network, thereby achieving the formation of stable and efficient network grouping.

[0049] For example, in any IoT device p i If the number of other IoT devices within the preset cluster range varepsilon is greater than or equal to the preset data volume Minpts, the IoT device p i As the cluster core point, starting from the cluster core point, the IoT devices whose relative distances to the cluster core point are less than varepsilon are added to the same cluster where the cluster core point is located in sequence until it cannot be expanded. In the case where any IoT device is between multiple cluster core points and the relative distances between the IoT device and multiple cluster core points are consistent, the IoT device can be randomly added to the cluster where any cluster core point among the multiple cluster core points is located, or the IoT device can be added to a specific cluster core point among the multiple cluster core points, wherein the number of IoT devices contained in the cluster where the specific cluster core point is located is less than the number of IoT devices contained in the cluster where the other cluster core points among the multiple cluster core points are located. The IoT device can also be added to the cluster with the least resource consumption in each cluster corresponding to the multiple cluster core points. In this embodiment, there is no specific limitation on the device adding method in the case where any IoT device is between multiple cluster core points and the relative distances between the IoT device and multiple cluster core points are consistent.

[0050] Step S24, after traversing the IoT devices, remove the IoT devices that do not belong to the cluster to obtain the denoised IoT devices; It is understandable that there are often isolated devices occupying network resources in the network structure, so step S24 is performed to purify the network structure by removing isolated devices, thereby improving the purity of the network and work efficiency.

[0051] Step S25, determining the core device and the auxiliary device in each of the clusters according to the state information of each IoT device after denoising, and obtaining the networking result.

[0052] It should be noted that status information refers to parameters such as the operating status and resource usage of IoT devices. Core devices refer to devices that perform the main functions in the cluster, and auxiliary devices refer to devices that provide support or backup functions in the cluster.

[0053] It is understandable that, since the role of the device in the network needs to be allocated according to its status capability in the same cluster, performing step S25 can avoid the problem of unclear function allocation of devices in the network, and the effect achieved is to ensure that each device in the network can play its best role, thereby improving the performance and stability of the entire network.

[0054] In this embodiment, by performing relative distance calculation and device clustering based on location information, the problems of uneven distribution of IoT devices in the network and waste of resources by isolated devices are avoided, and devices with close geographical locations are effectively organized into clusters. The functional allocation of core and auxiliary devices within the cluster is determined based on device status information, thereby optimizing the network structure and improving the utilization efficiency of network resources and the overall performance of the system.

[0055] In a feasible implementation manner, the state information includes the computing power, network bandwidth and point mutual information value of each IoT device after denoising. The step of determining the core device and the auxiliary device in each cluster according to the state information of each IoT device after denoising in step S25 may include steps S251-S252: Step S251, calculating the index score of each IoT device after denoising, wherein the index score is obtained by weighted summation of the computing power, network bandwidth and point mutual information value of the IoT device; It should be noted that the index score refers to the score obtained by comprehensively evaluating the computing power, network bandwidth and point mutual information value of the device according to preset weights. Computing power refers to the ability of the device to process data, network bandwidth refers to the data transmission rate of the device's network connection, and point mutual information value refers to the correlation measure of information interaction between devices.

[0056] It is understandable that, in the networking process of traditional IoT devices, there is often a mismatch between device capabilities and the tasks undertaken by the devices in the network. Therefore, step S251 is performed to avoid the mismatch between device capabilities and roles by evaluating the comprehensive performance of each device in the network, thereby providing an effective and reliable basis for judgment to ensure that each device can be appropriately assigned a role according to its own capabilities.

[0057] Exemplarily, the index score S(i) of each IoT device i after denoising is calculated according to the following formula:

[0058] in, , , is the preset weight, is the computing power of the target IoT device i (such as the quantified value of the number of CPU cores, memory size, etc.), is the network bandwidth of the target IoT device i (a quantitative value used to process performance indicators such as network latency and throughput), is the point mutual information value of the target IoT device i (used to measure the information sharing and dependency between devices).

[0059] Step S252: for any cluster, the IoT device with the highest indicator score in the cluster is used as the core device in the cluster, and the remaining IoT devices in the cluster except the core device are used as auxiliary devices in the cluster.

[0060] It is understandable that, since it is difficult to determine or lacks clear device role allocation in the networking process of traditional IoT devices, step S252 is performed to ensure that each cluster can operate efficiently and improve the overall performance of the network by determining the leader device in each cluster.

[0061] In this implementation, a weighted sum of multiple parameters based on preset weights is performed to comprehensively evaluate the computing power, network bandwidth, and point mutual information value of the denoised IoT device, thereby avoiding the problem of mismatch between device role assignment and its actual capabilities, and achieving the effect of assigning core or auxiliary roles to devices based on their performance, thereby ensuring that each device in the network can play a better role and optimizing the overall performance and resource utilization of the network.

[0062] Step S30: When the change amount of the characteristic information exceeds a preset threshold, the networking result is adjusted based on the changed characteristic information.

[0063] It should be noted that the amount of change refers to the degree of change in the characteristic information of the IoT device (such as location, status, etc.).

[0064] It is understandable that the environment or state of the IoT device may change, so step S30 is performed to dynamically adjust the network structure to adapt to changes in the environment or state of the IoT device, thereby avoiding problems such as improper resource allocation and reduced system responsiveness due to static network configuration. The effect achieved is to ensure the real-time and adaptability of the network configuration, thereby improving the flexibility and efficiency of the network.

[0065] In a feasible implementation, step S30 may include steps S31-S32: Step S31, when the change amount of the location information of any IoT device exceeds a preset distance threshold, returning to execute the step of calculating the relative distance between the IoT devices based on the location information based on the changed location information; It is understandable that since IoT devices may move or change positions during actual operation, thereby affecting the topology of the network, performing step S31 can avoid the problem of outdated network configuration due to changes in device location, and ensure dynamic updating of the network structure to adapt to the new location of the device, thereby maintaining the efficiency and connectivity of the network.

[0066] Exemplarily, when the change in location information of any IoT device (i.e., moving distance) exceeds a preset distance threshold of 100 meters, the relative distances between the IoT devices are recalculated based on the location information of the device after moving 100 meters, i.e., clustering is performed again.

[0067] Step S32: when the change amount of the status information of any IoT device exceeds a preset status threshold, return to the step of determining the core device and the auxiliary device in each of the clusters based on the changed status information.

[0068] It is understandable that, during the actual operation of an IoT device, the state of the device (such as computing power, network bandwidth, etc.) may change over time, affecting the role and function of the device in the network. Therefore, performing step S32 can avoid the problem of network performance degradation due to changes in device status, and ensure that the role and function allocation of devices in the network are always based on the latest status information, thereby optimizing the network's operating efficiency and resource utilization.

[0069] Exemplarily, when the change in status information of any IoT device (such as the computing resource usage of the device) exceeds a preset status threshold of 80%, the core device in the cluster is re-determined as an auxiliary device based on the computing resource usage greater than 80%, that is, the role allocation in the cluster is redetermined.

[0070] In this implementation, by monitoring changes in characteristic information and dynamically adjusting the network configuration, the problem of network configuration inadaptability caused by changes in the location or status of IoT devices is avoided. Regardless of whether the change in device location information exceeds a preset distance threshold, or the change in status information exceeds a preset status threshold, the relative distance between devices can be recalculated in time or the core and auxiliary devices in the cluster can be re-determined, ensuring efficient operation of the network and optimal allocation of resources, thereby improving the flexibility and responsiveness of the network.

[0071] This embodiment provides a networking method for IoT devices. By adopting edge computing nodes to collect feature information of IoT devices and dynamically clustering IoT devices, the problem that traditional static networking methods are difficult to adapt to changes in device status and environment is avoided, and the network structure is adjusted according to real-time changes in device feature information, thereby improving resource utilization efficiency, enhancing the system's responsiveness, and ensuring that IoT devices can be efficiently and flexibly networked in complex and changing environments.

[0072] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the networking method of the Internet of Things devices of the present application.

[0073] The networking method of the Internet of Things device also includes steps S01-S02: Step S01, for any one of the networked target IoT devices, monitoring the task load assigned to the target IoT device, and calculating the resource allocation amount of the target IoT device according to the task load and the computing capacity of the target IoT device; It should be noted that the target IoT device refers to the IoT device after networking is completed, the task load refers to the computing resources, memory, network bandwidth, etc. required to perform the specific tasks assigned to the target IoT device, and the resource allocation amount refers to the amount of resources that should be allocated to the device calculated based on the computing power of the device and the current task load.

[0074] It is understandable that, since it is difficult to make the device undertake reasonable tasks according to the actual capabilities and task load of the device in the traditional static networking method, performing step S01 can avoid the problem of device overload or resource waste, optimize the resource utilization rate of the IoT device, and ensure the stable operation of the network.

[0075] Exemplarily, for any target IoT device i among the IoT devices, the task load T assigned to the target IoT device is monitored. i, and calculate the resource allocation amount R(i) of the target IoT device i according to the following formula:

[0076] Where Ci represents the computing power of the target IoT device.

[0077] Step S02: adjusting the task load allocated to the target IoT device based on the resource allocation amount.

[0078] It is understandable that since the resource allocation of IoT devices may not be well adapted as the task execution or device status changes, performing step S02 can avoid performance bottlenecks or task failures caused by the mismatch between task load and device capabilities. By dynamically adjusting the task load, the rational use of device resources is ensured, and the overall performance of the network and the reliability of task processing are improved.

[0079] In this embodiment, by adjusting the task load of the IoT device in real time, performance degradation and system instability caused by excessive tasks or improper resource allocation of the device are avoided, and the task load is dynamically adjusted according to the actual capabilities of the device, thereby ensuring that the IoT device can perform tasks efficiently and evenly, thereby improving the resource utilization and task processing efficiency of the entire network.

[0080] For example, in order to help understand the implementation process of the networking method of the Internet of Things device obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 3 , Figure 3 A brief flowchart of a networking method for IoT devices is provided, specifically: First, the characteristic information (including location information, status information, etc.) of each IoT device is collected through the edge computing node into the super object model, and clustered according to the characteristic information to obtain the networking result. Among them, the super object model is a data model / semantic model designed to solve the large-scale distribution and dynamic management of IoT devices. The model aims to describe and manage distributed data covering multiple physical hardware nodes, software middleware (such as AI (Artificial Intelligence) algorithms, map services, BIM (Building Information Modeling) middleware) capabilities, and cross-domain physical objects with spatial attributes. The capability described by the super object model is not only a static information display, but also a dynamically adjusted platform, which can realize intelligent networking of devices and optimal scheduling of resources according to the collected device characteristics.

[0081] Then, the super-thing model adjusts the networking results based on the changes in the monitored feature information, that is, re-clustering and role allocation in the clusters after clustering (calibration of core devices and auxiliary devices). In addition, the super-thing model adjusts the task load assigned to the IoT devices that have completed the networking in real time based on the task load and computing power of the IoT devices.

[0082] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the networking method of the IoT device of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0083] This application also provides a networking device for IoT devices, please refer to Figure 4 , the networking device of the Internet of Things device includes: The collection module 10 is used to collect characteristic information of each IoT device through the edge computing node; A clustering module 20, configured to cluster the IoT devices based on the feature information, obtain a networking result, and monitor changes in the feature information; The adjustment module 30 is used to adjust the networking result based on the changed characteristic information when the change amount of the characteristic information exceeds a preset threshold.

[0084] Optionally, the characteristic information includes location information and status information of each IoT device, and the clustering module 20 is further used to: Calculate the relative distance between the IoT devices based on the location information; For any IoT device, determine the number of IoT devices included in a preset cluster range corresponding to the IoT device based on the relative distances, wherein the preset cluster range is an area centered on the IoT device; When the number of IoT devices included in the preset cluster range of the IoT device is greater than or equal to the preset number, the IoT device is used as a cluster core point for clustering, and the IoT devices included in the preset cluster range of the IoT device are added to the cluster where the cluster core point is located; After traversing the IoT devices, IoT devices that do not belong to the cluster are removed to obtain denoised IoT devices; According to the state information of each IoT device after denoising, the core device and the auxiliary device in each cluster are determined to obtain the networking result.

[0085] Optionally, the state information includes the computing power, network bandwidth and point mutual information value of each IoT device after denoising, and the clustering module 20 is further used for: Calculating the index score of each IoT device after denoising, wherein the index score is obtained by weighted summation of the computing power, network bandwidth and point mutual information value of the IoT device; For any cluster, the IoT device with the highest indicator score in the cluster is used as the core device in the cluster, and the remaining IoT devices in the cluster except the core device are used as auxiliary devices in the cluster.

[0086] Optionally, the adjustment module 30 is further used for: When the change in the location information of any IoT device exceeds a preset distance threshold, returning to execute the step of calculating the relative distance between the IoT devices based on the location information based on the changed location information; When the amount of change in the status information of any IoT device exceeds a preset status threshold, the step of determining the core device and the auxiliary device in each of the clusters is returned based on the changed status information.

[0087] Optionally, the allocation module 40 in the networking device of the Internet of Things device is used to: For any one of the networked target IoT devices, monitor the task load assigned to the target IoT device, and calculate the resource allocation amount of the target IoT device according to the task load and the computing capacity of the target IoT device; The task load allocated to the target IoT device is adjusted based on the resource allocation amount.

[0088] Optionally, the sampling module 10 is further used for: In the edge computing node, the indicator data of each IoT device is sampled at a preset sampling frequency to obtain sampled data, wherein the sampled data includes the location data and status data of each IoT device; The edge computing node processes the location data and the status data to obtain the feature information.

[0089] Optionally, the sampling module 10 is further used for: In the edge computing node, the location data and the status data are cleaned according to a preset standard data reference range to obtain cleaned sampling data; The edge computing node converts the data format of the cleaned sampled data into a preset standard format to obtain converted sampled data; The converted sampling data is denoised by the edge computing node to obtain position information corresponding to the position data and state information corresponding to the state data, and the position information and the state information are combined into the feature information.

[0090] The networking device of the Internet of Things device provided by the present application adopts the networking method of the Internet of Things device in the above-mentioned embodiment, which can solve the technical problem that the networking method of the Internet of Things device is difficult to adapt to changes in the device status and environment. Compared with the prior art, the beneficial effects of the networking device of the Internet of Things device provided by the present application are the same as the beneficial effects of the networking method of the Internet of Things device provided by the above-mentioned embodiment, and the other technical features in the networking device of the Internet of Things device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0091] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the networking method of the Internet of Things device in the above-mentioned embodiment 1.

[0092] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] like Figure 5As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0094] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0095] The electronic device provided by the present application adopts the networking method of the Internet of Things device in the above embodiment, which can solve the technical problem that the networking method of the Internet of Things device is difficult to adapt to changes in the device state and environment. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as the beneficial effects of the networking method of the Internet of Things device provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0096] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0097] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0098] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the networking method of the Internet of Things devices in the above-mentioned embodiment.

[0099] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0100] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0101] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: collects characteristic information of each Internet of Things device through an edge computing node; clusters the various Internet of Things devices based on the characteristic information to obtain a networking result, and monitors changes in the characteristic information; when the change in the characteristic information exceeds a preset threshold, adjusts the networking result based on the changed characteristic information.

[0102] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0103] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0104] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0105] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the networking method of the above-mentioned Internet of Things devices, and can solve the technical problem that the networking method of the Internet of Things devices is difficult to adapt to changes in device status and environment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the networking method of the Internet of Things devices provided by the above-mentioned embodiments, and will not be repeated here.

[0106] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the networking method of the Internet of Things devices as described above.

[0107] The computer program product provided by the present application can solve the technical problem that the networking method of the Internet of Things device is difficult to adapt to the changes in the device status and environment. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the networking method of the Internet of Things device provided by the above embodiment, which will not be repeated here.

[0108] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A networking method for Internet of Things devices, characterized in that: The networking method of the Internet of Things device includes: Collect characteristic information of each IoT device through edge computing nodes; Clustering the IoT devices based on the characteristic information to obtain a networking result, and monitoring changes in the characteristic information; When the change amount of the characteristic information exceeds a preset threshold, the networking result is adjusted based on the changed characteristic information.

2. The networking method of the Internet of Things device according to claim 1, characterized in that: The characteristic information includes the location information and status information of each IoT device, and the step of clustering the IoT devices based on the characteristic information to obtain a networking result includes: Calculate the relative distance between the IoT devices based on the location information; For any IoT device, determine the number of IoT devices included in a preset cluster range corresponding to the IoT device based on the relative distances, wherein the preset cluster range is an area centered on the IoT device; When the number of IoT devices included in the preset cluster range of the IoT device is greater than or equal to the preset number, the IoT device is used as a cluster core point for clustering, and the IoT devices included in the preset cluster range of the IoT device are added to the cluster where the cluster core point is located; After traversing the IoT devices, IoT devices that do not belong to the cluster are removed to obtain denoised IoT devices; According to the state information of each IoT device after denoising, the core device and the auxiliary device in each cluster are determined to obtain the networking result.

3. The networking method of the Internet of Things device as claimed in claim 2, characterized in that: The state information includes the computing power, network bandwidth and point mutual information value of each IoT device after denoising. The step of determining the core device and the auxiliary device in each cluster according to the state information of each IoT device after denoising includes: Calculating the index score of each IoT device after denoising, wherein the index score is obtained by weighted summation of the computing power, network bandwidth and point mutual information value of the IoT device; For any cluster, the IoT device with the highest indicator score in the cluster is used as the core device in the cluster, and the remaining IoT devices in the cluster except the core device are used as auxiliary devices in the cluster.

4. The networking method of the Internet of Things device as claimed in claim 2, characterized in that: When the change amount of the characteristic information exceeds a preset threshold, the step of adjusting the networking result based on the changed characteristic information includes: When the change in the location information of any IoT device exceeds a preset distance threshold, returning to execute the step of calculating the relative distance between the IoT devices based on the location information based on the changed location information; When the amount of change in the state information of any IoT device exceeds a preset state threshold, the step of determining the core device and the auxiliary device in each of the clusters is returned based on the changed state information.

5. The networking method of the Internet of Things device according to claim 1, characterized in that: The networking method of the Internet of Things device also includes: For any one of the networked target IoT devices, monitor the task load assigned to the target IoT device, and calculate the resource allocation amount of the target IoT device according to the task load and the computing capacity of the target IoT device; The task load allocated to the target IoT device is adjusted based on the resource allocation amount.

6. The networking method of the Internet of Things device according to claim 1, characterized in that: The step of collecting characteristic information of each IoT device through the edge computing node includes: In the edge computing node, the indicator data of each IoT device is sampled at a preset sampling frequency to obtain sampled data, wherein the sampled data includes the location data and status data of each IoT device; The edge computing node processes the location data and the status data to obtain the feature information.

7. The networking method of the Internet of Things device as claimed in claim 6, characterized in that: The step of processing the location data and the status data by the edge computing node to obtain the feature information comprises: In the edge computing node, the location data and the status data are cleaned according to a preset standard data reference range to obtain cleaned sampling data; The edge computing node converts the data format of the cleaned sampled data into a preset standard format to obtain converted sampled data; The converted sampling data is denoised by the edge computing node to obtain position information corresponding to the position data and state information corresponding to the state data, and the position information and the state information are combined into the feature information.

8. A networking device for Internet of Things devices, characterized in that: The networking device of the Internet of Things device includes: The collection module is used to collect characteristic information of each IoT device through edge computing nodes; A clustering module, used to cluster the IoT devices based on the feature information, obtain a networking result, and monitor changes in the feature information; The adjustment module is used to adjust the networking result based on the changed characteristic information when the change amount of the characteristic information exceeds a preset threshold.

9. An electronic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the networking method of the Internet of Things device according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the networking method of the Internet of Things device according to any one of claims 1 to 7 are implemented.

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