A device fast self-organizing network method, system, terminal and readable storage medium

Abstract

The present invention relates to the field of computer technology, and discloses a method, system, terminal and readable storage medium for rapid self-organizing networking of devices, wherein the method comprises: collecting device information of all devices, establishing a networking model according to the device information; dividing all the devices into multiple subnets according to the networking model; and networking each of the subnets according to the networking model. The present invention greatly reduces the complexity of networking by dividing an entire network into multiple subnets and networking each subnet separately, thereby effectively reducing the solvable space of networking and improving the speed of solving networking. Therefore, it is possible to quickly network a complex multi-device network, and when the network situation changes, the network can be adjusted in time to meet the dynamic needs of the network and avoid problems of network congestion or shortage.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system, terminal and readable storage medium for rapid self-organizing networking of devices. Background Art

[0002] With the popularization of IoT devices, the number of smart devices is growing exponentially around the world. These devices are widely used in smart cities, industrial IoT, smart homes, and Internet of Vehicles. These smart devices are widely distributed, large in number, and have diverse functions. The collaborative work of different devices in various application scenarios has become increasingly important. Due to the large number of devices and their different functions, how to divide the various devices and then network them has become a technical challenge.

[0003] Traditional networking methods mostly rely on static configuration and cannot respond to changes in the environment and device status in real time, which easily leads to waste of network resources or inability to quickly make adaptive adjustments when network equipment fails, and therefore cannot adapt to rapidly changing application requirements and device status.

[0004] To this end, the existing technology proposes a method of self-organizing networking through set rules and algorithms, but the existing networking methods are often more complicated. As the number of devices increases, the solvable space of the network also grows explosively. In a dynamic environment, the status and location of the device may change at any time, and the excessively large networking space makes it very difficult to solve. Even if the solution can be found, the networking time is relatively long and often cannot be adjusted in time, resulting in low networking efficiency and limited network performance, making it difficult for these complex methods to effectively manage and schedule these devices. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, terminal and readable storage medium for rapid self-organizing networking of devices, which aims to solve the problems of complex networking algorithms and low efficiency in the prior art.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] The present invention provides a method for rapid self-organizing networking of devices, the method comprising:

[0008] Collect device information of all devices and establish a networking model based on the device information;

[0009] Divide all the devices into multiple subnets according to the networking model;

[0010] Each of the subnets is networked separately according to the networking model.

[0011] Furthermore, the collecting of device information of all devices and establishing a networking model according to the device information specifically includes:

[0012] Obtaining location information, hardware information, function information, environment information, and operation status information uploaded by all devices, and performing data cleaning, denoising, and standardization on the location information, the hardware information, the function information, the environment information, and the operation status information in turn to obtain the device information;

[0013] A twin digital model of all devices is established based on the device information, and the twin digital model is used as the networking model.

[0014] Furthermore, dividing all the devices into a plurality of subnets according to the networking model specifically includes:

[0015] Dividing the networking model into multiple areas according to geographical locations;

[0016] For each region, a location is selected as the cluster center, and multiple cluster centers are obtained;

[0017] All devices are clustered according to each cluster center to obtain devices included in multiple subnets.

[0018] Furthermore, clustering all devices according to each cluster center to obtain multiple cluster centers specifically includes:

[0019] Calculating the spatial distance between each of the devices and each of the cluster centers;

[0020] Calculating the functional distance between each of the devices and each of the cluster centers;

[0021] Clustering is performed using the weighted sum of the spatial distance and the functional distance as the distance to obtain devices included in multiple subnets.

[0022] Further, the calculating the functional distance between each of the devices and each of the cluster centers specifically includes:

[0023] Vectorizing the hardware information and the function information of each device to obtain a distance vector of each device;

[0024] According to each of the distance vectors, the functional distance between each of the devices and each of the cluster centers is calculated.

[0025] Furthermore, after clustering all devices according to the cluster centers to obtain devices included in multiple subnets, the method further includes:

[0026] Determining whether the hardware information and function information of each subnet meet the set conditions according to the networking model;

[0027] The subnets that do not meet the set conditions are adjusted according to the networking model.

[0028] Further, networking each of the subnets according to the networking model specifically includes:

[0029] For each of the subnets, allocating functions to all the devices in the subnet according to the networking model;

[0030] For each of the subnets, the data flow direction of all devices in the subnet is set according to the result of the function allocation.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also provides a device rapid self-organizing network system, the device rapid self-organizing network system comprising:

[0032] A model building module collects device information of all devices and builds a networking model based on the device information;

[0033] A subnet division module, dividing all the devices into multiple subnets according to the networking model;

[0034] The subnet networking module is used to respectively network each of the subnets according to the networking model.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, which includes: a memory, a processor, and a device rapid self-organizing network program stored in the memory and executable on the processor, and when the device rapid self-organizing network program is executed by the processor, the terminal is controlled to implement the steps of the device rapid self-organizing network method as described above.

[0036] In addition, to achieve the above objectives, the present invention also provides a readable storage medium, which stores a device rapid self-organizing network program, and when the device rapid self-organizing network program is executed by a processor, the steps of the device rapid self-organizing network method as described above are implemented.

[0037] The present invention adopts the above technical solution to achieve the following effects:

[0038] The present invention greatly reduces the complexity of networking by dividing an entire network into multiple subnets and networking each subnet separately, thereby effectively reducing the solvable space of networking and improving the solution speed of networking. Therefore, it is possible to quickly network complex multi-device networks and adjust the network in time when the network situation changes to meet the dynamic needs of the network and avoid network congestion or shortage problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1It is a flowchart of the steps of a method for rapid self-organizing network of devices in a preferred embodiment of the present invention;

[0040] Figure 2 It is a structural diagram of a device rapid self-organizing network system in a preferred embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention. All other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0043] Embodiment 1

[0044] See also Figure 1 , Embodiment 1 of the present application is a method for rapid self-organizing networking of devices, which can be applied to management terminals of various types of Internet of Things systems, such as management terminals of edge computing systems, management terminals of smart home systems, or management terminals of other types of Internet of Things systems. In addition, a device-related self-organizing networking method of this embodiment can also be run outside the Internet of Things system to provide networking support services for the Internet of Things system. It can be run in a server, which can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms.

[0045] In this embodiment, the method for rapid self-organizing networking of devices is specifically operated in an Internet of Things system in the field of enhanced technology, and includes the steps of:

[0046] S1. Collect device information of all devices and establish a networking model based on the device information.

[0047] Specifically, in this embodiment, each uploaded device data is collected, and the device data includes the location information, hardware information, function information, environment information and operation status information of the device, wherein the location information refers to information describing the location of the device, including coordinate information describing the coordinates of the device, and considering that the device may exist on different floors, height information describing the height of the device. In this embodiment, the coordinate information is obtained through the GPS (Global Positioning System) integrated in the device. The hardware information refers to information describing the unchanged hardware performance of the device, including central processing unit (CPU) information, memory information, storage information and sensor type information. The function information is information describing the software functions supported by the device, including protocol information describing the communication protocols supported by the device and application information describing the special software functions of the device, such as having a graphics processing function. The environment information is information about the environment in which the device is located, which is used to describe the operating environment of the device, including temperature information, humidity information and light information. The operation status information is the operating status of the device under the current task, including central processing unit load, memory usage, storage usage and network delay.

[0048] After acquiring the device data, the device data needs to be preprocessed to obtain clean and effective data as device information. In this embodiment, the preprocessing includes data cleaning, denoising and data format standardization.

[0049] Data cleaning refers to removing incomplete, abnormal or duplicate data. For example, by checking the strength and credibility of the GPS signal used to obtain coordinate information, low-precision location information can be filtered out. Another example is marking and processing abnormal values ​​in hardware monitoring data, such as obvious data jumps from sensors.

[0050] After the data cleaning is completed, the cleaned data is subjected to denoising, and a variety of denoising algorithms are used to process the cleaned data to remove noise in the cleaned data to ensure the accuracy and consistency of the data. In this embodiment, the denoising algorithm specifically includes Kalman filtering and wavelet transform.

[0051] Finally, the processed denoised data is standardized to convert the data format of the same device into a standardized form for subsequent processing and analysis. For example, the temperature and humidity data collected by different sensors are uniformly converted to the same unit and precision to obtain the final device information.

[0052] Afterwards, a networking model is established based on the device information. In this embodiment, the networking model is a super object model used to describe each device involved in the networking. The super object model is an abstract model used to describe and manage device capabilities, covering multiple aspects such as the device's hardware resources, software functions, current status, and environmental information. This can not only help manage a single device, but also serve as a unified representation of a collection of virtual devices.

[0053] A unified model is used to describe the spatial attributes, hardware characteristics, software functions, real-time operating status and other information of each device, and the devices are classified and managed according to different functional levels, such as perception layer (sensor equipment), processing layer (edge ​​computing nodes), communication layer (network equipment), etc. A corresponding super object model is established for each layer, eventually forming a hierarchical super object model system.

[0054] S2. Divide all the devices into multiple subnets according to the networking model.

[0055] Specifically, in this embodiment, subnet division is performed based on device location information, user hardware information, and user function information.

[0056] First, the devices are preliminarily divided according to their location information. Specifically, the entire system is divided into multiple different areas. Specifically, relatively independent geographical areas are divided into a single area. The relatively independent scope is determined based on the scale of the network and the distribution density of the equipment to ensure that each area has a network of a certain scale, while not having a network that is too large, which would lead to complex network configuration for each area. The specific division method can be manual division or the use of an algorithm to divide according to coordinate information, such as the use of a graph algorithm.

[0057] Afterwards, a location is selected in each area as the cluster center to serve as the basis for subsequent clustering. Specifically, the center point of each area can be calculated as the initial cluster center, or the cluster center can be calculated according to other set rules. For example, a device that interacts most frequently with other devices in the area can be selected as the cluster center, that is, the cluster center.

[0058] Afterwards, the distance between each device and each cluster center is calculated to cluster each device and divide each device into different clusters. The devices in each cluster form a subnet.

[0059] In this embodiment, the distance adopts a multi-dimensional distance measurement method, including spatial distance and functional distance, wherein the spatial distance adopts Euclidean distance, and the Euclidean distance between each device and each cluster center is calculated according to the coordinate information of each device. The functional distance is calculated using Manhattan distance, which firstly needs to vectorize the functional information and hardware information of each device, wherein, for the hardware information, the central processing unit information, memory information, storage information and sensor type information are converted using a set ratio value, and each becomes an element of the distance vector, and for the functional information, if the function is present, the corresponding element is set to 1, and if the function is not present, the corresponding element is set to 0, and then the converted distance vector is used to calculate the Manhattan distance between each device and each cluster center, and finally clustering is performed according to the Manhattan distance and the Euclidean distance.

[0060] It is worth noting that in this embodiment, fine-tuning is performed based on the functional distance of the device in order to ensure that devices with complementary functions can be in the same cluster. Such a choice can improve the efficiency of networking. Similarly, in an optional embodiment, the distance can also include the load distance, which is also calculated using the Manhattan distance and weighted calculation, so as to further optimize the clustering effect. It is worth noting that since the distance is a measure of the similarity between two devices, and in this embodiment, it is necessary to achieve complementarity between different devices, the weighted value of the Manhattan distance in this embodiment is a negative number.

[0061] In this embodiment, the K-means clustering method is specifically used for clustering. In this embodiment, the K value is obtained by counting the number of clusters. In other embodiments, it can also be automatically adjusted according to the algorithm to adapt to the dynamic changes of the device. Finally, after completing the subnet division of each device, the present invention also judges and corrects all subnets through the networking model. Specifically, the super object model as a networking model is a unified description of various attributes of the device, including hardware resources, software functions, current status and environmental information, etc. Based on this information, the system can identify some devices that are unreasonable due to special functions or status, and make adjustments.

[0062] This includes determining whether each indicator of each subnet is within a set range, for example, determining whether the communication bandwidth of the subnet is within a set bandwidth range, determining whether the central processing unit performance of the subnet is within a set central processing unit performance range, and if an indicator is not within the set range, making adjustments thereto.

[0063] In addition, it also includes determining the type of sensors in each subnet. If the type of sensors cannot meet the needs of the subnet, adjustments need to be made and sensors need to be deployed from subnets with excess sensors. Similarly, if the subnet lacks a server with a certain communication protocol or a server with a certain function, it needs to be deployed from other subnets.

[0064] S3. Network each of the subnets according to the networking model.

[0065] Specifically, in this embodiment, the core device of each subnet is selected according to the networking model. In this embodiment, the core device is the device that interacts most with other devices. The core device serves as the central node of the subnet and is responsible for communication coordination and data processing within the subnet.

[0066] After the core device is selected, the function of each device in the subnet is allocated according to the networking model. In this embodiment, the Internet of Things system is specifically used in the field of augmented reality, so the function allocation is specifically allocated to edge nodes, data processing nodes and sensor nodes. Among them, the edge nodes are mainly responsible for data preprocessing and transmission, the data processing nodes are responsible for complex calculations, and the sensor nodes are responsible for environmental data collection.

[0067] After dividing the roles of all nodes, determine the specific network structure based on the networking model. Specifically, define the data flow and fault handling mechanism of each device based on the communication protocols supported by each device, the role of each device, and the performance it has.

[0068] For example, based on the performance of the downstream nodes that each node task needs to occupy, the performance constraints of the network are defined, that is, the constraints are used to ensure that each node can meet the tasks of the upstream nodes, and then the specific networking structure is defined according to the indicators. For example, the total delay of the system is calculated based on the delay between each node and other nodes, and a solution model is established with the total delay as the target, and then the solution model is solved to obtain the specific structure of the subnet.

[0069] After the network is established, the device capability description and status information provided by the super object model are still used to dynamically allocate computing resources and network bandwidth, and the unified management of the virtual resource pool is realized through the soft bus technology, and resources are allocated to each device in the network. In this way, load balancing is achieved. During operation, the load of each device is monitored in real time, and resource allocation is adjusted dynamically to avoid overloading of certain devices or waste of resources.

[0070] In addition, in this embodiment, the edge computing node regularly updates the device information to the super object model to ensure that the model reflects the latest status and capabilities of the device, and in the updating process, automatically detects device anomalies (such as device overload, network interruption), and marks these abnormal states in the super object model to provide a basis for subsequent networking adjustments, and uses historical data to optimize the super object model through machine learning algorithms to gradually improve the model's adaptability and predictive ability to device status changes. In order to promptly discover problems with the networking model, adjust the networking situation, and determine whether re-networking is needed based on actual conditions.

[0071] For example, load balancing needs to be considered in the optimization of networking results, which means that the system will reallocate devices in certain subnets based on the current load of the devices to ensure that the computing resources and communication bandwidth of each subnet are within a reasonable range, thereby avoiding overcrowding or insufficient resources in certain subnets.

[0072] This embodiment divides an entire network into multiple subnets and networks each subnet separately, which effectively reduces the solution space of the overall networking task, greatly reduces the complexity of networking, improves the speed of networking, and improves the timeliness of networking. Therefore, it can quickly network a complex multi-device network, and adjust the network in time when the network situation changes, meet the needs of real-time adjustment of dynamic adjustment, and avoid network congestion or shortage. After completing the formation of the subnet, fine-tune it according to the actual operation situation, so as to gradually approach the effect of the method of networking using overall information, so as to achieve a balance between the dynamic efficiency of networking and the overall effect of networking.

[0073] Embodiment 2

[0074] See also Figure 2 Based on the above method, the present invention also provides a device rapid self-organizing network system, the device rapid self-organizing network system comprising:

[0075] A model building module 51 collects device information of all devices and builds a networking model based on the device information;

[0076] Furthermore, the collecting of device information of all devices and establishing a networking model according to the device information specifically includes:

[0077] Obtaining location information, hardware information, function information, environment information, and operation status information uploaded by all devices, and performing data cleaning, denoising, and standardization on the location information, the hardware information, the function information, the environment information, and the operation status information in turn to obtain the device information;

[0078] A twin digital model of all devices is established based on the device information, and the twin digital model is used as the networking model.

[0079] A subnet division module 52, dividing all the devices into a plurality of subnets according to the networking model;

[0080] Furthermore, dividing all the devices into a plurality of subnets according to the networking model specifically includes:

[0081] Dividing the networking model into multiple areas according to geographical locations;

[0082] For each region, a location is selected as the cluster center, and multiple cluster centers are obtained;

[0083] All devices are clustered according to each cluster center to obtain devices included in multiple subnets.

[0084] Furthermore, clustering all devices according to each cluster center to obtain multiple cluster centers specifically includes:

[0085] Calculating the spatial distance between each of the devices and each of the cluster centers;

[0086] Calculating the functional distance between each of the devices and each of the cluster centers;

[0087] Clustering is performed using the weighted sum of the spatial distance and the functional distance as the distance to obtain devices included in multiple subnets.

[0088] Further, the calculating the functional distance between each of the devices and each of the cluster centers specifically includes:

[0089] Vectorizing the hardware information and the function information of each device to obtain a distance vector of each device;

[0090] According to each of the distance vectors, the functional distance between each of the devices and each of the cluster centers is calculated.

[0091] Furthermore, after clustering all devices according to the cluster centers to obtain devices included in multiple subnets, the method further includes:

[0092] Determining whether the hardware information and function information of each subnet meet the set conditions according to the networking model;

[0093] The subnets that do not meet the set conditions are adjusted according to the networking model.

[0094] The subnet networking module 53 is configured to respectively network each of the subnets according to the networking model.

[0095] Further, networking each of the subnets according to the networking model specifically includes:

[0096] For each of the subnets, allocating functions to all the devices in the subnet according to the networking model;

[0097] For each of the subnets, the data flow direction of all devices in the subnet is set according to the result of the function allocation.

[0098] Embodiment 3

[0099] See also Figure 3Based on the above method, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 3 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0100] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a device fast self-organizing network program 40, which can be executed by the processor 10.

[0101] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the relevant programs of the device rapid self-organizing network method.

[0102] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0103] Embodiment 4

[0104] This embodiment provides a storage medium, wherein the readable storage medium stores a device rapid self-organizing network program, and when the device rapid self-organizing network program is executed by a processor, the steps of the device rapid self-organizing network method as described above are implemented.

[0105] In summary, the present invention greatly reduces the complexity of networking by dividing an entire network into multiple subnets and networking each subnet separately, thereby effectively reducing the solvable space of networking and improving the speed of network solution. Therefore, it is possible to quickly network complex multi-device networks and adjust the network in time when the network situation changes to meet the dynamic needs of the network and avoid network congestion or shortages.

[0106] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0107] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, an optical disk, etc.

[0108] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for rapid self-organizing networking of devices, characterized in that: The device fast self-organizing network method comprises: Collect device information of all devices and establish a networking model based on the device information; Divide all the devices into multiple subnets according to the networking model; Networking each of the subnets according to the networking model; The collecting of device information of all devices and establishing a networking model according to the device information specifically includes: Obtaining location information, hardware information, function information, environment information, and operation status information uploaded by all devices, and performing data cleaning, denoising, and standardization on the location information, the hardware information, the function information, the environment information, and the operation status information in turn to obtain the device information; Establishing twin digital models of all devices according to the device information, and using the twin digital models as the networking model; The dividing all the devices into a plurality of subnets according to the networking model specifically includes: Dividing the networking model into multiple areas according to geographical locations; For each region, a location is selected as the cluster center, and multiple cluster centers are obtained; Cluster all devices according to each cluster center to obtain devices included in multiple subnets; The clustering of all devices according to each cluster center to obtain multiple cluster centers specifically includes: Calculating the spatial distance between each of the devices and each of the cluster centers; Calculating the functional distance between each of the devices and each of the cluster centers; Clustering is performed using a weighted sum of the spatial distance and the functional distance as the distance to obtain devices included in multiple subnets; The calculating the functional distance between each of the devices and each of the cluster centers specifically includes: Vectorize the function information and hardware information of each device to obtain a distance vector; The Manhattan distance between each device and each cluster center is calculated as the functional distance based on the distance vector of each device.

2. A method for rapid self-organizing networking of devices according to claim 1, characterized in that: The calculating the functional distance between each of the devices and each of the cluster centers specifically includes: Vectorizing the hardware information and the function information of each device to obtain a distance vector of each device; According to each of the distance vectors, the functional distance between each of the devices and each of the cluster centers is calculated.

3. A method for rapid self-organizing networking of devices according to claim 1, characterized in that: After clustering all devices according to the cluster centers to obtain devices included in multiple subnets, the method further includes: Determining whether the hardware information and function information of each subnet meet the set conditions according to the networking model; The subnets that do not meet the set conditions are adjusted according to the networking model.

4. A method for rapid self-organizing networking of devices according to claim 3, characterized in that: The networking of each of the subnets according to the networking model specifically includes: For each of the subnets, allocating functions to all the devices in the subnet according to the networking model; For each of the subnets, the data flow direction of all devices in the subnet is set according to the result of the function allocation.

5. A device rapid self-organizing network system, characterized in that: The device rapid self-organizing network system comprises: A model building module collects device information of all devices and builds a networking model based on the device information; A subnet division module, dividing all the devices into multiple subnets according to the networking model; A subnet networking module is used to respectively network each of the subnets according to the networking model; The collecting of device information of all devices and establishing a networking model according to the device information specifically includes: Obtaining location information, hardware information, function information, environment information, and operation status information uploaded by all devices, and performing data cleaning, denoising, and standardization on the location information, the hardware information, the function information, the environment information, and the operation status information in turn to obtain the device information; Establishing twin digital models of all devices according to the device information, and using the twin digital models as the networking model; The dividing all the devices into a plurality of subnets according to the networking model specifically includes: Dividing the networking model into multiple areas according to geographical locations; For each region, a location is selected as the cluster center, and multiple cluster centers are obtained; Cluster all devices according to each cluster center to obtain devices included in multiple subnets; The clustering of all devices according to each cluster center to obtain multiple cluster centers specifically includes: Calculating the spatial distance between each of the devices and each of the cluster centers; Calculating the functional distance between each of the devices and each of the cluster centers; Clustering is performed using a weighted sum of the spatial distance and the functional distance as the distance to obtain devices included in multiple subnets; The calculating the functional distance between each of the devices and each of the cluster centers specifically includes: Vectorize the function information and hardware information of each device to obtain a distance vector; The Manhattan distance between each device and each cluster center is calculated as the functional distance based on the distance vector of each device.

6. A terminal, characterized in that: The terminal includes: a memory, a processor, and a device rapid self-organizing network program stored in the memory and executable on the processor. When the device rapid self-organizing network program is executed by the processor, the terminal is controlled to implement the steps of the device rapid self-organizing network method as described in any one of claims 1 to 4.

7. A readable storage medium, characterized in that: The readable storage medium stores a device rapid self-organizing network program, and when the device rapid self-organizing network program is executed by a processor, the steps of the device rapid self-organizing network method according to any one of claims 1 to 4 are implemented.

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