Dynamic adjustment method, system and device of network and storage medium

Abstract

The invention discloses a dynamic adjustment method, system and device of a network and a storage medium, the network comprises a gateway node, a relay node and a network management device, the relay node is connected with the gateway node and the network management device, and the dynamic adjustment method is applied to the network management device. Comprising the following steps: acquiring real-time monitoring parameters of a network; inputting the real-time monitoring parameter into a trained neural network model to obtain a network state and a channel quality parameter; determining whether the network topology needs to be adjusted according to the state of the network and the channel quality parameter; and if the network topology needs to be adjusted, determining a network adjustment scheme according to the state of the network and the channel quality parameters. The embodiment of the invention can improve the data transmission performance of the network, and can be widely applied to the technical field of network transmission.

Description

Technical Field

[0001] The present invention relates to the field of network transmission technology, and in particular to a method, system, device and storage medium for dynamically adjusting a network. Background Art

[0002] In recent years, with the rapid development of Internet of Things (IoT) technology, LoRaWAN (Long Range Wide Area Network) as a low-power wide area network technology has been widely used in smart cities, agricultural monitoring, environmental monitoring and other fields due to its long-distance communication, low power consumption and good network coverage. With the expansion of the deployment scale of LoRaWAN network, the number of nodes in the network has increased dramatically, which has put higher requirements on the network topology and data transmission. Although LoRaWAN technology itself has the characteristics of strong scalability, in large-scale network applications, how to optimize the network topology to improve data transmission performance has become a problem that needs to be solved urgently. Summary of the invention

[0003] In view of this, in order to solve one of the above problems, an object of an embodiment of the present invention is to provide a method, system, device and storage medium for dynamically adjusting a network, which can improve the data transmission performance of the network.

[0004] On the one hand, an embodiment of the present invention provides a method for dynamic adjustment of a network, wherein the network includes a gateway node, a relay node, and a network management device, wherein the relay node connects the gateway node and the network management device, and the dynamic adjustment method is applied to the network management device, including:

[0005] Get real-time monitoring parameters of the network;

[0006] Inputting the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters;

[0007] Determining whether the network topology needs to be adjusted according to the network status and channel quality parameters;

[0008] If the network topology needs to be adjusted, the network adjustment plan is determined based on the network status and channel quality parameters.

[0009] Optionally, determining whether the network topology needs to be adjusted according to the network state and channel quality parameters includes:

[0010] Calculating the network state and channel quality index according to the network state and channel quality parameters and a preset calculation formula;

[0011] Whether the network topology needs to be adjusted is determined according to the network status, the channel quality index and a preset threshold range.

[0012] Optionally, the determining whether the network topology needs to be adjusted according to the network state and the channel quality index and a preset threshold range includes:

[0013] If the network status and channel quality index are outside the preset threshold range, the network topology needs to be adjusted;

[0014] If the network status and the channel quality index are within the preset threshold range, there is no need to adjust the network topology.

[0015] Optionally, determining a network adjustment scheme according to a network state and a channel quality parameter includes:

[0016] Determine to add nodes, delete nodes or reallocate network nodes based on the network status and channel quality parameters.

[0017] Optionally, according to the network status and channel quality parameters including traffic load and signal coverage, determining the network adjustment scheme according to the network status and channel quality parameters includes:

[0018] If the traffic load is less than a preset load and the signal coverage is greater than a preset coverage, a number of the relay nodes or a number of the gateway nodes are deleted.

[0019] Optionally, the method further comprises:

[0020] If the traffic load is greater than the preset load and the signal coverage is less than the preset coverage, a plurality of the relay nodes or a plurality of the gateway nodes are added.

[0021] Optionally, the method further comprises:

[0022] If the traffic load of multiple nodes is unbalanced, reallocate network nodes.

[0023] On the other hand, an embodiment of the present invention provides a dynamic adjustment system for a network, including:

[0024] The first module is used to obtain real-time monitoring parameters of the network;

[0025] The second module is used to input the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters;

[0026] A third module is used to determine whether the network topology needs to be adjusted according to the state of the network and the channel quality parameters;

[0027] The fourth module is used to determine a network adjustment plan according to the network status and channel quality parameters if the network topology needs to be adjusted.

[0028] On the other hand, an embodiment of the present invention provides a network dynamic adjustment device, including:

[0029] at least one processor;

[0030] at least one memory for storing at least one program;

[0031] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0032] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to perform the above method.

[0033] Implementation of the embodiments of the present invention includes the following beneficial effects: In this embodiment, the network includes a gateway node, a relay node and a network management device, the relay node connects the gateway node and the network management device, the network management device obtains real-time monitoring parameters of the network, inputs the real-time monitoring parameters into a trained neural network model, obtains the network status and channel quality parameters, determines whether the network topology needs to be adjusted according to the network status and channel quality parameters, and if the network topology needs to be adjusted, determines the network adjustment plan according to the network status and channel quality parameters, and adaptively and dynamically adjusts and optimizes the network topology according to the real-time monitoring parameters to improve signal coverage and signal quality in complex environments, etc., to ensure that each terminal device can stably access the network and improve the data transmission performance of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic diagram of a network topology structure provided by an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the steps of a method for dynamic adjustment of a network provided by an embodiment of the present invention;

[0036] Figure 3 is a flow chart of a method for dynamically adjusting a network according to a specific embodiment of the present invention;

[0037] Figure 4 It is a structural block diagram of a network dynamic adjustment system provided by an embodiment of the present invention;

[0038] Figure 5 It is a structural block diagram of a network dynamic adjustment device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0040] See also Figure 1 The terminal is connected to the gateway node, the gateway node is connected to the relay node and the network management device, and the relay node is connected to the network management device. The terminal can communicate with the network management device through the gateway node and the relay node, and the terminal can also communicate with the network management device through the gateway node.

[0041] like Figure 2 As shown, an embodiment of the present invention provides a dynamic adjustment method for a network, wherein the network includes a gateway node, a relay node, and a network management device, wherein the relay node connects the gateway node and the network management device, and the dynamic adjustment method is applied to the network management device, including:

[0042] S100: Obtain real-time monitoring parameters of the network.

[0043] The real-time monitoring parameters are determined according to the actual application, and this embodiment does not impose specific restrictions. For example, the real-time monitoring parameters include but are not limited to any one or more of signal strength, signal-to-noise ratio, network delay, traffic load, packet loss rate, etc. The real-time monitoring parameters are measured by the gateway node or relay node and sent to the network management device. Among them, the signal strength is used to evaluate the receiving strength of the signal, which affects the stability of the network connection, etc.; the signal-to-noise ratio is a parameter to measure the signal quality, which affects the reliability of data transmission; the network delay refers to the time delay from the source device to the clear device, etc., which affects the network performance; the traffic load refers to the amount of data transmitted by the network, which is used to judge the bandwidth requirements of the network, etc.

[0044] S200: Input the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters.

[0045] The state of the network and the channel quality parameters are parameter indicators that characterize the network transmission performance. The state of the network and the channel quality parameters are determined according to the actual application, and this embodiment does not impose specific restrictions. For example, link quality, signal coverage, etc. Link quality can be evaluated based on signal strength and signal-to-noise ratio, and signal coverage can be determined based on signal strength and communication distance. Before prediction, the neural network model needs to be trained using sample data. The neural network model includes an input layer, a hidden layer, and an output layer. The input layer receives real-time monitoring parameters, the hidden layer is used to extract features from the input real-time monitoring parameters, and the output layer is used to output the state of the network and channel quality parameters. It should be noted that the number of layers and the number of neurons in the hidden layer are determined according to the actual application, and this embodiment does not impose specific restrictions. The training sample data includes historical monitoring parameters, historical network topology, historical state and channel quality parameters.

[0046] S300: Determine whether the network topology needs to be adjusted according to the network status and channel quality parameters.

[0047] The quality of network transmission performance is determined based on the network status and channel quality parameters. If the network transmission performance is good, the network topology does not need to be adjusted; if the network transmission performance is not good, the network topology needs to be adjusted.

[0048] S400: If the network topology needs to be adjusted, determine a network adjustment plan according to the network status and channel quality parameters.

[0049] The network status and channel quality parameters include a variety of indicator parameters, and different indicator parameters represent different meanings. If the network topology needs to be adjusted, a network adjustment plan can be determined based on the status and channel quality parameters of the specific unqualified network.

[0050] Optionally, determining whether the network topology needs to be adjusted according to the network state and channel quality parameters includes:

[0051] S310, calculating the network state and channel quality index according to the network state and channel quality parameters and a preset calculation formula;

[0052] S320: Determine whether the network topology needs to be adjusted according to the network status, the channel quality index and a preset threshold range.

[0053] The preset calculation formula is determined according to the actual application, and this embodiment does not make specific restrictions. For example, the weighted summation formula, different network states and channel quality parameters correspond to different weights. The network state and channel quality index characterize the quality of network transmission performance. The network state and channel quality index can be a positive index or a negative index. If the network state and channel quality index are positive indexes, the larger the network state and channel quality index is, the better the network transmission performance is. If the network state and channel quality index are negative indexes, the smaller the network state and channel quality index is, the better the network transmission performance is.

[0054] The preset threshold range is determined according to actual application, and this embodiment does not impose specific restrictions. For example, the preset threshold range is determined according to acceptable network transmission performance. Whether the network topology needs to be adjusted is determined based on whether the network status and channel quality index are within the preset threshold range.

[0055] Optionally, determining whether the network topology needs to be adjusted according to the network state and the channel quality index and a preset threshold range includes:

[0056] S321, if the network status and channel quality index are outside the preset threshold range, the network topology needs to be adjusted;

[0057] S322: If the network status and the channel quality index are within a preset threshold range, there is no need to adjust the network topology.

[0058] If the network status and channel quality index are outside the preset threshold range, it indicates that the network cannot meet the set network transmission performance and the network topology needs to be adjusted; if the network status and channel quality index are within the preset threshold range, it indicates that the network can meet the set network transmission performance and there is no need to adjust the network topology.

[0059] Optionally, determining a network adjustment scheme according to a network state and a channel quality parameter includes:

[0060] S410: Determine whether to add nodes, delete nodes, or reallocate network nodes according to the network status and channel quality parameters.

[0061] Adding a node includes adding a gateway node and / or adding a relay node, deleting a node includes deleting a gateway node and / or adding a relay node, and reallocating network nodes includes reallocating the positions and numbers of gateway nodes and relay nodes.

[0062] Optionally, according to the network status and channel quality parameters including traffic load and signal coverage, determining a network adjustment scheme according to the network status and channel quality parameters includes:

[0063] S411. If the traffic load is less than the preset load and the signal coverage is greater than the preset coverage, delete several relay nodes or several gateway nodes.

[0064] The preset load is determined according to the actual application, and this embodiment does not make specific restrictions. For example, the preset load is determined according to the traffic load that the node can bear. Signal coverage refers to the coverage degree of the signal. The preset coverage is determined according to the actual application, and this embodiment does not make specific restrictions. For example, the preset coverage is determined according to the signal that can be transmitted to the network management device. If the traffic load is less than the preset load, it means that the traffic load of the node is light, and the signal coverage is greater than the preset coverage, it means that the signal coverage of the node overlaps with that of other nodes, and several redundant relay nodes or several redundant gateway nodes can be deleted. In addition, if the link quality of some nodes is continuously poor and cannot be improved by adjusting the topology or channel, you can consider deleting these nodes to avoid wasting network resources; when some nodes cannot provide stable connections for a long time, it may be due to hardware failure or other problems, and you can consider deleting these nodes.

[0065] Optionally, the method further comprises:

[0066] S412: If the traffic load is greater than the preset load and the signal coverage is less than the preset coverage, add several relay nodes or several gateway nodes.

[0067] If the traffic load is greater than the preset load, it indicates that the node's traffic load is heavy. If the signal coverage is less than the preset coverage, it indicates that there may be a lack of signal coverage between the node and other nodes. Add several relay nodes or gateway nodes to share the load, reduce data transmission delay or packet loss rate, and improve signal quality and communication quality.

[0068] Optionally, the method further comprises:

[0069] S413: If the traffic loads of multiple nodes are unbalanced, reallocate the network nodes.

[0070] Traffic load imbalance means that the traffic load between different nodes is too different. When the load on some nodes is too high and the load on other nodes is light, the network nodes may need to be reallocated. If the number of devices in the network changes too quickly (for example, adding new nodes or deleting nodes), the topology needs to be dynamically adjusted. If the channels of multiple nodes are interfered with, the network performance can be improved by adjusting the channel configuration or changing the channel.

[0071] See also Figure 3 In a specific embodiment, the dynamic adjustment process of the network is as follows:

[0072] Start to initialize the network, obtain the network real-time monitoring parameters, and input the network real-time monitoring parameters into the trained dynamic neural network to obtain the network status and channel quality parameters, then calculate the network status and channel quality index based on the network status and channel quality parameters, and determine whether the network topology needs to be adjusted based on the network status and channel quality index. If the network topology does not need to be adjusted, the devices will self-organize the connection. If the network topology needs to be adjusted, determine whether it is sufficient to add network nodes. If it is sufficient, add new network nodes and update the network topology after adding them. If it is not sufficient to add new network nodes, determine whether it is sufficient to delete network nodes. If it is sufficient to delete network nodes, delete the network nodes with the lowest dedication and update the network.

[0073] Implementation of the embodiments of the present invention includes the following beneficial effects: In this embodiment, the network includes a gateway node, a relay node and a network management device, the relay node connects the gateway node and the network management device, the network management device obtains real-time monitoring parameters of the network, inputs the real-time monitoring parameters into a trained neural network model, obtains the network status and channel quality parameters, determines whether the network topology needs to be adjusted according to the network status and channel quality parameters, and if the network topology needs to be adjusted, determines the network adjustment plan according to the network status and channel quality parameters, and adaptively and dynamically adjusts and optimizes the network topology according to the real-time monitoring parameters to improve signal coverage and signal quality in complex environments, etc., to ensure that each terminal device can stably access the network and improve the data transmission performance of the network.

[0074] like Figure 4 As shown, an embodiment of the present invention provides a dynamic adjustment system for a network, including:

[0075] The first module is used to obtain real-time monitoring parameters of the network;

[0076] The second module is used to input the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters;

[0077] The third module is used to determine whether the network topology needs to be adjusted according to the network status and channel quality parameters;

[0078] The fourth module is used to determine a network adjustment plan according to the network status and channel quality parameters if the network topology needs to be adjusted.

[0079] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] like Figure 5As shown, an embodiment of the present invention provides a dynamic adjustment device for a network, including:

[0081] at least one processor;

[0082] at least one memory for storing at least one program;

[0083] When at least one program is executed by at least one processor, the at least one processor implements the above method.

[0084] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored. The program executable by the processor is used to perform the above method when executed by the processor.

[0085] Among them, the memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. The memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a remote memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0086] It can be seen that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0087] In addition, the embodiment of the present application also discloses a computer program product or a computer program, which is stored in a computer-readable storage medium. The processor of the computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the above method.

[0088] The embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above method when executed by the processor. Similarly, the contents of the above method embodiment are all applicable to the present storage medium embodiment, and the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those achieved by the above method embodiment.

[0089] It is understood that all or some steps and systems in the disclosed method above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0090] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for dynamically adjusting a network, characterized in that: The network includes a gateway node, a relay node and a network management device, the relay node connects the gateway node and the network management device, and the dynamic adjustment method is applied to the network management device, including: Obtain real-time monitoring parameters of the network; Inputting the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters; Determining whether the network topology needs to be adjusted according to the network status and channel quality parameters; If the network topology needs to be adjusted, the network adjustment plan is determined based on the network status and channel quality parameters.

2. The method according to claim 1, characterized in that: Determining whether to adjust the network topology according to the network status and channel quality parameters includes: Calculating the network state and channel quality index according to the network state and channel quality parameters and a preset calculation formula; Whether the network topology needs to be adjusted is determined according to the network status, the channel quality index and a preset threshold range.

3. The method according to claim 2, characterized in that The determining whether the network topology needs to be adjusted according to the network status and the channel quality index and a preset threshold range includes: If the network status and channel quality index are outside the preset threshold range, the network topology needs to be adjusted; If the network status and the channel quality index are within the preset threshold range, there is no need to adjust the network topology.

4. The method according to claim 1, characterized in that: The determining of the network adjustment scheme according to the network status and channel quality parameters includes: Determine to add nodes, delete nodes or reallocate network nodes based on the network status and channel quality parameters.

5. The method according to claim 4, characterized in that According to the network status and channel quality parameters including traffic load and signal coverage, the network adjustment scheme is determined according to the network status and channel quality parameters, including: If the traffic load is less than a preset load and the signal coverage is greater than a preset coverage, a number of the relay nodes or a number of the gateway nodes are deleted.

6. The method according to claim 5, characterized in that The method further comprises: If the traffic load is greater than the preset load and the signal coverage is less than the preset coverage, a plurality of the relay nodes or a plurality of the gateway nodes are added.

7. The method according to claim 5, characterized in that The method further comprises: If the traffic load of multiple nodes is unbalanced, reallocate network nodes.

8. A network dynamic adjustment system, characterized in that: include: The first module is used to obtain real-time monitoring parameters of the network; The second module is used to input the real-time monitoring parameters into the trained neural network model to obtain the network status and channel quality parameters; A third module is used to determine whether the network topology needs to be adjusted according to the state of the network and the channel quality parameters; The fourth module is used to determine a network adjustment plan according to the network status and channel quality parameters if the network topology needs to be adjusted.

9. A network dynamic adjustment device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 7 when executed by the processor.

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