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CN-122027646-A - Cloud intelligent scheduling method and system for large-scale networking equipment

CN122027646ACN 122027646 ACN122027646 ACN 122027646ACN-122027646-A

Abstract

The invention discloses a cloud intelligent scheduling method and system for large-scale networking equipment, which relate to the field of equipment cloud and comprise the following operation steps of S1, establishing cloud and gateway connection, S2, accessing the gateway to the equipment, S3, establishing a network node signal intensity quantization model, S4, a group scheduling algorithm based on signal intensity, and S5, establishing an equipment-gateway connection relation. The cloud intelligent scheduling method for large-scale networking equipment can be applied to the situation that a large number of equipment is used for wireless networking, and in the actual project scene, such as a large-scale factory, a hospital, a park, a large-scale parking lot and the like, a large number of wireless intelligent equipment needs to be arranged.

Inventors

  • LI XUANZHENG

Assignees

  • 德微电技术(深圳)有限公司

Dates

Publication Date
20260512
Application Date
20260127

Claims (5)

  1. 1. The cloud intelligent scheduling method for the large-scale networking equipment is characterized by comprising the following steps of: S1, establishing cloud connection with a gateway, namely deploying a cloud service platform, communicating with at least one gateway in a wireless connection mode, and downwards sending a network allocation instruction by the gateway; s2, a device access gateway connects the device to be cloud-loaded to the gateway in a wireless mode, so that the device can receive and respond to the instruction from the cloud; S3, establishing a network node signal intensity quantization model, namely dividing a signal coverage area into a plurality of subareas by taking a central node as a reference, and quantizing the signal intensity into corresponding weights I (A1), I (A2), I (Ak) according to the range of the received signal intensity of each area; s4, a group scheduling algorithm based on signal strength comprises the following steps: A. The group election comprises the steps of obtaining the signal intensity weight value of each neighbor node and storing the signal intensity weight value into a neighbor node table, calculating the sum Sn of the signal intensity weight values of the neighbor nodes, calculating the node stability weight value Cn by periodically detecting the network topology change, obtaining the node residual electric quantity Dn, obtaining the node information quantity weight value Fn and expressing the node information quantity weight value as the reciprocal of communication flow; calculating node comprehensive weight H=k1· Sn-k2.Cn+k3.Fn+k4.Dn, wherein k1, k2, k3, k4 are weight factors, and k1+k2+k3+k4=1; selecting the node with the largest H value as the cluster head, and selecting the node with smaller ID as the cluster head if the H values are the same; B. The group range is determined by presetting an ideal node number N and setting the node number range as [ N-N, N+m ]; the N nodes with the strongest signal strength are selected by the group first choice to be added into the group, the node which is not allocated selects the group head with the strongest signal strength to be added, if the number of the nodes in the group is lower than the lower limit, the group is broken, and the node reselects the group head to be added; C. The dynamic updating in the group is that the node joining condition is that the signal strength from the node joining condition to the head of the group does not belong to any group, and the signal strength from the node joining condition to the head of the group meets the threshold value, and the number of the nodes in the group does not exceed the upper limit; and S5, establishing a device-gateway connection relation, wherein the device judges whether to respond to the network allocation instruction of the gateway according to an algorithm rule, the device meeting the access condition replies registration information to the gateway in batches, the device with insufficient signal strength does not respond, and the registered device ignores the network allocation instructions of other gateways.
  2. 2. The cloud intelligent scheduling method of large-scale networking equipment according to claim 1, wherein in the step S3, the preset condition of the network node signal intensity quantization model is that the node is free space propagation, and the node works at a conventional fixed power.
  3. 3. The cloud intelligent scheduling method of large-scale networking equipment according to claim 1, wherein In the step S3, the signal intensity I received by the nodes In different areas is assigned as I (A1) =4I (A2), I (A3) =3, I (A4) =1, and I (A5) =0.5, the signal intensity weight In of the node received by the nodes In different areas can be represented by a graph, when a certain node is In the range of A4, after the node receives a signal from a central node, the node quantifies the signal intensity as 1, and the signal intensity is stored In a neighbor node table of the node.
  4. 4. The cloud intelligent scheduling method of large-scale networking equipment according to claim 1, wherein in the step S3, in actual conditions, according to different environments, the positions of nodes and the signal intensities of the nodes do not have direct corresponding relation, so that the nodes are assigned according to the range of the signal intensities of the signals received by the nodes, the signal intensity weight of each node is more stable by quantizing the signal intensity of each node, the back and forth movement of the node, the relative movement of the node and the movement within a small range of the node are ignored, and the sum Sn of the signal intensity weights of the nodes is more stable, thereby reducing the times of cluster head conversion and simplifying calculation.
  5. 5. A large-scale networking equipment cloud intelligent scheduling system for implementing the method of any one of claims 1-4, comprising: the cloud platform is used for providing equipment access and management services; the gateway is in wireless connection with the cloud end and is used for issuing a network allocation instruction and receiving registration information of the equipment; and the networking devices are connected with the cloud through a gateway and execute the group scheduling algorithm to realize self-organizing network access and group maintenance.

Description

Cloud intelligent scheduling method and system for large-scale networking equipment Technical Field The invention relates to the field of equipment cloud, in particular to a cloud intelligent scheduling method and system for large-scale networking equipment. Background With the rapid development of the internet of things technology and the continuous expansion of application scenes, large-scale equipment networking and access to the cloud become normal. In the traditional equipment cloud and networking method, a gateway is accessed by adopting a direct equipment competition response or simple polling mechanism, and when the equipment is huge in quantity, dense in distribution and dynamic in network environment, the problems of signal interference, access conflict, network congestion and the like are extremely easy to occur, so that the access efficiency is low, the network topology is unstable, and the processing load of the gateway and the management complexity of the whole system are increased. In addition, the lack of effective self-organization and cooperative mechanism between devices makes intelligent scheduling and optimization according to real-time signal quality, node state and network load difficult, and limits the expandability and stability of the system. In order to overcome the defects, the technology provides an intelligent scheduling mechanism based on self-adaptive grouping. The core idea is that the equipment to be accessed is regarded as a distributed node, and the equipment can autonomously and intelligently elect the cluster head and form a stable subgroup by establishing a signal intensity quantization model and a multiparameter comprehensive evaluation algorithm. According to the method, the access process of the mass equipment is changed from unordered competition to ordered and batch collaborative registration, so that an access storm is effectively avoided, and the influence caused by network dynamic property is reduced by optimizing a group structure, thereby realizing efficient, stable and self-organized cloud access and resource scheduling of the equipment in a large-scale networking environment. Disclosure of Invention The invention mainly aims to provide a cloud intelligent scheduling method and system for large-scale networking equipment, which can effectively solve at least one technical problem in the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: A cloud intelligent scheduling method for large-scale networking equipment comprises the following operation steps: S1, establishing cloud connection with a gateway, namely deploying a cloud service platform, communicating with at least one gateway in a wireless connection mode, and downwards sending a network allocation instruction by the gateway; s2, a device access gateway connects the device to be cloud-loaded to the gateway in a wireless mode, so that the device can receive and respond to the instruction from the cloud; S3, establishing a network node signal intensity quantization model, namely dividing a signal coverage area into a plurality of subareas by taking a central node as a reference, and quantizing the signal intensity into corresponding weights I (A1), I (A2), I (Ak) according to the range of the received signal intensity of each area; s4, a group scheduling algorithm based on signal strength comprises the following steps: A. The group election comprises the steps of obtaining the signal intensity weight value of each neighbor node and storing the signal intensity weight value into a neighbor node table, calculating the sum Sn of the signal intensity weight values of the neighbor nodes, calculating the node stability weight value Cn by periodically detecting the network topology change, obtaining the node residual electric quantity Dn, obtaining the node information quantity weight value Fn and expressing the node information quantity weight value as the reciprocal of communication flow; calculating node comprehensive weight H=k1· Sn-k2.Cn+k3.Fn+k4.Dn, wherein k1, k2, k3, k4 are weight factors, and k1+k2+k3+k4=1; selecting the node with the largest H value as the cluster head, and selecting the node with smaller ID as the cluster head if the H values are the same; B. The group range is determined by presetting an ideal node number N and setting the node number range as [ N-N, N+m ]; the N nodes with the strongest signal strength are selected by the group first choice to be added into the group, the node which is not allocated selects the group head with the strongest signal strength to be added, if the number of the nodes in the group is lower than the lower limit, the group is broken, and the node reselects the group head to be added; C. The dynamic updating in the group is that the node joining condition is that the signal strength from the node joining condition to the head of the group does not belong to any group, and the signal stre