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

Abstract

The invention discloses a large-scale networking equipment clouding intelligent scheduling method and system, and relates to the field of equipment clouding, and the method comprises the following operation steps: S1, building a connection between a cloud end and a gateway; s2, accessing the equipment to the gateway; s3, establishing a network node signal intensity quantification model; s4, a group scheduling algorithm based on signal intensity; and S5, establishing a device-gateway connection relationship. The cloud intelligent scheduling method for the large-scale networking equipment can be applied to a scene in which a large number of equipment is used for wireless networking, and in an actual project scene such as a large factory area, a hospital, a park, a large parking lot and the like, a large number of wireless intelligent equipment needs to be arranged, so that a large number of wireless intelligent equipment can be distributed in the scene. By using the measurement of the ad-hoc network and the cloud, the project implementation can be quickly carried out, and the problem that a large number of wireless intelligent devices are clouded in a region with a larger area and in a scene with multiple intelligent devices at present can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of cloud-based equipment, and in particular to a method and system for intelligent scheduling of cloud-based large-scale networked equipment. Background Technology

[0002] With the rapid development of IoT technology and the continuous expansion of application scenarios, large-scale device networking and cloud access have become commonplace. Traditional methods of device cloudification and networking typically employ direct contention or simple polling mechanisms for gateway access. When the number of devices is large, their distribution is dense, and the network environment is dynamically changing, this can easily lead to signal interference, access conflicts, and network congestion, resulting in low access efficiency, unstable network topology, and increased gateway processing load and overall system management complexity. Furthermore, the lack of effective self-organization and collaboration mechanisms among devices makes intelligent scheduling and optimization based on real-time signal quality, node status, and network load difficult, limiting system scalability and stability.

[0003] To overcome the aforementioned shortcomings, this technology proposes an intelligent scheduling mechanism based on adaptive clustering. Its core idea is to treat devices to be connected as distributed nodes. By establishing a signal strength quantification model and a multi-parameter comprehensive evaluation algorithm, devices can autonomously and intelligently elect a group leader and form a stable subgroup. This method transforms the access process of massive devices from disordered competition to orderly, batch-based collaborative registration, effectively avoiding access storms. Furthermore, by optimizing the group structure, it reduces the impact of network dynamics, thereby achieving efficient, stable, and self-organizing cloud-based access and resource scheduling for devices in large-scale networking environments. Summary of the Invention

[0004] The main objective of this invention is to provide a cloud-based intelligent scheduling method and system for large-scale networked devices, which can effectively solve at least one of the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A cloud-based intelligent scheduling method for large-scale networked devices includes the following steps:

[0007] S1: Establish cloud-gateway connection: Deploy a cloud service platform and communicate with at least one gateway via wireless connection, with the gateway sending network configuration instructions downwards;

[0008] S2: Device access gateway: Connect the device to be connected to the cloud to the gateway wirelessly, so that the device can receive and respond to instructions from the cloud;

[0009] S3: Establish a network node signal strength quantization model: Based on the central node, divide its signal coverage area into multiple sub-regions, and quantize the signal strength into corresponding weights I(A1), I(A2), ..., I(Ak) according to the range of received signal strength in each region.

[0010] S4: A group scheduling algorithm based on signal strength, including:

[0011] A. Group leader election: Each node obtains the signal strength weights of its neighboring nodes and stores them in the neighboring node table, and calculates the sum of its signal strength weights Sn; by periodically detecting network topology changes, it calculates the node stability weight Cn; it obtains the node's remaining power Dn; it obtains the node's information weight Fn, which is represented as the reciprocal of the communication traffic.

[0012] The overall weight of the nodes is calculated as H = k1·Sn - k2·Cn + k3·Fn + k4·Dn, where k1, k2, k3, and k4 are weight factors, and k1 + k2 + k3 + k4 = 1. The node with the largest H value is selected as the group head. If the H values ​​are the same, the node with the smaller ID is selected as the group head.

[0013] B. Group range determination: The ideal number of nodes N is preset, and the range of the number of nodes is set to [Nn, N+m]; the group first selects the N nodes with the strongest signal strength to join the group, and unassigned nodes select the group leader with the strongest signal strength to join. If the number of nodes in the group is lower than the lower limit, the group is disbanded, and the nodes re-select the group leader to join.

[0014] C. Dynamic updates within the group: The conditions for a node to join are: not belonging to any group, the signal strength to the group head meets the threshold, and the number of nodes in the group has not exceeded the upper limit; the conditions for a node to leave are: the signal strength is lower than the set value or there is no response after a timeout.

[0015] S5: Establish device-gateway connection: The device determines whether to respond to the gateway's network configuration command based on the algorithm rules. Devices that meet the access conditions reply with registration information to the gateway in batches, while devices with insufficient signal strength do not respond; registered devices ignore the network configuration commands of other gateways.

[0016] Preferably, in step S3, the preset condition for the network node signal strength quantization model is: the node propagates in free space and operates at a conventional fixed power.

[0017] Preferably, in step S3, the signal strength I received by nodes in different regions is assigned the values ​​I(A1)=4I(A2), I(A3)=3, I(A4)=1, and I(A5)=0.5. The signal strength weight In received by the nodes in different regions can be obtained from the figure. When a node is in the range of A4, after the node receives the signal from the central node, the node quantizes the signal strength as 1 and stores it in its neighbor node table.

[0018] Preferably, in step S3, in actual situations, depending on the environment, there is no direct correspondence between the position of a node and its signal strength. Therefore, the node is assigned a value based on the range of the signal strength received by the node. By quantifying the signal strength of each node, the node signal strength weight is made more stable. The back-and-forth movement of the node, the relative movement of the node, and the movement of the node within a small range are ignored. The sum of the node signal strength weights Sn is also more stable, thereby reducing the number of group head changes and simplifying the calculation.

[0019] Beneficial effects

[0020] Compared with existing technologies, this invention provides a cloud-based intelligent scheduling method and system for large-scale networked devices, which has the following beneficial effects:

[0021] This invention introduces a signal strength-based group scheduling algorithm to achieve intelligent filtering and orderly scheduling of access devices. Devices do not blindly respond to gateway commands, but rather decide whether to initiate registration based on dynamically calculated signal strength and a comprehensive weighting. This effectively avoids wireless channel conflicts and network storms caused by a large number of devices responding simultaneously in dense scenarios, significantly improving the success rate and efficiency of gateway access and ensuring the initial stability of the network topology.

[0022] The adaptive clustering-based scheduling mechanism proposed in this invention achieves complete automation and intelligence in the process of large-scale device cloudification. The system transforms disordered access competition into batch-based, cluster-based collaborative registration by having devices autonomously elect group leaders and dynamically define and maintain groups. This process requires no manual on-site configuration or intervention, greatly simplifying the deployment process. It is particularly suitable for scenarios with a large number of devices and a wide distribution, such as large factories, industrial parks, and parking lots, and can significantly shorten project delivery cycles.

[0023] This invention not only provides an intelligent scheduling method, but its corresponding system architecture forms a complete solution. This system can optimize connection relationships based on real-time network conditions, suppressing responses from devices with weak signals and ignoring redundant commands from registered devices. This reduces excessive reliance on gateway processing capabilities and enhances the overall network robustness and manageability. From an implementation perspective, this method and system can significantly reduce the manual configuration costs and post-maintenance complexity of large-scale wireless networking projects, demonstrating high practical value and promising prospects for widespread adoption. Attached Figure Description

[0024] Figure 1 This is a flowchart of the cloud-based intelligent scheduling method for large-scale networked devices proposed in this invention;

[0025] Figure 2 This is a schematic diagram of the network node signal strength quantization model proposed in this paper. Detailed Implementation

[0026] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0027] like Figure 1 As shown, a cloud-based intelligent scheduling method for large-scale networked devices includes the following steps:

[0028] S1: Establish cloud-gateway connection: Deploy a cloud service platform and communicate with at least one gateway via wireless connection, with the gateway sending network configuration instructions downwards;

[0029] S2: Device access gateway: Connect the device to be connected to the cloud to the gateway wirelessly, so that the device can receive and respond to instructions from the cloud;

[0030] S3: Establish a network node signal strength quantization model: Taking the central node as the benchmark, divide its signal coverage area into multiple sub-regions. Based on the range of received signal strength in each region, quantize the signal strength into corresponding weights I(A1), I(A2), ..., I(Ak). The preset conditions for the network node signal strength quantization model are: nodes propagate in free space, nodes operate at a conventional fixed power, and the signal strength I received by nodes in different regions is assigned the values ​​I(A1) = 4I(A2), I(A3) = 3, I(A4) = 1, and I(A5) = 0.5. The signal strength weight In received by nodes in different regions can be referenced... Figure 2The value is assigned as follows: when a node is within the range of A4, after receiving a signal from the central node, the node quantizes the signal strength as 1 and stores it in its neighbor node table. In reality, depending on the environment, there is no direct correspondence between the node's position and its signal strength. Therefore, the value of the node is assigned based on the range of the signal strength received by the node. By quantizing the signal strength of each node, the node signal strength weight is made more stable. The movement of nodes back and forth, relative movement of nodes, and movement of nodes within a small range are ignored. The sum of the node signal strength weights Sn is also more stable, which is used to reduce the number of group head changes and to simplify the calculation.

[0031] S4: A group scheduling algorithm based on signal strength, including:

[0032] A: Selection of the group leader: Each node n determines the signal strength In of its received neighboring nodes and stores it in the neighboring node table. Then, the signal strength In of all neighboring nodes is added together to obtain Sn, that is, Sn = I1 + 2 + ... + In. Sn is the sum of the signal strength weights of all neighboring nodes of each node, which is called the sum of the signal strength weights of that node.

[0033] Every certain period of time ΔT, each node performs a topology probe. Each node compares its neighbor node table at time T with the node table at time T, which is the time period before time T. For newly added nodes, the signal strength weight I(Ti) at time Ti is taken, and for nodes that have left, the signal strength weight I(Tz) at time Tz is taken. The signal strength weights of the newly added and left nodes are then added together to obtain Cn = I(Ti) + I(Tz), where Cn is used to directly reflect the stability of a node relative to its neighbor nodes.

[0034] Each node determines its remaining battery power Dn;

[0035] The information weight Fn of a node is represented by the reciprocal of the communication traffic of each node;

[0036] The formula for selecting the group leader is: H=k1Sn-k2Cn+k3Fn+k4Dn, where Sn is the node signal strength weight, Cn is the node stability weight, Fn is the information content weight, and Dn is the battery power. k4, k1, k2, and k3 are weighting factors used to represent the importance of various parameters and satisfy k+k2+k+k4=1.

[0037] If a node finds that it has the largest H value among its neighboring nodes, it will set itself as the group leader. If the H values ​​are equal, the node with the smaller ID number will be chosen as the group leader.

[0038] B: Group range determination: The size of the group has an ideal number of nodes N. The group head determines the upper and lower limits of the number of nodes in the group based on the ideal number of nodes N. The range is expressed as [Nn, N+m], where the values ​​of m and n are set according to the specific environment.

[0039] After the group leader is selected, the N nodes with the strongest signal strength (N being the ideal number of nodes) among the neighboring nodes will be assigned to this group.

[0040] After the partitioning is complete, any nodes in the undetermined state will choose the leader with the strongest signal strength as their new leader, unless that leader explicitly states it will not accept any new nodes.

[0041] If the number of nodes in a group is less than the minimum limit for the number of nodes in a group, the group will be automatically disbanded, and each node will find the group leader with the strongest signal strength and join that group.

[0042] C: Group Update: For a node to join a group, it must meet the following conditions: the node does not currently belong to any group, the signal strength from the node to the group head meets the joining conditions, and the number of nodes in the group after joining does not exceed the upper limit. Any node that meets all three conditions will be allowed to join the group.

[0043] The condition for a node to leave is that the signal strength of the node drops to a certain value. The signal strength is used as a criterion to optimize large, stable clusters and disband small, unstable clusters.

[0044] If no signal is received from the node within the time specified by the SAOW algorithm, the node is judged to have disappeared, and the node table in the group is updated.

[0045] S5: Establish device-gateway connection: Based on algorithm rules, determine whether to respond to the gateway's information. According to the calculation rules, if the device meets the conditions for accessing the gateway, it will respond to the gateway information in batches to register. Devices with weak signals will not respond to the gateway's information, while devices with strong signals will reply to the gateway and register their own information with the gateway, thereby establishing a connection. If a device has already established a relationship with the gateway, it will not process the network configuration instructions received from other gateways.

[0046] In some preferred embodiments, a cloud-based intelligent scheduling system for large-scale networked devices is also proposed, including:

[0047] A cloud platform used to provide device access and management services;

[0048] At least one gateway, wirelessly connected to the cloud, is used to issue network configuration commands and receive device registration information;

[0049] Several network devices are connected to the cloud through a gateway and execute the cloud-based intelligent scheduling method for large-scale network devices to achieve self-organized network access and group maintenance.

[0050] 1. This method primarily involves a gateway issuing network configuration commands. Surrounding devices receive these commands and, based on an algorithm, calculate their signal strength. Devices with weak signals will not respond to the gateway, while those with strong signals will reply and register their information with the gateway, thus establishing a connection. Devices already connected to the gateway will not process commands from other gateways. All devices receive commands wirelessly and transmit them from the cloud to the gateway. Upon receiving the commands, the gateway synchronously forwards them, and surrounding devices receive the commands. After receiving the network configuration command, a signal strength algorithm is performed based on the corresponding information. The algorithm rules determine whether to respond to the gateway's information. According to the calculation rules, if the device meets the conditions for accessing the gateway, it will respond to the gateway information in batches to register. This enables all devices to automatically complete the gateway's cloud access capability without any on-site intervention. This method can be applied to scenarios with a large number of devices for wireless networking. In actual project scenarios such as large factories, hospitals, parks, and large parking lots, a large number of wireless intelligent devices need to be deployed. In such scenarios, by using self-organizing network and self-cloud measurement, the project can be implemented quickly.

[0051] This method solves the current problem of large-scale self-organizing network cloud access for devices. Taking a parking lot as an example: there are tens of thousands of smart lights on the project site that need to access the internet. These numerous smart lights need to report data through a gateway via wireless networking. Therefore, the gateway needs to be able to accurately access the cloud access of the devices around it. This method solves the current problem of accessing the cloud access of a large number of wireless smart devices in large areas such as parks, hospitals, factories, and parking lots, as well as in scenarios with many smart devices. As a result, it can accelerate the project delivery cycle and reduce labor and maintenance costs.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A cloud-based intelligent scheduling method for large-scale networked devices, characterized in that, Includes the following steps: S1: Establish cloud-gateway connection: Deploy a cloud service platform and communicate with at least one gateway via wireless connection, with the gateway sending network configuration instructions downwards; S2: Device access gateway: Connect the device to be connected to the cloud to the gateway wirelessly, so that the device can receive and respond to instructions from the cloud; S3: Establish a network node signal strength quantization model: Based on the central node, divide its signal coverage area into multiple sub-regions, and quantize the signal strength into corresponding weights I(A1), I(A2), ..., I(Ak) according to the range of received signal strength in each region. S4: A group scheduling algorithm based on signal strength, including: A. Group leader election: Each node obtains the signal strength weights of its neighboring nodes and stores them in the neighboring node table, and calculates the sum of its signal strength weights Sn; by periodically detecting network topology changes, it calculates the node stability weight Cn; it obtains the node's remaining power Dn; it obtains the node's information weight Fn, which is represented as the reciprocal of the communication traffic. The overall weight of the nodes is calculated as H = k1·Sn - k2·Cn + k3·Fn + k4·Dn, where k1, k2, k3, and k4 are weight factors, and k1 + k2 + k3 + k4 = 1. The node with the largest H value is selected as the group head. If the H values ​​are the same, the node with the smaller ID is selected as the group head. B. Group range determination: The ideal number of nodes N is preset, and the range of the number of nodes is set to [Nn, N+m]; the group first selects the N nodes with the strongest signal strength to join the group, and unassigned nodes select the group leader with the strongest signal strength to join. If the number of nodes in the group is lower than the lower limit, the group is disbanded, and the nodes re-select the group leader to join. C. Dynamic updates within the group: The conditions for a node to join are: not belonging to any group, the signal strength to the group head meets the threshold, and the number of nodes in the group has not exceeded the upper limit; the conditions for a node to leave are: the signal strength is lower than the set value or there is no response after a timeout. S5: Establish device-gateway connection: The device determines whether to respond to the gateway's network configuration command based on the algorithm rules. Devices that meet the access conditions reply with registration information to the gateway in batches, while devices with insufficient signal strength do not respond; registered devices ignore the network configuration commands of other gateways.

2. The cloud-based intelligent scheduling method for large-scale networked equipment according to claim 1, characterized in that: In step S3, the preset conditions for the network node signal strength quantization model are: the node propagates in free space and operates at a conventional fixed power.

3. The cloud-based intelligent scheduling method for large-scale networked equipment according to claim 1, characterized in that: In step S3, the signal strength I received by nodes in different regions is assigned the values ​​I(A1)=4I(A2), I(A3)=3, I(A4)=1, and I(A5)=0.

5. The signal strength weight In received by the nodes in different regions can be obtained from the figure. When a node is in the range of A4, after the node receives the signal from the central node, the node quantizes the signal strength as 1 and stores it in its neighbor node table.

4. The cloud-based intelligent scheduling method for large-scale networked equipment according to claim 1, characterized in that: In step S3, in reality, depending on the environment, there is no direct correspondence between the position of a node and its signal strength. Therefore, the node is assigned a value based on the range of the signal strength received by the node. By quantifying the signal strength of each node, the node signal strength weight is made more stable. The movement of the node back and forth, the relative movement of the node, and the movement of the node within a small range are ignored. The sum of the node signal strength weights Sn is also more stable, thereby reducing the number of group head changes and simplifying the calculation.

5. A cloud-based intelligent scheduling system for large-scale networked devices implementing the method as described in any one of claims 1-4, characterized in that, include: A cloud platform used to provide device access and management services; At least one gateway, wirelessly connected to the cloud, is used to issue network configuration commands and receive device registration information; Several network devices connect to the cloud via a gateway and execute the group scheduling algorithm to achieve self-organized network access and group maintenance.

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