A method, system, and apparatus for switch planning based on ap power budgeting

Abstract

The application relates to the technical field of network planning, and discloses a switch planning method, system and device based on AP power budget, which comprises the following steps: defining the power demand and physical position of all access points AP, and the power supply capacity constraint and port capacity constraint of a switch; calculating the theoretical minimum value of the required switch quantity; dividing all the access points AP into a plurality of initial groups and independent groups through a spatial clustering algorithm according to the physical position of the access points AP; checking the initial groups with the theoretical minimum value as an optimization target, splitting the initial groups that do not meet the switch power supply capacity constraint or the port capacity constraint into subgroups that meet the constraints; and merging and optimizing all the subgroups that meet the constraints and the independent groups to form a final grouping scheme; and outputting the final grouping scheme and the total required switch quantity. The application ensures the high reliability of the planning scheme in actual deployment.

Description

Technical Field

[0001] This invention relates to the field of network planning technology, and specifically to a switch planning method, system, and device based on AP power budget for scientific planning of switches in large-scale network infrastructures, taking into account Ethernet power budget, port capacity, and physical location of devices. Background Technology

[0002] In modern network infrastructures, such as smart buildings, smart parks, and large venues, hundreds or even thousands of wireless access points (APs) are deployed. These APs not only require switches to provide network connectivity, but also typically need to be powered by the switches via PoE technology. Therefore, how to rationally plan the number of switches and their connection relationships with the APs has become a key technical challenge.

[0003] Currently, existing switch planning methods mainly rely on manual experience or simple quantity matching, lacking coordinated optimization based on power constraints and geographical space. These methods typically suffer from the following drawbacks:

[0004] First, the lack of detailed consideration of PoE power budget leads to insufficient planning reliability. Traditional planning often only focuses on whether the number of switch ports is sufficient to connect all APs, severely neglecting the limitations of their power supply capacity. Switches not only have a total power output limit, but also a maximum power limit for each individual port. Existing methods have not established effective models to verify whether the total power consumption of a group of APs and the individual power consumption of each AP simultaneously meet the overall and single-port power constraints of the switch. This may lead to serious problems after actual deployment, such as the switch being unable to power all APs normally even though the number of ports is sufficient, or an AP being unable to connect due to excessive power consumption, thus affecting the stability and reliability of the network.

[0005] Secondly, the planning process is disconnected from the physical location of the access points (APs), leading to high deployment costs and low efficiency. Existing solutions typically treat connectivity planning and physical location deployment as two separate processes. Planning only logically assigns APs to switches without considering their actual geographical distribution within the building. This easily results in APs assigned to the same switch being geographically dispersed, leading to excessively long network cabling (such as Ethernet cables), complex paths, difficult construction, and significantly increased costs. Furthermore, this violates the fundamental design principle of "grouping devices that are close together onto the same switch" to optimize network topology and reduce latency.

[0006] Finally, the optimization objective is singular, failing to achieve optimal resource allocation under multiple constraints. Traditional planning methods have a single optimization objective, typically pursuing only the minimization of the number of switches. However, in practical engineering, this is a multi-objective optimization problem that requires satisfying power, port, and space constraints. Simply pursuing the minimization of the number of devices easily leads to low resource utilization of high-performance switches. Even if there is a large amount of redundancy in their ports and power supply capabilities, these remaining resources are physically isolated and cannot be allocated because they are already used to connect remote access points, ultimately resulting in wasted equipment investment and increased total cost of ownership. An ideal solution needs to strike a balance between minimizing the number of devices and maximizing device utilization, and can flexibly merge small clusters that are spatially adjacent and whose total power allows. Current technologies lack such a dynamic and adaptive merging optimization mechanism.

[0007] Therefore, there is an urgent need in this field for a switch planning system that can comprehensively consider power budget, port capacity and spatial distribution to improve the efficiency, economy and reliability of network deployment. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a switch planning method, system, device and medium based on AP power budget, which can realize the scientific estimation of the number of switches and the reasonable allocation of AP connection relationships, while meeting power supply and connection requirements, reducing equipment costs and improving network compactness and operational reliability.

[0009] This invention is achieved through the following technical solution:

[0010] A switch planning method based on AP power budget includes:

[0011] Define the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches.

[0012] Based on the total power requirements and total number of all access points (APs), as well as the power supply capacity and port capacity constraints of the switches, calculate the theoretical minimum number of switches required.

[0013] Based on the physical location of the access points (APs), a spatial clustering algorithm is used to divide all the access points into several initial groups and independent groups to ensure that access points that belong to the same area in terms of physical location are preferentially assigned to the same initial group.

[0014] The initial group is verified with the theoretical minimum value as the optimization target. The initial group that does not meet the power supply capacity constraint or port capacity constraint of the switch is split into sub-groups that meet the constraints. All sub-groups that meet the constraints and the independent group are merged and optimized. Groups that are spatially adjacent and still meet the constraints after merging are merged to form the final grouping scheme.

[0015] Output the final packet scheme and the total number of switches required.

[0016] As an optimization, the power supply capacity constraint includes the switch's maximum PoE output power, the maximum PoE output power of a single port, and the actual total available power of the switch; the packet verification specifically verifies that the power demand of any access point AP in the packet does not exceed the maximum PoE output power of a single port, the total power demand of the packet does not exceed the actual total available power of the switch, and the number of access point APs in the packet does not exceed the port capacity constraint.

[0017] As an optimization, the specific process of splitting initial packets that do not meet the switch power supply capacity constraints or port capacity constraints into sub-packets that meet the constraints is a recursive splitting process, including:

[0018] Within the initial group, the access points (APs) are re-clustered by gradually reducing the neighborhood radius parameter of the spatial clustering algorithm, thereby dividing the initial group into several sub-groups;

[0019] The sub-groups that do not meet the constraints are taken as new sub-groups to be split, and the above recursive splitting process is repeated until each sub-group generated simultaneously meets the power supply capacity constraint and port capacity constraint of the switch.

[0020] As an optimization, the spatial clustering algorithm is the DBSCAN density clustering algorithm.

[0021] As an optimization, all groups that satisfy the constraints are merged. The specific process of merging spatially adjacent groups that still satisfy the constraints after merging to form the final grouping scheme is as follows:

[0022] Calculate the spatial distance between any two groups that satisfy the constraints;

[0023] Determine whether two groups meet the merging conditions. The merging conditions include: the total power demand of the merged group does not exceed the actual total available power of the whole machine, the total number of access points (APs) of the merged group does not exceed the port capacity constraint, and the spatial distance between the two groups is less than a preset merging distance threshold.

[0024] Perform a merge operation on all group pairs that meet the merge criteria to form a new grouping scheme.

[0025] As an optimization, the spatial distance is the Euclidean distance between the geometric centers of the two groups.

[0026] As an optimization, the theoretical minimum value is calculated. The specific formula is:

[0027] ;

[0028] Where A is the ratio of the total power demand of all APs to the total actual available power of a single switch, rounded up; and B is the ratio of the total number of all APs to the port capacity constraint of a single switch, rounded up.

[0029] This invention also discloses a switch planning system based on AP power budget, used to execute the aforementioned switch planning method based on AP power budget, comprising:

[0030] The parameter definition module defines the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches.

[0031] The calculation module calculates the theoretical minimum number of required switches based on the total power requirements and total number of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches.

[0032] The clustering and grouping module divides all the access points (APs) into several initial groups and independent groups based on their physical locations using a spatial clustering algorithm, so as to ensure that the access points that belong to the same area in terms of physical location are preferentially assigned to the same initial group.

[0033] The optimization and adjustment module verifies the initial group with the theoretical minimum value as the optimization target, splits the initial group that does not meet the switch power supply capacity constraint or port capacity constraint into sub-groups that meet the constraint; and merges and optimizes all sub-groups that meet the constraint and the independent group, merging spatially adjacent groups that still meet the constraint after merging to form the final grouping scheme.

[0034] The result output module outputs the final grouping scheme and the total number of switches required.

[0035] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a switch planning method based on AP power budget as described above.

[0036] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a switch planning method based on AP power budget as described above.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] This invention significantly improves the planning efficiency and cost-effectiveness of large-scale wireless network deployments. Its core significance lies in integrating the traditionally experience-dependent and often disconnected processes of power calculation, port allocation, and physical layout planning into an automated, model-driven decision-making system. This fundamentally solves industry pain points such as unreliable planning, resource waste, and high costs caused by neglecting PoE power budgets and spatial location. Through rigorous mathematical modeling and spatial clustering algorithms, the system ensures that the planning scheme simultaneously meets multiple requirements of power supply reliability, port capacity, and cabling costs, resulting in outputs that are both engineering feasible and economically viable.

[0039] This invention establishes a unified power, port, and space constraint model, and for the first time simultaneously verifies the matching relationship between the power supply capacity of the entire switch and a single port and the power consumption of the AP during the planning stage. This fundamentally avoids the risks of insufficient power supply or equipment incompatibility caused by neglecting the PoE budget in traditional methods, and ensures the high reliability of the planning scheme in actual deployment.

[0040] By introducing spatial clustering algorithms and recursive splitting mechanisms, geographically proximate APs are prioritized for grouping under all constraints, significantly reducing network cabling length and complexity. Further intelligent merging strategies effectively eliminate resource silos and improve the port and power resource utilization of individual switches, thereby reducing the total number of switches and lowering the overall cost of equipment procurement and cabling deployment.

[0041] The core algorithm of this invention does not depend on specific equipment models or site plans. Its parameters can be flexibly configured according to actual switch specifications, AP power consumption and building floor plans. Therefore, it has strong versatility and can be widely applied to various network construction scenarios such as smart buildings, parks and large venues that require dense deployment of APs, providing effective decision support for PoE network planning of different scales. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 A flowchart of a switch planning method based on AP power budget provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0045] This invention proposes a switch planning method, system, device, and medium based on AP power budget. A specific embodiment will be used to illustrate this in detail below.

[0046] Example 1:

[0047] This embodiment describes a switch planning method based on AP power budget, and its overall process is as follows: Figure 1 As shown, the main steps include:

[0048] S1. Define the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches.

[0049] This step involves mathematically modeling the entities and constraints involved in the planning problem. The specific process is as follows:

[0050] S101, Sets and Indexes: Define the AP set as... The total number of APs is The switches are numbered as follows: Let's assume that M units are ultimately needed.

[0051] S102, AP parameter definition: For each AP, assuming the number is... Define its power (PoE requirement). (Unit: W), and its planar coordinates (two-dimensional). , , They are The first and second components represent the x and y coordinates, respectively. It represents the set of real numbers.

[0052] S103. Switch Parameter Definition: Defines the key parameters of the switch, including:

[0053] Maximum PoE output power of the whole machine: ;

[0054] Maximum PoE output power per port: ;

[0055] Total number of ports ;

[0056] Number of reserved ports ;

[0057] It is a set of integers;

[0058] Number of allocatable ports (port capacity constraint) .

[0059] S104, A single switch can calculate the power of an AP:

[0060] Calculate the maximum total power that can be simultaneously carried by each port: .

[0061] Calculate the actual total power available to the entire machine. . This is the total PoE output power of the entire switch; This serves as the basis for determining whether the group power exceeds the limit.

[0062] S105. Assignment Mapping and Local Statistics: Defining the Assignment Function , Indicates AP Assigned to the i-th switch. For any switch i, its assigned set of APs is: Define the number of APs in this set. Total power , Let J represent the power requirement of the j-th AP (access point).

[0063] S106. Necessary Constraints: The model must satisfy the following constraints:

[0064] Single-port power constraint: For each assigned AP, its power must meet the following requirements. .

[0065] Total power constraint: For each switch, the total power of the APs it supports must meet the following requirements. .

[0066] Port quantity constraint: The number of APs that each switch can support must meet the following requirements. .

[0067] Regional priority constraint: APs in the same area should be connected to the same switch first. In clustering constraints, this is reflected in the goal of prioritizing APs that are spatially close to each other to be grouped together.

[0068] S107. Optimization Objective: The main objective is to minimize the total number of switches M while satisfying the above constraints, i.e. The secondary objective is to optimize the spatial compactness of APs within each switch when M is constant, i.e., to minimize the sum of squared distances within the group. ,in For clusters The geometric center, Let be the physical location coordinates of the j-th AP (access point).

[0069] S2: Based on the total power requirements and total number of all access points (APs), as well as the power supply capacity and port capacity constraints of the switches, calculate the theoretical minimum number of switches required. This step involves calculating the maximum AP power and number that each switch can support.

[0070] This step provides a theoretical basis and optimization objectives for subsequent planning. The specific process is as follows:

[0071] S201. Maximum number of APs supported by a single switch: Calculate the worst-case scenario for a single switch (all APs have equal power). Under the given power limit, the number of APs that can be supported. The actual maximum number of APs is... .

[0072] S202, Single Switch Bearer Decision: Given a subset C of APs, their total power... AP number A single switch can carry out the load if and only if: .

[0073] S203. Lower bound of minimum required number of switches: Based solely on capacity and port count, without considering geographical location, for any given subset C, the lower bound of the minimum required number of switches is: When C is the set of all APs, this lower bound is the global theoretical minimum. Its calculation formula .

[0074] In other words, the theoretical minimum value The calculation process is as follows:

[0075] Calculate the total power requirement of all APs. .

[0076] Calculate the total number N of all APs.

[0077] Calculate the theoretical minimum value Where ⌈*⌉ represents rounding up. This is the ideal lower limit without considering spatial location.

[0078] S3. Based on the physical location of the access points (APs), a spatial clustering algorithm is used to divide all APs into several initial groups. This ensures that APs belonging to the same physical area are preferentially assigned to the same initial group, thus making the APs within the same group spatially adjacent. This step involves calculating the number of switches and their connection relationships with the APs. The specific process is as follows:

[0079] S301, AP Distance Definition and Clustering Parameters: Define the Euclidean distance between any two APs. The initial parameters of the selected spatial clustering algorithm (DBSCAN is preferred in this embodiment) include the neighborhood radius. (Can be set according to the maximum allowable cabling length) and minimum number of neighbors In the DBSCAN algorithm, if the access point is... With the center, and Within a circular region of radius (inclusive) If the number of access points (of itself) is not less than a preset positive integer minPts (i.e., minPts∈Z⁺), then These are called core points. The algorithm connects mutually reachable core points and points in their neighborhoods, forming several clusters. Access points not included in any cluster are marked as noise points. These clusters constitute the initial packet set. For subsequent processing, each noise point is treated as an independent packet containing only a single AP.

[0080] S302. Perform clustering and form groups: using the selected initial parameters. Run the DBSCAN algorithm on the set A of AP with minPts. Algorithm output:

[0081] Several initial groups (clusters): set each It is a set of APs that are spatially adjacent.

[0082] Several independent groups: These are noise points that are not assigned to any cluster. Each noise point is considered as an independent group containing only a single AP.

[0083] S303, Initial Verification and Ideal Case Judgment:

[0084] Verify each cluster Does it meet the carrying capacity of a single switch (i.e., the judgment condition in step S202: total power consumption of the cluster)? and the number of APs included None of them exceeded the upper limit for a single switch.

[0085] For each cluster Calculate its total power. With points .

[0086] ;

[0087] ;

[0088] If verified, each cluster If all the above carrying capacity conditions are met, it means that the clustering result has achieved optimal performance in terms of both spatial compactness and engineering constraints. At this point, the planning scheme is very simple: allocate one switch to each cluster, with a one-to-one correspondence between clusters and switches. The total number of switches M required is then equal to the number of clusters K. In this case, no further splitting and merging steps are needed, and this scheme can be directly output as the final planning result.

[0089] S4: Validate the initial grouping with the theoretical minimum value as the optimization target, split the initial grouping that does not meet the switch power supply capacity constraint or port capacity constraint into sub-grouping that meets the constraint; and merge and optimize all sub-groupings that meet the constraint and the independent grouping, merging spatially adjacent groups that still meet the constraint after merging to form the final grouping scheme.

[0090] This step involves verification, splitting, and merging optimization.

[0091] S401, Recursive Splitting: Responding to the initial groups that failed the verification in S303. (Right now or ), perform recursive splitting:

[0092] 1. Determine the target number of subgroups: Calculate the minimum number of subgroups that this group needs to be split into. That is, any single switch can only handle a maximum of Power and maximum capacity One AP, therefore At least A switch is used to carry out the task.

[0093] 2. Perform recursive clustering splitting: in this group Within this, the neighborhood radius parameter of the DBSCAN algorithm is gradually reduced. (For example, let) (and continue to decrease), re-clustering with the goal of... Split into at least Smaller, more compact sub-groups, and each sub-cluster meets the single-switch bearer determination criteria: Take the smallest one The partitioning, i.e., minimizing the number of subclusters to satisfy the constraints, aims to reduce the total number of switches M and ensure spatial proximity of points within the cluster.

[0094] 3. Recursive Verification: For the subgroups obtained from the splitting, verify them again using condition S202. Subgroups that still do not meet the constraints are treated as new objects to be split, and steps 1-2 are repeated until all generated subgroups meet the S201 condition. This process is the recursive splitting process.

[0095] S402. Merge Optimization: After splitting, all subgroups satisfying condition S202 are obtained, along with the independent groups generated in S302. Merge optimization is then performed on these groups:

[0096] 1. Calculate the spatial distance between groups: Calculate the Euclidean distance between the geometric centers of any two groups. , , It is the geometric center.

[0097] 2. Merge criteria: Two groups and They can be merged if and only if the following conditions are met simultaneously:

[0098] That is, the total power after the merger shall not exceed the total power actually available by the whole machine;

[0099] That is, the total number of APs after merging does not exceed the port capacity constraint;

[0100] That is, the spatial distance is less than the preset merging distance threshold.

[0101] 3. Perform the merge: Traverse all group pairs, prioritizing the merging of the closest spatially distributed group pairs that also meet the above conditions, forming new groups. Repeat this process until no more groups can be merged, ultimately obtaining the final grouping scheme.

[0102] Merging decisions are based on satisfying the requirement of not exceeding the capacity limit and maximizing the objective function. The principle of not causing significant deterioration is followed, prioritizing the merging of cluster pairs that minimize the number of switches and are spatially closest.

[0103] S5. Output the final grouping scheme and the total number of switches required.

[0104] S501. After the above splitting and merging, the final cluster set is obtained. Each cluster meets the single-switch bearer determination criteria:

[0105] ;

[0106] ;

[0107] S502. Define the final assignment function g: for any ,like but .

[0108] S503, Obtain the final total number of switches required. Output the final grouping scheme (i.e., the connection relationship between APs and switches) and the total number of switches M.

[0109] In summary, this invention establishes a power, port, and space constraint model, and combines spatial clustering, recursive splitting, and merging optimization to achieve efficient utilization of switch resources and optimal deployment cost planning.

[0110] This invention establishes a switch planning model that satisfies multiple constraints. First, it provides a precise mathematical model of the switch planning problem, uniformly quantifying the three core constraints: power, ports, and space. This includes explicitly defining the switch's total power budget, maximum power per port, number of available ports, and the power requirements and physical coordinates of the access points (APs). This model forms the basis for all subsequent automated calculations, ensuring the theoretical feasibility and reliability of the planning scheme.

[0111] This invention proposes an initial grouping algorithm based on spatial clustering. It innovatively introduces the DBSCAN spatial clustering algorithm into the field of network device planning. This method automatically prioritizes geographically proximate access points (APs) into the same group (cluster), aiming to minimize future deployment cabling distances and costs. This step directly reflects the optimization goal of connecting the closest APs to the same switch, and is the core solution to the problem of traditional methods being disconnected from physical topology.

[0112] This invention proposes a constraint-driven recursive splitting and verification mechanism. For initial clusters that do not meet the carrying capacity of a single switch due to excessive AP power or too many APs, the system employs an adaptive recursive splitting method. This involves continuously reducing the neighborhood radius. The parameters recursively subdivide large clusters into multiple smaller subclusters that satisfy power and port constraints without compromising space compactness. This mechanism ensures that at any stage, each group strictly meets the criteria for whether any set of APs can be carried by a single switch, thus guaranteeing the engineering practicality of the planning results.

[0113] This invention proposes a cost-optimal merging strategy. After generating initial groups that satisfy all constraints, the invention proceeds with the merging optimization step. This strategy intelligently determines whether two or more clusters whose combined power is significantly lower than the switch capacity and are spatially adjacent can be merged onto the same switch. This step directly addresses the problem of idle and wasted equipment resources. By improving the resource utilization of individual switches, and while ensuring feasibility, it further reduces the total number of switches, ultimately achieving optimal equipment costs.

[0114] The implementation of this invention will significantly improve the planning efficiency and economy of large-scale wireless network deployments. Its core significance lies in integrating the traditionally experience-dependent and often disconnected processes of power calculation, port allocation, and physical layout planning into an automated, model-driven decision-making system. This fundamentally solves industry pain points such as unreliable planning, resource waste, and high costs caused by neglecting PoE power budgets and spatial location. Through rigorous mathematical modeling and spatial clustering algorithms, the system ensures that the planning scheme simultaneously meets multiple requirements of power supply reliability, port capacity, and cabling costs, resulting in outputs that are both engineering feasible and economically viable. Its method has strong versatility and adaptability, and can be widely applied to various scenarios requiring dense deployment of APs and PoE switches, such as smart buildings, smart parks, and large venues, providing effective technical support for the scientific planning and lean management of network infrastructure.

[0115] The core effect of this invention lies in its significant improvement in the accuracy and economy of switch planning through automated multi-constraint modeling and optimization. Specifically, this can be summarized in the following three points:

[0116] Enhancing planning reliability and engineering feasibility: By establishing a unified power, port, and space constraint model, this system verifies the matching relationship between the power supply capacity of the entire switch and its single port and the power consumption of the AP during the planning stage for the first time. This fundamentally avoids the risks of insufficient power supply or equipment incompatibility caused by neglecting the PoE budget in traditional methods, ensuring the high reliability of the planning scheme in actual deployment.

[0117] Optimize resource utilization and reduce overall costs: By introducing spatial clustering algorithms and recursive splitting mechanisms, geographically proximate APs are prioritized for grouping under all constraints, significantly reducing network cabling length and complexity; further intelligent merging strategies effectively eliminate "resource silos" and improve the port and power resource utilization of individual switches, thereby reducing the total number of switches while lowering the overall cost of equipment procurement and cabling deployment.

[0118] Enhanced versatility and scenario adaptability: The core algorithm of this system does not depend on specific equipment models or site plans. Its parameters can be flexibly configured according to actual switch specifications, AP power consumption and building floor plans. Therefore, it has strong versatility and can be widely applied to various network construction scenarios such as smart buildings, parks and large venues that require dense deployment of APs, providing effective decision support for PoE network planning of different scales.

[0119] Example 2

[0120] This embodiment provides a switch planning system based on AP power budget for implementing the method described in Embodiment 1. The system includes:

[0121] The parameter definition module is used to define the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches.

[0122] The calculation module calculates the theoretical minimum number of switches required, based on the total power demand and total number of all access points (APs) and the power supply capacity and port capacity constraints of the switches.

[0123] The clustering and grouping module is used to divide all the access points (APs) into several initial groups and independent groups based on their physical locations using a spatial clustering algorithm, so as to ensure that the access points that belong to the same area in terms of physical location are preferentially assigned to the same initial group.

[0124] The optimization and adjustment module is used to verify the initial group with the theoretical minimum value as the optimization target, split the initial group that does not meet the switch power supply capacity constraint or port capacity constraint into sub-groups that meet the constraint; and to merge and optimize all sub-groups that meet the constraint and the independent group, and merge spatially adjacent groups that still meet the constraint after merging to form the final grouping scheme.

[0125] The result output module is used to output the final grouping scheme and the total number of switches required.

[0126] The various modules of the system work together to automatically complete the entire planning process from parameter input to solution output.

[0127] Example 3:

[0128] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a switch planning method based on AP power budget as described in any of the embodiments in Embodiment 1. The electronic device may be a server, personal computer, workstation, or other computing device.

[0129] Example 4:

[0130] This embodiment provides a computer-readable storage medium, such as flash memory, hard disk, optical disk, etc., on which a computer program is stored. When the program is executed by a processor, it implements a switch planning method based on AP power budget as described in any one of the embodiments.

[0131] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A switch planning method based on AP power budget, characterized in that, include: Define the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches. The specific process is as follows: S101, Sets and Indexes: Define the AP set as... The total number of APs is The switches are numbered as follows Assume that M units are ultimately needed; S102, AP parameter definition: For each AP, assuming the number is... Define the power of the AP. and AP plane coordinates , , They are The first and second components represent the x and y coordinates, respectively. Represents the set of real numbers; S103. Switch Parameter Definition: Defines the key parameters of the switch, including: Maximum PoE Output Power of the entire unit: Maximum PoE output power per port: Total number of ports Number of reserved ports ; Set of integers; number of allocable ports ; S104. Power calculation for a single switch's APs: Calculate the maximum total power that can be simultaneously supported by port limits. ; Calculate the actual total power available to the entire machine ; S105. Assignment Mapping and Local Statistics: Defining the Assignment Function , Indicates AP Assigned to the i-th switch, for any switch i, the set of APs assigned to it is: Define the number of APs in this set. Total power , Let j be the power requirement of the j-th AP (access point); S106. Necessary Constraints: The model must satisfy the following constraints: Single Port Power Constraint: For each assigned AP, its power must satisfy... Total power constraint: For each switch, the total power of the APs it supports must meet the following requirements. Port quantity constraint: For each switch, the number of APs it supports must meet the following requirements. Regional priority constraint: APs in the same area should be connected to the same switch first. In clustering constraints, this is reflected in the goal of prioritizing APs that are spatially closer to each other and putting them in the same group. S107. Optimization Objective: The main objective is to minimize the total number of switches M while satisfying the above constraints, i.e. The secondary objective is to optimize the spatial compactness of APs within each switch when M is constant, i.e., to minimize the sum of squared distances within the group. ,in For clusters The geometric center, Let be the physical location coordinates of the j-th AP (access point); Based on the total power requirements and total number of all access points (APs), as well as the power supply capacity and port capacity constraints of the switches, calculate the theoretical minimum number of switches required. The specific process is as follows: S201. Maximum number of APs supported by a single switch: Calculate the maximum number of APs that a single switch can support under worst-case conditions, based on its maximum power. The actual maximum number of APs is The worst-case scenario is that the power of all APs is equal to... ; S202, Single Switch Bearer Decision: Given a subset C of APs, their total power... AP number Then it can be carried by a single switch if and only if: ; S203. Lower bound of minimum required number of switches: Based solely on capacity and port count, without considering geographical location, for any given subset C, the lower bound of the minimum required number of switches is: When C is the set of all APs, this lower bound is the global theoretical minimum. Its calculation formula Based on the physical location of the access points (APs), a spatial clustering algorithm is used to divide all the access points into several initial groups and independent groups to ensure that access points that belong to the same area in terms of physical location are preferentially assigned to the same initial group. The initial group is verified with the theoretical minimum value as the optimization target. The initial group that does not meet the power supply capacity constraint or port capacity constraint of the switch is split into sub-groups that meet the constraints. All sub-groups that meet the constraints and the independent group are merged and optimized. Groups that are spatially adjacent and still meet the constraints after merging are merged to form the final grouping scheme. Output the final packet scheme and the total number of switches required.

2. The switch planning method based on AP power budget according to claim 1, characterized in that, The power supply capacity constraints include the switch's maximum PoE output power, maximum PoE output power per port, and the total actual available power of the switch. The packet verification specifically involves verifying that the power requirement of any access point AP within the packet does not exceed the maximum PoE output power per port, the total power requirement of the packet does not exceed the total actual available power of the switch, and the number of access point APs in the packet does not exceed the port capacity constraint.

3. The switch planning method based on AP power budget according to claim 2, characterized in that, The specific process of splitting initial packets that do not meet the switch power supply capacity constraints or port capacity constraints into sub-packets that meet the constraints is a recursive splitting process, which includes: Within the initial group, the access points (APs) are re-clustered by gradually reducing the neighborhood radius parameter of the spatial clustering algorithm, thereby dividing the initial group into several sub-groups; The sub-groups that do not meet the constraints are taken as new sub-groups to be split, and the above recursive splitting process is repeated until each sub-group generated simultaneously meets the power supply capacity constraint and port capacity constraint of the switch.

4. The switch planning method based on AP power budget according to claim 1, characterized in that, The spatial clustering algorithm is the DBSCAN density clustering algorithm.

5. The switch planning method based on AP power budget according to claim 2, characterized in that, The specific process of merging and optimizing all groups that satisfy the constraints, and merging spatially adjacent groups that still satisfy the constraints after merging, to form the final grouping scheme is as follows: Calculate the spatial distance between any two groups that satisfy the constraints; Determine whether two groups meet the merging conditions. The merging conditions include: the total power demand of the merged group does not exceed the actual total available power of the whole machine, the total number of access points (APs) of the merged group does not exceed the port capacity constraint, and the spatial distance between the two groups is less than a preset merging distance threshold. Perform a merge operation on all group pairs that meet the merge criteria to form a new grouping scheme.

6. The switch planning method based on AP power budget according to claim 5, characterized in that, The spatial distance is the Euclidean distance between the geometric centers of the two groups.

7. The switch planning method based on AP power budget according to claim 1, characterized in that, Calculate the theoretical minimum value The specific formula is: ； in, Let N be the total power requirement of all APs, and N be the total number of APs. This refers to the actual total power available to the entire machine. The port capacity constraint.

8. A switch planning system based on AP power budget, characterized in that, A switch planning method based on AP power budget as described in any one of claims 1-7 includes: The parameter definition module defines the power requirements and physical location of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches. The specific process is as follows: S101, Sets and Indexes: Define the AP set as... The total number of APs is The switches are numbered as follows Assume that M units are ultimately needed; S102, AP parameter definition: For each AP, assuming the number is... Define the power of the AP. and AP plane coordinates , , They are The first and second components represent the x and y coordinates, respectively. Represents the set of real numbers; S103. Switch Parameter Definition: Defines the key parameters of the switch, including: Maximum PoE Output Power of the entire unit: Maximum PoE output power per port: Total number of ports Number of reserved ports ; Set of integers; number of allocable ports ; S104. Power calculation for a single switch's APs: Calculate the maximum total power that can be simultaneously supported by port limits. ; Calculate the actual total power available to the entire machine ; S105. Assignment Mapping and Local Statistics: Defining the Assignment Function , Indicates AP Assigned to the i-th switch, for any switch i, the set of APs assigned to it is: Define the number of APs in this set. Total power , Let j be the power requirement of the j-th AP (access point); S106. Necessary Constraints: The model must satisfy the following constraints: Single Port Power Constraint: For each assigned AP, its power must satisfy... Total power constraint: For each switch, the total power of the APs it supports must meet the following requirements. Port quantity constraint: For each switch, the number of APs it supports must meet the following requirements. Regional priority constraint: APs in the same area should be connected to the same switch first. In clustering constraints, this is reflected in the goal of prioritizing APs that are spatially closer to each other and putting them in the same group. S107. Optimization Objective: The main objective is to minimize the total number of switches M while satisfying the above constraints, i.e. The secondary objective is to optimize the spatial compactness of APs within each switch when M is constant, i.e., to minimize the sum of squared distances within the group. ,in For clusters The geometric center, Let be the physical location coordinates of the j-th AP (access point); The calculation module calculates the theoretical minimum number of required switches based on the total power requirements and total number of all access points (APs), as well as the power supply capacity constraints and port capacity constraints of the switches. The specific process is as follows: S201. Maximum number of APs supported by a single switch: Calculate the maximum number of APs that a single switch can support under worst-case conditions, based on its maximum power. The actual maximum number of APs is The worst-case scenario is that the power of all APs is equal to... ; S202, Single Switch Bearer Decision: Given a subset C of APs, their total power... AP number Then it can be carried by a single switch if and only if: ; S203. Lower bound of minimum required number of switches: Based solely on capacity and port count, without considering geographical location, for any given subset C, the lower bound of the minimum required number of switches is: When C is the set of all APs, this lower bound is the global theoretical minimum. Its calculation formula ; The clustering and grouping module divides all the access points (APs) into several initial groups and independent groups based on their physical locations using a spatial clustering algorithm, so as to ensure that the access points that belong to the same area in terms of physical location are preferentially assigned to the same initial group. The optimization and adjustment module verifies the initial group with the theoretical minimum value as the optimization target, splits the initial group that does not meet the switch power supply capacity constraint or port capacity constraint into sub-groups that meet the constraint; and merges and optimizes all sub-groups that meet the constraint and the independent group, merging spatially adjacent groups that still meet the constraint after merging to form the final grouping scheme. The result output module outputs the final grouping scheme and the total number of switches required.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a switch planning method based on AP power budget as described in any one of claims 1 to 7.

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