Computing node decision method, device, equipment and storage medium

Abstract

The present application discloses a computing node decision method, apparatus, device and computer-readable storage medium, including: obtaining operating load parameters, edge network bandwidth and cloud network bandwidth when at least two network base stations provide wireless network coverage for a vehicle; determining the edge link load score of each edge server and the cloud link load score of the cloud server when each operating load parameter is greater than or equal to a first load threshold parameter and at least one operating load parameter is less than or equal to a second load threshold parameter; determining the server corresponding to the maximum value of all edge link load scores and cloud link load scores in the network cluster as the target server; establishing a computing power connection between the vehicle and the target server to assist the vehicle in computing power calculation through the target server. This solves the problem of unbalanced load on edge servers.

Description

Technical Field

[0001] The present application relates to the field of edge computing in the Internet of Vehicles, and specifically relates to a computing node decision method, apparatus, device, and computer-readable storage medium. Background Art

[0002] In the field of intelligent transportation, edge computing can achieve real-time vehicle positioning and intelligent traffic signal control, improving the efficiency and safety of the transportation system. With the rapid development of vehicle networking technology, the application of edge computing in these fields is becoming increasingly important.

[0003] Existing edge computing technologies primarily achieve low latency and efficient data processing by deploying computing and storage resources at the edge of the network, close to users. The overall architecture of edge computing typically consists of three layers: cloud computing processing centers, edge nodes, and end nodes. Through computational selection, computing power from cloud computing processing centers or edge nodes is allocated to end nodes to facilitate vehicle management.

[0004] However, in existing solutions, when the load of edge servers is unbalanced, some servers may be overloaded while other servers are idle, resulting in ineffective utilization of computing resources. Summary of the Invention

[0005] This application aims to provide a computing node decision method, device, equipment and computer-readable storage medium, at least to solve the problem of ineffective utilization of computing resources when selecting edge computing in the Internet of Vehicles.

[0006] In a first aspect, an embodiment of the present application discloses a computing node decision method, comprising:

[0007] In a case where there are at least two network base stations providing wireless network coverage for the vehicle, obtaining an operating load parameter and an edge network bandwidth of each target edge server in the network cluster, as well as a cloud network bandwidth of a cloud server in the network cluster; the target edge server is an edge server providing wireless network coverage for the vehicle through a corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; the cloud network bandwidth is the network bandwidth between the cloud server and an external network of the network cluster;

[0008] When each of the operating load parameters is greater than or equal to a first load threshold parameter and at least one of the operating load parameters is less than or equal to a second load threshold parameter, determining an edge link load score value of each edge server according to the corresponding operating load parameter, and determining a cloud link load score value of the cloud server according to the total edge network bandwidth and the cloud network bandwidth;

[0009] Determine the server corresponding to the maximum value among all the edge link load score values ​​and the cloud link load score values ​​in the network cluster as the target server;

[0010] A computing power connection is established between the vehicle and the target server so as to assist the vehicle in computing power calculations through the target server.

[0011] In a second aspect, an embodiment of the present application further discloses a computing node decision device, comprising:

[0012] An activation module is configured to obtain, when at least two network base stations provide wireless network coverage for a vehicle, an operating load parameter and an edge network bandwidth of each target edge server in a network cluster, as well as a cloud network bandwidth of a cloud server in the network cluster; the target edge server is an edge server that provides wireless network coverage for the vehicle through a corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; and the cloud network bandwidth is the network bandwidth between the cloud server and an external network of the network cluster;

[0013] an evaluation module, configured to determine, when each of the operating load parameters is greater than or equal to a first load threshold parameter and at least one of the operating load parameters is less than or equal to a second load threshold parameter, an edge link load score value for each edge server based on the corresponding operating load parameter, and determine a cloud link load score value for the cloud server based on the total edge network bandwidth and the cloud network bandwidth;

[0014] A selection module is configured to determine, in the network cluster, a server corresponding to a maximum value among all the edge link load score values ​​and the cloud link load score values ​​as a target server;

[0015] A computing module is used to establish a computing power connection between the vehicle and the target server so as to assist the vehicle in computing power calculation through the target server.

[0016] In a third aspect, an embodiment of the present application further discloses an electronic device comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present application further discloses a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0018] In summary, in the embodiment of the present application, by obtaining the operating load parameters and network bandwidth of each target edge server, the current load situation and network transmission capacity of each server are accurately understood, providing data support for subsequent load balancing decisions; then, based on the operating load parameters and network bandwidth, the edge link load score value of each edge server and the cloud link load score value of the cloud server are calculated to quantify the load situation of each server, which is convenient for comparison and selection of the optimal computing node; then, the server with the smallest load score value is selected as the target server, ensuring that the selected server has the lightest load, thereby avoiding server overload and improving the utilization efficiency of computing resources; finally, a computing power connection is established between the vehicle and the target server, realizing real-time computing assistance for the vehicle and improving the efficiency and accuracy of vehicle management. Therefore, based on the method of the embodiment of the present application, by obtaining the operating load parameters and network bandwidth, calculating the load score value, and selecting the optimal server to establish a computing power connection, efficient utilization of the edge computing resources of the Internet of Vehicles is achieved, solving the problem of unbalanced load on edge servers in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In the attached figure:

[0020] Figure 1 This is a flowchart of the steps of a computing node decision method provided in an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of the relationship between the vehicle and the server in an embodiment of the present application;

[0022] Figure 3 This is a flowchart of another computing node decision method provided by an embodiment of the present application;

[0023] Figure 4 This is a block diagram of a computing node decision-making device provided in an embodiment of the present application;

[0024] Figure 5 is a block diagram of an electronic device according to an embodiment of the present application;

[0025] Figure 6 This is a block diagram of an electronic device according to another embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0028] Figure 1 This is a computing node decision method provided by this embodiment.

[0029] The method may include the following steps:

[0030] Step 101: When there are at least two network base stations providing wireless network coverage for the vehicle, obtain the operating load parameters and edge network bandwidth of each target edge server in the network cluster, and the cloud network bandwidth of the cloud server in the network cluster.

[0031] Among them, the target edge server is the edge server that provides wireless network coverage for the vehicle through the corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; the cloud network bandwidth is the network bandwidth between the cloud server and the external network of the network cluster.

[0032] In some embodiments of the present application, when there are at least two network base stations providing wireless network coverage for the vehicle, the operating load parameters and edge network bandwidth of each target edge server in the network cluster, as well as the cloud network bandwidth of the cloud server in the network cluster, are obtained. The target edge server is the edge server that provides wireless network coverage for the vehicle through the corresponding network base station. The edge network bandwidth refers to the network bandwidth between the network base station corresponding to the target edge server and the cloud server, while the cloud network bandwidth refers to the network bandwidth between the cloud server and the external network of the network cluster. The acquisition of these parameters is to provide necessary data support for the subsequent selection of computing nodes.

[0033] For example, Figure 2As shown in the figure, assume that in an intelligent transportation system, the dotted circle represents the base station range. Vehicle A is covered by base stations 1 and 2. The system first determines that the target edge servers corresponding to base stations 1 and 2 are edge servers E1 and E2, respectively. Next, the system obtains the operating load parameters of edge servers E1 and E2, assuming that E1's operating load parameter is 40% and E2's operating load parameter is 60%. Simultaneously, the system obtains the edge network bandwidth, assuming that the bandwidth between E1 and cloud server C is 100 Mbps and the bandwidth between E2 and cloud server C is 150 Mbps. Furthermore, the system obtains the cloud network bandwidth of cloud server C, assuming that the bandwidth between the cloud server and the external network of the network cluster is 200 Mbps. With this information, the system accurately understands the current load and network transmission capacity of each target edge server, providing data support for subsequent computing node selection.

[0034] Step 102: When each operating load parameter is greater than or equal to a first load threshold parameter and at least one operating load parameter is less than or equal to a second load threshold parameter, determine the edge link load score value of each edge server based on the corresponding operating load parameter, and determine the cloud link load score value of the cloud server based on the total edge network bandwidth and the cloud network bandwidth.

[0035] In some embodiments of the present application, when each operating load parameter is greater than or equal to a first load threshold parameter and at least one operating load parameter is less than or equal to a second load threshold parameter, the edge link load score value of each edge server is determined according to the corresponding operating load parameter, and the cloud link load score value of the cloud server is determined based on the total edge network bandwidth and the cloud network bandwidth. The edge link load score value is used to quantify the load situation of the edge server, while the cloud link load score value is used to quantify the load situation of the cloud server. These score values ​​will be used for subsequent computing node selection to ensure that the optimal computing node is selected.

[0036] For example, assume that in an intelligent transportation system, vehicle A is confirmed to be covered by base stations 1 and 2. The system obtains the operating load parameters of edge servers E1 and E2. Assume that the operating load parameter of E1 is 75% and the operating load parameter of E2 is 65%. Set the first load threshold parameter to 70% and the second load threshold parameter to 30%. Since the operating load parameters of E1 and E2 are both greater than or equal to the first load threshold parameter, and the operating load parameter of E2 is less than or equal to the second load threshold parameter, the system calculates the edge link load score based on these parameters. Assume that the edge link load score of E1 is 0.85 and the edge link load score of E2 is 0.99. The cloud link load score of the cloud server is 0.5. Using these scores, the system can quantify the load of each server and provide data support for the subsequent selection of computing nodes.

[0037] Step 103: Determine the server corresponding to the maximum value among all edge link load scores and cloud link load scores in the network cluster as the target server.

[0038] In some embodiments of the present application, the server corresponding to the maximum value of all edge link load scores and cloud link load scores in the network cluster is determined as the target server. The target server refers to the server that is most suitable for performing the computing task under the current load and network bandwidth conditions. By selecting the server with the largest load score, it is possible to ensure that the computing task is assigned to the server with the lightest load, thereby improving the utilization efficiency of computing resources and avoiding server overload.

[0039] For example, assume that in an intelligent transportation system, vehicle A is confirmed to be covered by base stations 1 and 2. The system has also obtained the edge link load scores of edge servers E1 and E2, which are 0.85 and 0.99, respectively, and the cloud link load score of the cloud server is 0.5. In this case, the system will compare these scores and select the server with the smallest score as the target server. Since edge server E2 has the largest score of 0.99, the system determines E2 as the target server. Next, the system will establish a computing power connection between vehicle A and edge server E2, so that E2 can provide computing power assistance to vehicle A. This selection ensures that computing tasks are assigned to the lightest-loaded server, improving the utilization efficiency of computing resources and avoiding server overload.

[0040] Step 104: Establish a computing power connection between the vehicle and the target server to assist the vehicle in computing power calculations through the target server.

[0041] In some embodiments of this application, a computing connection is established between the vehicle and a target server, enabling the target server to assist the vehicle in computing. A computing connection establishes a data transmission channel between the vehicle and the target server, allowing the vehicle to send data to the target server for computation and receive the results. This step ensures that computational tasks can be efficiently executed on the target server, thereby improving the efficiency and accuracy of vehicle management.

[0042] For example, suppose in an intelligent transportation system, vehicle A is confirmed to be covered by base stations 1 and 2. The system has also identified edge server E2 as the target server. The system will then establish a computing connection between vehicle A and edge server E2 via a wireless network. Specifically, the system first sends a connection request to E2 through vehicle A's onboard communication module. After receiving the request, E2 verifies vehicle A's identity and allocates the necessary network resources to establish a stable data transmission channel. Vehicle A then sends the data to be processed (such as real-time traffic information, sensor data, etc.) to E2. E2 calculates the received data and returns the calculation results to vehicle A. In this way, vehicle A can obtain the calculation results in real time, thereby realizing functions such as intelligent traffic signal control and real-time positioning, improving the efficiency and safety of the transportation system.

[0043] In summary, in the embodiment of the present application, by obtaining the operating load parameters and network bandwidth of each target edge server, the current load situation and network transmission capacity of each server are accurately understood, providing data support for subsequent load balancing decisions; then, based on the operating load parameters and network bandwidth, the edge link load score value of each edge server and the cloud link load score value of the cloud server are calculated to quantify the load situation of each server, which is convenient for comparison and selection of the optimal computing node; then, the server with the smallest load score value is selected as the target server, ensuring that the selected server has the lightest load, thereby avoiding server overload and improving the utilization efficiency of computing resources; finally, a computing power connection is established between the vehicle and the target server, realizing real-time computing assistance for the vehicle and improving the efficiency and accuracy of vehicle management. Therefore, based on the method of the embodiment of the present application, by obtaining the operating load parameters and network bandwidth, calculating the load score value, and selecting the optimal server to establish a computing power connection, efficient utilization of the edge computing resources of the Internet of Vehicles is achieved, solving the problem of unbalanced load on edge servers in the prior art.

[0044] Figure 3 Another computing node decision method provided in the application embodiment is referred to Figure 3 , the method may include the following steps:

[0045] Step 201: Acquire the location information of the vehicle and the location information of each network base station.

[0046] In some embodiments of the present application, the vehicle's location information and the location information of each network base station are obtained. This step is to determine the vehicle's current location and the network base stations covering that location, thereby providing basic data for subsequent computing node selection. The vehicle's location information can be obtained using positioning technology, while the network base station location information can be obtained from the base station location database provided by the network operator.

[0047] For example, imagine a vehicle A driving in an intelligent transportation system. The system first obtains vehicle A's current location information through onboard satellite equipment. Simultaneously, the system obtains information about all network base stations covering the area from the network operator's base station location database. Using this information, the system determines that vehicle A is currently covered by base stations 1 and 2, providing data support for subsequent computational node selection.

[0048] Step 202: Determine the number of target network base stations based on the vehicle's location information and the location information of each network base station.

[0049] The target network base station is a network base station that provides wireless network coverage for the vehicle.

[0050] In some embodiments of the present application, the number of target network base stations is determined based on the vehicle's location information and the location information of each network base station. Target network base stations are those that provide wireless network coverage to the vehicle. By determining the number of these base stations, further analysis and selection of optimal computing nodes can be performed to ensure efficient utilization of computing resources.

[0051] For example, assume that in an intelligent transportation system, the location of vehicle A has been determined. The system also obtains information about all network base stations covering the area from the network operator's base station location database. Assume there are three base stations in the area. By analyzing this location information, the system determines that vehicle A is currently covered by base stations 1, 2, and 3, making the number of target network base stations three. This information is used for subsequent computational node selection and resource allocation.

[0052] Optionally, step 202 includes the following sub-steps:

[0053] Sub-step 2021, determining the wireless network signal strength of each network base station to the vehicle based on the vehicle's location information and the location information of each network base station;

[0054] In some embodiments of the present application, the signal strength of each network base station's wireless network to the vehicle is determined based on the vehicle's location information and the location information of each network base station. Signal strength refers to the strength of the wireless signal emitted by the network base station at the vehicle's location, typically expressed in decibel milliwatts (dBm). By measuring and calculating signal strength, the coverage of each network base station to the vehicle can be evaluated, providing a basis for subsequent target network base station selection.

[0055] For example, assume that in an intelligent transportation system, the location of vehicle A, as well as the locations of base stations 1 and 2, is known. The system first uses the onboard communication module to measure the signal strength of each base station received by vehicle A at its current location. Assume that the signal strength of base station 1 is 65dBm and the signal strength of base station 2 is 70dBm. Based on these measurement results, the system can determine the wireless network signal strength of base stations 1 and 2 to vehicle A. Base station 1 has a stronger signal strength, indicating good coverage for vehicle A, while base station 2 has a slightly weaker signal strength, but still within an acceptable range. This information is then used to select target network base stations and calculate node allocation.

[0056] In sub-step 2022, the network base stations corresponding to the signal strengths greater than a preset signal strength threshold are determined as target network base stations to obtain the number of target network base stations.

[0057] In some embodiments of the present application, network base stations corresponding to signal strengths greater than a preset signal strength threshold are identified as target network base stations to determine the number of target network base stations. The signal strength threshold is a preset criterion used to screen out network base stations with sufficiently strong signal strengths to ensure that the vehicle can obtain a stable wireless network connection. This screening process can determine which network base stations can effectively cover the vehicle, thus providing a basis for subsequent computing node selection.

[0058] For example, assume that in an intelligent transportation system, the location of vehicle A, as well as the locations of base stations 1 and 2, is known. The system has measured the signal strength of each base station received by vehicle A at its current location. Assume that the signal strength of base station 1 is 65dBm and the signal strength of base station 2 is 70dBm. The set signal strength threshold is 68dBm. In this case, the system compares the signal strength of each base station with the preset signal strength threshold. Base station 1's signal strength of 65dBm is less than the threshold of 68dBm, so base station 1 is not determined as the target network base station. Base station 2's signal strength of 70dBm is greater than the threshold of 68dBm, so base station 2 is not determined as the target network base station. Ultimately, the system determines that the number of target network base stations is one, namely base station 2. This information is used for subsequent computing node selection and resource allocation.

[0059] Step 203 : When there are at least two network base stations providing wireless network coverage for the vehicle, obtain the operating load parameters and edge network bandwidth of each target edge server in the network cluster, and the cloud network bandwidth of the cloud server in the network cluster.

[0060] Among them, the target edge server is the edge server that provides wireless network coverage for the vehicle through the corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; the cloud network bandwidth is the network bandwidth between the cloud server and the external network of the network cluster.

[0061] The method shown in this step has been described in step 101 and will not be repeated here.

[0062] In step 204, when each operating load parameter is greater than or equal to the first load threshold parameter and at least one operating load parameter is less than or equal to the second load threshold parameter, an edge link load score value of each edge server is determined based on the corresponding operating load parameter, and a cloud link load score value of the cloud server is determined based on the total edge network bandwidth and the cloud network bandwidth.

[0063] The method shown in this step has been described in step 102 and will not be repeated here.

[0064] Optionally, in order to determine the edge link load score value of each edge server according to the corresponding operating load parameter, step 204 includes the following sub-steps:

[0065] Sub-step 2041: determining the absolute value of the difference between the second load threshold parameter and the corresponding operating load parameter as the edge link load score value of the corresponding edge server.

[0066] In some embodiments of the present application, the absolute value of the difference between the second load threshold parameter and the corresponding operating load parameter is determined as the edge link load score of the corresponding edge server. The edge link load score is used to quantify the load of the edge server. The smaller the difference, the closer the load of the edge server is to the threshold, and the lighter the load. This calculation method allows for intuitive comparison of the load of each edge server, thereby providing a basis for subsequent computing node selection.

[0067] For example, assume that in an intelligent transportation system, vehicle A is determined to be covered by base stations 1 and 2. The system also obtains the operating load parameters of edge servers E1 and E2. Assume that E1's operating load parameter is 75% and E2's is 62%. The second load threshold parameter is set to 70%. In this case, the system calculates the edge link load score for each edge server. For edge server E1, its edge link load score is: |70% - 75%| = 5%. For edge server E2, its edge link load score is: |70% - 62%| = 8%. Based on these calculation results, the system determines that E2's load score is 5%, while E1's is 8%. Because E2's higher load score indicates a lighter load, E2 may be prioritized in subsequent computing node selection. This calculation method ensures that computing tasks are assigned to less loaded edge servers, improving computing resource utilization efficiency.

[0068] The above process can be expressed as the formula:

[0069] Average ci =|η i -η2|;

[0070] Among them, Average ci represents the edge link load score, η i represents the operating load parameter of edge server i, and η2 represents the second load threshold parameter.

[0071] Optionally, in order to determine the cloud link load score of the cloud server based on the total edge network bandwidth and the cloud network bandwidth, step 204 includes the following sub-steps:

[0072] Sub-step 2042: determining the minimum value of all edge network bandwidths as the target edge network bandwidth.

[0073] In some embodiments of the present application, the minimum value among all edge network bandwidths is determined as the target edge network bandwidth. Edge network bandwidth refers to the network bandwidth between the edge server and the cloud server. By selecting the minimum value as the target edge network bandwidth, it is possible to ensure that the optimal edge network bandwidth is used when calculating the cloud link load score, thereby improving the utilization efficiency of computing resources.

[0074] For example, assume that in an intelligent transportation system, vehicle A is determined to be covered by base stations 1 and 2. The system obtains the edge network bandwidth of edge servers E1 and E2. Assume that E1's edge network bandwidth is 100 Mbps and E2's is 150 Mbps. In this case, the system compares the edge network bandwidths of E1 and E2 and selects the minimum as the target edge network bandwidth. Because E2's edge network bandwidth of 150 Mbps is greater than E1's 100 Mbps, the system selects 100 Mbps as the target edge network bandwidth. This selection ensures that the optimal edge network bandwidth is used when subsequently calculating the cloud link load score, thereby improving the utilization efficiency of computing resources.

[0075] In sub-step 2043, according to a preset weight, a weighted average of the absolute value of the difference between the target edge network bandwidth and the second load threshold parameter and the absolute value of the difference between the cloud network bandwidth and the second load threshold parameter is determined as the cloud link load score value.

[0076] In some embodiments of the present application, the absolute value of the difference between the target edge network bandwidth and the second load threshold parameter, and the absolute value of the difference between the cloud network bandwidth and the second load threshold parameter, are weighted averaged according to preset weights to determine the cloud link load score value. The weighted average value refers to assigning different weights according to the importance of different factors when calculating the average value. In this way, the difference between the edge network bandwidth and the cloud network bandwidth can be comprehensively considered to quantify the load of the cloud server, thereby providing a basis for the subsequent selection of computing nodes.

[0077] For example, assume that in an intelligent transportation system, vehicle A is determined to be covered by base station 1 and base station 2. The system has determined that the target edge network bandwidth is 150 Mbps and the cloud network bandwidth is 200 Mbps. The second load threshold parameter is set to 100 Mbps, and the preset weights are α = 0.6 and β = 0.4. In this case, the system will calculate the absolute value of the difference between the target edge network bandwidth and the second load threshold parameter, as well as the absolute value of the difference between the cloud network bandwidth and the second load threshold parameter. The absolute value of the difference between the target edge network bandwidth and the second load threshold parameter is: |150-100|=50 Mbps. The absolute value of the difference between the cloud network bandwidth and the second load threshold parameter is: |200-100|=100 Mbps. Next, the system calculates a weighted average using the preset weights: Cloud Link Load Score = α × |150 - 100 | + β × |200 - 100 | = 0.6 × 50 + 0.4 × 100 = 30 + 40 = 70 Mbps. Using this calculation method, the system determines the Cloud Link Load Score to be 70 Mbps. This score is used in subsequent compute node selection to ensure optimal selection and improve computing resource utilization efficiency.

[0078] The above process can be expressed as the formula:

[0079] Average s =α*|θ-η2|+β*|(min(BW i )-η2|;

[0080] Among them, Average s Indicates the cloud link load score, BW i Denotes the edge network bandwidth of edge server i, θ denotes the cloud network bandwidth, η2 denotes the second load threshold parameter, α and β are weights, and α+β=1, 0<α<1, 0<β<1.

[0081] The ratio of α and β can be adjusted according to the dimension of focus.

[0082] Optionally, in a preferred embodiment of the present application, the first load threshold parameter is selected as 30%, and the second load threshold parameter is selected as 70%.

[0083] In step 205 , the server corresponding to the maximum value among all edge link load scores and cloud link load scores in the network cluster is determined as the target server.

[0084] The method shown in this step has been described in step 103 and will not be repeated here.

[0085] Step 206: Establish a computing power connection between the vehicle and the target server to assist the vehicle in computing power calculation through the target server.

[0086] The method shown in this step has been described in step 104 and will not be repeated here.

[0087] Optionally, as a supplement to the solution for situations other than step 204, the embodiment of the present application further includes the following additional steps:

[0088] Step 207: When each operating load parameter is less than or equal to the first load threshold parameter, or when each operating load parameter is less than or equal to the first load threshold parameter, or greater than or equal to the second load threshold parameter, or when each operating load parameter is less than or equal to the second load threshold parameter, the edge server corresponding to the minimum operating load parameter is determined as the target server.

[0089] In some embodiments of the present application, when each operating load parameter is less than or equal to a first load threshold parameter, or when each operating load parameter is less than or equal to the first load threshold parameter, or greater than or equal to a second load threshold parameter, or when each operating load parameter is less than or equal to the second load threshold parameter, the edge server corresponding to the smallest operating load parameter is determined as the target server. This step is to select the edge server with the lightest load as the target server under specific load conditions to ensure that computing tasks are assigned to the most suitable server and improve the utilization efficiency of computing resources.

[0090] For example, suppose that in an intelligent transportation system, vehicle A is determined to be covered by base station 1 and base station 2. The system obtains the operating load parameters of edge servers E1 and E2. Assume that the operating load parameter of E1 is 25% and the operating load parameter of E2 is 35%. Set the first load threshold parameter to 30% and the second load threshold parameter to 70%. In this case, the operating load parameters of E1 and E2 are both less than or equal to the first load threshold parameter (30%). The system will compare these parameters and select the edge server corresponding to the smallest operating load parameter as the target server. Since E1's operating load parameter of 25% is the smallest, the system determines E1 as the target server. Next, the system will establish a computing power connection between vehicle A and edge server E1 to assist vehicle A in computing power calculation through E1. This selection ensures that the computing task is assigned to the server with the lightest load, improves the utilization efficiency of computing resources, and avoids server overload.

[0091] Optionally, as another supplement to the solution for situations other than step 204, the embodiment of the present application further includes the following additional steps:

[0092] Step 208 : When each operating load parameter is greater than or equal to the second load threshold parameter and the cloud network bandwidth is greater than or equal to the second load threshold parameter, the edge server corresponding to the minimum operating load parameter is determined as the target server.

[0093] In some embodiments of the present application, when each operating load parameter is greater than or equal to a second load threshold parameter and the cloud network bandwidth is greater than or equal to the second load threshold parameter, the edge server corresponding to the smallest operating load parameter is determined as the target server. This step is intended to ensure that under high load conditions, edge servers with relatively light loads can still be selected to perform computing tasks, thereby optimizing the utilization efficiency of computing resources. Cloud network bandwidth refers to the bandwidth between the cloud server and the external network of the network cluster, ensuring that it is large enough to support high-load computing tasks.

[0094] For example, assume that in an intelligent transportation system, vehicle A is determined to be covered by base stations 1 and 2. The system obtains the operating load parameters of edge servers E1 and E2, assuming E1's operating load parameter is 75% and E2's operating load parameter is 80%. A second load threshold parameter is set to 70%. Simultaneously, the system obtains the cloud network bandwidth of the cloud server, assuming it is 250 Mbps, which is also greater than the second load threshold parameter. In this case, the operating load parameters of both E1 and E2 are greater than or equal to the second load threshold parameter (70%), and the cloud network bandwidth is also greater than or equal to the second load threshold parameter (70%). The system compares the operating load parameters of E1 and E2 and selects the edge server with the smallest operating load parameter as the target server. Since E1's operating load parameter of 75% is less than E2's 80%, the system selects E1 as the target server. Next, the system establishes a computing power connection between vehicle A and edge server E1, using E1 to provide computing power assistance to vehicle A. This selection ensures that computing tasks are assigned to edge servers with relatively light loads, improving the efficiency of computing resource utilization.

[0095] Step 209 : When each operating load parameter is greater than or equal to the second load threshold parameter and the cloud network bandwidth is less than the second load threshold parameter, the cloud server is determined as the target server.

[0096] In some embodiments of the present application, when each operating load parameter is greater than or equal to the second load threshold parameter and the cloud network bandwidth is less than the second load threshold parameter, the cloud server will be determined as the target server. This step is to select the cloud server as the computing node when the edge server load is too high and the cloud network bandwidth is insufficient to support high-load computing tasks, so as to ensure that the computing tasks can proceed smoothly. The cloud server is selected as the target server in this case because it can provide relatively stable computing resources even if its network bandwidth is low.

[0097] For example, suppose that in an intelligent transportation system, vehicle A is determined to be covered by base station 1 and base station 2. The system obtains the operating load parameters of edge servers E1 and E2, assuming that the operating load parameter of E1 is 85% and the operating load parameter of E2 is 90%. Set the second load threshold parameter to 70%. At the same time, the system obtains the cloud network bandwidth of the cloud server, assuming it is 50Mbps, which is less than the second load threshold parameter. In this case, the operating load parameters of E1 and E2 are both greater than or equal to the second load threshold parameter (70%), and the cloud network bandwidth is less than the second load threshold parameter (70%). Therefore, the system determines the cloud server as the target server. Next, the system will establish a computing power connection between vehicle A and the cloud server to assist vehicle A in computing power calculations through the cloud server.

[0098] In summary, in the embodiment of the present application, by obtaining the operating load parameters and network bandwidth of each target edge server, the current load situation and network transmission capacity of each server are accurately understood, providing data support for subsequent load balancing decisions; then, based on the operating load parameters and network bandwidth, the edge link load score value of each edge server and the cloud link load score value of the cloud server are calculated to quantify the load situation of each server, which is convenient for comparison and selection of the optimal computing node; then, the server with the smallest load score value is selected as the target server, ensuring that the selected server has the lightest load, thereby avoiding server overload and improving the utilization efficiency of computing resources; finally, a computing power connection is established between the vehicle and the target server, realizing real-time computing assistance for the vehicle and improving the efficiency and accuracy of vehicle management. Therefore, based on the method of the embodiment of the present application, by obtaining the operating load parameters and network bandwidth, calculating the load score value, and selecting the optimal server to establish a computing power connection, efficient utilization of the edge computing resources of the Internet of Vehicles is achieved, solving the problem of unbalanced load on edge servers in the prior art.

[0099] refer to Figure 4 , which shows a computing node decision device 30 provided in an embodiment of the present application, including:

[0100] Activation module 301 is configured to obtain, when at least two network base stations provide wireless network coverage for the vehicle, operating load parameters and edge network bandwidth of each target edge server in the network cluster, as well as cloud network bandwidth of cloud servers in the network cluster; the target edge server is an edge server that provides wireless network coverage for the vehicle via a corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; and the cloud network bandwidth is the network bandwidth between the cloud server and an external network of the network cluster;

[0101] Evaluation module 302 is configured to determine an edge link load score for each edge server based on the corresponding operating load parameter, if each operating load parameter is greater than or equal to a first load threshold parameter and at least one operating load parameter is less than or equal to a second load threshold parameter, and determine a cloud link load score for the cloud server based on the total edge network bandwidth and the cloud network bandwidth;

[0102] A first selection module 303 is configured to determine a server corresponding to a maximum value among all edge link load scores and cloud link load scores in the network cluster as a target server;

[0103] The computing module 304 is used to establish a computing power connection between the vehicle and the target server so as to assist the vehicle in computing power calculation through the target server.

[0104] Optionally, the evaluation module 302 includes:

[0105] The edge link load scoring submodule is configured to determine the absolute value of the difference between the second load threshold parameter and the corresponding operating load parameter as the edge link load score value of the corresponding edge server.

[0106] Optionally, the evaluation module 302 includes:

[0107] A target bandwidth submodule, configured to determine the minimum value of all edge network bandwidths as the target edge network bandwidth;

[0108] The cloud link load scoring submodule is used to determine the cloud link load score value as a weighted average of the absolute value of the difference between the target edge network bandwidth and the second load threshold parameter and the absolute value of the difference between the cloud network bandwidth and the second load threshold parameter according to a preset weight.

[0109] Optionally, the device 30 further includes:

[0110] The second selection module is used to determine the edge server corresponding to the minimum operating load parameter as the target server when each operating load parameter is less than or equal to the first load threshold parameter, or when each operating load parameter is respectively less than or equal to the first load threshold parameter, or greater than or equal to the second load threshold parameter, or when each operating load parameter is respectively less than or equal to the second load threshold parameter.

[0111] Optionally, the device 30 further includes:

[0112] a third selection module, configured to, when each operating load parameter is greater than or equal to a second load threshold parameter and the cloud network bandwidth is greater than or equal to the second load threshold parameter, determine the edge server corresponding to the minimum operating load parameter as the target server;

[0113] The fourth selection module is configured to determine the cloud server as the target server when each operating load parameter is greater than or equal to the second load threshold parameter and the cloud network bandwidth is less than the second load threshold parameter.

[0114] Optionally, the device 30 further includes:

[0115] A location acquisition module is used to obtain the location information of the vehicle and the location information of each network base station;

[0116] The quantity analysis module is used to determine the number of target network base stations based on the location information of the vehicle and the location information of each network base station; the target network base stations are network base stations that provide wireless network coverage for the vehicle.

[0117] Optional quantitative analysis modules include:

[0118] A signal strength analysis submodule is used to determine the signal strength of the wireless network from each network base station to the vehicle based on the vehicle's location information and the location information of each network base station;

[0119] The quantity analysis submodule is configured to determine the network base stations corresponding to the signal strengths greater than a preset signal strength threshold as target network base stations, so as to obtain the number of target network base stations.

[0120] In summary, in the embodiment of the present application, by obtaining the operating load parameters and network bandwidth of each target edge server, the current load situation and network transmission capacity of each server are accurately understood, providing data support for subsequent load balancing decisions; then, based on the operating load parameters and network bandwidth, the edge link load score value of each edge server and the cloud link load score value of the cloud server are calculated to quantify the load situation of each server, which is convenient for comparison and selection of the optimal computing node; then, the server with the smallest load score value is selected as the target server, ensuring that the selected server has the lightest load, thereby avoiding server overload and improving the utilization efficiency of computing resources; finally, a computing power connection is established between the vehicle and the target server, realizing real-time computing assistance for the vehicle and improving the efficiency and accuracy of vehicle management. Therefore, based on the method of the embodiment of the present application, by obtaining the operating load parameters and network bandwidth, calculating the load score value, and selecting the optimal server to establish a computing power connection, efficient utilization of the edge computing resources of the Internet of Vehicles is achieved, solving the problem of unbalanced load on edge servers in the prior art.

[0121] Reference Figure 5 , electronic device 500 may include one or more of the following components: a processing component 502 , a memory 504 , a power component 506 , a multimedia component 508 , an audio component 510 , an input / output (I / O) interface 512 , a sensor component 514 , and a communication component 516 .

[0122] The processing component 502 generally controls the overall operation of the electronic device 500, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 502 may include one or more modules to facilitate interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.

[0123] The memory 504 is used to store various types of data to support operations on the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phone book data, messages, pictures, multimedia, etc. The memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0124] The power supply assembly 506 provides power to the various components of the electronic device 500. The power supply assembly 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 500.

[0125] The multimedia component 508 includes an interface that provides an output interface between the electronic device 500 and the user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of touch or slide actions, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front camera and / or a rear camera. When the electronic device 500 is in an operating mode, such as a shooting mode or a multimedia mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0126] The audio component 510 is used to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC) that is used to receive external audio signals when the electronic device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting audio signals.

[0127] The input / output I / O interface 512 provides an interface between the processing component 502 and peripheral interface modules, such as a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0128] The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the electronic device 500. For example, the sensor assembly 514 can detect the open / closed state of the electronic device 500, the relative positioning of components, such as the display and keypad of the electronic device 500. The sensor assembly 514 can also detect changes in the position of the electronic device 500 or a component of the electronic device 500, the presence or absence of user contact with the electronic device 500, the orientation or acceleration / deceleration of the electronic device 500, and temperature changes of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0129] The communication component 516 is used to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0130] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement the methods provided in the embodiments of the present application.

[0131] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, which can be executed by the processor 520 of the electronic device 500 to perform the above method. For example, the non-transitory storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0132] Figure 6FIG. 6 is a block diagram of an electronic device 600 according to another embodiment of the present invention. For example, the electronic device 600 may be provided as a server. Figure 6 The electronic device 600 includes a processing component 622, which further includes one or more processors, and a memory resource represented by a memory 632 for storing instructions executable by the processing component 622, such as an application. The application stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 622 is configured to execute the instructions to perform the method provided in the embodiments of the present application.

[0133] The electronic device 600 may further include a power supply component 626 configured to perform power management of the electronic device 600, a wired or wireless network interface 650 configured to connect the electronic device 600 to a network, and an input / output (I / O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0134] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0135] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A computing node decision method, characterized in that: include: In a case where there are at least two network base stations providing wireless network coverage for the vehicle, obtaining an operating load parameter and an edge network bandwidth of each target edge server in the network cluster, and a cloud network bandwidth of a cloud server in the network cluster; the target edge server is an edge server providing wireless network coverage for the vehicle through a corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; The cloud network bandwidth is the network bandwidth between the cloud server and the external network of the network cluster; When each of the operating load parameters is greater than or equal to a first load threshold parameter and at least one of the operating load parameters is less than or equal to a second load threshold parameter, determining an edge link load score value of each edge server according to the corresponding operating load parameter, and determining a cloud link load score value of the cloud server according to the total edge network bandwidth and the cloud network bandwidth; Determine the server corresponding to the maximum value among all the edge link load score values ​​and the cloud link load score values ​​in the network cluster as the target server; A computing power connection is established between the vehicle and the target server so as to assist the vehicle in computing power calculations through the target server.

2. The method according to claim 1, wherein Determining the edge link load score value of each edge server according to the corresponding operating load parameter includes: The absolute value of the difference between the second load threshold parameter and the corresponding operating load parameter is determined as the edge link load score value of the corresponding edge server.

3. The method according to claim 1, wherein Determining the cloud link load score of the cloud server based on the total edge network bandwidth and the cloud network bandwidth includes: Determine the minimum value among all the edge network bandwidths as the target edge network bandwidth; According to a preset weight, a weighted average of the absolute value of the difference between the target edge network bandwidth and the second load threshold parameter and the absolute value of the difference between the cloud network bandwidth and the second load threshold parameter is determined as the cloud link load score value.

4. The method according to claim 1, wherein The method further comprises: In the case where each of the operating load parameters is less than or equal to the first load threshold parameter, or in the case where each of the operating load parameters is respectively less than or equal to the first load threshold parameter, or greater than or equal to the second load threshold parameter, or in the case where each of the operating load parameters is respectively less than or equal to the second load threshold parameter, the edge server corresponding to the smallest operating load parameter is determined as the target server.

5. The method according to claim 1, wherein The method further comprises: When each of the operating load parameters is greater than or equal to a second load threshold parameter, and the cloud network bandwidth is greater than or equal to the second load threshold parameter, determining the edge server corresponding to the minimum operating load parameter as the target server; When each of the operating load parameters is greater than or equal to a second load threshold parameter and the cloud network bandwidth is less than the second load threshold parameter, the cloud server is determined as the target server.

6. The method according to claim 1, wherein The method further comprises: Obtaining the location information of the vehicle and the location information of each of the network base stations; The number of target network base stations is determined according to the location information of the vehicle and the location information of each of the network base stations; the target network base stations are network base stations that provide wireless network coverage for the vehicle.

7. The method according to claim 6, wherein The determining the number of target network base stations according to the location information of the vehicle and the location information of each of the network base stations includes: Determining the signal strength of the wireless network of each network base station to the vehicle based on the location information of the vehicle and the location information of each network base station; The network base stations corresponding to the signal strengths greater than a preset signal strength threshold are determined as the target network base stations to obtain the number of the target network base stations.

8. A computing node decision device, characterized in that: include: An activation module is configured to obtain, when at least two network base stations provide wireless network coverage for a vehicle, an operating load parameter and an edge network bandwidth of each target edge server in a network cluster, as well as a cloud network bandwidth of a cloud server in the network cluster; the target edge server is an edge server that provides wireless network coverage for the vehicle through a corresponding network base station; the edge network bandwidth is the network bandwidth between the network base station corresponding to the corresponding target edge server and the cloud server; and the cloud network bandwidth is the network bandwidth between the cloud server and an external network of the network cluster; an evaluation module configured to determine, when each of the operating load parameters is greater than or equal to a first load threshold parameter and at least one of the operating load parameters is less than or equal to a second load threshold parameter, an edge link load score value for each edge server based on the corresponding operating load parameter, and determine a cloud link load score value for the cloud server based on the total edge network bandwidth and the cloud network bandwidth; A selection module is configured to determine, in the network cluster, a server corresponding to a maximum value among all the edge link load score values ​​and the cloud link load score values ​​as a target server; A computing module is used to establish a computing power connection between the vehicle and the target server so as to assist the vehicle in computing power calculation through the target server.

9. An electronic device, characterized in that: include: a processor, a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method according to any one of claims 1 to 7.

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