Computing node decision-making method and device, equipment and storage medium

Abstract

The invention discloses a computing node decision-making method, device and equipment and a computer readable storage medium, and the method comprises the steps: obtaining an operation load parameter, an edge network bandwidth and a cloud network bandwidth under the condition that at least two network base stations carry out the wireless network coverage of a vehicle; when each operation load parameter is greater than or equal to a first load threshold parameter and at least one operation load parameter is less than or equal to a second load threshold parameter, determining an edge link load score value of each edge server, and determining a cloud link load score value of the cloud server; determining a server corresponding to the maximum value in all the edge link load score values and the cloud link load score values in the network cluster as a target server; and establishing computing power connection between the vehicle and the target server so as to carry out computing power calculation assistance on the vehicle through the target server. The problem that the load of the edge server is unbalanced is solved.

Description

Technical Field

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

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

[0003] Existing edge computing technologies mainly achieve low latency and efficient data processing by deploying computing and storage resources at the edge of the network, close to users. The overall structure of edge computing usually includes three levels: cloud computing processing center, edge nodes, and terminal nodes. Through computing selection, the computing power of the cloud computing processing center or edge nodes is allocated to the terminal nodes to achieve 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, making it impossible to effectively utilize computing resources. Summary of the invention

[0005] The present application aims to provide a computing node decision method, device, equipment and computer-readable storage medium, at least to solve the problem of inability to effectively utilize 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, including:

[0007] In the case where there are at least two network base stations that provide wireless network coverage for the vehicle, 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 are obtained; the target edge server is an 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;

[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, an edge link load score value of each edge server is determined according to the corresponding operating load parameter, and a cloud link load score value of the cloud server is determined according to all of the edge network bandwidth and the cloud network bandwidth;

[0009] Determine the server corresponding to the maximum value of 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 calculation through the target server.

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

[0012] An activation module is used to 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 when at least two network base stations provide wireless network coverage for the vehicle; the target edge server is an 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;

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

[0014] A selection module is used to determine the server corresponding to the maximum value of all the edge link load score values ​​and the cloud link load score values ​​in the network cluster as the 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 program 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; and then according to 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, the efficient utilization of the edge computing resources of the Internet of Vehicles is realized, and the problem of unbalanced load of edge servers in the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In the attached picture:

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

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

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

[0023] Figure 4 is a block diagram of a computing node decision 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 It is a block diagram of an electronic device of another embodiment provided by the embodiments of the present application. DETAILED DESCRIPTION

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

[0027] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other 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 and the cloud network bandwidth of the cloud server in the network cluster are obtained. The target edge server is an 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, assuming that in an intelligent transportation system, the dotted circle in the figure represents the range of the base station, and vehicle A is covered by base station 1 and base station 2. The system first determines that the target edge servers corresponding to base station 1 and base station 2 are edge servers E1 and E2 respectively. Next, the system obtains the operating load parameters of edge servers E1 and E2, assuming that the operating load parameter of E1 is 40% and the operating load parameter of E2 is 60%. At the same time, the system obtains the edge network bandwidth, assuming that the bandwidth between E1 and cloud server C is 100Mbps, and the bandwidth between E2 and cloud server C is 150Mbps. In addition, the system also 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 200Mbps. Through this information, the system can accurately understand the current load situation and network transmission capacity of each target edge server, and provide data support for the subsequent selection of computing nodes.

[0034] Step 102, 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, determine the edge link load score value of each edge server according to the corresponding operating load parameter, and determine the cloud link load score value of the cloud server according to 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 the first load threshold parameter and at least one operating load parameter is less than or equal to the 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 according to the total edge network bandwidth and the cloud network bandwidth. The edge link load score value is used to quantify the load of the edge server, while the cloud link load score value is used to quantify the load 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, suppose that in an intelligent transportation system, vehicle A is confirmed 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 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 value based on these parameters. Assume that the edge link load score value of E1 is 0.85 and the edge link load score value of E2 is 0.99. The cloud link load score value of the cloud server is 0.5. Through these score values, the system can quantify the load conditions 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 of all edge link load score values ​​and cloud link load score values ​​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 score values ​​and cloud link load score values ​​in the network cluster is determined as the target server. The target server refers to the server that is most suitable for computing tasks under the current load and network bandwidth conditions. By selecting the server with the largest load score value, it can be ensured 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, suppose that in an intelligent transportation system, vehicle A is confirmed to be covered by base station 1 and base station 2. And the system has obtained the edge link load score values ​​of edge servers E1 and E2, which are 0.85 and 0.99 respectively, and the cloud link load score value of the cloud server is 0.5. In this case, the system will compare these scores and select the server corresponding to the smallest score as the target server. Since the score value of edge server E2, 0.99, is the largest, the system determines E2 as the target server. Next, the system will establish a computing power connection between vehicle A and edge server E2 to assist vehicle A in computing power calculation through E2. This choice ensures that the computing tasks are assigned to the server with the lightest load, improves the utilization efficiency of computing resources, and avoids server overload.

[0040] Step 104, establishing 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.

[0041] In some embodiments of the present application, a computing power connection is established between the vehicle and the target server to assist the vehicle in computing power calculations through the target server. Computing power connection refers to establishing a data transmission channel between the vehicle and the target server, so that the vehicle can send the data to be processed to the target server for calculation and receive the calculation results. This step ensures that the computing tasks can be efficiently executed on the target server, thereby improving the efficiency and accuracy of vehicle management.

[0042] For example, suppose that in an intelligent transportation system, vehicle A is confirmed to be covered by base station 1 and base station 2. And the system has determined that edge server E2 is the target server. The system will next establish a computing power connection between vehicle A and edge server E2 through a wireless network. Specifically, the system first sends a connection request to E2 through the on-board communication module of vehicle A. After receiving the request, E2 verifies the identity of vehicle A 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, and 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; and then according to 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, the efficient utilization of the edge computing resources of the Internet of Vehicles is realized, and the problem of unbalanced load of edge servers in the prior art is solved.

[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, obtaining the location information of the vehicle and the location information of each network base station.

[0046] In some embodiments of the present application, the location information of the vehicle and the location information of each network base station will be obtained. This step is to determine the current location of the vehicle and the network base station covering the location, so as to provide basic data for the subsequent selection of computing nodes. The location information of the vehicle can be obtained through positioning technology, and the location information of the network base station can be obtained through the base station location database provided by the network operator.

[0047] For example, suppose that in an intelligent transportation system, there is a car A driving. The system first obtains the current location information of car A through the on-board satellite equipment. At the same time, the system obtains the information of all network base stations covering the area from the base station location database of the network operator. With this information, the system can determine that car A is currently covered by base station 1 and base station 2, providing data support for the subsequent selection of computing nodes.

[0048] Step 202, determining the number of target network base stations based on the location information of the vehicle 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 location information of the vehicle and the location information of each network base station. The target network base stations are those base stations that provide wireless network coverage for the vehicle. By determining the number of these base stations, the optimal computing nodes can be further analyzed and selected to ensure efficient use of computing resources.

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

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

[0053] Sub-step 2021, 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;

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

[0055] For example, suppose that in an intelligent transportation system, the location of vehicle A and the locations of base stations 1 and 2 are known. The system first measures the signal strength of each base station received by vehicle A at its current location through the vehicle communication module. Assume that the signal strength of base station 1 is 65dBm and the signal strength of base station 2 is 70dBm. Through these measurement results, the system can determine the wireless network signal strength of base station 1 and base station 2 to vehicle A. The signal strength of base station 1 is stronger, indicating that it has better coverage for vehicle A, while the signal strength of base station 2 is slightly weaker, but still within an acceptable range. This information will be used for subsequent target network base station selection and calculation node allocation.

[0056] Sub-step 2022, determining the network base stations corresponding to the signal strengths greater than the preset signal strength threshold as target network base stations, so as to obtain the number of target network base stations.

[0057] In some embodiments of the present application, the network base station corresponding to the signal strength greater than the preset signal strength threshold is determined as the target network base station to obtain the number of target network base stations. The signal strength threshold is a preset standard for screening out network base stations with sufficiently strong signal strength to ensure that the vehicle can obtain a stable wireless network connection. Through this screening, it is possible to determine which network base stations can effectively cover the vehicle, thereby providing a basis for subsequent computing node selection.

[0058] For example, suppose that in an intelligent transportation system, the location of vehicle A and the locations of base stations 1 and 2 are known. The system has measured the signal strength of each base station received by vehicle A at the current location, assuming 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 will compare the signal strength of each base station with the preset signal strength threshold. The signal strength of base station 1, 65dBm, is less than the threshold of 68dBm, so base station 1 will not be determined as the target network base station. The signal strength of base station 2, 70dBm, is greater than the threshold of 68dBm, so base station 2 is not determined as the target network base station. Finally, the system determines that the number of target network base stations is 1, that is, base station 2. This information will be 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 explained in step 101 and will not be repeated here.

[0062] 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, determine the edge link load score value of each edge server according to the corresponding operating load parameter, and determine the cloud link load score value of the cloud server according to the total edge network bandwidth and the cloud network bandwidth.

[0063] The method shown in this step has been explained 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 operation 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. Through this calculation method, the load of each edge server can be intuitively compared, thereby providing a basis for the subsequent selection of computing nodes.

[0067] For example, suppose that in an intelligent transportation system, vehicle A is determined to be covered by base station 1 and base station 2. And the system obtains the operating load parameters of edge servers E1 and E2, assuming that the operating load parameter of E1 is 75% and the operating load parameter of E2 is 62%. Set the second load threshold parameter to 70%. In this case, the system will calculate the edge link load score value of each edge server. For edge server E1, its edge link load score value is: |70%-75%|=5%. For edge server E2, its edge link load score value is: |70%-62%|=8%. Through these calculation results, the system can determine that the load score value of E2 is 5%, while the load score value of E1 is 8%. Since E2 has a larger load score value, indicating that its load is lighter, E2 may be given priority in the subsequent selection of computing nodes. This calculation method ensures that computing tasks are assigned to edge servers with lighter loads, thereby improving the utilization efficiency of computing resources.

[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 the edge server i, and η2 represents the second load threshold parameter.

[0071] Optionally, in order to determine the cloud link load score value of the cloud server according to 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 of all edge network bandwidths is determined as the target edge network bandwidth. The 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 can be ensured that the optimal edge network bandwidth is used when calculating the cloud link load score value, thereby improving the utilization efficiency of computing resources.

[0074] 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 edge network bandwidth of edge servers E1 and E2. Assume that the edge network bandwidth of E1 is 100Mbps and the edge network bandwidth of E2 is 150Mbps. In this case, the system will compare the edge network bandwidths of E1 and E2 and select the minimum value as the target edge network bandwidth. Since the edge network bandwidth of E2, 150Mbps, is greater than the 100Mbps of E1, the system determines 100Mbps as the target edge network bandwidth. This selection ensures that the optimal edge network bandwidth is used when the cloud link load score value is subsequently calculated, thereby improving the utilization efficiency of computing resources.

[0075] Sub-step 2043, according to the preset weight, the 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 150Mbps and the cloud network bandwidth is 200Mbps. Set the second load threshold parameter to 100Mbps, and the preset weights are α=0.6 and β=0.4 respectively. 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, and 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|=50Mbps. The absolute value of the difference between the cloud network bandwidth and the second load threshold parameter is: |200-100|=100Mbps. Next, the system calculates the weighted average according to the preset weights: Cloud Link Load Score = α×|150-100|+β×|200-100|=0.6×50+0.4×100=30+40=70Mbps. Through this calculation method, the system determines that the cloud link load score is 70Mbps. This score will be used for subsequent computing node selection to ensure the selection of the optimal computing node and improve the utilization efficiency of computing resources.

[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 represents the edge network bandwidth of edge server i, θ represents the cloud network bandwidth, η2 represents 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 that is given priority.

[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] Step 205: determine the server corresponding to the maximum value of all edge link load score values ​​and cloud link load score values ​​in the network cluster as the target server.

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

[0085] Step 206, establishing 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.

[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 the case 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 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, determine the edge server corresponding to the minimum operating load parameter as the target server.

[0089] In some embodiments of the present application, 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. This step is to select the edge server with the lightest load as the target server under specific load conditions to ensure that the computing task is 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, assuming 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 choice ensures that the computing tasks are 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 the case 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, determine the edge server corresponding to the minimum operating load parameter as the target server.

[0093] 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 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. 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 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 75% and the operating load parameter of E2 is 80%. Set the second load threshold parameter to 70%. At the same time, the system obtains the cloud network bandwidth of the cloud server, assuming that it is 250Mbps, which is also greater 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 also greater than or equal to the second load threshold parameter (70%). The system will compare the operating load parameters of E1 and E2 and select the edge server corresponding to the smallest operating load parameter as the target server. Since the operating load parameter of E1 of 75% is less than 80% of E2, 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 tasks are assigned to edge servers with relatively light loads, improving the utilization efficiency of computing resources.

[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 is 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 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; and then according to 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, the efficient utilization of the edge computing resources of the Internet of Vehicles is realized, and the problem of unbalanced load of edge servers in the prior art is solved.

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

[0100] The activation module 301 is used to 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 when at least two network base stations provide wireless network coverage for the vehicle; the target edge server is an 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;

[0101] An evaluation module 302 is used to determine an edge link load score value of each edge server according to the corresponding operation load parameter when each operation load parameter is greater than or equal to a first load threshold parameter and at least one operation load parameter is less than or equal to a second load threshold parameter, and determine a cloud link load score value of the cloud server according to the total edge network bandwidth and the cloud network bandwidth;

[0102] The first selection module 303 is used to determine the server corresponding to the maximum value of all edge link load score values ​​and cloud link load score values ​​in the network cluster as the 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 used to determine the absolute value of the difference between the second load threshold parameter and the corresponding operation load parameter as the edge link load scoring value of the corresponding edge server.

[0106] Optionally, the evaluation module 302 includes:

[0107] A target bandwidth submodule, used 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 scoring 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 is used to determine the edge server corresponding to the minimum operating load parameter 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 greater than or equal to the second load threshold parameter;

[0113] The fourth selection module is used 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 station is the network base station that provides wireless network coverage for the vehicle.

[0117] Optional, quantitative analysis modules include:

[0118] A signal strength analysis submodule, used to determine 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;

[0119] The quantity analysis submodule is used to determine the network base stations corresponding to the signal strengths greater than a preset signal strength threshold as target network base stations 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; and then according to 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, the efficient utilization of the edge computing resources of the Internet of Vehicles is realized, and the problem of unbalanced load of edge servers in the prior art is solved.

[0121] Reference Figure 5 , the 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 operations, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 502 may include one or more modules to facilitate the interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate the 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 the operation of 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 component 506 provides power to the various components of the electronic device 500. The power supply component 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 touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, 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 may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0126] The audio component 510 is used to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC), and when the electronic device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is used to receive an external audio signal. The received audio signal can be further stored in the memory 504 or sent 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 the peripheral interface module, which may be 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, and the sensor assembly 514 can also detect the position change 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 the temperature change 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 an optical 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, a carrier 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, and the instructions can be executed by a processor 520 of an 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 6600 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 that can be executed 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 instructions to perform the method provided in the embodiment of the present application.

[0133] The electronic device 600 may also 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. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be 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 precise structures that have been 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, It is characterized in that include: In the case where there are at least two network base stations providing wireless network coverage for the vehicle, obtaining 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; the target edge server is an edge server providing 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; 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 of each edge server is determined according to the corresponding operating load parameter, and a cloud link load score value of the cloud server is determined according to all of the edge network bandwidth and the cloud network bandwidth; Determine the server corresponding to the maximum value of 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 calculation through the target server.

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

3. The method according to claim 1, It is characterized in that Determining the cloud link load score value of the cloud server according to all the edge network bandwidths and the cloud network bandwidths includes: Determine the minimum value among all the edge network bandwidths as the target edge network bandwidth; According to the preset weight, the 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, It is characterized in that 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, It is characterized in that 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, It is characterized in that 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 1, It is characterized in that 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: Determine the signal strength of the wireless network of each network base station to the vehicle according to the location information of the vehicle and the location information of each network base station; The network base station corresponding to the signal strength greater than a preset signal strength threshold is determined as the target network base station to obtain the number of the target network base stations.

8. A computing node decision device, It is characterized in that include: An activation module is used to 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 when at least two network base stations provide wireless network coverage for the vehicle; the target edge server is an 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; an evaluation module, configured to determine an edge link load score value of each edge server according to the corresponding operation load parameter when each of the operation load parameters is greater than or equal to a first load threshold parameter and at least one of the operation load parameters is less than or equal to a second load threshold parameter, and determine a cloud link load score value of the cloud server according to all of the edge network bandwidth and the cloud network bandwidth; A selection module is used to determine the server corresponding to the maximum value of all the edge link load score values ​​and the cloud link load score values ​​in the network cluster as the 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, It is 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, It is 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 as claimed in any one of claims 1 to 7.

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