Vehicle-side cooperative task unloading and resource allocation method based on vehicle moving and parking assistance

Abstract

The invention discloses a vehicle-side cooperative task unloading and resource allocation method based on moving and parking vehicle assistance, and the method comprises the steps: sensing the task information of all current equipment and the current wireless environment information of a system through a network controller; inputting current task information and system wireless environment information into a trained optimization model deployed in a network controller, and calculating an equipment task unloading decision and a resource allocation condition based on a current state; according to the obtained equipment task unloading decision information, whether the equipment task is executed on an edge server of the base station or on a moving or parking vehicle under the corresponding base station is determined; according to the method, the unloading decision of equipment tasks is comprehensively considered, the transmission power of equipment is allocated, the computing resources of edge servers are allocated, and the computing resources of vehicles are allocated under the constraint condition of maximum cost so as to minimize the weighted sum of all task processing delay and equipment energy consumption; and the system efficiency and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking and mobile edge computing technology, and in particular to a vehicle-edge collaborative task unloading and resource allocation method based on mobile and parked vehicle assistance. Background Art

[0002] As an emerging technology, vehicle edge computing is an innovative fusion of mobile edge computing and Internet of Vehicles technology. Vehicle edge computing technology sinks vehicle tasks and places the computing tasks of vehicle terminals at the edge of the network, which is an effective way to solve the problem of limited computing power of vehicle terminals. At the same time, vehicle edge computing technology uses network edge resources for computing and data processing, which can quickly respond to the needs of vehicle terminals, improve the response speed and efficiency of the Internet of Vehicles, and provide advanced support for the realization of vehicle intelligence and automation.

[0003] As an important component of vehicle-mounted edge computing, edge servers can not only meet the computing needs of the vehicle itself, such as collision warning, autonomous driving, and intelligent transportation, but also provide computing power and storage resources for other smart devices connected to the edge computing network (such as smartphones, laptops, etc.). Mobilizing the computing power of edge servers and expanding their service scope can significantly enhance the application value and development potential of vehicle edge computing.

[0004] When the edge server carries too many tasks, its computing resources may not be able to meet the needs of all tasks, which in turn leads to server overload and deterioration of task service quality. Faced with the contradiction between the computing power limit of the edge server and the computing power demand of the user device. In order to expand the computing resources of the vehicle edge computing system, we can consider introducing external resources from other entities. For example, these tasks can be further transferred from the edge server to the cloud server or other edge servers connected to it. However, heterogeneous resource computing is underutilized in vehicle edge computing scenarios, especially in vehicles. Vehicles have built-in chips, and vehicles are equipped with equipment to handle applications such as autonomous driving and smart cars. Edge computing resources can be expanded by utilizing the underutilized resources of nearby vehicles.

[0005] Considering the influence of complex factors such as vehicle mobility characteristics, vehicle service mission willingness, multi-cell and multi-user distribution, the existing resource management methods in the field of vehicle edge computing are difficult to adapt to the needs of multi-edge intelligent collaborative systems, and further improvement and innovation are urgently needed in terms of reliability, efficiency and scalability. To this end, it is necessary to explore and establish vehicle resource management strategies and methods that adapt to multi-edge collaborative scenarios from a systematic and holistic perspective to achieve efficient scheduling and utilization of resources and improve the overall system performance and user experience.

[0006] In the application scenario of vehicle edge computing, by making full use of the computing power of the vehicle, we can not only improve the efficiency of task processing, but also significantly reduce the working pressure of the edge server. Vehicle-to-infrastructure communication, vehicle-to-vehicle communication, and vehicle-to-device communication can relieve the pressure on the edge server. However, many studies only focus on how to make full use of parking resources, but ignore the resources owned by mobile vehicles. Moreover, the scope of application is relatively limited, and a wide range of problems (uneven edge arithmetic load, uneven temporal and spatial distribution of vehicles) are not considered, and only the situation under a single edge node is considered. In addition, the vehicle is private, and will not disclose its private information (such as the number of idle resources), nor will it provide computing resources unconditionally. Consider that the vehicle rents the vehicle computing resources based on the willingness of the device to purchase the vehicle computing resources at the highest acceptable price.

[0007] In the daily life scenes of the city, the share of mobile vehicles is very large. Using the computing power of these mobile vehicles to help devices complete task offloading can significantly improve the overall performance of the mobile edge computing system. This assisted mobile vehicle method helps to increase the overall computing resources, thereby improving the overall performance of the mobile edge computing system, and provides strong supplementary support for the parked vehicle assisted mobile edge computing solution. At the same time, the device can offload tasks to any edge server and the vehicle under the edge server to achieve load balancing. When a device's task is transferred to an edge server covering the device, the task can be further transferred from the edge server to the vehicle within the coverage area of ​​the edge server. The above shortcomings greatly limit the performance of task offloading decisions and resource allocation. Summary of the invention

[0008] 1. Technical issues to be resolved

[0009] In order to overcome the deficiencies of the above-mentioned prior art, the present invention is aimed at tasks based on vehicle edge computing. Taking into account the limited resources of edge servers and the underutilization of idle resources in vehicles, we use mobile and parked vehicles to expand the resources of edge servers. Each device can offload its tasks to the edge server of the base station, or further offload them from the base station to the moving and parked vehicles. Determine the task offloading decision and resource allocation strategy under the maximum cost constraint of purchasing vehicle resources to minimize the total task processing delay and energy consumption of all devices. The present invention is inherited to the network controller deployment module and resource configuration module of the edge server layer, combined with the network status perception and collection module, to realize the dynamic optimization of task offloading decisions and resource allocation strategies, accelerate the neural network training process and optimization process on the basis of considering practicality, and improve user experience.

[0010] (II) Technical solution

[0011] To solve the above technical problems, the present invention proposes a vehicle-side collaborative task offloading and resource allocation method based on mobile and parked vehicle assistance, comprising the following steps:

[0012] S1: The network controller senses all current device task information and the current wireless environment information of the system; at the same time, the network controller senses the computing resources of all base stations and vehicles, the transmission rate between base stations and the status information of all vehicles; and uploads relevant information to the network controller through wireless and wired connections;

[0013] S2: Input the current task information and the current wireless environment information of the system described in step S1 into the trained optimization model deployed in the network controller, calculate the device task offloading decision based on the current state, the transmission power allocation during the task transmission process, the computing resource allocation to be used during the task calculation process, and the task transmission rate allocation between base stations;

[0014] S3: Extract the device task offloading decision information obtained in step S2. If the information indicates that the task is executed on the edge server of the base station, process the vehicle task according to the task offloading decision. If the task is planned to be offloaded to the edge server of the base station, the processing delay of the task includes the uplink transmission delay from the device to the edge server of the attached base station, the transmission delay between base stations, and the calculation delay on the edge server.

[0015] S4: Extract the device task offloading decision information obtained in step S2. If the information indicates that the task is executed on the vehicle under the base station, the network controller sends the corresponding task offloading decision instruction to the corresponding vehicle through a wired connection, and processes the device task according to the task offloading decision. If the task is planned to be offloaded to the vehicle under the base station, the processing delay of the task includes the uplink transmission delay from the device to the attached base station edge server, the transmission delay between base stations, the downlink transmission delay from the base station to the vehicle, and the calculation delay on the vehicle. If the unloaded vehicle is a mobile vehicle, the residence time of the vehicle in the corresponding base station must be greater than the processing delay of the task, otherwise the task offloading service provided by the vehicle may be interrupted, resulting in the failure of task offloading.

[0016] Furthermore, the step S1 is specifically as follows:

[0017] The network architecture of this method is characterized by: A parking and moving vehicle assisted mobile edge computing scenario is considered, in which M edge servers are included, all of which can communicate through local area network interconnection. We denote the set of edge servers as M = {1, 2, ..., M}. Assume that there is I in the mth edge server m Devices, Parked vehicles and Mobile vehicle. Equipment collection I m ={1,2,…,I m} indicates that the sets of parked vehicles and moving vehicles are We use a collection to include these two sets. In addition, a network controller is placed at the edge layer to control the operation of the entire method. We use "vehicle nm" (vehicle n under the coverage of edge server m) to represent parked vehicles or moving vehicles. is the duration that vehicle nm maintains communication with the affiliated edge server m before leaving the coverage of edge server m.

[0018] The algorithm is deployed on a network controller to work. The network controller perceives the current task information of all devices and the current wireless environment information of the system. Among them, we name the task of device im under the coverage of edge server m as task im. The information of task im includes: (1) c im (based on the CPU operating cycle) represents the computing resources required for task processing; (2)d im (in bits) represents the amount of data required for task transmission; (3) (in seconds) represents the maximum task processing delay that the device im can bear; the current wireless environment characteristics are composed of the channel gain from the device to the base station and the channel gain from the base station to the vehicle. At the same time, the network controller perceives the computing resources of all base stations and vehicles, the transmission rate between base stations, and the status information of all vehicles.

[0019] During the working process, the network controller will upload the above information to the network controller through wireless and wired connections.

[0020] Furthermore, the step S2 is specifically as follows:

[0021] The current task calculation amount obtained in step S1 Task data volume The information of the wireless environment G is input into the trained optimization model deployed on the network controller to calculate the device task offloading decision A, B based on the current state, the transmission power allocation P during the task transmission process, and the computing resource allocation F to be used during the task calculation process. es ,F vec , and the task transmission rate distribution R between base stations. If A, B, F are given es ,F vec and R, the transmission power control sub-problem of each device can be naturally decomposed from P0, and the device transmission power The solution of is decoupled from other variables.

[0022] The characteristic of task scheduling in this method is that for task im, each task can be unloaded and processed in the following ways: the first way is to unload the task to the edge server and then calculate it on the edge server; the second way is to unload the task to the mobile vehicle for processing; the third way is to unload the task to the parked vehicle for processing. These methods all require that the task be unloaded to the edge server first, and then transmitted to the corresponding computing node for task calculation. Once the task is completed, its result will be returned to the relevant device in the opposite route. The delay factors of task processing include the transmission delay of the task, the calculation delay of the task, and the transmission delay of the task results. Considering that the transmission delay of the task is relatively short, this paper decides not to consider it.

[0023] We use α imj To indicate whether the task is processed on the edge server, α imj =1 means that task im is processed on edge server j. imnj To indicate whether the task is processed on the vehicle, β imnj = 1 means that task im is processed on vehicle nj under edge server j. We adopt A = {α imj},m,j∈M,i∈I m and B = {β imnj},m,j∈M,i∈I m ,n∈N j Two sets of task offloading indicators, which cover all task offloading strategies respectively.

[0024] At the same time, regarding the computing resources used in the task calculation process and the allocation of wired transmission rates between base stations, we will denotes the total computing resources of edge server j. Let Representative from The computing resources allocated to the computing task im, we will represents the total computing resources of vehicle nj. express The computing resources allocated to the computing task im, We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j, The above optimization variables are designed based on the deep reinforcement learning technology to design an optimization algorithm and establish a Markov decision process, where the state space of this method is set to s = {C, D, G} and the action space is set to a = {A, B, F es ,F vec ,R}. Finally, the optimization result is obtained according to the algorithm output.

[0025] Furthermore, the step S3 is specifically as follows:

[0026] Extract the offloading decision information obtained in step S2. If the information indicates that the device task is executed on the edge server on the base station, the network controller sends task offloading decision instructions A and B to the device and sends computing resource allocation instructions F to the edge server on the base station. es The transmission rate allocation instruction R between the vehicle and the base station, the device itself makes a transmission power allocation instruction P, executes the instruction in the manner described in step S3 of claim 1, and transmits the result back to the device. After the execution of the corresponding instruction is completed, the task processing cost of the vehicle equipment task is obtained, that is, the weighted sum of the task processing delay and energy consumption.

[0027] Furthermore, the step S4 is specifically as follows:

[0028] Extract the offloading decision information obtained in step S2. If the information indicates that the task is executed on the vehicle under the base station, the network controller sends the task offloading decision instruction A, V to the device and sends the computing resource allocation instruction F to the vehicle under the base station. vec , sending a transmission rate allocation instruction R between base stations to the edge server on the base station, the device itself makes a transmission power allocation instruction P, and executes the instruction in the manner described in step S4 of claim 1, and after the task calculation process and the transmission process are all completed, the result is returned to the device. After the execution of the corresponding instruction is completed, the task processing cost of the vehicle task is obtained, that is, the weighted sum of the task processing delay and energy consumption.

[0029] (III) Beneficial effects

[0030] Compared with the prior art, the present invention adopts the above technical solution and has the following beneficial effects:

[0031] 1. When making task offloading decisions and resource allocation, through analysis of actual scenarios, in order to solve the problem of low vehicle resource utilization, the present invention proposes a task offloading and resource allocation strategy that combines moving and parked vehicles. By jointly scheduling task offloading requests, allocating computing resources of all vehicles under the maximum cost constraint of purchasing vehicle resources, allocating computing resources of edge servers, allocating transmission rates between edge servers, and allocating transmission power of devices, an optimization problem is proposed to minimize the total task processing delay and energy consumption of all devices, making the technology more practical;

[0032] 2. When making task offloading decisions and resource allocation, a double-delayed deep deterministic policy gradient deep reinforcement learning algorithm is designed to solve complex optimization problems through neural networks, which is more practical;

[0033] 3. By analyzing the optimization target structure, we embedded an optimization subroutine to solve the corresponding device transmit power allocation subproblem through numerical methods, and then let the double-delayed deep deterministic policy gradient part of our algorithm handle the optimization problem of other parts. This reduces the search space of the double-delayed deep deterministic policy gradient model, greatly reduces the learning difficulty of deep reinforcement learning, and greatly improves the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a schematic diagram of a system model of an embodiment;

[0035] Figure 2 It is a schematic diagram of the training process of task offloading decision and resource allocation;

[0036] Figure 3 It is a schematic diagram of the application process of task offloading decision and resource allocation;

[0037] Figure 4 This is a schematic diagram comparing the rewards of this scheme with other schemes as the number of devices increases;

[0038] Figure 5 It is a schematic diagram comparing the rewards of this scheme with other schemes as the number of mobile vehicles increases;

[0039] Figure 6 This is a schematic diagram comparing the rewards of this scheme with other schemes as the number of parked vehicles increases. DETAILED DESCRIPTION

[0040] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0041] The present invention proposes a vehicle-side collaborative task unloading and resource allocation method based on mobile and parked vehicle assistance, and the embodiment includes the following steps:

[0042] Step 1: The network controller senses all current device task information and the current wireless environment information of the system; at the same time, the network controller senses the computing resources of all base stations and vehicles, the transmission rate between base stations, and the status information of all vehicles; and uploads the relevant information to the network controller through wireless and wired connections;

[0043] Step 2: Input the current task information and the current wireless environment information of the system described in step 1 into the trained optimization model deployed in the network controller, and calculate the device task offloading decision based on the current state, the transmission power allocation during the task transmission, the computing resource allocation to be used during the task calculation, and the task transmission rate allocation between base stations;

[0044] Step 3: extract the device task offloading decision information obtained in step 2. If the information indicates that the task is executed on the edge server of the base station, process the vehicle task according to the task offloading decision; if the task is planned to be offloaded to the edge server of the base station, the processing delay of the task includes the uplink transmission delay from the device to the edge server of the attached base station, the transmission delay between the base stations, and the calculation delay on the edge server;

[0045] Step 4: Extract the device task offloading decision information obtained in step 2. If the information indicates that the task is executed on the vehicle under the base station, the network controller sends the corresponding task offloading decision instruction to the corresponding vehicle through a wired connection, and processes the device task according to the task offloading decision; if the task is planned to be offloaded to the vehicle under the base station, the processing delay of the task includes the uplink transmission delay from the device to the attached base station edge server, the transmission delay between base stations, the downlink transmission delay from the base station to the vehicle, and the calculation delay on the vehicle; if the unloaded vehicle is a mobile vehicle, the residence time of the vehicle in the corresponding base station must be greater than the processing delay of the task, otherwise the task offloading service provided by the vehicle may be interrupted, resulting in the failure of task offloading;

[0046] Furthermore, the step 1 includes:

[0047] The network architecture of this method is characterized by: A parking and moving vehicle assisted mobile edge computing scenario is considered, in which M edge servers are included, all of which can communicate through local area network interconnection. We denote the set of edge servers as M = {1, 2, ..., M}. Assume that there is I in the mth edge server m Devices, Parked vehicles and Mobile vehicle. Equipment collection I m ={1,2,…,I m} indicates that the sets of parked vehicles and moving vehicles are We use a collection to include these two sets. In addition, a network controller is placed at the edge layer to control the operation of the entire method. We use "vehicle nm" (vehicle n under the coverage of edge server m) to represent parked vehicles or moving vehicles. is the duration that vehicle nm maintains communication with the affiliated edge server m before leaving the coverage of edge server m.

[0048] The algorithm is deployed on a network controller to work. The network controller perceives the current task information of all devices and the current wireless environment information of the system. Among them, we name the task of device im under the coverage of edge server m as task im. The information of task im includes: (1) c im (based on the CPU operating cycle) represents the computing resources required for task processing; (2)d im (in bits) represents the amount of data required for task transmission; (3) (in seconds) represents the maximum task processing delay that the device im can bear; the current wireless environment characteristics are composed of the channel gain from the device to the base station and the channel gain from the base station to the vehicle. At the same time, the network controller perceives the computing resources of all base stations and vehicles, the transmission rate between base stations, and the status information of all vehicles.

[0049] During the working process, the network controller will upload the above information to the network controller through wireless and wired connections.

[0050] Furthermore, the step 2 includes:

[0051] The current task calculation amount obtained in step 1 Task data volume The information of the wireless environment G is input into the trained optimization model deployed on the network controller to calculate the device task offloading decision A, B based on the current state, the transmission power allocation P during the task transmission process, and the computing resource allocation F to be used during the task calculation process. es ,F vec , and the transmission rate distribution R of tasks between base stations.

[0052] The objective function of P0 can be expanded as:

[0053]

[0054] Study u im The structure of , we find that if A, B, F are given es ,F vec and R, the transmit power control subproblem of each device can be naturally decomposed from P0. If device im offloads its task im(α imj =1 or β imnj =1), then the sub-problem corresponding to the device is:

[0055]

[0056] It can be seen that P1 is a quasi-convex optimization problem, because the objective function is composed of The problem consists of a linear numerator and a concave denominator, and the constraints are linear. Therefore, fractional programming methods such as the Dinkelbach algorithm can be used for optimal solution.

[0057] The characteristic of task scheduling in this method is that for task im, each task can be unloaded and processed in the following ways: the first way is to unload the task to the edge server and then calculate it on the edge server; the second way is to unload the task to the mobile vehicle for processing; the third way is to unload the task to the parked vehicle for processing. These methods all require that the task be unloaded to the edge server first, and then transmitted to the corresponding computing node for task calculation. Once the task is completed, its result will be returned to the relevant device in the opposite route. The delay factors of task processing include the transmission delay of the task, the calculation delay of the task, and the transmission delay of the task results. Considering that the transmission delay of the task is relatively short, this paper decides not to consider it.

[0058] We use α imj To indicate whether the task is processed on the edge server, α imj =1 means that task im is processed on edge server j. imnj To indicate whether the task is processed on the vehicle, β imnj = 1 means that task im is processed on vehicle nj under edge server j. We adopt A = {α imj},m,j∈M,i∈I m and B = {β imnj},m,j∈M,i∈I m ,n∈N j Two sets of task offloading indicators, these two sets of indicators cover all task offloading strategies. Therefore, we have:

[0059] α imj ∈{0,1},m,j∈M,∈I m

[0060] β imnj ∈{0,1},m,j∈M,i∈I m ,n∈N j .

[0061] We assume that the task of each device should not be further subdivided into subtasks. Therefore, when processing any task im, the goal of its task offloading must be unique, namely:

[0062]

[0063] The unit price of computing resources for different tasks is different for each vehicle. Assume that for task im, the unit price of vehicle nj under edge server j is ω imnj , for task im, the unit price index of all vehicle computing resources is The highest price that task im can accept is

[0064] At the same time, regarding the computing resources used in the task calculation process and the allocation of wired transmission rates between base stations, we will denotes the total computing resources of edge server j. Let Representative from The computing resources allocated to the computing task im, we will represents the total computing resources of vehicle nj. express The computing resources allocated to the computing task im, We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j, The above optimization variables are designed based on the deep reinforcement learning technology to design an optimization algorithm and establish a Markov decision process, where the state space of this method is set to s = {C, D, G} and the action space is set to a = {A, B, F es ,F vec ,R}. Finally, the optimization result is obtained according to the algorithm output.

[0065] Furthermore, the step three includes:

[0066] Extract the device task offloading decision information obtained in step 2. If the information indicates that the task is executed on the edge server of the base station, process the vehicle task according to the task offloading decision. If the task is planned to be offloaded to the edge server of the base station, the processing delay of the task includes the uplink transmission delay from the device to the edge server of the attached base station, the transmission delay between base stations, and the calculation delay on the edge server.

[0067] The computation delay of task im on edge server j is expressed as:

[0068]

[0069] We calculate the channel signal interference plus noise ratio of the uplink transmission from device im to edge server m. The calculation formula is:

[0070]

[0071] is the transmit power of device im for uplink transmission, and its maximum value is Not more than g im is the channel gain between device im and edge server m. The channel bandwidth is B, and the additive white Gaussian noise power spectral density is defined as N0, so BN0 is the additive white Gaussian noise power of the channel. We express the co-channel interference when device im transmits to edge server m as Therefore, the uplink transmission rate can be expressed as:

[0072]

[0073] Therefore, the uplink transmission delay from device im to edge server m is:

[0074]

[0075] We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j. The transmission delay of task im from edge server m to edge server j is:

[0076]

[0077] The total task processing delay is:

[0078]

[0079] For device im, the energy consumption of task offloading only includes the energy consumed during uplink transmission. It is expressed as:

[0080]

[0081] Furthermore, the step 4 includes:

[0082] Extract the device task offloading decision information obtained in step 2. If the information indicates that the task is executed on the vehicle under the base station, the network controller sends the corresponding task offloading decision instruction to the corresponding vehicle through a wired connection, and processes the device task according to the task offloading decision; if the task is planned to be offloaded to the vehicle under the base station, the processing delay of the task includes the uplink transmission delay from the device to the attached base station edge server, the transmission delay between base stations, the downlink transmission delay from the base station to the vehicle, and the calculation delay on the vehicle; if the unloaded vehicle is a mobile vehicle, the residence time of the vehicle in the corresponding base station must be greater than the processing delay of the task, otherwise the task offloading service provided by the vehicle may be interrupted, resulting in the failure of task offloading.

[0083] The computational delay of task im on vehicle nj is expressed as:

[0084]

[0085] Task im is offloaded to N j For vehicle nj in the channel, we get the channel signal interference plus noise ratio of the downlink transmission from edge server j to vehicle nj. Its value is:

[0086]

[0087] in, is the transmission power of edge server i sending tasks to vehicle j, g ij is the channel gain, BN0 is the additive white Gaussian noise power of the channel, is the co-channel interference. Therefore, we can calculate the downlink transmission rate from edge server i to vehicle j as:

[0088]

[0089] Therefore, the downlink transmission delay from edge server i to vehicle j is:

[0090]

[0091] We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j. The transmission delay of task im from edge server m to edge server j is:

[0092]

[0093] The total task processing delay is:

[0094]

[0095] If the unloading vehicle nj is a mobile vehicle, and the residence time of the vehicle nj in the edge server j is less than the processing delay of the task im, then the task unloading service provided by the vehicle may be interrupted, resulting in the failure of the task unloading. That is:

[0096]

[0097] The processing delay of task im is:

[0098]

[0099] The task processing delay cannot exceed the task processing delay tolerance:

[0100]

[0101] The total price of the vehicle resources purchased by the equipment im needs to be within the maximum price range acceptable to the task im:

[0102]

[0103] Therefore, we have the task processing cost:

[0104]

[0105] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A vehicle-side collaborative task offloading and resource allocation method based on mobile and parked vehicle assistance, characterized in that: The following steps are involved: S1: The network controller senses all current device task information and the current wireless environment information of the system; at the same time, the network controller senses the computing resources of all base stations and vehicles, the transmission rate between base stations and the status information of all vehicles; and uploads relevant information to the network controller through wireless and wired connections; S2: Input the current task information and the current wireless environment information of the system described in step S1 into the trained optimization model deployed in the network controller, calculate the device task offloading decision based on the current state, the transmission power allocation during the task transmission process, the computing resource allocation to be used during the task calculation process, and the task transmission rate allocation between base stations; S3: extracting the device task offloading decision information obtained in step S2, and if the information indicates that the task is executed on the edge server of the base station, processing the vehicle task according to the task offloading decision; if it is planned to offload the task to the edge server of the base station, the processing delay of the task includes the uplink transmission delay from the device to the edge server of the attached base station, the transmission delay between the base stations, and the calculation delay on the edge server; S4: Extract the device task offloading decision information obtained in step S2. If the information indicates that the task is executed on the vehicle under the base station, the network controller sends the corresponding task offloading decision instruction to the corresponding vehicle through a wired connection, and processes the device task according to the task offloading decision. If the task is planned to be offloaded to the vehicle under the base station, the processing delay of the task includes the uplink transmission delay from the device to the attached base station edge server, the transmission delay between base stations, the downlink transmission delay from the base station to the vehicle, and the calculation delay on the vehicle. If the unloaded vehicle is a mobile vehicle, the residence time of the vehicle in the corresponding base station must be greater than the processing delay of the task, otherwise the task offloading service provided by the vehicle may be interrupted, resulting in the failure of task offloading.

2. The method for vehicle-side collaborative task offloading and resource allocation based on mobile and parked vehicle assistance according to claim 1, characterized in that: The step S1 includes: The network architecture of the method is characterized by considering a parking and moving vehicle assisted mobile edge computing scenario, in which M edge servers are included, all of which can communicate through local area network interconnection. We represent the set of edge servers as M = {1, 2, ..., M}. Assume that there is I in the mth edge server m Devices, Parked vehicles and Mobile vehicle. Equipment collection I m ={1,2,…,I m } indicates that the sets of parked vehicles and moving vehicles are We use a collection to include these two sets. In addition, a network controller is placed at the edge layer to control the operation of the entire method. We use "vehicle nm" (vehicle n under the coverage of edge server m) to represent parked vehicles or moving vehicles. is the duration that vehicle nm maintains communication with the affiliated edge server m before leaving the coverage of edge server m. The algorithm is deployed on a network controller to work. The network controller perceives the current task information of all devices and the current wireless environment information of the system. Among them, we name the task of device im under the coverage of edge server m as task im. The information of task im includes: (1) c im (based on the CPU operating cycle) represents the computing resources required for task processing; (2)d im (unit: bit) represents the amount of data required for task transmission; (3) (in seconds) represents the maximum task processing delay that the device im can bear; the current wireless environment characteristics are composed of the channel gain from the device to the base station and the channel gain from the base station to the vehicle. At the same time, the network controller perceives the computing resources of all base stations and vehicles, the transmission rate between base stations, and the status information of all vehicles. During the working process, the network controller will upload the above information to the network controller through wireless and wired connections.

3. The method for vehicle-side collaborative task offloading and resource allocation based on mobile and parked vehicle assistance according to claim 1, characterized in that: The step S2 includes: calculating the current task calculation amount obtained in step S1 Task data volume The information of the wireless environment G is input into the trained optimization model deployed on the network controller to calculate the device task offloading decision A, B based on the current state, the transmission power allocation P during the task transmission process, and the computing resource allocation F to be used during the task calculation process. es ,F vec , and the task transmission rate distribution R between base stations. If A, B, F are given es ,F vec and R, the transmission power control sub-problem of each device can be naturally decomposed from P0, and the device transmission power The solution of is decoupled from other variables. The characteristic of task scheduling in this method is that for task im, each task can be unloaded and processed in the following ways: the first way is to unload the task to the edge server and then calculate it on the edge server; the second way is to unload the task to the mobile vehicle for processing; the third way is to unload the task to the parked vehicle for processing. These methods all require that the task be unloaded to the edge server first, and then transmitted to the corresponding computing node for task calculation. Once the task is completed, its result will be returned to the relevant device in the opposite route. The delay factors of task processing include the transmission delay of the task, the calculation delay of the task, and the transmission delay of the task results. Considering that the transmission delay of the task is relatively short, this paper decides not to consider it. We use α imj To indicate whether the task is processed on the edge server, α imj =1 means that task im is processed on edge server j. imnj To indicate whether the task is processed on the vehicle, β imnj = 1 means that task im is processed on vehicle nj under edge server j. We adopt A = {α imj },m,j∈M,i∈I m and B = {β imnj },m,j∈M,i∈I m ,n∈N j Two sets of task offloading indicators, which cover all task offloading strategies respectively. At the same time, regarding the computing resources used in the task calculation process and the allocation of wired transmission rates between base stations, we will denotes the total computing resources of edge server j. Let Representative from The computing resources allocated to the computing task im, we will represents the total computing resources of vehicle nj. express The computing resources allocated to the computing task im, We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j, The above optimization variables are designed based on the deep reinforcement learning technology to design an optimization algorithm and establish a Markov decision process, where the state space of this method is set to s = {C, D, G} and the action space is set to a = {A, B, F es ,F vec ,R}. Finally, the optimization result is obtained according to the algorithm output.

4. The method for vehicle-side collaborative task offloading and resource allocation based on mobile and parked vehicle assistance according to claim 1, characterized in that: The step S3 includes: extracting the offloading decision information obtained in step S2, if the information indicates that the device task is executed by the edge server on the base station, the network controller sends the task offloading decision instructions A, B to the device, and sends the computing resource allocation instruction F to the edge server on the base station. es The transmission rate allocation instruction R between the vehicle and the base station, the device itself makes a transmission power allocation instruction P, executes the instruction in the manner described in step S3 of claim 1, and transmits the result back to the device. After the execution of the corresponding instruction is completed, the task processing cost of the vehicle equipment task is obtained, that is, the weighted sum of the task processing delay and energy consumption. The computation delay of task im on edge server j is expressed as: We calculate the channel signal interference plus noise ratio of the uplink transmission from device im to edge server m. The calculation formula is: is the transmit power of device im for uplink transmission, and its maximum value is Not more than g im is the channel gain between device im and edge server m. The channel bandwidth is B, and the additive white Gaussian noise power spectral density is defined as N0, so BN0 is the additive white Gaussian noise power of the channel. We express the co-channel interference when device im transmits to edge server m as Therefore, the uplink transmission rate can be expressed as: Therefore, the uplink transmission delay from device im to edge server m is: We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j. The transmission delay of task im from edge server m to edge server j is: The total task processing delay is: For device im, the energy consumption of task offloading only includes the energy consumed during uplink transmission. It is expressed as:

5. The method for vehicle-side collaborative task offloading and resource allocation based on mobile and parked vehicle assistance according to claim 1, characterized in that: The step S4 includes: extracting the offloading decision information obtained in step S2, and if the information indicates that the task is executed on the vehicle under the base station, the network controller sends the task offloading decision instructions A and B to the device, and sends the computing resource allocation instruction F to the vehicle under the base station. vec , sending a transmission rate allocation instruction R between base stations to the edge server on the base station, the device itself makes a transmission power allocation instruction P, and executes the instruction in the manner described in step S4 of claim 1, and after the task calculation process and the transmission process are all completed, the result is returned to the device. After the execution of the corresponding instruction is completed, the task processing cost of the vehicle task is obtained, that is, the weighted sum of the task processing delay and energy consumption. The computational delay of task im on vehicle nj is expressed as: Task im is offloaded to N j For vehicle nj in the channel, we get the channel signal interference plus noise ratio of the downlink transmission from edge server j to vehicle nj. Its value is: in, is the transmission power of edge server i sending tasks to vehicle j, g ij is the channel gain, BN0 is the additive white Gaussian noise power of the channel, is the co-channel interference. Therefore, we can calculate the downlink transmission rate from edge server i to vehicle j as: Therefore, the downlink transmission delay from edge server i to vehicle j is: We use Represents the total wired transmission rate from edge server m to other edge servers. yes The transmission rate assigned to the connection from edge server m to edge server j. The transmission delay of task im from edge server m to edge server j is: The total task processing delay is: For device im, the energy consumption of task offloading only includes the energy consumed during uplink transmission. It is expressed as: The processing delay of task im is: Therefore, we have the task processing cost:

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