Roadside intelligent unit optimization layout method for realizing load balancing

Abstract

The invention relates to the technical field of smart roads, discloses a roadside smart unit optimization layout method for realizing load balancing, and aims to solve the problem of roadside unit deployment efficiency caused by rapid development of smart roads. The roadside intelligent unit optimization layout method for realizing load balancing comprises the following steps: S1, inputting basic data; s2, basic data processing; s3, carrying out random scene RSU optimization layout solving; and S4, performing visual output. According to the roadside intelligent unit optimization layout method for realizing load balancing, the randomness and discreteness of vehicle flow in a road network and the cooperative work capability between RSUs are comprehensively considered, the deployment number, position and load distribution of the RSUs are integrally optimized, potential conflicts in the deployment process of the roadside units are effectively reduced, and the deployment efficiency of the RSUs is improved. The road use efficiency is improved, and powerful technical support is provided for further development of an intelligent traffic system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent roads, and particularly to an optimized layout method for roadside intelligent units to achieve load balancing. Background Art

[0002] With the rapid development of Internet of Things technology, vehicle networking, as one of its important applications, has gradually penetrated into multiple fields such as traffic management and in-vehicle services. The popularization of vehicle communication technology (Vehicle-to-Everything, V2X) provides a technical foundation for realizing efficient communication between vehicles and between vehicles and infrastructure. As a key component of vehicle networking, the roadside unit (RSU) undertakes important tasks of data collection, processing, and transmission, and plays an important role in improving traffic efficiency and safety.

[0003] However, the existing RSU deployment and management methods have obvious limitations. On the one hand, traditional RSU deployments often adopt static and fixed modes and lack the ability to adapt to dynamic changes in traffic flow. This deployment method may lead to insufficient RSU service capabilities in areas with large traffic flows, while there is resource waste in areas with small traffic flows. On the other hand, most existing RSU management methods rely on manual operations and cannot achieve intelligent and automated optimized configuration, which not only increases operating costs but also affects the user experience. Especially in complex urban traffic environments, the driving paths and traffic flow distributions of vehicles have a high degree of uncertainty and dynamics. The existing one-dimensional road models and simplified treatments assuming infinite RSU service capabilities cannot accurately reflect the actual traffic conditions and are difficult to meet the growing vehicle networking service requirements.

[0004] In addition, existing research has mostly focused on optimizing data transmission delay, while insufficient consideration has been given to the flexibility, scalability of RSU deployment, and the comprehensive impact on the entire vehicle networking ecosystem. The existence of these problems limits the potential of vehicle networking technology in improving traffic efficiency and ensuring driving safety.

[0005] Therefore, there is an urgent need for an innovative technical solution that can dynamically optimize the deployment and management of RSUs according to real-time traffic flow and road conditions to improve the efficiency and quality of vehicle networking services, reduce operating costs, and ultimately enhance the user experience. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides an optimized layout method for roadside intelligent units to achieve load balancing.

[0007] The optimized layout method for roadside intelligent units to achieve load balancing provided by the present invention includes the following steps:

[0008] S1. Basic data input: Read road network data, including road section intersection nodes, road section lengths, and traffic flow information on each road section under multiple scenarios. Initialize the number of RSU deployments and the number of additional module deployments at road section intersection nodes, and construct the basic RSU deployment road network.

[0009] S2. Basic data processing: Import the road network traffic under multiple scenarios according to different traffic scenarios; generate the road section service sets under multiple scenarios. For the road network traffic under different scenarios, generate the road section service sets under multiple scenarios according to the stochastic optimization model.

[0010] S3. Solve the stochastic scenario RSU optimal deployment: Construct a mixed-integer linear programming model for the multi-scenario RSU deployment optimization. One objective is to minimize the vehicle data delivery delay, including the delay of data upload and the data processing delay, and the second objective is to minimize the RSU deployment cost, including the costs of both the RSU pedestal and its additional modules.

[0011] S4. Visualization output: According to the output results of the optimization model, display the optimized RSU deployment plan in the road network and mark the data transmission situation between RSUs.

[0012] Preferably, in step S1, read the road network data including road section intersection nodes, road section lengths, and traffic flow information to obtain the alternative location set I, the service road section set E, and the service scenario set ω;

[0013] During initialization, the number of RSU deployments and the number of additional module deployments at each road section intersection node are both set to the un-deployed state;

[0014] Using the alternative location set I and the service road section set E, connect the alternative locations according to the service road sections to obtain the basic RSU deployment network.

[0015] Preferably, in step S2, it is necessary to select the traffic flow data of peak, off-peak, and inspection scenarios as input data to avoid the problem of inaccurate data caused by unstable road network traffic in the real scenario, so as to better serve the dynamically randomly changing traffic flow in the real scenario.

[0016] Preferably, in the step S3 of optimal deployment solution, the objective function of the mixed-integer linear programming model of the RSU stochastic optimal deployment model constructed aims to minimize the vehicle data delivery delay and the RSU deployment cost:

[0017]

[0018] The objective function of the model, which is the maximum value of the objective function of the optimal random RSU deployment scheme in each scenario, aims to comprehensively optimize the RSU service delivery delay and deployment cost in each scenario to achieve full-scenario adaptation;

[0019] Among them, v refers to the maximum dual-objective coupling index in multiple scenarios, corresponding to the formula of constraint (1). This dual-objective coupling index refers to the linear weighting between the delivery delay and the deployment cost. The delivery delay corresponds to ∑ i∈ I[S(∑ e∈ E w eiω a ei d+∑ j∈J (y jiω -y ijω ))+∑ j∈I y ijω tr ijω , and the deployment cost corresponds to ∑ i∈I (ux iω +vz iω ).

[0020] Among them, ∑ e∈E w eiω a ei d+∑ j∈J (y jiω -y ijω ) represents the volume of tasks being processed at the alternative location i in the service scenario ω; S(∑ e∈E w eiω a ei d+∑ j∈J (y jiω -y ijω )) represents the time to process the corresponding tasks at the alternative location i in the service scenario ω; y ijω tr ijω represents the delay time when transmitting data packets from the alternative location i to the alternative location j in the service scenario ω; ux iω +vz iω represents the cost of deploying an RSU at the alternative location i in the service scenario ω.

[0021] Among them, Constraint (2) is a process of taking the maximum value of the difference between the objective function value of the optimal layout scheme of random RSUs in each scenario and the optimal value of the optimal layout scheme of fixed RSUs, which is a mathematical simplification technique; Constraint (3) describes that the traffic service requirements in each scenario must be met; Constraint (4) corresponds the traffic flow with its corresponding service requirements; Constraint (5) means that the load transfer between RSUs at a certain position must require the installation of RSUs at this position; Constraint (6) means that when installing the RSU additional module at a certain position, it must be required that the RSU base has been installed at this position; Constraint (7) defines the value range of each variable.

[0022] Preferably, in step S4, the result visualization output includes:

[0023] 4.1. Output the layout optimization scheme, including the RSU layout scale at each alternative position and the load status of each RSU;

[0024] 4.2. For the dynamic randomness of the actual road conditions, the model generates corresponding RSU operation suggestions according to different scenarios, including marking the data transmission situation between RSUs in the road network.

[0025] Compared with the related technologies, the method for optimizing the layout of roadside intelligent units to achieve load balance provided by the present invention has the following beneficial effects:

[0026] The method for optimizing the layout of roadside intelligent units to achieve load balance provided by the present invention constructs an optimization layout model of roadside units from the perspective of ensuring the service quality of intelligent roads and improving the utilization rate of roadside unit computing resources, generates an optimization layout scheme of roadside units in the whole scenario, and overall optimizes the deployment of roadside units and their subsequent multi-scenario targeted operations, which helps to ensure the vehicle service quality and improve the road operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flowchart of a method for optimizing the layout of roadside intelligent units to achieve load balance provided by the present invention;

[0028] Figure 2 It is the specific example Sioux Falls Netwok of the present invention;

[0029] Figure 3 It is the RSU basic layout network of the specific example of the present invention;

[0030] Figure 4 It is the RSU optimized layout scheme of the specific example of the present invention;

[0031] Figure 5 It is the roadside unit service map of the specific example of the present invention in the road off-peak scenario;

[0032] Figure 6 This is the load distribution diagram of the roadside unit in the flat peak scenario of the specific example of the present invention;

[0033] Figure 7 This is the service diagram of the roadside unit in the peak scenario of the specific example of the present invention;

[0034] Figure 8 This is the load distribution diagram of the roadside unit in the peak scenario of the specific example of the present invention;

[0035] Figure 9 This is the service diagram of the roadside unit in the road inspection scenario of the specific example of the present invention;

[0036] Figure 10 This is the load distribution diagram of the roadside unit in the road inspection scenario of the specific example of the present invention. Detailed implementation manners

[0037] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0038] As Figure 1 shown, a method for optimizing the layout of roadside intelligent units to achieve load balancing includes the following steps:

[0039] S1. Input of basic data: Read road network data, including road section intersection nodes, road section lengths, and traffic flow information on each road section under multiple scenarios, initialize the number of RSU layouts and the number of additional module layouts at road section intersection nodes, and construct a basic RSU layout road network;

[0040] S2. Processing of basic data: Import the road network traffic under each scenario according to different traffic scenarios; generate a road section service set under multiple scenarios, and for the road network traffic under different scenarios, uniformly generate a road section service set according to a stochastic optimization model;

[0041] S3. Solving for stochastic scenario RSU layout optimization: Construct a mixed-integer linear programming model for multi-scenario RSU layout optimization, where one objective is to minimize the vehicle data delivery delay, including the delay of data upload and the delay of data processing, and the second objective is to minimize the RSU layout cost, including the costs of both the RSU pedestal and its additional modules;

[0042] S4. Visual output: According to the output result of the optimization model, the optimized RSU layout plan can be displayed in the road network, and the data transmission situation between RSUs can be marked.

[0043] In the step S1, the specific process of basic data input is as follows:

[0044] Step 1.1: Read the Sioux Falls Network road network data, including road section intersection nodes, road section lengths, and traffic flow information on each road section under multiple scenarios;

[0045] Step 1.2: Initialize the number of RSU deployments and the number of additional module deployments at the road section intersection nodes. Here, the number of RSU pedestals and the number of additional module deployments at each intersection are both initialized to 0;

[0046] Step 1.3: Use the alternative location set I and the service road section set E to connect the alternative locations according to the service road sections, so as to obtain the basic RSU deployment network, as Figure 4 shown;

[0047] In the step S2, the specific process of basic data processing is as follows:

[0048] Step 2.1: Select and import multi-scenario road network traffic data: Since the road network traffic in the real scenario is not stable, in order to better serve the dynamically and randomly changing traffic flow in the real scenario, the present invention needs to select the road traffic under peak, flat peak, and road inspection scenarios as the input of the model;

[0049] Step 2.2: Generate the road section service set under multiple scenarios. For the road network traffic under different scenarios, generate the road section service set under multiple scenarios according to the stochastic optimization model;

[0050] In the path optimization solution of the step S3, the roadside unit optimization deployment model includes the following content,

[0051]

[0052] The objective function of the model, that is, the maximum value of the objective function value of the stochastic RSU optimal deployment scheme under each scenario, aims to overall optimize the RSU service delivery delay and deployment cost under each scenario to achieve full-scenario adaptation.

[0053] Among them, v refers to the maximum dual-objective coupling index under multiple scenarios, corresponding to the formula of constraint (1), and this dual-objective coupling index refers to the linear weighting between the delivery delay and the deployment cost. The delivery delay corresponds to ∑ i∈I [S(∑ e∈ E w eiω a ei d+∑ j∈J (y jiω -y ijω ))+∑ j∈I y ijω trijω , the deployment cost corresponds to ∑ i∈i (ux iω +vz iω ).

[0054] Among them, ∑ e∈E w eiω a ei d + ∑ j∈J (y ijω -y ijω ) represents the volume of tasks being processed at the alternative location i in the service scenario ω; S(∑ e∈E w eiω a ei d + ∑ j∈J (y jiω -y ijω )) represents the time to process the corresponding tasks at the alternative location i in the service scenario ω; y ijω tr ijω represents the delay time when transmitting data packets from the alternative location i to the alternative location j in the service scenario ω; ux iω +vz iω represents the cost of deploying RSU at the alternative location i in the service scenario ω.

[0055] Among them, the meanings of some symbols in the formula are as follows:

[0056] E: All road connections in the network, which are crucial for traffic flow management and RSU;

[0057] I: Intersections where actual traffic flow occurs in the traffic network, which are crucial for traffic analysis and optimization;

[0058] Ω: Traffic scenarios served by RSUs, and each scenario (ω) involves specific traffic demands and environmental conditions that affect RSU configuration and performance;

[0059] a ei : Indicates whether RSUi can cover the road section e, which is crucial for evaluating the RSU deployment effect;

[0060] u: The upfront construction cost of each RSU installation;

[0061] C1: The maximum data processing capacity of a basic roadside unit;

[0062] C2: The additional data processing capacity of the RSU integrated in each module;

[0063] d: The average size of data packets transmitted by vehicles, which affects RSU data processing requirements and network communication efficiency;

[0064] tr ij: The latency cost of transmitting unit data packets between candidate locations i and j is crucial for evaluating network communication efficiency.

[0065] S(x): The data processing time function under different load conditions, reflecting the performance of RSU under different traffic demands;

[0066] x i : Represents the binary decision variable for RSU deployment at candidate location i, which is crucial for network optimization;

[0067] y ijω : In scenario ω, the data transmission volume from location i to location j, which is an indicator for evaluating network traffic and RSU service efficiency;

[0068] z i : The number of additional RSU modules deployed at location i, which directly affects the RSU processing capacity and cost;

[0069] w ijω : In scenario ω, the data packet volume served by the roadside unit (RSU) at location i for edge j, which is a key indicator for evaluating RSU service capacity and network load.

[0070] Among them, constraint (2) is a process of taking the maximum value of the difference between the objective function value of the optimal layout scheme of random RSU in each scenario and the optimal value of the optimal layout scheme of fixed RSU, which is a kind of mathematical simplification technique. Constraint (3) describes that the vehicle flow service demand in each scenario must be met. Constraint (4) corresponds the traffic flow with its corresponding service demand. Constraint (5) means that the load transfer between RSU at a certain position requires the deployment of RSU at this position. Constraint (6) means that when deploying additional RSU modules at a certain position, it is necessary to require that the RSU base has been deployed at this position. Constraint (7) defines the value range of each variable.

[0071] In step S4, the specific process of result visualization output is as follows:

[0072] Step 4.1: Output the layout optimization scheme, including the RSU layout scale at each alternative location and the load status of each RSU;

[0073] Step 4.2: Regarding the dynamic randomness of the actual road conditions, the model generates corresponding RSU operation suggestions according to different scenarios, including marking the data transmission situation between RSUs in the road network.

[0074] Figure 4-10Schematic diagram of the optimized layout result of the roadside unit of the present invention, including the layout scale diagram of the roadside unit, the service situation diagram of the roadside unit under the flat peak scenario of the road, the load distribution diagram of the roadside unit, the service situation diagram of the roadside unit under the peak scenario of the road, the load distribution diagram of the roadside unit, the service situation diagram of the roadside unit under the road inspection scenario, and the load distribution diagram of the roadside unit.

[0075] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field of the present invention through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should fall within the protection scope determined by the claims.

Claims

1. An optimized layout method for roadside intelligent units to achieve load balancing, characterized in that, It includes the following steps: S1. Basic data input: Read road network data, including road section intersection nodes, road section lengths, and traffic flow information on each road section under multiple scenarios. Initialize the number of RSU deployments and the number of additional module deployments at road section intersection nodes, and construct a basic RSU deployment road network; S2. Basic data processing: Import road network traffic under multiple scenarios according to different traffic scenarios; generate a road section service set under multiple scenarios, and for the road network traffic under different scenarios, uniformly generate a road section service set according to a stochastic optimization model; S3. Solve the stochastic scenario RSU optimal deployment: Construct a mixed-integer linear programming model for the multi-scenario RSU deployment optimization. One objective is to minimize the vehicle data delivery delay, including the delay of data upload and the delay of data processing, and the second objective is to minimize the RSU deployment cost, including the costs of both the RSU pedestal and its additional modules; S4. Visual output: According to the output results of the optimization model, display the optimized RSU deployment plan in the road network and mark the data transmission situation between RSUs.

2. The method for optimizing the layout of roadside intelligent units for realizing load balancing according to claim 1, characterized in that, In step S1, read road network data including road section intersection nodes, road section lengths, and traffic flow information to obtain an alternative location set I, a service road section set E, and a service scenario set ω; During initialization, the number of RSU deployments and the number of additional module deployments at each road section intersection node are both set to the un-deployed state; Using the alternative location set I and the service road section set E, connect the alternative locations according to the service road sections to obtain a basic RSU deployment network.

3. The method for optimizing the layout of roadside intelligent units for realizing load balancing according to claim 1, wherein In step S2, it is necessary to select the traffic flow data of peak, off-peak, and inspection scenarios as input data to avoid inaccurate data caused by unstable road network traffic in the real scenario.

4. The method for optimizing the layout of roadside intelligent units for realizing load balancing according to claim 1, wherein, In the optimization deployment solution of step S3, the objective function of the mixed-integer linear programming model of the constructed RSU stochastic optimization deployment model aims to minimize the vehicle data delivery delay and the RSU deployment cost: minv s.t. The objective function of the model, that is, the maximum value of the objective function value of the stochastic RSU optimal deployment plan in each scenario, aims to overall optimize the RSU service delivery delay and deployment cost in each scenario and achieve full-scenario adaptation; Among them, v refers to the maximum dual-objective coupling index in multiple scenarios, corresponding to the formula of constraint (1), and this dual-objective coupling index refers to the linear weighting between the delivery delay and the deployment cost. The delivery delay corresponds to ∑ i∈I [S(∑ e∈E w eiω a ei d+∑ j∈J (y jiω -y ijω ))+∑ j∈I y ijω tr ijω , and the deployment cost corresponds to ∑ i∈i (ux iω +vz iω ). Among them, ∑ e∈E w eiω a ei d + ∑ j∈J (y ijω - y ijω ) represents the volume of the task being processed at the alternative location i in the service scenario ω; S(∑ e∈E w eiω a ei d + ∑ j∈J (y jiω - y ijω )) represents the time to process the corresponding task at the alternative location i in the service scenario ω; y ijω tr ijω represents the delay time when transmitting data packets from the alternative location i to the alternative location j in the service scenario ω; ux iω + vz iω represents the cost of deploying RSU at the alternative location i in the service scenario ω. Among them, constraint (2) is a process of taking the maximum value of the difference between the objective function value of the stochastic RSU optimal deployment plan in each scenario and the optimal value of the fixed RSU optimal deployment plan, which is a mathematical simplification technique; constraint (3) describes that the vehicle flow service demand in each scenario must be met; constraint (4) corresponds the traffic flow to its corresponding service demand; constraint (5) means that the load transfer between RSUs at a certain position must require the deployment of an RSU at this position; constraint (6) means that when deploying an RSU additional module at a certain position, it must require the deployment of an RSU pedestal at this position; constraint (7) defines the value range of each variable.

5. The method for optimizing the layout of roadside intelligent units for realizing load balancing according to claim 1, characterized in that, In step S4, the result visual output includes: 4.

1. Output the deployment optimization plan, including the RSU deployment scale at each alternative location and the load status of each RSU; 4.

2. Regarding the dynamic randomness of the actual road conditions, the model generates corresponding RSU operation suggestions according to different scenarios, including marking the data transmission situation between RSUs in the road network.

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