Vehicle networking equipment optimization method

Abstract

The embodiment of the invention provides an Internet of Vehicles equipment optimization method. The method comprises the following steps: acquiring basic data reported by an entity in the Internet of Vehicles; determining the actual carbon emission of the entity according to the basic data; according to the actual carbon emission, determining a minimum target device number meeting a preset use requirement of the Internet of Vehicles; and adjusting the target devices in the Internet of Vehicles according to the minimum number of the target devices. According to the embodiment of the invention, the actual carbon emission of each entity in the Internet of Vehicles is calculated by using the Internet of Vehicles data, and the dynamic optimization of the Internet of Vehicles infrastructure equipment is realized based on the Internet of Vehicles use requirements and the actual carbon emission, so that the problem that more extra carbon emission is generated due to the continuous increase of Internet of Vehicles infrastructure facilities in related technologies can be solved; and thus, the overall carbon emission of the Internet of Vehicles is reduced, and deployment optimization and use optimization of the Internet of Vehicles equipment are realized.

Description

Technical Field

[0001] The present application relates to the field of communications, and more specifically, to a method for optimizing Internet of Vehicles equipment. Background Art

[0002] Climate change and global warming caused by the use of fossil energy have become a pressing topic, and the transportation industry has always been one of the key industries to reduce the use of fossil energy and related greenhouse gas emissions. Greenhouse emissions generated during the operation of motor vehicles are the main source of greenhouse gas and air pollutant emissions in the transportation industry. Therefore, accurately accounting for greenhouse gas emissions during the operation of motor vehicles is the key to achieving low-carbon transportation.

[0003] The Internet of Vehicles is developing rapidly along with new energy vehicles. In the working environment of the Internet of Vehicles, it is divided into three parts: vehicles, roadside equipment, and cloud (server). These three parts together constitute the overall architecture of the Internet of Vehicles and the vehicle-road-cloud, realizing the close connection and information exchange between vehicles, vehicles and infrastructure, and vehicles and the cloud. This interconnected architecture provides a strong foundation for the development and optimization of intelligent transportation systems. Although with the rapid development of new energy vehicles, the proportion of clean energy continues to expand, which can reduce a certain amount of carbon emissions, but with the rapid development of cities and Internet of Vehicles related construction, the growing infrastructure (roadside and cloud) will also generate more additional carbon emissions.

[0004] In summary, how to optimize the deployment of Internet of Vehicles facilities and reduce the overall carbon emissions of the Internet of Vehicles has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiment of the present application provides a method for optimizing Internet of Vehicles equipment to at least solve the problem in the related art that the continuous growth of Internet of Vehicles infrastructure will generate more additional carbon emissions.

[0006] According to one embodiment of the present application, a method for optimizing Internet of Vehicles equipment is provided, the method comprising: obtaining basic data reported by entities in the Internet of Vehicles; determining the actual carbon emissions of the entities based on the basic data; determining the minimum target number of devices that meet preset Internet of Vehicles usage requirements based on the actual carbon emissions; and adjusting the target devices in the Internet of Vehicles based on the minimum target number of devices.

[0007] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above method embodiments are executed.

[0008] According to another embodiment of the present application, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0009] According to another embodiment of the present application, a computer program product is further provided, including a computer program, which implements the steps in any one of the above method embodiments when executed by a processor.

[0010] Through the embodiments of the present application, the actual carbon emissions of each entity in the vehicle networking can be calculated using vehicle networking data, and dynamic optimization of the vehicle networking infrastructure equipment is achieved based on the vehicle networking usage requirements and actual carbon emissions. It can solve the problem that the continuous growth of vehicle networking infrastructure in the related art will generate more additional carbon emissions, thereby reducing the overall carbon emissions of the vehicle networking and achieving deployment optimization and usage optimization of the vehicle networking equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a hardware structural block diagram of the vehicle networking equipment optimization method according to the embodiment of the present application;

[0012] Figure 2 is a flowchart of the vehicle networking equipment optimization method according to the embodiment of the present application;

[0013] Figure 3 is a flowchart of the carbon emissions determination scheme for networked vehicles in an embodiment of the present application;

[0014] Figure 4 is a flowchart of the server optimization strategy in an embodiment of the present application;

[0015] Figure 5 is a flowchart of the roadside equipment optimization strategy in an embodiment of the present application;

[0016] Figure 6 is the overall flowchart of the vehicle networking equipment optimization scheme in the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In the following, the embodiments of the present application will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence.

[0019] The embodiments of the present application are applied to vehicle networking. The method embodiments provided in the embodiments of the present application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a computer terminal as an example,Figure 1 is the hardware structure block diagram of the vehicle networking device optimization method according to an embodiment of the present application. As Figure 1 shown, the hardware single board may include one or more ( Figure 1 only one is shown in Figure 1 the figure) processor 12 (the processor 12 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device) and a memory 14 for storing data. Among them, the above-mentioned mobile terminal may further include a transmission device 16 for communication functions and an input / output device 18. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 the figure is only schematic and does not limit the structure of the above-mentioned mobile terminal. For example, the mobile terminal may further include more or fewer components than

[0020] shown in

[0021] the figure, or have a different configuration from

[0022] In an embodiment of the present application, a vehicle networking device optimization method is provided. Figure 2 is the flowchart of the vehicle networking device optimization method according to an embodiment of the present application. As Figure 2 shown, the process includes the following steps:

[0023] Step S202, obtaining the basic data reported by entities in the vehicle networking;

[0024] Step S204: Determine the actual carbon emissions of the entity according to the basic data;

[0025] Step S206: Determine the minimum target device quantity that meets the preset vehicle networking usage requirements according to the actual carbon emissions;

[0026] Step S208: Adjust the target devices in the vehicle networking according to the minimum target device quantity.

[0027] In the embodiment of the present application, through the above steps S202 to S208, the actual carbon emissions of each entity in the vehicle networking can be calculated by using the vehicle networking data, and the dynamic optimization of the vehicle networking infrastructure devices is realized based on the vehicle networking usage requirements and the actual carbon emissions, thereby solving the problem that the continuous growth of the vehicle networking infrastructure in the related art will generate more additional carbon emissions, reducing the overall carbon emissions of the vehicle networking, and realizing the deployment optimization and usage optimization of the vehicle networking devices.

[0028] In this embodiment, the target devices include at least one of the following: roadside devices, servers. Through the embodiment of the present application, the deployment and usage optimization of the roadside devices or the servers can be performed separately, or the deployment and usage optimization of the roadside devices and the servers can be performed. Exemplarily, since the roadside devices will affect the carbon emissions of the servers, the roadside devices are preferentially adjusted, and then the servers are adjusted after the adjustment of the roadside devices is completed.

[0029] In some embodiments, the basic data in step S202 may include: positioning terminal data, electric vehicle data, fuel vehicle data, roadside device data, server data, etc. Exemplarily, the electric vehicle data may include the number of electric vehicles, power consumption, driving distance, clean energy ratio, carbon emission factors of non-clean energy, etc., the fuel vehicle data may include the number of networked fuel vehicles, fuel consumption, driving distance, fuel consumption per unit distance, carbon emission factors of fuel, fuel consumption per unit time during congestion (idle), congestion time, etc., the roadside device data may include the power consumption of the roadside devices, the server data may include the power consumption of the servers, and the roadside device data and the server data may also include the clean energy ratio, carbon emission factors of non-clean energy, etc.

[0030] In this embodiment, entities such as people or non-motor vehicles in the vehicle networking that only upload location information at a fixed frequency can be collectively referred to as positioning terminals. Exemplarily, the carbon emissions of the positioning terminals can be measured by the number of connected positioning terminals. Therefore, the positioning terminal data may include the number of positioning terminals.

[0031] In some embodiments, fuel vehicles can be divided into networked fuel vehicles and non-networked fuel vehicles. This application focuses on the carbon emissions of networked fuel vehicles and electric vehicles, that is, the carbon emissions of networked vehicles.

[0032] In an exemplary embodiment, the networked oil trucks can directly report the fuel consumption and the number of networked oil trucks, and then obtain the carbon emissions of the networked oil trucks. Alternatively, the total number of oil trucks can be estimated based on the number of electric vehicles and the total number of vehicles detected by the intersection cameras, and the total carbon emissions of the oil trucks can be calculated based on the fixed parameters in the electric vehicle data and the oil truck data. Then, the carbon emissions of the networked oil trucks can be calculated based on the proportion of the networked oil trucks. Exemplarily, the total carbon emissions of the oil trucks can also be calculated based on two scenarios: normal driving and idling due to congestion.

[0033] In some embodiments, step S204 of determining the actual carbon emissions of the entity according to the basic data includes: determining the actual carbon emissions of the networked vehicles, positioning terminals, roadside devices, and servers respectively according to the basic data, where the entity includes the networked vehicles, the positioning terminals, the roadside devices, and the servers.

[0034] In some embodiments, when the target device is a server, step S206 of determining the minimum number of target devices that meet the preset vehicle networking usage requirements according to the actual carbon emissions may include the following steps:

[0035] Step S2061, determining the actual carbon emissions of the networked devices connected to the server from the actual carbon emissions, where the networked devices include at least one of the following: networked vehicles, roadside devices, and positioning terminals;

[0036] Step S2062, determining the minimum value of the total carbon emissions of multiple servers connected to the networked devices and the number of the multiple servers according to the actual carbon emissions of the networked devices and a pre-determined first conversion relationship, where the first conversion relationship is the conversion relationship between the total carbon emissions of the multiple servers and the carbon emissions and the number of servers of each type of networked device;

[0037] Step S2063, determining the minimum number of servers that meet the vehicle networking usage requirements according to the minimum value of the total carbon emissions, the number of servers, and a preset server carbon emission limit, where the minimum number of target devices includes the minimum number of servers, the total carbon emissions of the multiple servers corresponding to the minimum number of servers are greater than or equal to the minimum value of the total carbon emissions, and the carbon emissions of a single server corresponding to the minimum number of servers are less than or equal to the server carbon emission limit.

[0038] In some embodiments, step S2063 of determining the minimum number of servers that meet the vehicle networking usage requirements according to the minimum value of the total carbon emissions, the number of servers, and a preset server carbon emission limit may include the following steps:

[0039] Step S2063-1: Determine the average carbon emissions of each server according to the minimum value of the total carbon emissions and the number of servers.

[0040] Step S2063-2: When the average carbon emissions are less than or equal to the carbon emission upper limit of the server, determine the number of servers as the minimum number of servers.

[0041] Step S2063-3: When the average carbon emissions are greater than the carbon emission upper limit of the server, gradually increase the number of servers and determine the total carbon emissions and the average carbon emissions of the multiple servers corresponding to the number of servers until the average carbon emissions are less than or equal to the carbon emission upper limit of the server, and determine the corresponding number of servers as the minimum number of servers.

[0042] In some embodiments, the first conversion relationship in step S2062 is:

[0043]

[0044] where E X个服务器 is the total carbon emissions of X servers, E 联网车辆 is the carbon emissions of the connected vehicles, E 路侧设备 is the carbon emissions of the roadside equipment, E 定位终端 is the carbon emissions of the positioning terminal, K1, K2, and K3 are the first carbon emission conversion coefficient, the second carbon emission conversion coefficient, and the third carbon emission conversion coefficient respectively, and B is the static carbon emissions of the server.

[0045] In an exemplary embodiment, step S2062 may specifically include: substituting the actual carbon emissions of the connected devices, roadside equipment, and positioning terminal into the first conversion relationship to obtain the hyperbolic function relationship between the total carbon emissions of the multiple servers and the number of servers; determining the minimum value of the total carbon emissions of the multiple servers and the number of servers according to the hyperbolic function relationship.

[0046] In some embodiments, before step S2062, the method further includes the following steps:

[0047] Step S2051: Detect the carbon emission conversion coefficient when a single server is connected to each type of connected device separately.

[0048] Step S2052: Determine the conversion relationship between the total carbon emissions of the multiple servers and the carbon emissions of each type of connected device and the number of servers as the first conversion relationship according to the carbon emission conversion coefficient.

[0049] In some embodiments, step S2051 of detecting the carbon emission conversion coefficients when a single server is separately connected to each type of networked device may include the following steps:

[0050] Step S2051-1: Detect the static carbon emission B of a single server when no networked device is connected.

[0051] Step S2051-2: Detect the first carbon emission of a single server when only the networked vehicle, the roadside device, or the positioning terminal is connected, and detect the second carbon emission of the corresponding networked device.

[0052] Step S2051-3: Determine the carbon emission conversion coefficients when the server is separately connected to each type of networked device in the following manner:

[0053]

[0054] where E 服务器 is the carbon emission of a single server, E 联网车辆 is the carbon emission of the networked vehicle, E 路侧设备 is the carbon emission of the roadside device, E 定位终端 is the carbon emission of the positioning terminal. The carbon emission conversion coefficients include the first carbon emission conversion coefficient K1 when the server is separately connected to the networked vehicle, the second carbon emission conversion coefficient K2 when the server is separately connected to the roadside device, and the third carbon emission conversion coefficient K3 when the server is separately connected to the positioning terminal.

[0055] In an exemplary embodiment, the first carbon emission corresponding to the networked vehicle may be substituted into E 服务器 , the second carbon emission corresponding to the networked vehicle may be substituted into E 联网车辆 , and the carbon emissions of other networked devices may be set to 0. Then, the first carbon emission conversion coefficient of the networked vehicle may be determined in the following manner:

[0056]

[0057] The determination methods of the second and third carbon emission conversion coefficients are similar to that of the first carbon emission conversion coefficient, and are not elaborated herein in this application.

[0058] Through the embodiments of this application, on the basis of meeting the server usage requirements, unnecessary servers can be controlled to enter the sleep state, thereby reducing the total carbon emissions of the servers, achieving the optimization of the use of servers in the vehicle-to-everything (V2X) network. In the stage of deploying V2X devices, the servers can also be deployed according to the minimum number of target devices, achieving the optimization of the deployment of servers in the V2X network.

[0059] In some embodiments, when the target device is a server, step S208 adjusts the target devices in the vehicle networking according to the minimum number of target devices, including: when the number of currently working servers is greater than the minimum number of servers, controlling one or more servers exceeding the minimum number of servers to enter the sleep state.

[0060] In some embodiments, step S206 determines the minimum number of target devices that meet the preset vehicle networking usage requirements according to the actual carbon emissions, including:

[0061] Step S2064, when the target device is a roadside device, determining the carbon emissions of the connected vehicles at the target intersection and the carbon emissions of the connected vehicles at multiple adjacent intersections from the actual carbon emissions;

[0062] Step S2065, detecting the traffic flow speed at the target intersection and the traffic flow speeds at the multiple adjacent intersections;

[0063] Step S2066, determining the total carbon emissions of multiple roadside devices at the target intersection according to the carbon emissions of the connected vehicles at the target intersection, the carbon emissions of the connected vehicles at the multiple adjacent intersections, the traffic flow speed at the target intersection, and the traffic flow speeds at the multiple adjacent intersections;

[0064] Step S2067, determining the minimum number of roadside devices that meet the vehicle networking usage requirements according to the total carbon emissions of the multiple roadside devices and the preset carbon emission upper limit of the roadside devices.

[0065] In some embodiments, step S2067 determines the minimum number of roadside devices that meet the vehicle networking usage requirements according to the total carbon emissions of the multiple roadside devices and the preset carbon emission upper limit of the roadside devices, including the following steps:

[0066] Step S2067-1, obtaining the number of roadside devices at the target intersection and the number of redundant roadside devices of multiple redundant roadside devices;

[0067] Step S2067-2, determining the required number of roadside devices at the target intersection by taking the ratio of the total carbon emissions of the multiple roadside devices to the carbon emission upper limit of the roadside devices;

[0068] Step S2067-3, determining the maximum number of roadside devices in the sleep state by taking the difference between the number of roadside devices and the required number of roadside devices;

[0069] Step S2067-4, when the maximum number of roadside devices in the sleep state is greater than or equal to the number of redundant roadside devices, determining the number of roadside devices in the sleep state as the number of redundant roadside devices;

[0070] Step S2067-5, when the maximum number of dormant roadside devices is less than the number of redundant roadside devices, determining the maximum number of dormant roadside devices as the number of dormant roadside devices;

[0071] Step S2067-6, determining the difference between the number of roadside devices and the number of dormant roadside devices as the minimum number of roadside devices.

[0072] In some embodiments, when the target device is a roadside device, step S208 adjusts the target devices in the vehicle-to-everything network according to the minimum number of target devices, including: selecting at least one target redundant roadside device from the multiple redundant roadside devices according to the minimum number of target devices, and controlling the target redundant roadside device to enter the dormant state, so that the number of working roadside devices is equal to the minimum number of target devices. Exemplarily, the target redundant roadside devices may be evenly distributed among the multiple redundant roadside devices.

[0073] In some embodiments, step S2066 includes: determining the total carbon emissions of the multiple roadside devices by the following method:

[0074]

[0075] wherein, E 目标路口 is the total carbon emissions of the multiple roadside devices, M and A are preset linear correlation coefficients, E 路口联网车辆 is the carbon emissions of the connected vehicles at the target intersection, V 目标路口 is the traffic flow speed at the target intersection, E 路口联网车辆i is the carbon emissions of the connected vehicles at the multiple adjacent intersections, V 相邻路口i is the traffic flow speed at the multiple adjacent intersections, and i is the number of the multiple adjacent intersections.

[0076] In some embodiments, before S2066, the method further includes: determining multiple groups of intersection carbon emissions values, including performing linear fitting based on the multiple groups of intersection carbon emissions values to obtain M and A in the linear correlation coefficients. Exemplarily, the multiple groups of intersection carbon emissions values may correspond to different moments in the historical data, and the linear fitting may be performed based on the least squares method.

[0077] Through the embodiments of the present application, on the basis of meeting the usage requirements of roadside devices, redundant devices that are not needed can be controlled to enter the dormant state, thereby reducing the total carbon emissions of roadside devices, realizing the usage optimization of roadside devices in the vehicle-to-everything network. In the vehicle-to-everything device deployment stage, roadside devices can also be deployed according to the minimum number of target devices, realizing the deployment optimization of roadside devices in the vehicle-to-everything network.

[0078] Figure 3 is a flowchart of a carbon emission determination scheme for connected vehicles in an embodiment of the present application. As Figure 3 shown, this process includes the following steps:

[0079] Step S302, obtain the number of electric vehicles N1, the number of connected fuel vehicles N2, and the total number of vehicles N;

[0080] Step S304, determine the carbon emissions of connected fuel vehicles according to the carbon emissions of fuel vehicles and the proportionality coefficient N2 / (N - N1);

[0081] Step S306, sum the carbon emissions of electric vehicles and the carbon emissions of connected fuel vehicles to obtain the carbon emissions of connected vehicles.

[0082] In this embodiment, connected vehicles include electric vehicles and connected fuel vehicles.

[0083] In an exemplary embodiment, the carbon emissions of electric vehicles can be calculated in the following manner:

[0084] E 电车 = N1 × E1 × (1 - q) × η;

[0085] where E 电车 is the carbon emissions of electric vehicles, N1 is the number of electric vehicles, E1 is the power consumption of electric vehicles, which can be obtained from the vehicle-mounted system of new energy vehicles, q is the proportion of clean energy, which can be obtained from the local power grid system, and η is the carbon emission factor of non-clean energy.

[0086] In an exemplary embodiment, the carbon emissions of fuel vehicles E 油车 can be calculated in two parts. One part is the carbon emissions E 油动 during the driving process of fuel vehicles, and the other part is the carbon emissions E 油堵 generated when fuel vehicles are congested (idling).

[0087] The carbon emissions during the driving process of fuel vehicles can be calculated in the following manner:

[0088] E 油动 = (N - N1) × D × α × β;

[0089] where E 油动 is the carbon emissions during the driving process of fuel vehicles, N is the total number of vehicles, D is the driving distance, which can be estimated by referring to the driving distance reported by electric vehicles, α is the fuel consumption per unit distance, and β is the carbon emission factor of fuel.

[0090] The carbon emissions when fuel vehicles are congested can be calculated in the following manner:

[0091] E 油堵 = (N - N1) × γ × t拥堵 ;

[0092] Among them, E 油堵 is the carbon emission when the oil vehicle is congested, γ is the unit fuel consumption of the oil vehicle at idle caused by congestion, and t 拥堵 is the predicted congestion time.

[0093] In an exemplary embodiment, the vehicle congestion time can be calculated in the following manner:

[0094]

[0095] Among them, range is the specified time range, t is each moment, and θ t is the congestion probability at each moment under historical data, which can be obtained by aggregating and analyzing historical data through big data.

[0096] In an exemplary embodiment, the radar or camera at the vehicle networking intersection can be used to detect and identify the vehicles on the road surface to obtain the total number of vehicles N. Electric vehicles or networked oil vehicles with in-vehicle systems can directly report the number of electric vehicles or networked oil vehicles. The number of oil vehicles needs to be calculated by the difference between the total number of vehicles and the number of electric vehicles.

[0097] In another embodiment, the networked oil vehicles with in-vehicle systems can also directly report the fuel consumption and the number of networked oil vehicles of the networked oil vehicles, and then the carbon emissions of the networked oil vehicles can be directly determined based on the product of the number of networked oil vehicles, the fuel consumption, and the carbon emission factor of the fuel.

[0098] In some embodiments, the power consumption of roadside devices and the power consumption of servers can be directly obtained from the vehicle network. And the carbon emissions of roadside devices and the carbon emissions of servers can be determined in the following manner:

[0099] E 路侧设备 = E2×(1 - q)×η;

[0100] E 服务器 = E3×(1 - q)×η;

[0101] Among them, E 路侧设备 is the carbon emission of roadside devices, E2 is the power consumption of roadside devices, E 服务器 is the carbon emission of servers, E3 is the power consumption of servers, q is the proportion of clean energy, which can be obtained from the local power grid system, and η is the carbon emission factor of non-clean energy.

[0102] Figure 4 is the flowchart of the server optimization strategy in an embodiment of the present application. As Figure 4 shown, this process includes the following steps:

[0103] Step S401, obtain the carbon emissions of pre-determined connected vehicles;

[0104] Step S402, obtain the number of positioning terminals;

[0105] Step S403, obtain the carbon emissions of pre-determined roadside devices;

[0106] Step S404, determine the number of servers X when the total carbon emissions of the server are minimized according to the first conversion relationship;

[0107] Step S405, determine the total carbon emissions of X servers, and divide by X to obtain the average carbon emissions E of each server a ;

[0108] Step S406, determine whether E a is greater than the server carbon emission upper limit E max ;

[0109] Step S407, if the judgment is yes, let X = X + 1, gradually increase the number of servers, and return to step S405;

[0110] Step S408, if the judgment is no, put the other servers except the X servers into sleep.

[0111] In this embodiment, the first conversion relationship is:

[0112]

[0113] wherein, E X个服务器 is the total carbon emissions of X servers, E 联网车辆 is the carbon emissions of the connected vehicles, E 路侧设备 is the carbon emissions of the roadside devices, E 定位终端 is the carbon emissions of the positioning terminals, K1, K2, and K3 are the first carbon emission conversion coefficient, the second carbon emission conversion coefficient, and the third carbon emission conversion coefficient respectively, and B is the static carbon emissions of the server.

[0114] In this embodiment, step S404 can take the derivative of the first conversion relationship formula to obtain the number of servers when the total carbon emissions of the server are minimized. Since the positioning terminal is only used to upload location information at a fixed frequency and the power consumption is basically the same, the number of positioning terminals can be directly used to measure the carbon emissions of the positioning terminal in this application.

[0115] In this embodiment, the server carbon emission upper limit E max can be detected by additionally increasing the connected devices of the server. When the connected devices increase and the power consumption of the server itself remains unchanged, it is considered that the limit capacity of the server has been reached, and then the carbon emission upper limit of the corresponding server can be determined.

[0116] Through the embodiments of the present application, the requirements for each networked device in the vehicle networking to connect to the server can be met. Without exceeding the carbon emission limit of the server, the use of the server can be optimized, and the overall carbon emissions can be reduced. This solution can also be applied in the server deployment stage. Based on the finally determined number of servers, the servers can be deployed, and on the basis of ensuring the device connection requirements, the carbon emissions generated by large-scale device deployment can be minimized as much as possible.

[0117] In some embodiments, the carbon emissions of the roadside devices are proportional to the carbon emissions of the networked vehicles at the current intersection. The current data can be used to dynamically adjust the usage of the roadside devices, use the devices as needed, and reduce carbon emissions.

[0118] The roadside units (RSUs) are divided into two types. One is the RSU connected with sensing devices, which is uniformly called type-I roadside device. The other is the RSU for simple communication, which is called type-II roadside device. During the adjustment process, the type-I roadside devices keep running without losing the sensing function, and the type-II roadside devices can be adjusted as needed. Exemplarily, the roadside device optimization solution can be only for type-II RSU devices.

[0119] Figure 5 is the flowchart of the roadside device optimization strategy in an embodiment of the present application. As Figure 5 shown, the process includes the following steps:

[0120] Step S501, predicting the total carbon emissions E of multiple roadside devices at the target intersection 目标路口 ;

[0121] Step S502, calculating the minimum number of required roadside devices m = E 目标路口 / E 路侧max ;

[0122] Step S503, obtaining the number l of roadside devices at the target intersection;

[0123] Step S504, obtaining the number n of redundant roadside devices;

[0124] Step S505, judging whether l - m is greater than n;

[0125] Step S506, if the judgment is yes, putting these n redundant roadside devices into sleep mode;

[0126] Step S507, if the judgment is no, evenly putting (l - m) devices among the n redundant roadside devices into sleep mode.

[0127] In some embodiments, step S501 can predict the total carbon emissions of the multiple roadside devices in the following manner:

[0128]

[0129] Among them, E 目标路口 is the total carbon emissions of the multiple roadside devices, M and A are preset linear correlation coefficients, and E 路口联网车辆 is the carbon emissions of the connected vehicles at the target intersection, and V 目标路口 is the traffic flow speed at the target intersection, and E 路口联网车辆i is the carbon emissions of the connected vehicles at the multiple adjacent intersections, and V 相邻路口i is the traffic flow speed at the multiple adjacent intersections, and i is the number of the multiple adjacent intersections.

[0130] In this embodiment, the carbon emission prediction strategy of the roadside device at the target intersection may further include the following steps:

[0131] Step S5011, calculate the carbon emissions of the connected vehicles at the target intersection and detect the traffic flow speed at the target intersection;

[0132] Step S5012, calculate the carbon emissions of the connected vehicles at the multiple adjacent intersections and detect the traffic flow speed at the multiple adjacent intersections;

[0133] Step S5013, construct the total carbon emissions prediction model at the target intersection as:

[0134]

[0135] Step S5014, obtain the actual carbon emissions at the target intersection and adjacent intersections at different times, and fit E 目标路口 , and obtain the values of M and A, where M and A are linear correlation coefficients;

[0136] Step S5015, substitute the above values into the linear prediction model to obtain E 目标路口 .

[0137] In this embodiment, before step S502, it is also necessary to detect the upper limit of roadside device carbon emissions E 路侧max under the normal operation of the roadside device. Exemplarily, the vehicles connected to the roadside device can be continuously increased. When the number of connected vehicles increases and the power consumption of the server itself remains unchanged, it is considered that the limit capacity of the roadside device has been reached, and then the corresponding upper limit of roadside device carbon emissions can be determined.

[0138] In this embodiment, the redundant roadside devices in step S504 can be determined based on the coverage range of the roadside devices. The roadside devices whose coverage ranges overlap with those of other roadside devices and the overlapping part exceeds a preset redundancy threshold (such as 80% of the coverage range) are determined as redundant roadside devices.

[0139] Through the embodiments of the present application, the requirements for vehicles at intersections in the vehicle-to-everything (V2X) network to connect to roadside devices can be met. On the basis of not exceeding the carbon emission limit of roadside devices and not affecting the monitoring coverage of roadside devices, the optimization of the use of roadside devices can be realized, and the overall carbon emissions can be reduced. This solution can also be applied to the deployment stage of roadside devices. Based on the finally determined number of roadside devices, the roadside devices are deployed to minimize the carbon emissions generated by large-scale device deployment.

[0140] Figure 6 is the overall flowchart of the vehicle network device optimization solution in the embodiments of the present application. As Figure 6 shown, this process is mainly divided into the following steps:

[0141] Step S602, data acquisition, using various technologies to obtain the original data relied on, such as the basic data of V2X entities;

[0142] Step S604, model construction, constructing a carbon emission model based on the original data;

[0143] Step S606, calculation optimization, performing secondary calculation according to the original data and the model to obtain the overall carbon emissions, and optimizing starting from the dependency relationship of V2X facilities.

[0144] In this embodiment, the original data in step S602 depends on three important components in the V2X network: vehicle-side terminals, roadside devices, and servers. Vehicles can be divided into fuel vehicles and electric vehicles. Fuel vehicles calculate carbon emissions based on fuel consumption, and electric vehicles, roadside devices, and servers calculate carbon emissions based on power consumption.

[0145] In this embodiment, in data acquisition, the following steps can be included as the basic data for carbon emission calculation.

[0146] Step S6021, obtaining the power consumption and driving distance from the in-vehicle system of electric vehicles;

[0147] Step S6022, obtaining the driving distance from fuel vehicles equipped with in-vehicle systems. For the same intersection, the driving distance of fuel vehicles can also be estimated from the driving distance of electric vehicles;

[0148] Step S6023, obtaining the local clean energy ratio from the power grid;

[0149] Step S6024, using the radar or camera at the V2X intersection to detect and identify the total number of vehicles on the road surface. When vehicles with in-vehicle systems report, they can be distinguished as electric vehicles and fuel vehicles, and the number of reported electric vehicles is recorded;

[0150] Step S6025, obtaining the power consumption of roadside devices;

[0151] Step S6026, obtaining the power consumption of the V2X server;

[0152] Step S6027: Aggregate and analyze big data to obtain the congestion probability at each time point under historical data.

[0153] The basic data in the above steps S6021 to S6027 is only used as an example, and this application is not limited thereto.

[0154] In this embodiment, the model construction in step S604 can refer to the process of determining the carbon emission conversion coefficient and the process of determining the linear correlation coefficient by linear fitting in the above embodiment, which will not be elaborated herein.

[0155] In this embodiment, the calculation optimization in step S606 can refer to the process of determining the minimum number of target devices in the above embodiment and adjust the corresponding target devices based on the minimum number of target devices.

[0156] Through the embodiments of this application, the current data can be used to dynamically adjust the usage of roadside devices or servers, use devices on demand, thereby reducing the overall carbon emissions in the vehicle networking and realizing the optimization of the deployment and use of vehicle networking devices.

[0157] The embodiments of this application also provide a computer-readable storage medium, in which a computer program is stored. When the computer program is run by a processor, it executes the steps in any one of the above method embodiments.

[0158] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: USB flash drive, read-only memory (ROM for short), random access memory (RAM for short), mobile hard disk, magnetic disk or optical disc, etc., various media that can store computer programs.

[0159] The embodiments of this application also provide an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0160] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0161] The embodiments of this application also provide a computer program product, including a computer program, which realizes the steps in any one of the above method embodiments when executed by a processor.

[0162] For the specific examples in this embodiment, reference may be made to the examples described in the above embodiments and the exemplary embodiments, and details thereof will not be repeated here.

[0163] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device, so that they can be stored in the storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. Thus, the present application is not limited to any specific combination of hardware and software.

[0164] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. An optimization method for vehicle networking devices, characterized in that, The method includes: Obtaining the basic data reported by entities in the vehicle networking; Determining the actual carbon emissions of the entities according to the basic data; Determining the minimum target device quantity that meets the preset vehicle networking usage requirements according to the actual carbon emissions; Adjusting the target devices in the vehicle networking according to the minimum target device quantity.

2. The method according to claim 1, characterized in that, Determining the actual carbon emissions of the entities according to the basic data includes: Respectively determining the actual carbon emissions of the connected vehicles, positioning terminals, roadside devices, and servers according to the basic data, where the entities include the connected vehicles, the positioning terminals, the roadside devices, and the servers.

3. The method according to claim 1, characterized in that Determining the minimum target device quantity that meets the preset vehicle networking usage requirements according to the actual carbon emissions includes: When the target device is a server, determining the actual carbon emissions of the connected devices connected to the server from the actual carbon emissions, where the connected devices include at least one of the following: connected vehicles, roadside devices, and positioning terminals; Determining the minimum value of the total carbon emissions of multiple servers connected to the connected devices and the number of the multiple servers according to the actual carbon emissions of the connected devices and a pre-determined first conversion relationship, where the first conversion relationship is the conversion relationship between the total carbon emissions of the multiple servers and the carbon emissions and the number of servers of each type of connected device; Determining the minimum number of servers that meets the vehicle networking usage requirements according to the minimum value of the total carbon emissions, the number of servers, and a preset server carbon emission upper limit, where the minimum target device quantity includes the minimum number of servers, the total carbon emissions of the multiple servers corresponding to the minimum number of servers is greater than or equal to the minimum value of the total carbon emissions, and the carbon emissions of a single server corresponding to the minimum number of servers is less than or equal to the server carbon emission upper limit.

4. The method according to claim 3, wherein Determining the minimum number of servers that meets the vehicle networking usage requirements according to the minimum value of the total carbon emissions, the number of servers, and a preset server carbon emission upper limit includes: Determining the average carbon emissions of each server according to the minimum value of the total carbon emissions and the number of servers; When the average carbon emissions is less than or equal to the server carbon emission upper limit, determining the number of servers as the minimum number of servers; When the average carbon emissions is greater than the server carbon emission upper limit, gradually increasing the number of servers and determining the total carbon emissions and the average carbon emissions of the multiple servers corresponding to the number of servers until the average carbon emissions is less than or equal to the server carbon emission upper limit, and determining the corresponding number of servers as the minimum number of servers.

5. The method according to claim 3, characterized in that The first conversion relationship is: Among them, E X个服务器 is the total carbon emissions of X servers, E 联网车辆 is the carbon emissions of the connected vehicles, E 路侧设备 is the carbon emissions of the roadside equipment, E 定位终端 is the carbon emissions of the positioning terminal, K1, K2, and K3 are the first carbon emissions conversion coefficient, the second carbon emissions conversion coefficient, and the third carbon emissions conversion coefficient respectively, and B is the static carbon emissions of the server.

6. The method according to claim 3, characterized in that Before determining the minimum value of the total carbon emissions of multiple servers connected to the connected devices and the number of the multiple servers according to the actual carbon emissions of the connected devices and a pre-determined first conversion relationship, the method further includes: Detect the carbon emission conversion coefficient when a single server is connected to each type of networked device separately; Determine the conversion relationship between the total carbon emissions of the multiple servers, the carbon emissions of each type of networked device, and the number of servers as the first conversion relationship according to the carbon emission conversion coefficient.

7. The method according to claim 6, characterized in that Detecting the carbon emission conversion coefficient when a single server is connected to each type of networked device separately includes: Detect the static carbon emission B of a single server when no networked device is connected; Detect the first carbon emissions of a single server when only the networked vehicle, the roadside device, or the positioning terminal is connected respectively, and detect the second carbon emissions of the corresponding networked device; Determine the carbon emission conversion coefficient when the server is connected to each type of networked device separately in the following ways: Among them, E 服务器 is the carbon emission of a single server, E 联网车辆 is the carbon emission of the connected vehicle, E 路侧设备 is the carbon emission of the roadside device, E 定位终端 is the carbon emission of the positioning terminal. The carbon emission conversion coefficient includes a first carbon emission conversion coefficient K1 when the server is separately connected to the connected vehicle, a second carbon emission conversion coefficient K2 when the server is separately connected to the roadside device, and a third carbon emission conversion coefficient K3 when the server is separately connected to the positioning terminal.

8. The method according to claim 3, characterized in that, Adjust the target devices in the vehicle-to-everything network according to the minimum target device quantity, including: When the number of currently working servers is greater than the minimum number of servers, control one or more servers exceeding the minimum number of servers to enter the sleep state.

9. The method according to claim 1, wherein Determine the minimum target device quantity that meets the preset vehicle-to-everything network usage requirements according to the actual carbon emissions, including: When the target device is a roadside device, determine the carbon emissions of the networked vehicles at the target intersection and the carbon emissions of the networked vehicles at multiple adjacent intersections from the actual carbon emissions; Detect the traffic flow speed at the target intersection and the traffic flow speeds at the multiple adjacent intersections; Determine the total carbon emissions of the multiple roadside devices at the target intersection according to the carbon emissions of the networked vehicles at the target intersection, the carbon emissions of the networked vehicles at the multiple adjacent intersections, the traffic flow speed at the target intersection, and the traffic flow speeds at the multiple adjacent intersections; Determine the minimum number of roadside devices that meets the vehicle-to-everything network usage requirements according to the total carbon emissions of the multiple roadside devices and the preset carbon emission upper limit of the roadside devices.

10. The method according to claim 9, wherein Determining the minimum number of roadside devices that meets the vehicle-to-everything network usage requirements according to the total carbon emissions of the multiple roadside devices and the preset carbon emission upper limit of the roadside devices includes: Obtain the number of roadside devices at the target intersection and the number of redundant roadside devices of the multiple redundant roadside devices; Determine the required number of roadside devices at the target intersection by taking the ratio of the total carbon emissions of the multiple roadside devices to the carbon emission upper limit of the roadside devices; Determine the maximum number of roadside devices in the sleep state by taking the difference between the number of roadside devices and the required number of roadside devices; When the maximum number of roadside devices in the sleep state is greater than or equal to the number of redundant roadside devices, determine the number of redundant roadside devices as the number of roadside devices in the sleep state; When the maximum number of roadside devices in the sleep state is less than the number of redundant roadside devices, determine the maximum number of roadside devices in the sleep state as the number of roadside devices in the sleep state; Determine the minimum number of roadside devices by taking the difference between the number of roadside devices and the number of roadside devices in the sleep state.

11. The method according to claim 10, characterized in that, Adjust the target devices in the vehicle-to-everything network according to the minimum target device quantity, including: Select at least one target redundant roadside device from the multiple redundant roadside devices according to the minimum target device number, and control the target redundant roadside device to enter the sleep state, so that the number of working roadside devices is equal to the minimum target device number.

12. The method according to claim 9, wherein Determine the total carbon emissions of the multiple roadside devices at the target intersection according to the carbon emissions of the connected vehicles at the target intersection, the carbon emissions of the connected vehicles at the multiple adjacent intersections, the traffic flow speed at the target intersection, and the traffic flow speed at the multiple adjacent intersections, including: Determine the total carbon emissions of the multiple roadside devices in the following manner: Among them, E 目标路口 is the total carbon emission of the multiple roadside devices, M and A are preset linear correlation coefficients, and E 路口联网车辆 is the carbon emission of the connected vehicles at the target intersection, V 目标路口 is the traffic flow speed at the target intersection, and E 路口联网车辆i is the carbon emission of the connected vehicles at the multiple adjacent intersections, V 相邻路口i is the traffic flow speed at the multiple adjacent intersections, and i is the number of the multiple adjacent intersections.

13. A computer-readable storage medium, characterized in that, A computer program is stored in the storage medium, wherein the computer program, when run by a processor, executes the method described in any one of claims 1 to 12.

14. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of claims 1 to 12.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method described in any one of claims 1 to 12 are implemented.

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