Charging scheduling method based on Internet of Things technology and related device

Abstract

The embodiment of the invention discloses a charging scheduling method based on the Internet of Things technology and a related device, the method is applied to a cloud platform, the cloud platform is connected with edge equipment on a target road, the charging pressure of a plurality of charging areas can be balanced, and the use efficiency of charging facilities is improved. The method comprises the following steps: acquiring current traffic information of a target road, energy consumption information of a plurality of electric vehicles and infrastructure information of a plurality of charging areas through edge equipment; according to the current traffic information, traffic situation prediction is generated, the traffic situation prediction is used for representing the traffic condition of the target road in a first time period, and the first time period is a time period after the current time; according to the traffic situation prediction, the energy consumption information and the infrastructure information, a scheduling strategy is generated, the scheduling strategy is used for indicating a target charging area of the target electric vehicle, and the target charging area is a charging area used for providing charging service for the target electric vehicle in the multiple charging areas; and sending the scheduling strategy.

Description

[0001] This application claims the priority of a Chinese patent application with the application number 202410135501.4 and the invention title "A New Energy Operation System" filed with the National Intellectual Property Administration on January 30, 2024, the entire content of which is incorporated herein by reference. Technical Field

[0002] This application relates to the field of the Internet of Things, and particularly to a charging scheduling method and related devices based on Internet of Things technology. Background Art

[0003] With the development of electric vehicle technology, electric vehicles have been increasingly popularized due to their advantages such as high efficiency and low pollution. Compared with fuel vehicles, the charging time of electric vehicles is often longer. Especially during peak travel periods such as holidays, when new energy vehicles are driving on highways, the estimated charging mileage is insufficient, and it is easy to miss charging stations or encounter the situation of multiple vehicles queuing for charging at the same station, thus affecting the travel arrangements of vehicle owners.

[0004] Currently, the judgment of electric vehicle charging demand mainly relies on the cloud control platform. This platform will monitor the energy consumption and location information of vehicles in real time, and query the nearby charging areas, and then generate a charging scheduling strategy. However, such a scheduling strategy is based on ideal situations, and there may be various uncertain factors in reality, such as random selection or local trap problems. These factors often lead to errors in the scheduling results and a large deviation from the actual situation. Summary of the Invention

[0005] Embodiments of this application provide a charging scheduling method and related devices based on Internet of Things technology, which are used to balance the charging pressure of multiple charging areas and improve the utilization efficiency of charging facilities.

[0006] In a first aspect, embodiments of this application provide a charging scheduling method. This method is applied to a cloud platform, and the cloud platform is connected to edge devices on a target road. The method includes:

[0007] First, use the edge devices to collect the current traffic information of the target road in real time, which includes information such as traffic flow and vehicle speed. At the same time, the energy consumption information of multiple electric vehicles driving on this road is also collected, such as remaining battery power, power consumption, etc. In addition, the infrastructure information of multiple charging areas located within a preset distance range of the target road is obtained, and the infrastructure information includes the power supply load situation to evaluate the charging capacity of each charging area.

[0008] Subsequently, based on the currently collected traffic information, conduct traffic situation prediction. The traffic situation prediction can depict the traffic conditions of the target road in a future period of time, that is, the first time period.

[0009] Next, a scheduling strategy is generated by combining traffic situation prediction, energy consumption information, and infrastructure information of the charging areas. The scheduling strategy indicates which charging area at least one target electric vehicle should go to for charging, ensuring that the selected target charging area can meet the vehicle's charging needs while maintaining the balanced utilization of the charging facilities.

[0010] Finally, send this scheduling strategy.

[0011] In this application, the cloud platform deploys the V2XEdge application to each edge computing unit to collect detailed data at multiple points on the target road. With the help of radar and camera devices, the system can intelligently identify and count the passing vehicles, thereby generating the current traffic information.

[0012] Traffic situation prediction covers two aspects: short-term prediction of normal traffic flow and prediction of sudden traffic events. Using the traffic situation prediction algorithm model integrated in the cloud platform, with the flow, density, and speed data at the section level, and the highway network topology model as the core input data, precise analysis is carried out through AI inference calculation. After being processed by the model, it can output the predicted traffic situation data within a short time range, providing strong support for traffic management and scheduling. For example, it is assumed that it is predicted that the traffic flow on section A will increase significantly within the next 30 minutes and traffic congestion is likely to occur.

[0013] By adopting the above method, by incorporating the traffic situation prediction of the target road into the calculation process of the charging scheduling strategy, the scheduling strategy can not only adapt to the normal state of daily traffic changes but also quickly respond to sudden traffic conditions, improving the accuracy and efficiency of the adjustment of the charging scheduling strategy and achieving more meticulous management. When constructing the charging scheduling strategy, the method of group behavior analysis is used to effectively avoid the local optimization problem that may be caused by only pursuing the optimal strategy for a single vehicle, thereby reducing the congestion phenomenon that may occur in some service areas. This strategy makes the distribution of traffic pressure in each service area more balanced and greatly improves the overall utilization efficiency of the charging facilities.

[0014] In one possible implementation, the current traffic information includes the passing vehicle flow information, traffic event information, and driving trajectory information of each section of the target road.

[0015] In one possible implementation, obtaining the energy consumption information of multiple electric vehicles includes: collecting the energy consumption information of multiple electric vehicles through edge devices, and / or calculating the energy consumption information of multiple electric vehicles according to the current traffic information.

[0016] In this application, for electric vehicles in the vehicle-to-everything (V2X) network, the vehicle can upload its own energy consumption information to the cloud platform through the V2X network; for electric vehicles without network connection, clustering fitting can be performed on them, and using the energy consumption information of the reported electric vehicles, the power consumption curve of the vehicle category can be obtained.

[0017] Using the above method, the power consumption curve of unconnected electric vehicles is predicted by using the energy consumption information of the reported electric vehicles. A way to indirectly understand the vehicle energy consumption status is provided. The coverage rate and accuracy of vehicle energy consumption data in the entire transportation system are improved.

[0018] In a possible implementation manner, according to traffic situation prediction, energy consumption information, and infrastructure information, a scheduling strategy is generated, including: calculating the charging decision of each electric vehicle among multiple electric vehicles during subsequent driving according to the traffic situation prediction and energy consumption information; generating a first distribution according to the charging decision, where the first distribution is used to represent the vehicle distribution in multiple charging areas during the foregoing first time period; making a decision adjustment to the charging decisions of multiple electric vehicles according to the infrastructure information and the first distribution to obtain a second distribution, where the overall charging queue duration corresponding to the vehicle distribution represented by the second distribution is less than the overall charging queue duration corresponding to the vehicle distribution represented by the first distribution; generating a scheduling strategy according to the second distribution.

[0019] Using the above method, the first distribution generated according to the charging decisions of multiple electric vehicles can intuitively understand the vehicle distribution in each charging area in the future period of time. This helps to identify charging areas that may be congested or overloaded. By further considering infrastructure information (such as the power supply capacity and current load situation of the charging station), the charging decision can be adjusted to obtain a second distribution. This adjustment aims to balance the load of each charging area and avoid the situation where some areas are overcrowded while other areas have idle resources. The scheduling strategy generated according to the second distribution improves the user experience and reduces the time cost caused by waiting for charging.

[0020] In a possible implementation manner, the scheduling strategy includes: charging recommendation information of a first electric vehicle, where the charging recommendation information is used to indicate the recommended charging area of the first electric vehicle, and the first electric vehicle is any one of the multiple electric vehicles.

[0021] In a possible implementation manner, the scheduling strategy further includes: charging expansion information of the target charging area, where the charging expansion information is used to indicate deploying temporary charging facilities in the target charging area.

[0022] In a possible implementation, the method further includes: obtaining historical traffic information of a target road and historical operation information of the multiple charging areas, where the historical operation information is used to represent the charging queue situation of the multiple charging areas and the usage situation of charging facilities; generating an operation strategy according to the historical traffic information and the historical operation information, where the operation strategy is used to update the number of charging facilities and / or the charging rules of the multiple charging areas.

[0023] In this application, through the historical traffic information of the target road, key features such as the traffic flow and congestion status of the road can be understood. Combining the historical operation information of multiple charging areas, such as the charging queue situation and the utilization rate of charging facilities, it is possible to identify whether the existing charging facilities meet the demand, and whether there is a situation of resource waste or shortage.

[0024] By adopting the above method to optimize the layout of charging facilities and charging rules, the charging waiting time of electric vehicle owners can be reduced, and the charging efficiency can be improved. This helps to enhance the usage experience of the owners and increase their satisfaction with charging facilities.

[0025] In a possible implementation, the infrastructure information includes at least one of the following: grid load information, charging pile information, and mobile charging vehicle information.

[0026] In a possible implementation, the target road is an expressway.

[0027] In a second aspect, an embodiment of this application provides a charging scheduling device, which is applied to a cloud platform. The cloud platform is connected to edge devices on a target road. The device includes:

[0028] An obtaining module, configured to obtain the current traffic information of the target road, the energy consumption information of multiple electric vehicles, and the infrastructure information of multiple charging areas through edge devices. The multiple charging areas are charging areas within a preset distance range of the target road, and the infrastructure information is used to indicate the power supply load situation of the multiple charging areas;

[0029] A prediction module, configured to generate a traffic situation prediction according to the current traffic information. The traffic situation prediction is used to represent the traffic conditions of the target road within a first time period, and the first time period is a time period after the current time;

[0030] A processing module, configured to generate a scheduling strategy according to the traffic situation prediction, the energy consumption information, and the infrastructure information. The scheduling strategy is used to indicate the target charging area of the target electric vehicle. The target charging area is a charging area among the multiple charging areas that is used to provide charging services for the target electric vehicle, and the target electric vehicle is at least one electric vehicle among the multiple electric vehicles;

[0031] A sending module, configured to send the scheduling strategy.

[0032] In a possible implementation, the current traffic information includes the vehicle passing flow information of each section of the target road, traffic event information, and the driving trajectory information of each vehicle.

[0033] In a possible implementation, the obtaining module is specifically configured to: collect the energy consumption information of multiple electric vehicles through edge devices, and / or calculate the energy consumption information of multiple electric vehicles according to the current traffic information.

[0034] In a possible implementation, the processing module is specifically configured to: calculate the charging decision of each electric vehicle among multiple electric vehicles during subsequent driving according to the traffic situation prediction and the energy consumption information of each vehicle; generate a first distribution according to the charging decisions of multiple electric vehicles, where the first distribution is used to represent the vehicle distribution in multiple charging areas within a first time period; make a decision adjustment to the charging decisions of multiple electric vehicles according to the infrastructure information and the first distribution to obtain a second distribution, where the overall charging queue duration corresponding to the vehicle distribution represented by the second distribution is less than the overall charging queue duration corresponding to the vehicle distribution represented by the first distribution; generate a scheduling strategy according to the second distribution.

[0035] In a possible implementation, the scheduling strategy includes: charging recommendation information for a first electric vehicle, where the charging recommendation information is used to indicate the recommended charging area for the first electric vehicle, and the first electric vehicle is any one of the target electric vehicles.

[0036] In a possible implementation, the scheduling strategy further includes: charging capacity expansion information for a target charging area, where the charging capacity expansion information is used to indicate the deployment of temporary charging facilities in the target charging area.

[0037] In a possible implementation, the obtaining module is further configured to: obtain the historical traffic information of the target road and the historical operation information of multiple charging areas, where the historical operation information is used to represent the charging queue situation of multiple charging areas and the usage situation of charging facilities; the processing module is further configured to generate an operation strategy according to the historical traffic information and the historical operation information, where the operation strategy is used to update the number and / or charging rules of charging facilities in multiple charging areas.

[0038] In a possible implementation, the infrastructure information includes at least one of the following: grid load information, charging pile information, and mobile charging vehicle information.

[0039] In a possible implementation, the target road is an expressway.

[0040] In a third aspect of the present application, a charging scheduling device is provided, which may include a processor. The processor is coupled to a memory that stores program instructions. When the program instructions stored in the memory are executed by the processor, the method of the first aspect or any implementation manner of the first aspect is implemented. For the steps executed by the processor in each possible implementation manner of the first aspect, reference may specifically be made to the first aspect, and details are not elaborated herein.

[0041] In a fourth aspect, an embodiment of the present application provides a computing device cluster, which includes at least one computing device. Each computing device includes a processor and a memory, and a computer program or computer instructions are stored in the memory. The processor is used to call and run the computer program or computer instructions stored in the memory, so that the computing device cluster executes the method of the first aspect and any optional method thereof.

[0042] In a fifth aspect of the present application, a computer-readable storage medium is provided. A computer program is stored in the computer-readable storage medium. When it runs on a computer, the computer is caused to execute the method of any implementation manner of the first aspect.

[0043] In a sixth aspect of the present application, a computer program product is provided. When it runs on a computer, the computer is caused to execute the method of any implementation manner of the first aspect.

[0044] In a seventh aspect of the present application, a chip system is provided. The chip system includes a processor for supporting a server or a charging scheduling device to implement the functions involved in any implementation manner of the first aspect. For example, sending or processing data and / or information involved in the above method. In a possible design, the chip system further includes a memory for storing necessary program instructions and data of the server or communication device. The chip system may be composed of chips or may include chips and other discrete devices.

[0045] The beneficial effects of the second to seventh aspects above may refer to the introduction of the first aspect, and details are not elaborated herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic diagram of a system architecture provided by an embodiment of the present application;

[0048] Figure 2Schematic diagram of the intelligent transportation cloud control platform architecture provided by the embodiments of the present application;

[0049] Figure 3 Schematic diagram of the edge side structure provided by the embodiments of the present application;

[0050] Figure 4 Schematic flow chart of a charging scheduling method provided by the embodiments of the present application;

[0051] Figure 5 Schematic diagram of the comprehensive perception subsystem provided by the embodiments of the present application;

[0052] Figure 6 Schematic diagram of the charging scheduling subsystem provided by the embodiments of the present application;

[0053] Figure 7 Schematic diagram of the operation optimization subsystem provided by the embodiments of the present application;

[0054] Figure 8 Schematic illustration of an embodiment of a charging scheduling device provided by the embodiments of the present application;

[0055] Figure 9 Schematic diagram of a structure of a computing device provided by the embodiments of the present application;

[0056] Figure 10 Schematic diagram of a structure of a computing device cluster provided by the embodiments of the present application;

[0057] Figure 11 Schematic diagram of another structure of a computing device cluster provided by the embodiments of the present application. Detailed implementation manners

[0058] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0059] The terms "first", "second", "third", "fourth", etc. (if any) in the description, claims and above-mentioned drawings of this application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments of this application described herein, for example, can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0060] The charging problem of electric vehicles has always been a core challenge in their development. If an electric vehicle is charged when the remaining battery power is relatively sufficient, it may cause greater damage to the battery. On the contrary, when charging is carried out when the battery power is low, although it usually only takes about one hour to fully charge, compared with the fact that a fuel vehicle only needs 5 minutes to fill up with fuel, this waiting time seems relatively long.

[0061] Due to the long charging time of electric vehicles and the insufficient layout density of charging facilities to support long-distance driving, especially in the case of a sharp increase in vehicle travel volume during holidays, when driving an electric vehicle on the highway, the charging station is often missed due to inaccurate estimated cruising range. This results in some charging stations being idle without vehicles to charge, while some charging stations waste a lot of time due to vehicles queuing up to charge, which not only causes waste of resources but also seriously affects the travel plans of vehicle owners.

[0062] For the convenience of understanding, the relevant terms and concepts mainly involved in the embodiments of this application will be introduced first below.

[0063] (1) Vehicle to Everything Edge Computing Platform (V2XEdge)

[0064] V2X refers to the communication technology between vehicles and everything (Vehicle to Everything), including communication between vehicles and vehicles (V2V), vehicles and infrastructure (V2I), vehicles and pedestrians (V2P), and vehicles and the network (V2N). Edge Computing represents edge computing, which is a distributed computing paradigm that pushes computing tasks and data storage from a centralized data center to the edge of the network, that is, devices or terminals, to improve processing speed and response capabilities.

[0065] The V2X Edge platform is generally designed to enable real-time information exchange and processing between vehicles and other traffic participants, enhancing road safety and traffic efficiency. It utilizes edge computing technology to process and analyze data near vehicles, reducing data transmission latency, increasing decision-making speed, and providing support for autonomous driving, intelligent transportation systems, and other vehicle-related applications.

[0066] (2) Intelligent Transportation Cloud Control Platform

[0067] The intelligent transportation cloud control platform combines the Internet of Things, intelligent analysis technology, 3D visualization technology with the data of multiple systems and the actual business scenarios, serving as a support tool for operators to achieve visual perception of the holographic intelligent transportation. Traffic industry developers can build corresponding intelligent applications based on the platform capabilities and the specific business requirements of different scenarios. For example, it can transform business scenarios such as urban traffic, highway operation, and large hub scheduling in an intelligent way to serve traffic managers and travelers.

[0068] Based on this, this application provides a charging scheduling system 10 for a cloud platform, which can provide appropriate charging scheduling strategies for electric vehicles. Please refer to Figure 1 , a schematic structural diagram of a charging scheduling system provided by this application. As Figure 1 shown, the system includes a cloud platform side 11, an edge side 12, and an Internet side 13.

[0069] Among them, the cloud platform side 11 includes the basic capabilities of cloud services and the cloud platform. Cloud services are service models provided based on cloud computing technology, including computing, storage, network, database, and applications, etc. It can be roughly divided into three levels of services, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). In this application, the main cloud services involved include platform services such as data analysis and operation optimization suggestions.

[0070] This application designs a comprehensive perception subsystem for high-speed vehicles by leveraging the traffic data analysis capabilities of the cloud platform. At the same time, relying on the group decision-making and planning capabilities of the cloud platform, a charging scheduling subsystem for electric vehicles is designed. In addition, combined with the continuous evaluation and optimization capabilities of the cloud platform, a service area operation optimization subsystem is also designed. These three subsystems cooperate with each other to aim at improving traffic efficiency, optimizing the electric vehicle charging experience, and improving the service area operation efficiency. The following will elaborate on the three subsystems in detail and will not make too much statement here.

[0071] The cloud platform provided by this application is an intelligent transportation cloud control platform integrating advanced technologies such as cloud computing, 5G, Internet of Things, big data, and artificial intelligence. The edge computing units and edge devices on the cloud platform side 11 and the edge side 12 can be connected by wired or wireless means. For example, the cloud platform side 11 and the edge side 12 can be connected by wireless means such as z-wave, long range radio (LoRa), Zigbee, narrowband internet of things (NB-IOT), and cellular networks. The cloud platform side 11 and the edge side 12 can also be connected by wired means such as Ethernet, RS232, RS485, and universal serial bus (USB).

[0072] The edge devices in the edge side 12 can be roadside unit devices (RSU), intelligent sensors, edge intelligent routers, etc. for data collection. The edge devices can be directly connected to the cloud platform side 11 or indirectly connected to the cloud platform side 11 through the edge computing units in the edge side 12.

[0073] In some embodiments, the cloud platform side 11 communicates with the edge devices on the edge side 12 using an IoT controller and an IoT gateway. The IoT controller can be a virtual IoT control device. For example, the IoT controller is an IoT control system deployed on a certain computing device. The IoT controller can also be a physical IoT control device. Similarly, the IoT gateway can be a virtual IoT gateway. For example, the IoT gateway is a gateway system deployed on a certain computing device. The IoT gateway can also be a physical IoT gateway. The computing device can refer to devices such as servers and computers in the cloud platform side 11.

[0074] The edge side 12 mainly refers to the computing locations at the network edge, as well as the hardware and software at these physical locations. In the charging scheduling system 10, it includes an edge computing unit: corresponding edge device management applications can be deployed to process real-time requests from edge-connected devices; providing the basic capabilities required for edge-side data calculation, which are used in AI recognition scenarios such as radar-vision fitting. The edge side 12 also has the ability to cooperate in cloud-edge connection, reporting data to the cloud for real-time processing; providing an application development and deployment environment for the deployment of third-party management applications.

[0075] In some embodiments, the cloud platform side 11 can be connected to the Internet side 13. For example, the cloud platform can support the access of real-time traffic condition data provided by map navigation service providers to supplement sections not covered by the sensing points of edge devices.

[0076] Specifically, please refer to Figure 2 , Figure 2Schematic diagram of the intelligent transportation cloud control platform architecture provided by this application. In the cloud platform side 11, it includes cloud services and the basic capabilities of the cloud platform. Among them, cloud services include operation services and V2X Server, and the basic capabilities of the cloud platform include elastic load balance (ELB), object storage service (OBS), cloud container engine (CCE), intelligent edge fabric (IEF), software repository for container (SWR), and application operations management (AOM), etc.

[0077] The edge side 12 mainly uses the V2XEdge application to process the data collected by edge devices and communicates with the cloud platform side 11 through HTTPS / MQTTS.

[0078] Specifically, the schematic diagram of the edge side 12 is as Figure 3 shown. Among them, the edge computing unit generally uses the Atlas500 intelligent small station, Atlas 500Pro intelligent edge server, and ITS800 traffic perception edge as the hardware platform for hosting, and can be deployed according to specific computing power requirements. Edge devices mainly include radars, cameras, charging piles, power monitoring meters, mobile charging vehicle-mounted devices, etc.

[0079] Among them, the V2XEdge application, as the edge application of the intelligent transportation cloud control platform, is responsible for accessing roadside devices and processing traffic information; including traffic data statistics of lane-level flow density and speed; performing radar-vision fitting of vehicle trajectories based on radars and cameras and identifying their license plate numbers and vehicle models; real-time detection of traffic events, including traffic accidents, pedestrian recognition, slow vehicle movement, congestion and other events. [[ID=,12]]

[0080] The data collection application, that is, the data collection edge side application, collects device information through a dedicated device communication protocol or interface, and after aggregating the data at each point, reports it to the cloud platform in batches.

[0081] For the device management application, some edge devices have remote management functions, so instructions can be sent from the cloud platform to the device management application, and then specific devices can be controlled to perform corresponding actions.

[0082] Based on this, the embodiment of this application provides a charging scheduling method, which is applied to the aforementioned intelligent transportation cloud control platform. As Figure 4As shown in the figure, the charging scheduling method provided by the embodiment of the present application includes the following steps 401-404.

[0083] 401. Obtain the current traffic information of the target road, the energy consumption information of each vehicle, and the infrastructure information of multiple charging areas through the edge device.

[0084] The multiple charging areas are the charging areas within a preset distance range of the target road, and the infrastructure information is used to indicate the power supply load conditions of the multiple charging areas, and can be collected through the data acquisition application and device management application in the aforementioned edge computing unit.

[0085] In a possible implementation manner, the target road is a highway, and the multiple charging areas are service areas passed by the highway.

[0086] Specifically, the infrastructure information includes the load details of the service area power grid, the real-time usage status and charging power data of the charging piles in the service area, and the location and remaining power data of the mobile charging vehicle. These data are collected by the edge computing unit and reported to the cloud platform in real time for unified summary.

[0087] In a possible implementation manner, the cloud platform deploys the V2XEdge application to each edge computing unit to collect detailed data at multiple points on the target road. With the help of radar and camera devices, the system can intelligently identify and count the passing vehicles, thereby generating the current traffic information. The current traffic information includes key information such as lane-level traffic flow, density, and average speed, and also covers vehicle driving trajectory information, such as basic data such as license plates and vehicle models. In addition, the system can also monitor and record various traffic events occurring on the target road in real time.

[0088] Specifically, on the cloud platform side, the lane-level traffic flow, density, and speed data are statistically summarized to form a detailed traffic condition analysis of each road network sub-section, including traffic flow, density (time occupancy and space occupancy), and average speed, etc.

[0089] In addition, the cloud platform also supports Internet-side map navigation service providers to access real-time traffic condition data to supplement sections of the target road that are not covered by the sensing points.

[0090] There are two different methods for obtaining the energy consumption information of each vehicle driving on the target road:

[0091] Exemplarily, for electric vehicles in the vehicle network, the vehicle can upload its own energy consumption information, such as driving range, power consumption per 100 kilometers, charging time, etc., to the edge computing center through the vehicle network.

[0092] Exemplarily, for electric vehicles without network connection, clustering fitting can be performed on them. By using the energy consumption information of the reported electric vehicles, the power consumption curve (average speed - power consumption per 100 kilometers curve) of the vehicle categories can be obtained. The vehicle categories are mainly divided into small and medium-sized sedans, small and medium-sized SUVs, large sedans, and large SUVs. According to the predicted and fitted driving route, at a certain sampling distance (such as 1 KM), the energy consumption of each sub-sampling section is discretely calculated, and finally the energy consumption information of different vehicle types on the target road is obtained. For example, the position - energy consumption curves of different vehicle types are drawn.

[0093] Exemplarily, the information reported by electric vehicle a through the network platform shows that this vehicle is a medium-sized SUV, and its energy consumption per 100 kilometers is 20 degrees of electricity. If it is detected by edge devices such as radar cameras that another electric vehicle b also belongs to the medium-sized SUV category, based on the existing data, it can be reasonably inferred that the energy consumption per 100 kilometers of electric vehicle b may also be 20 degrees of electricity. It should be understood that this inference is based on the similarity assumption of the energy consumption of the same type of vehicles. The actual energy consumption may vary due to factors such as the specific configuration of the vehicle and driving habits, but the differences are within a similar range.

[0094] 402. Generate a traffic situation prediction based on the current traffic information.

[0095] Please refer to Figure 5 , Figure 5 which is a schematic diagram of the processing flow of the integrated perception subsystem on the cloud platform side. The edge computing unit on the edge side collects the detailed vehicle flow information of each section, real-time traffic event information, and the driving trajectory information of each vehicle, and constructs a high-speed road network model on the cloud platform side. Combining the real-time road condition data provided by the map navigation service provider on the Internet side, the cloud platform performs microscopic traffic simulation and spatio-temporal data engine processing to generate a traffic situation prediction for a future period of time.

[0096] In a possible implementation, the traffic situation prediction includes short-term prediction of the normal traffic flow and prediction of sudden traffic events. For the normal traffic flow, the traffic situation prediction algorithm model of the cloud control platform is enabled, and the section-level flow density and speed data and the high-speed road network topology model are used as data inputs for AI inference calculation to output the predicted traffic situation data within a short time range.

[0097] For example, within the next 30 minutes, section A will encounter traffic congestion. For electric vehicles driving before section A, if their remaining cruising range is not sufficient to cope with the additional energy consumption during the congestion, they should be advised to choose a detour route or drive into a nearby charging area in advance for charging to ensure that they can reach the destination smoothly.

[0098] In a possible implementation, the map navigation accessed from the Internet side can also provide the driving behavior patterns of the drivers, providing a reference for the traffic situation prediction.

[0099] 403. Generate a scheduling strategy based on traffic situation prediction, energy consumption information of each vehicle, and infrastructure information.

[0100] Please refer to Figure 6 , Figure 6 which is a schematic diagram of the processing flow of the charging scheduling subsystem on the cloud platform side. After obtaining the traffic situation prediction of the target road, combined with the energy consumption information of each vehicle collected and inferred by the edge computing unit, and infrastructure information including the aforementioned grid load information, charging pile information, and mobile charging vehicle information, a scheduling strategy is generated through group behavior decision analysis.

[0101] The group behavior decision analysis pre-integrates group behavior analysis algorithms, takes all electric vehicles on the current road as the overall research object, and based on the traffic situation prediction and the energy consumption information of each vehicle, evaluates and predicts the charging behavior decision probability in the subsequent driving path. Through multiple rounds of simulation iteration, a charging behavior distribution map of each electric vehicle can be generated. Combining the current charging status and operating status of each service area, predicting the future charging load and average charging waiting time of each service area, and formulating a global scheduling strategy.

[0102] Specifically, the scheduling strategy uses the Monte Carlo method to perform strategic intervention and adjustment on some vehicles according to the group simulation results to ensure the smoothness of the overall traffic and the satisfaction of charging requirements.

[0103] Exemplarily, the optimal charging interval of a single vehicle covers multiple service area charging stations. Through multi-vehicle differential selection, vehicles can charge in different service areas, achieving the effect of the shortest waiting time. For example, when electric vehicle c faces a charging choice, it has two service areas to choose from: Service Area A, which is 5 km away, and Service Area B, which is 10 km away. If only considering the interests of electric vehicle c itself, obviously, choosing the nearer Service Area A seems to be the best choice because it helps to save driving distance and time. However, considering the charging behavior decisions of multiple electric vehicles comprehensively, it is possible that a large number of electric vehicles tend to choose Service Area A for charging. Then, the charging queue length of Service Area A will increase rapidly. In contrast, the charging queue length of Service Area B may be relatively loose. In this case, considering the overall efficiency and the minimization of waiting time, adjusting the charging decision of electric vehicle c to choose to go to Service Area B for charging can not only reduce its waiting time during the charging process, but also help to balance the charging demand between the two service areas, thereby optimizing the overall charging efficiency.

[0104] 404. Send the scheduling strategy.

[0105] The scheduling strategy includes the charging advice information of the target electric vehicle, which can be pushed by the edge device to the central control system of the networked electric vehicle, or the charging advice information can be sent through the Internet navigation platform and the charging pile management platform.

[0106] In addition, to cope with the short-term surge in charging demand, the scheduling strategy also includes the charging capacity expansion information of the target charging area. For example, Figure 6 as shown, the charging control module of the cloud platform can also be connected to the edge computing unit, and then sent to the corresponding terminal device (i.e., the mobile charging vehicle) to temporarily expand the service area with a large future load.

[0107] Furthermore, by integrating the historical data of the cloud platform, an operation strategy for each charging area is generated. For example, Figure 7 as shown, Figure 7 is a schematic diagram of the processing flow of the operation optimization subsystem on the cloud platform side. By analyzing the historical traffic information and historical operation information, operation optimization suggestions are given, and these suggestions are provided to the charging operation module for adjustment.

[0108] Exemplarily, the historical traffic information includes the historical traffic flow distribution, driving trajectory distribution, etc.; the historical operation information includes the historical charging pile utilization rate, historical service area queuing status, etc.

[0109] Exemplarily, by analyzing the historical traffic information of the target road, the core characteristics such as the traffic flow change, congestion occurrence time period and location of this road can be obtained. At the same time, by combining the historical operation data of multiple charging areas, such as key indicators such as the queuing duration during charging and the actual usage frequency of charging facilities, it can be judged whether the existing charging facilities can meet the current charging demand, and whether there are problems such as uneven resource allocation or facility idle waste.

[0110] Specifically, the process of data analysis is to process this data by operation experts, including mathematical statistical pre-analysis, data rule mining, manual data review, data visualization, etc. By combining optimization means such as adjusting the number of charging piles and charging rules, the operating income of the service area is maximized.

[0111] Optionally, a single-objective nonlinear programming model is used for optimal solution to give operation optimization suggestions. Some operation optimization suggestions can be sent through the cloud platform after being confirmed by operation personnel, such as: dynamic electricity price and competitive bidding reservation charging function.

[0112] In the embodiments of the present application, by integrating the traffic situation prediction of the target road into the calculation process of the scheduling strategy, it not only adapts to the normal traffic conditions, but also can flexibly respond to sudden traffic conditions, and can more accurately give the energy consumption curve of electric vehicles, improving the granularity of the adjustment of the charging scheduling strategy and making it more accurate and efficient. When formulating the charging scheduling strategy, the method of group behavior analysis is adopted, effectively avoiding the local optimal trap that may be caused by the optimal strategy of a single vehicle, thus reducing the congestion phenomenon in a small number of service areas, making the pressure distribution of each service area more balanced, and improving the utilization efficiency of charging facilities. In addition, the present application also collaborates with operation data experts to mine data value and continuously provide operation suggestions. This collaborative cooperation not only gives full play to the long-term benefits of the system, but also can timely adjust the operation strategy through the negative feedback mechanism to ensure the continuous optimization of the overall operation.

[0113] The method provided by the embodiments of the present application has been introduced in detail above. Next, the device for executing the above method provided by the embodiments of the present application will be introduced.

[0114] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of a charging scheduling device provided by an embodiment of the present application. As Figure 8 shown, the charging scheduling device provided by the embodiments of the present application includes:

[0115] An acquisition module 801, configured to acquire the current traffic information of the target road, the energy consumption information of each vehicle, and the infrastructure information of multiple charging areas through an edge device. The multiple charging areas are the charging areas within a preset distance range of the target road, and the infrastructure information is used to indicate the power supply load situation of the multiple charging areas;

[0116] A prediction module 802, configured to generate a traffic situation prediction according to the current traffic information. The traffic situation prediction is used to represent the traffic conditions of the target road within a first time period, and the first time period is the time period after the current time;

[0117] A processing module 803, configured to generate a scheduling strategy according to the traffic situation prediction, the energy consumption information of each vehicle, and the infrastructure information. The scheduling strategy is used to indicate the target charging areas of multiple electric vehicles. The target charging areas are the charging areas among the multiple charging areas that are used to provide charging services for electric vehicles;

[0118] A sending module 804, configured to send the scheduling strategy.

[0119] In a possible implementation manner, the current traffic information includes the vehicle passing flow information, traffic event information, and driving trajectory information of each section of the target road.

[0120] In a possible implementation, the obtaining module 801 is specifically configured to: collect the energy consumption information of the first electric vehicle through an edge device, and / or calculate the energy consumption information of the second electric vehicle according to the current traffic information.

[0121] In a possible implementation, the processing module 803 is specifically configured to: calculate a charging decision for the third electric vehicle during subsequent driving according to the traffic situation prediction and the energy consumption information of each vehicle, where the third electric vehicle is any one of multiple electric vehicles; generate a first distribution according to the charging decisions of the multiple electric vehicles, where the first distribution is used to represent the vehicle distribution in multiple charging areas during a second time period; make a decision adjustment to the charging decisions of the multiple electric vehicles according to the infrastructure information and the first distribution to obtain a second distribution, where the overall charging queue duration corresponding to the vehicle distribution represented by the second distribution is less than the overall charging queue duration corresponding to the vehicle distribution represented by the first distribution; generate a scheduling strategy according to the second distribution.

[0122] In a possible implementation, the scheduling strategy includes: charging recommendation information for the first electric vehicle, where the charging recommendation information is used to indicate the recommended charging area for the first electric vehicle, and the first electric vehicle is any one of multiple electric vehicles.

[0123] In a possible implementation, the scheduling strategy further includes: charging capacity expansion information for the target charging area, where the charging capacity expansion information is used to indicate the deployment of temporary charging facilities in the target charging area.

[0124] In a possible implementation, the obtaining module 801 is further configured to: obtain the historical traffic information of the target road and the historical operation information of multiple charging areas, where the historical operation information is used to represent the charging queue situation of the multiple charging areas and the usage situation of the charging facilities.

[0125] The processing module 803 is further configured to generate an operation strategy according to the historical traffic information and the historical operation information, where the operation strategy is used to update the number and / or billing rules of the charging facilities in the multiple charging areas.

[0126] In a possible implementation, the infrastructure information includes at least one of the following: grid load information, charging pile information, and mobile charging vehicle information.

[0127] In a possible implementation, the target road is a highway.

[0128] Among them, both the obtaining module and the processing module can be implemented by software or by hardware. Exemplarily, next, taking the obtaining module as an example, the implementation manner of the obtaining module is introduced. Similarly, the implementation manner of the processing module can refer to the implementation manner of the obtaining module.

[0129] As an example of a software functional unit, the acquisition module may include code running on a computing instance. The computing instance may include at least one of a physical host (computing device), a virtual machine, and a container. Further, the above computing instance may be one or more. For example, the acquisition module may include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers for running the code may be distributed in the same region or in different regions. Further, the multiple hosts / virtual machines / containers for running the code may be distributed in the same availability zone (AZ) or in different AZs, and each AZ includes one data center or multiple geographically proximate data centers. Usually, one region may include multiple AZs.

[0130] Similarly, the multiple hosts / virtual machines / containers for running the code may be distributed in the same virtual private cloud (VPC) or in multiple VPCs. Usually, one VPC is set within one region. For cross-region communication between two VPCs within the same region and between VPCs in different regions, a communication gateway needs to be set in each VPC, and the interconnection between VPCs is achieved through the communication gateway.

[0131] As an example of a hardware functional unit, the acquisition module may include at least one computing device, such as a server. Alternatively, the acquisition module may also be a device implemented using a central processing unit (CPU), or may be implemented using an application-specific integrated circuit (ASIC), a programmable logic device (PLD), etc. The above PLD may be implemented by a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), a data processing unit (DPU), a neural network processing unit (NPU), a system on chip (SoC), an offload card, an acceleration card, or any combination thereof.

[0132] The multiple computing devices included in the acquisition module may be distributed in the same region or in different regions. The multiple computing devices included in the acquisition module may be distributed in the same AZ or in different AZs. Similarly, the multiple computing devices included in the acquisition module may be distributed in the same VPC or in multiple VPCs. Among them, the multiple computing devices may be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, GALs, DPUs, NPUs, SoCs, offloading cards, and acceleration cards.

[0133] It should be noted that in other embodiments, the acquisition module may be used to execute any step in the charging scheduling method, and the processing module may be used to execute any step in the charging scheduling method. The steps to be implemented by the acquisition module and the processing module can be specified as needed. The entire function of the charging scheduling device is realized by separately implementing different steps in the charging scheduling method through the acquisition module and the processing module.

[0134] This application also provides a computing device 100. As Figure 9 shown, the computing device 100 includes: a bus 102, a processor 104, a memory 106, and a communication interface 108. The processor 104, the memory 106, and the communication interface 108 communicate with each other through the bus 102. The computing device 100 may be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computing device 100.

[0135] The bus 102 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 only one line is shown in the figure, but it does not mean that there is only one bus or one type of bus. The bus 104 may include a path for transmitting information between various components (such as the memory 106, the processor 104, and the communication interface 108) of the computing device 100.

[0136] The processor 104 may include any one or more of processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0137] The memory 106 may include volatile memory, such as random access memory (RAM). The processor 104 may also include non-volatile memory, such as read-only memory (ROM), flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0138] The memory 106 stores executable program code, and the processor 104 executes the executable program code to implement the functions of the aforementioned acquisition module, prediction module, processing module, and sending module respectively, so as to implement the charging scheduling method. That is to say, the memory 106 stores instructions for executing the charging scheduling method.

[0139] Alternatively, the memory 106 stores executable code, and the processor 104 executes the executable code to implement the functions of the aforementioned charging scheduling device respectively, so as to implement the charging scheduling method. That is to say, the memory 106 stores instructions for executing the charging scheduling method.

[0140] The communication interface 108 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement the communication between the computing device 100 and other devices or communication networks.

[0141] The embodiment of the present application also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device may be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device may also be a terminal device such as a desktop computer, a laptop computer, or a smart phone.

[0142] As Figure 10 shown, the computing device cluster includes at least one computing device 100. The memory 106 in one or more of the computing devices 100 in the computing device cluster may store the same instructions for executing the charging scheduling method.

[0143] In some possible implementation manners, the memory 106 of one or more of the computing devices 100 in the computing device cluster may also store partial instructions for executing the charging scheduling method respectively. In other words, a combination of one or more computing devices 100 may jointly execute the instructions for executing the charging scheduling method.

[0144] It should be noted that the memories 106 in different computing devices 100 in the computing device cluster may store different instructions, which are respectively used to execute partial functions of the charging scheduling device. That is to say, the instructions stored in the memories 106 in different computing devices 100 can implement the functions of one or more devices in the acquisition module and the processing module.

[0145] In some possible implementation manners, one or more computing devices in the computing device cluster may be connected through a network. Among them, the network may be a wide area network or a local area network, etc. Figure 11 A possible implementation manner is shown. As Figure 11 shown, two computing devices 100A and 100B are connected through a network. Specifically, they are connected to the network through the communication interfaces in each computing device. In this type of possible implementation manner, the memory 106 in the computing device 100A stores instructions for executing the function of the acquisition module. At the same time, the memory 106 in the computing device 100B stores instructions for executing the functions of the prediction module, the processing module, and the sending module.

[0146] It should be understood that Figure 11 the functions of the computing device 100A shown in

[0147] This application embodiment also provides a computer program product containing instructions. The computer program product may be software or a program product containing instructions that can run on a computing device or be stored in any available medium. When the computer program product runs on at least one computing device, it causes at least one computing device to execute the charging scheduling method.

[0148] This application embodiment also provides a computer-readable storage medium. The computer-readable storage medium may be any available medium that a computing device can store or a data storage device such as a data center containing one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc. The computer-readable storage medium includes instructions that instruct the computing device to execute the charging scheduling method.

[0149] In addition, it should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A charging scheduling method based on Internet of Things technology, characterized in that The method is applied to a cloud platform, which is connected to edge devices on a target road. The method includes: Obtaining, by the edge devices, current traffic information of the target road, energy consumption information of multiple electric vehicles, and infrastructure information of multiple charging areas. The multiple charging areas are charging areas within a preset distance range of the target road, and the infrastructure information is used to indicate the power supply load conditions of the multiple charging areas; Generating a traffic situation prediction according to the current traffic information. The traffic situation prediction is used to represent the traffic conditions of the target road within a first time period, and the first time period is a time period after the current time; Generating a scheduling strategy according to the traffic situation prediction, the energy consumption information, and the infrastructure information. The scheduling strategy is used to indicate a target charging area for a target electric vehicle. The target charging area is a charging area among the multiple charging areas that provides charging services to the target electric vehicle, and the target electric vehicle is at least one electric vehicle among the multiple electric vehicles; Sending the scheduling strategy.

2. The method according to claim 1, wherein The current traffic information includes vehicle passing flow information, traffic event information, and driving trajectory information of each vehicle on each section of the target road.

3. The method according to claim 1 or 2, characterized in that, The obtaining of the energy consumption information of multiple electric vehicles includes: Collecting, by the edge devices, the energy consumption information of the multiple electric vehicles, and / or calculating the energy consumption information of the multiple electric vehicles according to the current traffic information.

4. The method according to any one of claims 1 to 3, characterized in that, The generating of the scheduling strategy according to the traffic situation prediction, the energy consumption information, and the infrastructure information includes: Calculating a charging decision for each electric vehicle among the multiple electric vehicles during subsequent driving according to the traffic situation prediction and the energy consumption information; Generating a first distribution according to the charging decision. The first distribution is used to represent the vehicle distribution situation in the multiple charging areas within the first time period; Making a decision adjustment to the charging decisions of the multiple electric vehicles according to the infrastructure information and the first distribution to obtain a second distribution. The overall charging queue duration corresponding to the vehicle distribution situation represented by the second distribution is less than the overall charging queue duration corresponding to the vehicle distribution situation represented by the first distribution; Generating the scheduling strategy according to the second distribution.

5. The method according to any one of claims 1 to 4, characterized in that The scheduling strategy includes: charging recommendation information for a first electric vehicle. The charging recommendation information is used to indicate a recommended charging area for the first electric vehicle, and the first electric vehicle is any one of the target electric vehicles.

6. The method according to claim 5, wherein The scheduling strategy further includes: charging capacity expansion information for the target charging area. The charging capacity expansion information is used to indicate the deployment of temporary charging facilities in the target charging area.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Obtaining historical traffic information of the target road and historical operation information of the multiple charging areas. The historical operation information is used to represent the charging queue situation of the multiple charging areas and the usage situation of charging facilities; Generate an operation strategy based on the historical traffic information and the historical operation information, where the operation strategy is used to update the number and / or billing rules of the charging facilities in the multiple charging areas.

8. The method according to any one of claims 1-7, characterized in that The infrastructure information includes at least one of the following: grid load information, charging pile information, and mobile charging vehicle information.

9. The method according to any one of claims 1-8, characterized in that, The target road is a highway.

10. A charging scheduling device based on Internet of Things technology, characterized in that, The device is applied to a cloud platform, and the cloud platform is connected to edge devices on the target road. The device includes: An acquisition module, configured to obtain the current traffic information of the target road, the energy consumption information of multiple electric vehicles, and the infrastructure information of multiple charging areas through the edge devices. The multiple charging areas are the charging areas within a preset distance range of the target road, and the infrastructure information is used to indicate the power supply load conditions of the multiple charging areas; A prediction module, configured to generate a traffic situation prediction according to the current traffic information, where the traffic situation prediction is used to represent the traffic conditions of the target road within a first time period, and the first time period is a time period after the current time; A processing module, configured to generate a scheduling strategy according to the traffic situation prediction, the energy consumption information, and the infrastructure information. The scheduling strategy is used to indicate the target charging area of the target electric vehicle, and the target charging area is the charging area among the multiple charging areas that provides charging services to the target electric vehicle. The target electric vehicle is at least one electric vehicle among the multiple electric vehicles; A sending module, configured to send the scheduling strategy.

11. The device according to claim 10, characterized in that, The current traffic information includes the vehicle passing flow information, traffic event information, and driving trajectory information of each vehicle on each section of the target road.

12. The device according to claim 10 or 11, characterized in that, The acquisition module is specifically configured to: Collect the energy consumption information of the multiple electric vehicles through the edge devices, and / or calculate the energy consumption information of the multiple electric vehicles according to the current traffic information.

13. The device according to any one of claims 10 to 12, characterized in that, The processing module is specifically configured to: Calculate the charging decision of each electric vehicle among the multiple electric vehicles during subsequent driving according to the traffic situation prediction and the energy consumption information; Generate a first distribution according to the charging decision, where the first distribution is used to represent the vehicle distribution situation in the multiple charging areas within the first time period; Make a decision adjustment to the charging decisions of the multiple electric vehicles according to the infrastructure information and the first distribution to obtain a second distribution, where the overall charging queue duration corresponding to the vehicle distribution situation represented by the second distribution is less than the overall charging queue duration corresponding to the vehicle distribution situation represented by the first distribution; Generate the scheduling strategy according to the second distribution.

14. The device according to any one of claims 10-13, characterized in that, The scheduling strategy includes: charging recommendation information for a first electric vehicle, where the charging recommendation information is used to indicate the recommended charging area of the first electric vehicle, and the first electric vehicle is any one of the target electric vehicles.

15. The device according to claim 14, characterized in that, The scheduling strategy further includes: charging expansion information for the target charging area, where the charging expansion information is used to indicate the deployment of temporary charging facilities in the target charging area.

16. The device according to any one of claims 10-15, characterized in that, The acquisition module is further configured to: Obtain the historical traffic information of the target road and the historical operation information of the multiple charging areas, where the historical operation information is used to represent the charging queuing situation of the multiple charging areas and the usage situation of charging facilities; The processing module is further configured to generate an operation strategy according to the historical traffic information and the historical operation information, where the operation strategy is used to update the quantity and / or charging rules of the charging facilities in the multiple charging areas.

17. The device according to any one of claims 10-16, characterized in that, The infrastructure information includes at least one of the following: grid load information, charging pile information, and mobile charging vehicle information.

18. The device according to any one of claims 10-17, characterized in that, The target road is an expressway.

19. A charging scheduling device, characterized in that, The device includes at least one processor, coupled to a memory; The memory is used to store programs or instructions; The at least one processor is configured to execute the programs or instructions to enable the device to implement the method according to any one of claims 1 to 9.

20. A cluster of computing devices, characterized in that, Includes at least one computing device, and each computing device includes a processor and a memory; The processor of the at least one computing device is configured to execute the instructions stored in the memory of the at least one computing device, so that the computing device cluster executes the method according to claims 1-9.

21. A computer program product comprising instructions, characterized in that, When the instructions are run by the computing device cluster, the computing device cluster executes the method according to any one of claims 1-9.

22. A computer-readable storage medium, characterized in that, Includes computer program instructions, and when the computer program instructions are executed by the computing device cluster, the computing device cluster executes the method according to any one of claims 1-9.

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