Vehicle scheduling method and electronic equipment

Abstract

The invention discloses a vehicle scheduling method and electronic equipment, and the method comprises the steps: determining a demand index of a target region according to a real-time demand and a historical demand of the target region; determining traffic characteristics of the target area according to the type of the target area and traffic facilities in the area; optimizing the health degree of the target area through a convolution algorithm; and scheduling the vehicle according to the demand index, the traffic characteristic and the health degree.

Description

Technical Field

[0001] This application belongs to the field of transportation, and specifically relates to a method and electronic equipment for dispatching vehicles. Background Technology

[0002] With the acceleration of urbanization, vehicle dispatching, such as that of ride-hailing services, suffers from several technical shortcomings. These include the inability of dispatching algorithms to dynamically optimize based on real-time traffic conditions, a lack of flexibility in vehicle dispatching routes, and the underutilization of traffic flow. These shortcomings result in low operational efficiency of ride-hailing systems in densely populated urban areas, impacting passenger travel experience and exacerbating urban traffic congestion.

[0003] Currently, while many ride-hailing dispatch systems have introduced dispatch strategies based on historical data, most systems have not fully considered dynamic traffic data, regional health, and the application of V2X technology. Therefore, existing dispatch systems have not effectively solved the problems of low vehicle dispatch efficiency and inaccurate route planning in large urban environments. Summary of the Invention

[0004] The purpose of this application is to provide a method and electronic device for dispatching vehicles, which can effectively solve the problems of low vehicle dispatching efficiency and inaccurate route planning in large urban environments.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for dispatching vehicles, the method comprising: determining a demand index for a target area based on real-time and historical demand; determining traffic characteristics of the target area based on the type of the target area and the traffic facilities within the area; optimizing the health of the target area using a convolution algorithm; and dispatching vehicles based on the demand index, the traffic characteristics, and the health.

[0006] In a second aspect, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0007] Thirdly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0008] Fourthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0009] In the embodiments of this application, the dispatching system has not effectively solved the problems of low vehicle dispatching efficiency and inaccurate route planning in large urban environments. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a method for scheduling vehicles according to an embodiment of this application; Figure 2 This is a schematic diagram of a vehicle dispatching system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the region weighting provided in an embodiment of this application; Figure 4 This is a schematic flowchart of a vehicle dispatching device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of another electronic device provided in an embodiment of this application. Detailed Implementation

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0013] The embodiments of this application will be described in detail below with reference to the accompanying drawings and specific examples and application scenarios.

[0014] Figure 1 This diagram illustrates a flowchart of the query method provided in an embodiment of this application. Figure 1 As shown, the method includes: S102: Determine the demand index of the target area based on the real-time and historical demand of the target area.

[0015] like Figure 2As shown, the intelligent ride-hailing dispatch system proposed in this application based on V2X technology aims to improve urban traffic efficiency, optimize vehicle dispatch routes, reduce empty mileage, and alleviate urban traffic congestion. The system architecture consists of three layers: platform layer, edge layer, and terminal layer. The responsibilities and functions of each layer are as follows: The terminal layer includes the onboard terminals equipped in each ride-hailing vehicle. These terminals acquire the real-time status of the vehicle through devices such as GPS positioning modules, V2X communication modules, and onboard sensors, and upload this information to the edge layer. The terminal layer also receives dispatch instructions from the edge layer and executes vehicle route planning and scheduling.

[0016] The edge layer, supported by intelligent devices such as traffic lights, road condition monitoring, and ride-hailing vehicles, is responsible for real-time data collection and preprocessing, providing real-time updates on information such as traffic flow and regional health. Leveraging V2X communication technology, the edge layer can exchange information with vehicles in real time, ensuring that each vehicle can respond quickly and receive dispatch instructions.

[0017] The platform layer is responsible for optimizing and managing scheduling strategies, and undertakes big data analysis and computation. By storing and analyzing historical data, the platform layer can achieve demand forecasting over long time scales and formulate scheduling strategies based on this forecast data. Simultaneously, the platform layer is also responsible for receiving real-time data from the edge layer and terminal layer for further analysis and decision-making.

[0018] A city's transportation network is divided into multiple smaller zones, the size of which can be adjusted according to the city's specific circumstances. Typically, this zoning is based on the following factors: Traffic flow: The density of road capacity, traffic signals, and other facilities within a region.

[0019] Regional functions: such as commercial areas, residential areas, entertainment venues, etc. Different areas have different travel needs and transportation modes.

[0020] Historical demand data: Travel data over a certain period of time can reflect changes in transportation demand during a specific time period.

[0021] The step of determining the demand index of the target area based on the real-time and historical demand of the target area includes: determining the demand index of the target area based on the weighted sum of the real-time and historical demand of the target area.

[0022] Regional Demand Index It is derived by calculating the weighted sum of real-time demand and historical demand within the region. The specific formula is:

[0023] Among them Traffic flow weighting coefficient This refers to real-time traffic data (such as the average traffic flow of vehicles over a certain period of time). The coefficient representing historical demand. This refers to historical demand data (such as the number of trips within a certain period in the past).

[0024] The data collected by the actual traffic monitoring system is used for real-time calculations. For example, during peak hours, traffic flow in a commercial area increases rapidly, while during off-peak hours, traffic flow is relatively low. Therefore, the regional demand index can reflect the dynamics of travel demand in various areas in real time.

[0025] For example: Suppose the real-time traffic flow of a certain commercial area. The historical demand data is 500 vehicles per hour. Assuming a traffic flow weight of 1000 passengers per hour in this area. The historical demand coefficient is 0.7. If the value is 0.3, then the demand index for this region can be expressed as:

[0026] S104: Determine the traffic characteristics of the target area based on its type and the traffic facilities within the area.

[0027] This step may include: determining the weight of the target area based on its function; and determining the traffic characteristics of the target area based on its weight.

[0028] The transportation convenience of the target area is determined based on the distribution of public transportation within the target area.

[0029] Based on Fourier series, determine the changes in traffic flow over different time periods.

[0030] The purpose of regional characteristic assessment is to evaluate the transportation characteristics and adaptability of a region based on its different types and public transportation facilities. Regional type weights, public transportation convenience, and time characteristics are key factors in calculating regional health.

[0031] Each region is assigned a different weight based on its function. The formula is as follows:

[0032] in, Functional factors representing the business district Functional factors representing residential areas These represent the functional factors of entertainment venues. For example... Figure 3 As shown.

[0033] The accessibility of a region is assessed by calculating the distribution of public transportation (subway, bus, shared bicycles, etc.). The formula is as follows:

[0034] in, , and These represent the number of subway cars, buses, and shared bicycles, respectively. , , This is for adjusting the coefficient.

[0035] To account for the impact of time variations on demand, we can use Fourier series to simulate traffic flow changes over different time periods. The formula is as follows:

[0036] The formula uses a periodic... Internally, it combines multiple frequency components. Time is modeled to predict traffic demand at different times.

[0037] S106: Optimize the health of the target region using a convolution algorithm.

[0038] By using different convolution kernels, different types of target regions are subjected to convolution processing according to their type, in order to optimize the health of the target regions.

[0039] In this system, the convolution algorithm is used to calculate the health of a region. In particular, during the health assessment of a region, the convolution operation can effectively capture the mutual influence and spatial characteristics between regions.

[0040] Regional Health Matrix We can smooth regional features by applying convolution kernels to optimize the health relationships between regions. By using convolution kernels, we can perform weighted averaging of the health data for each region, thereby reflecting the mutual influence between regions.

[0041] Specific convolution kernels The design is as follows, with a size of 3×3:

[0042] Each element of the convolution kernel This represents a weighting coefficient, which, after training, can be dynamically adjusted according to the actual situation of the region. Through convolution operations, we can effectively smooth regional health data, thereby more accurately predicting regional demand and traffic conditions.

[0043] For example: Suppose we have a regional health matrix and convolution kernel The new health matrix is ​​obtained through convolution operation. Weighted smoothing can effectively eliminate noise and obtain a more accurate estimate of regional health.

[0044] To further optimize the calculation of regional health scores, this scheme employs a categorical convolution method. The convolution kernels for different regions can be adjusted based on the region type. For example, for commercial areas, the kernel weights are more weighted towards the impact of traffic flow; while for residential areas, the kernel weights are more weighted towards the impact of public transportation convenience. This categorical convolution method can refine the calculation of health scores based on the different characteristics of each region, thereby improving the adaptability and accuracy of scheduling strategies.

[0045] The ride-hailing dispatch system of this invention mainly consists of the following core components to achieve functions such as dynamic dispatching, real-time traffic flow prediction, regional health optimization, and dynamic dispatching instruction generation for vehicles. These components work together to ensure the system's efficient operation and real-time response.

[0046] Kafka is used to cache dynamic scheduling message queues in various regions of the ride-hailing dispatch system. The vehicle terminals, edge computing nodes, and platform layer in the system communicate asynchronously through Kafka, ensuring the timely processing and transmission of dispatch instructions, vehicle status information, and health data. Kafka improves the system's write performance, ensuring that dispatch messages and alarm information can still be recorded and processed efficiently and stably under high concurrency.

[0047] Redis is used to cache real-time health data for ride-hailing areas. Because the dispatch system has high real-time requirements, Redis, as an in-memory database, can quickly respond to system read requests, ensuring the stability and timeliness of area health data and vehicle locations even under rapidly changing conditions. Furthermore, Redis's caching mechanism reduces the pressure on the database, further improving the overall system performance.

[0048] HBase is primarily used to store vehicle behavior data collected by in-vehicle terminals. This includes vehicle location information, speed information, traffic flow change data, etc. Due to the large volume and rapid growth of vehicle data, HBase, as a distributed storage system, can efficiently handle large-scale data read and write operations, ensuring the storage and fast access of historical data.

[0049] Spark Streaming is used for real-time stream processing and data analysis of vehicle behavior data stored in HBase. Through Spark Streaming's streaming capabilities, the system can monitor vehicle dynamics in real time, quickly respond to changes in regional health and traffic flow fluctuations, and automatically adjust scheduling strategies or regional health data. This real-time analysis module supports instant detection of abnormal events, such as abnormal fluctuations in traffic flow or vehicle shortages in a specific area during a certain period, thereby providing the platform with accurate scheduling optimization suggestions.

[0050] Through the collaborative work of these core components, the ride-hailing dispatch system of the present invention can realize real-time monitoring and processing of multi-dimensional data such as vehicle dynamics, regional demand, and traffic flow, thereby achieving efficient and intelligent dynamic dispatch in different regions and time periods, and improving the overall efficiency and responsiveness of ride-hailing services.

[0051] S108: Dispatch vehicles according to the demand index, the traffic characteristics, and the health status.

[0052] This application's embodiments are based on real-time data acquisition and scheduling optimization using V2X communication: V2X technology is used to acquire data in real time from multiple vehicles, monitoring equipment, and infrastructure for intelligent scheduling within the region. Dynamic regional health assessment and convolutional calculation: Scheduling efficiency is optimized using block convolutional algorithms based on multi-dimensional data such as regional traffic flow and occupancy rate. Real-time traffic and demand forecasting: Scheduling strategies are adjusted in real time, comprehensively considering various modes of transportation and travel demand within the region.

[0053] The embodiments of this application have the following beneficial effects: 1. Improve dispatch efficiency: Optimize dispatch strategies through real-time traffic data and regional health analysis to improve the response speed and efficiency of ride-hailing services.

[0054] 2. Reduce traffic congestion: Allocate vehicles to high-demand areas to avoid excessive concentration of vehicles or idle vehicles, thereby reducing the risk of traffic congestion.

[0055] 3. High adaptability: The system can flexibly adjust the scheduling strategy according to the traffic characteristics of different cities and regions, and has good scalability and adaptability.

[0056] 4. Save resources: Dynamic scheduling avoids resource waste and improves the system's resource utilization rate.

[0057] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described query method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0058] Figure 4 The diagram shows the structure of the device 400 provided in the embodiment of this application. The device 400 includes: a first determining module 110, a second determining module 120, a processing module 130, and a scheduling module 140.

[0059] The first determining module 110 determines the demand index of the target area based on the real-time and historical demand of the target area; The second determining module 120 determines the traffic characteristics of the target area based on the type of the target area and the traffic facilities within the area; Processing module 130 optimizes the health of the target region using a convolution algorithm; The dispatch module 140 dispatches vehicles based on the demand index, the traffic characteristics, and the health status.

[0060] Optionally, determining the demand index of the target area based on real-time and historical demand includes: The demand index for the target area is determined by weighting the real-time demand and historical demand for the target area.

[0061] Optionally, determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: The weight of the target region is determined based on its function. The traffic characteristics of the target area are determined based on the weight of the target area.

[0062] Optionally, determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: The transportation convenience of the target area is determined based on the distribution of public transportation within the target area.

[0063] Optionally, determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: Based on Fourier series, determine the changes in traffic flow over different time periods.

[0064] Optionally, optimizing the health of the target region using a convolution algorithm includes: By using different convolution kernels, different types of target regions are subjected to convolution processing according to their type, in order to optimize the health of the target regions.

[0065] Figure 5The diagram illustrates the hardware structure of an electronic device implementing the embodiments of this application. Referring to the diagram, at the hardware level, the electronic device includes a processor and optionally, an internal bus, a network interface, and a memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0066] The processor, network interface, and memory can be interconnected via an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus.

[0067] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0068] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a device at the logical level that locates the target user. The processor executes the program stored in memory and specifically performs the following: Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0069] The above is as stated in this application. Figure 1The methods disclosed in the illustrated embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0070] The electronic device can also execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0071] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0072] This application also proposes a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0073] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0074] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0075] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for dispatching vehicles, characterized in that, The method includes: The demand index for the target area is determined based on the real-time and historical demand of the target area. Based on the type of the target area and the traffic facilities within the area, determine the traffic characteristics of the target area; The health of the target region is optimized using a convolution algorithm; Vehicles are dispatched based on the demand index, traffic characteristics, and health status.

2. The method according to claim 1, characterized in that, The process of determining the demand index of the target area based on its real-time and historical demand includes: The demand index for the target area is determined by weighting the real-time demand and historical demand for the target area.

3. The method according to claim 1, characterized in that, Determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: The weight of the target region is determined based on its function. The traffic characteristics of the target area are determined based on the weight of the target area.

4. The method according to claim 1, characterized in that, Determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: The transportation convenience of the target area is determined based on the distribution of public transportation within the target area.

5. The method according to claim 1, characterized in that, Determining the traffic characteristics of the target area based on its type and the traffic facilities within the area includes: Based on Fourier series, determine the changes in traffic flow over different time periods.

6. The method according to claim 1, characterized in that, The optimization of the health of the target region using a convolution algorithm includes: By using different convolution kernels, different types of target regions are subjected to convolution processing according to their type, in order to optimize the health of the target regions.

7. A device for dispatching vehicles, comprising: The first determining module determines the demand index of the target area based on the real-time and historical demand of the target area; The second determining module determines the traffic characteristics of the target area based on the type of the target area and the traffic facilities within the area. The processing module optimizes the health of the target region using a convolution algorithm; The scheduling module schedules vehicles based on the demand index, traffic characteristics, and health status.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, use the processor to perform the steps of the session method according to any one of claims 1-6.

9. A computer-readable medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the steps of the session method according to any one of claims 1-6.

10. A computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, perform the steps of the session method according to any one of claims 1-6.

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