An automatic driving vehicle scheduling method, system, device and storage medium

Abstract

The application discloses an automatic driving vehicle scheduling method, system, device and storage medium, relates to the technical field of vehicle scheduling, and has the technical scheme as follows: the scheduling method comprises the following steps: acquiring the generated cargo quantity of each cargo loading and unloading point per unit time and the maximum cargo quantity carried by the vehicle at a time; a plurality of cargo loading and unloading points matched with the maximum cargo quantity carried by the vehicle are taken as order sharing points; the real-time generated cargo quantity of each order sharing point is acquired; when the sum of the real-time generated cargo quantities of the order sharing points meets the vehicle scheduling task threshold condition, the current cargo quantity of each order sharing point is locked, and the vehicle is scheduled to load cargo at each order sharing point in turn. Through the scheduling method, the vehicle can be reasonably scheduled according to the cargo quantity, multiple point order sharing transportation can be realized, the vehicle carrying capacity can be maximized, and the operation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle scheduling, in particular to an automatic driving vehicle scheduling method, system, device and storage medium. BACKGROUND

[0002] This section is intended to provide background or context to the embodiments recited in the claims. The description herein does not constitute admission that the prior art is prior art nor does it constitute an admission of any description in this section as prior art to an application described herein and infringed thereby.

[0003] The current automatic driving policy in specific scenarios such as parks and ports has few restrictions, and as labor costs in China gradually increase, the demand for automatic driving landing applications in specific scenarios is gradually increasing. How to optimize the scheduling of automatic driving vehicles to improve operational efficiency has become a research hotspot in the industry.

[0004] The existing scheduling scheme mainly includes park unmanned express delivery and park unmanned intelligent car-hailing system. The park unmanned express delivery vehicle is used to solve the last mile problem of logistics distribution. Usually, after the express personnel load the express pieces at the park entrance, the corresponding information is sent to the cloud data center, the data center generates the password of each password cabinet and the automatic delivery time and the station owner's mobile phone. The unmanned express delivery vehicle can automatically deliver goods to the owner's door according to the fixed time line or the reserved time line. The owner can open the cabinet and take the goods by himself through the password generated by the system. The park unmanned intelligent car-hailing service usually sends a vehicle scheduling request to the automatic driving vehicle management cloud through the mobile terminal. The cloud automatically plans the route according to the location of the idle vehicle and the current location of the user. Through the cloud, order management, user travel management and other functions can be realized. However, the actual amount of goods at each logistics point is different, while the number of vehicles allocated to each logistics point is consistent, which leads to the fact that the carrying capacity of the vehicle is not fully utilized, the vehicle scheduling and distribution is unreasonable, and thus the operational efficiency of the automatic driving vehicle is affected. SUMMARY

[0005] In view of the above technical problems, the present application provides an automatic driving vehicle scheduling method, system, device and storage medium, which can reasonably schedule vehicles according to the amount of goods and realize multi-point order transportation, maximize the utilization of vehicle carrying capacity, and has the effect of improving operational efficiency.

[0006] To solve the above technical problems, the technical scheme adopted by the present application includes five aspects.

[0007] In a first aspect, an automatic driving vehicle scheduling method is provided, comprising the following steps:

[0008] Obtaining the amount of goods generated by each goods loading and unloading point per unit time, and the maximum amount of goods carried by the vehicle per single time;

[0009] a plurality of goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the pickup points;

[0010] the real-time generated goods quantity of each pickup point is obtained;

[0011] when the sum of the real-time generated goods quantity of each pickup point meets the vehicle scheduling task threshold condition, the current goods quantity of each pickup point is locked, and the vehicle is dispatched to each pickup point in turn to load goods.

[0012] In some embodiments, the plurality of goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the pickup points, comprising:

[0013] the single round-trip operation time of the vehicle and the number of vehicles are obtained;

[0014] the maximum carrying capacity of all vehicles per unit time is determined according to the number of vehicles and the single round-trip operation time;

[0015] the goods quantity generated per unit time by each goods loading and unloading point is added in turn, and when the sum of the added goods quantity is less than or equal to the maximum carrying capacity of all vehicles per unit time, the added goods loading and unloading point is selected as the pickup point.

[0016] In some embodiments, the plurality of goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the pickup points, which meet the following conditions:

[0017]

[0018] wherein Yi is the goods quantity generated per unit hour by the i-th pickup point; N is the number of pickup points; t is the single round-trip operation time of the vehicle; K is the number of vehicles; Q max is the maximum carrying capacity of the vehicle.

[0019] In some embodiments, the vehicle scheduling task threshold condition is:

[0020]

[0021] Max(X′ i )≥K2;

[0022] wherein is the sum of the goods quantity of each pickup point to be allocated to the vehicle; K1 and K2 are constants, K2 is greater than K1 / 2, and K1≤Q max .

[0023] In some embodiments, when the sum of the real-time goods quantity of each pickup point meets the vehicle scheduling task threshold condition, the current goods quantity of each pickup point is locked, and the vehicle is dispatched to each pickup point in turn to load goods, comprising:

[0024] The goods information of the pickup point is updated in real time when the vehicle starts, until the vehicle reaches the pickup point and completes loading;

[0025] The real-time updated goods information is continuously compared with the triggering threshold condition, and a vehicle is added when the triggering threshold condition is met.

[0026] In some embodiments, the continuously comparing the real-time updated goods information with the triggering threshold condition, and adding a vehicle when the triggering threshold condition is met includes:

[0027] When the following conditions are met, a second vehicle is added:

[0028]

[0029] When the following conditions are met, the Mth vehicle is added:

[0030]

[0031]

[0032] In the formula, Q is the sum of the goods quantity of each pickup point to be allocated to the vehicle; K1 and K2 are constants, K2 is greater than K1 / 2 in the specific value process, and K1≤Q max J is the number of times of locking the loading quantity.

[0033] In some embodiments, the vehicle starts, the goods information of the pickup point is updated in real time, until the vehicle reaches the pickup point and completes loading, including:

[0034] When the vehicle goes to the pickup point and has not arrived, and when the vehicle arrives at the pickup point and has not completed loading, updating the goods information includes: Locking the loading quantity when the first vehicle is allocated to the i pickup point; The goods quantity of the i pickup point to be allocated to the transport vehicle; The sum of the goods quantity of each pickup point to be allocated to the vehicle.

[0035] In some embodiments, the vehicle is dispatched to go to each pickup point to load in turn, including:

[0036] Arranging the locked goods quantity of each pickup point in turn from large to small;

[0037] Controlling the vehicle to run from the pickup point with more goods quantity to the pickup point with less goods quantity in turn;

[0038] When the goods quantity of each pickup point is consistent, controlling the vehicle to run to the closest pickup point.

[0039] In a second aspect, the present application provides an automatic driving vehicle scheduling system, comprising:

[0040] a sensor arranged at a cargo loading and unloading point, configured to detect cargo quantity change information and real-time cargo quantity;

[0041] a logistics information management platform connected to the sensor, configured to count real-time cargo quantity of each cargo loading and unloading point;

[0042] a vehicle operation management platform connected to the logistics information management platform, configured to execute the steps of the vehicle scheduling method as described above.

[0043] In a third aspect, the present application provides a vehicle scheduling control device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to execute the steps of the vehicle scheduling method as described above.

[0044] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program executable by one or more processors, and the computer program is used to implement the steps of the vehicle scheduling method as described above.

[0045] In a fifth aspect, the present application provides a computer program product, comprising computer programs / instructions, wherein the computer programs / instructions are executed by a processor to implement the steps of the vehicle scheduling method as described above.

[0046] Compared with the prior art, one or more embodiments of the above scheme can have the following advantages or beneficial effects:

[0047] The present application provides an automatic driving vehicle scheduling method, system, device and storage medium, the vehicle scheduling method comprising: acquiring cargo quantity generated by each cargo loading and unloading point per unit time, and maximum cargo carrying capacity of the vehicle per time; taking a plurality of cargo loading and unloading points matched with the maximum cargo carrying capacity of the vehicle as a single point; acquiring real-time cargo quantity generated by each single point; when the sum of the real-time cargo quantity generated by each single point meets the vehicle scheduling task threshold condition, locking the current cargo quantity of each single point, and scheduling the vehicle to load cargo at each single point in turn. Through the vehicle scheduling method, multiple cargo loading and unloading points can be combined for transportation, the number of automatic driving vehicles can be reasonably allocated, the carrying capacity of the vehicle can be maximized, the driving mileage of the vehicle can be reduced, and energy consumption can be saved; at the same time, the vehicle is allocated according to the actual cargo quantity of each cargo loading and unloading point, each link is smoothly connected, the operation efficiency of the automatic driving vehicle can be improved, and the overall work efficiency can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings;

[0049] Figure 1 a flowchart of an embodiment of the automatic driving vehicle scheduling method of the present application;

[0050] Figure 2 an exemplary flowchart corresponding to step S2 shown in Figure 1

[0051] Figure 3 an exemplary flowchart corresponding to step S4 shown in Figure 1

[0052] Figure 4 a further exemplary flowchart corresponding to step S4 shown in Figure 1

[0053] Figure 5 a schematic block diagram of an embodiment of the automatic driving vehicle scheduling system of the present application;

[0054] Figure 6 a schematic block diagram of an embodiment of the control device of the present application;

[0055] Figure 7 a schematic diagram of an embodiment of the computer readable storage medium of the present application.

[0056] In the drawings, the same components are designated by the same reference numerals, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0057] The present disclosure will be further described below with reference to the embodiments shown in the drawings.

[0058] The existing park unmanned intelligent car-hailing service is usually that a user sends a vehicle scheduling request to a vehicle management cloud of an automatic driving vehicle through a mobile terminal, the cloud automatically plans a route according to the positions of idle vehicles and the current position of the user, and the cloud can realize order management and user travel management functions at the same time. However, the actual cargo quantities of various logistics points are different, and the number of vehicles allocated to each logistics point is consistent, which leads to that the carrying capacity of the vehicles is not fully utilized and the vehicle scheduling and distribution are unreasonable, and further affects the operation efficiency of the automatic driving vehicle.

[0059] Embodiments of the present application disclose an automatic driving vehicle scheduling method, system, device and storage medium, such as Figure 1 ​​​As shown, the vehicle scheduling method comprises: acquiring the amount of goods generated by each goods loading and unloading point per unit time, and the maximum amount of goods carried by the vehicle per time; matching a plurality of goods loading and unloading points with the maximum amount of goods carried by the vehicle as a single point; acquiring the real-time amount of goods generated by each single point; when the sum of the real-time amount of goods generated by each single point meets the vehicle scheduling task threshold condition, locking the current amount of goods of each single point, and scheduling the vehicle to load goods at each single point in turn. Through the vehicle scheduling method, multiple goods loading and unloading points can be matched for transportation, the number of automatic driving vehicles can be reasonably allocated, the carrying capacity of the vehicle can be maximized, the driving mileage of the vehicle can be reduced, and energy consumption can be saved. At the same time, the vehicle is allocated according to the actual amount of goods of each goods loading and unloading point, each link is smoothly connected, the operation efficiency of the automatic driving vehicle can be improved, and the overall work efficiency can be improved.

[0060] Some embodiments of the present disclosure also provide a vehicle scheduling system, a vehicle scheduling control device, a storage medium and a computer program product corresponding to the above-mentioned vehicle scheduling method.

[0061] At least one embodiment of the present disclosure provides an automatic driving vehicle scheduling method, which can be implemented in software, hardware, firmware or any combination thereof, loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a network server, etc., so as to allocate vehicles according to the actual amount of goods of each goods loading and unloading point, smoothly connect each link, improve the operation efficiency of the automatic driving vehicle, and improve the overall work efficiency.

[0062] Reference will be made to the following Figure 1 As shown, the automatic driving vehicle scheduling method provided by at least one embodiment of the present disclosure is described, which comprises steps S1 to S4.

[0063] S1, acquiring the amount of goods generated by each goods loading and unloading point per unit time, and the maximum amount of goods carried by the vehicle per time.

[0064] In some embodiments, the amount of goods can be the number of goods, the quality of goods or the volume of goods, only one of which is changed as a point, in this embodiment, the goods of each goods loading and unloading point are consistent by default, only the number of goods is changed as a point, and the number of goods is taken as a limiting condition for vehicle single loading and unloading. The logistics information management platform is used to statistically calculate the goods information generated by each goods loading and unloading point in real time, so as to calculate the number of goods generated by each goods loading and unloading point per unit time, and combine the maximum amount of goods carried by each automatic driving vehicle per time, so as to efficiently schedule the automatic driving vehicle, reasonably schedule the vehicle, and improve the overall work efficiency.

[0065] S2, several goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the order sharing point.

[0066] In some embodiments, as shown in Figure 2 the several goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the order sharing point, including:

[0067] S21, the single round-trip operation time of the vehicle and the number of vehicles are obtained;

[0068] S22, according to the number of vehicles and the single round-trip operation time, the maximum carrying capacity of all vehicles in unit time is determined;

[0069] S23, the amount of goods generated by each goods loading and unloading point in unit time is sequentially superimposed, and when the superimposed sum is less than or equal to the maximum carrying capacity of all vehicles in unit time, the goods loading and unloading point being superimposed is selected as the order sharing point.

[0070] In some embodiments, it is assumed that K autonomous vehicles are equipped, and the maximum carrying capacity of each autonomous vehicle is Q max Therefore, the several goods loading and unloading points matching the maximum carrying capacity of the vehicle are selected as the order sharing point, which satisfies the following conditions:

[0071]

[0072] In the formula, Yi is the amount of goods generated by the i-th order sharing point in unit time; N is the number of order sharing points; t is the single round-trip operation time of the vehicle; K is the number of vehicles; is the maximum carrying capacity of the vehicle.

[0073] S3, the amount of goods generated by each of the order sharing points in real time is obtained.

[0074] In some embodiments, a sensor for recording the change information and real-time quantity information of the goods is installed on each goods loading and unloading point, and the goods of each goods loading and unloading point are consistent by default, only the quantity is changed, so that the above two feedbacks of the goods detected by the sensor are fed back to the logistics information management platform for real-time statistics, and further fed back to the autonomous vehicle operation management platform, so as to dispatch each vehicle according to the quantity of the goods.

[0075] S4, when the sum of the amount of goods generated by each of the order sharing points in real time satisfies the vehicle dispatching task threshold condition, the current amount of goods of each order sharing point is locked, and the vehicle is dispatched to each order sharing point in turn to load goods.

[0076] In some embodiments, in order to fully utilize the carrying capacity of each vehicle, while avoiding the accumulation of goods at each single point of goods loading and unloading, the vehicle is dispatched to the pickup point for loading when the sum of the real-time goods quantity of each pickup point meets the vehicle dispatching task threshold condition. The vehicle dispatching task threshold condition is:

[0077]

[0078] Max(X i ′ )≥K2;

[0079] In the formula, is the sum of the goods quantity of each pickup point to be allocated to the vehicle; K1 and K2 are constants, K2 is greater than K1 / 2, and K1≤Q max .

[0080] In some embodiments, in order to facilitate the pickup and transportation of each pickup point, as shown in Figure 3 , the vehicle is dispatched to each pickup point in turn for loading, including:

[0081] S41, arranging the goods quantity of each pickup point in descending order;

[0082] S42, controlling the vehicle to run from the pickup point with the most goods quantity to the pickup point with the least goods quantity;

[0083] S43, when the goods quantity of each pickup point is consistent, controlling the vehicle to run to the closest pickup point.

[0084] The running route of the dispatched vehicle follows the principle of preferentially loading the pickup point with the most goods quantity. If the goods quantity is equal, the pickup point where the vehicle arrives first is preferentially loaded, so as to reasonably plan the driving route of the autonomous vehicle and improve the overall operation efficiency.

[0085] In some embodiments, when the vehicle is dispatched to each pickup point for loading, the goods quantity of each pickup point is locked, so that after the vehicle is dispatched, the goods quantity of each pickup point is further updated in real time. As shown in Figure 4 , step S4 includes:

[0086] S401, when the vehicle is dispatched, the goods information of the pickup point is updated in real time until the vehicle arrives at the pickup point and completes loading;

[0087] S402, continuously comparing the updated goods information with the trigger threshold condition, and adding a dispatched vehicle when the trigger threshold condition is met.

[0088] In some embodiments, when the vehicle is dispatched, the goods information of the pickup point is updated in real time until the vehicle arrives at the pickup point and completes loading, including:

[0089] When the vehicle goes to the order point and has not arrived, and when the vehicle arrives at the order point and has not completed loading, the updated cargo information includes:

[0090] The loading amount locked when the first vehicle is assigned to the i-order point;

[0091] The cargo quantity waiting for transportation vehicle assignment for the i-order point;

[0092] The sum of the cargo quantities waiting for vehicle assignment for each order point.

[0093] Through the above information update, the vehicle dispatching task threshold condition is repeatedly substituted, so that a vehicle is dispatched under the condition that the dispatching task threshold condition is met.

[0094] In some embodiments, the real-time updated cargo information includes three stages from the start of the vehicle to the completion of loading by the vehicle arriving at the order point.

[0095] In the first stage, the vehicle goes to the order point and has not arrived at the order point, at which time the real-time cargo quantity of each order point is still in production, and the logistics information management platform updates the real-time data of each order point to X i The automatic driving vehicle operation management platform updates the cargo quantity of the order point to:

[0096] (1) The loading amount locked when the first automatic driving vehicle is assigned to the i-order point:

[0097] (2) The cargo quantity waiting for transportation vehicle assignment for the i-order point:

[0098] (3) The sum of the cargo quantities waiting for transportation vehicle assignment for each order point:

[0099] In the second stage, the vehicle arrives at the order point for loading and has not completed loading; the newly added cargo quantity in the first stage is denoted as ΔX i At this time, the logistics information management platform updates the cargo quantity information of each order point to X i = X i + ΔX i The automatic driving vehicle operation management platform updates the data as in the first stage.

[0100] In the third stage, the vehicle completes loading, at which time the i-order point jth vehicle completes loading and is assigned a value of zero, and other data is updated as in the first and second stages.

[0101] In some embodiments, the real-time updated goods information is continuously compared with the triggering threshold condition, and when the triggering threshold condition is met, the newly added dispatch vehicle includes:

[0102] When the following conditions are met, a second vehicle is newly added:

[0103]

[0104] X i ′ ≥K2;

[0105] When the following conditions are met, the Mth vehicle is newly added:

[0106]

[0107]

[0108] wherein, is the sum of the goods quantity of each order-matching point to be allocated to the vehicle; K1 and K2 are constants, K2 is greater than K1 / 2, and K1≤Q max ; j is the number of times of locking the task loading quantity.

[0109] By continuously performing step S4, complete automation of task dispatching is achieved, ensuring that the entire business operates in a closed loop until all transportation tasks are completed, and after completing the tasks, each autonomous vehicle is dispatched back to the initial point.

[0110] In some embodiments, a charging pile and a parking space are provided at the initial point of the vehicle, so that after the autonomous vehicle completes the task and returns to the initial point, it can automatically park in the parking space for scheduling and use for the next task dispatch.

[0111] The autonomous vehicle scheduling method provided by the embodiments of the present disclosure reasonably calculates the goods quantity of each goods loading and unloading point to confirm the order-matching point, thereby performing order-matching transportation for each order-matching point, reasonably allocating the number of autonomous vehicles, and maximizing the carrying capacity of the vehicle, reducing the vehicle mileage and saving energy consumption; at the same time, the vehicle is allocated according to the actual goods quantity of each goods loading and unloading point, realizing smooth connection of each link, which can improve the operation efficiency of the autonomous vehicle and improve the overall work efficiency.

[0112] At least some embodiments of the present disclosure also provide an autonomous vehicle scheduling system, as shown in Figure 5 The vehicle scheduling system includes:

[0113] The sensor 11 provided at the goods loading and unloading point is used to detect the goods quantity change information and the real-time goods quantity;

[0114] The logistics information management platform 12 connected with the sensor 11 is used to count the real-time cargo quantity of each cargo loading and unloading point.

[0115] The vehicle operation management platform 13 connected with the logistics information management platform 12 is used to execute the steps of the vehicle scheduling method provided by any embodiment of the present disclosure.

[0116] In some embodiments, the cargo quantity is the cargo number, the cargo of each cargo loading and unloading point is consistent by default, and only the number is a single change point. The sensor 11 and the logistics information management platform 12 can be connected through network communication, the logistics information management platform 12 and the vehicle operation management platform 13 can be connected through network communication, and the vehicle operation management platform 13 and the autonomous vehicle can be connected through a wireless communication network, facilitating real-time transmission of various data. The network connection can use a 5G network to improve data transmission speed and ensure overall operation efficiency.

[0117] In some embodiments, the sensor 11 uses an infrared sensor in cooperation with a counter to monitor the passing of cargo in real time by installing an infrared sensor at the cargo loading and unloading point, and connects the infrared sensor with the counter, so that the infrared sensor feeds back a signal to the counter for counting each time a cargo passes, thereby feeding back the cargo information to the logistics information management platform 12 for statistical calculation, and then feeding back to the vehicle operation management platform 13 for calculation to allocate autonomous vehicles to go to each single point for loading, reasonably schedule vehicles, and improve overall operation efficiency.

[0118] In some embodiments, by linking and matching the information between the vehicle, the logistics information management platform, and the vehicle operation management platform, each link is smoothly connected, so that the vehicle can perform single transportation between multiple points, maximize the carrying capacity of the vehicle, reasonably schedule vehicles, not only save the number of vehicles used, but also reduce the vehicle mileage, reduce operation cost and save energy, and greatly improve the work efficiency of the entire vehicle scheduling use.

[0119] At least some embodiments of the present disclosure also provide a vehicle scheduling control device, as shown in the figure, which includes a memory 21 and a processor 22. The memory 21 stores a computer program, and the processor executes the computer program to perform the steps of the vehicle scheduling method provided by any embodiment of the present disclosure. Figure 6

[0120] In some embodiments, the processor 22 is used to execute all or part of the steps in the vehicle scheduling method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may, for example, include instructions of any application program or method in the electronic device, and application program related data.​

[0121] The processor 22 can be implemented by an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic elements, for executing the application management method in the above embodiment one.

[0122] The memory 21 can be implemented by any type of volatile or nonvolatile memory device or a combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0123] The present disclosure at least some embodiments further provide a computer readable storage medium, such as a floppy disk, a CD-ROM, a DVD, a Blu-ray disc, a hard disk, or a solid state disk, or any other magnetic, optical, or electronic device, which stores computer programs 31. Figure 7 As shown, the computer readable storage medium stores computer programs 31, which, when executed by a processor, implement the steps of the vehicle dispatching method provided by any embodiment of the present disclosure.

[0124] In some embodiments, a storage medium can be a tangible medium which can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. Machine-readable media can be machine-readable signal media or machine-readable storage media. Machine-readable media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include one or more lines of electrical wire, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0126] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0128] The embodiment of the present application also provides a computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of the vehicle scheduling method provided by any one of the embodiments of the present application.

[0129] To sum up, the automatic driving vehicle scheduling method, system, device and storage medium provided by the present application can confirm the pickup point by reasonably calculating the cargo quantity of each cargo loading and unloading point, combine the real-time updated cargo information of the pickup point, and perform pickup transportation for each pickup point, reasonably allocate the number of automatic driving vehicles, maximize the carrying capacity of the vehicles, reduce the driving mileage of the vehicles, save energy consumption, and realize smooth connection of each link according to the actual cargo quantity of each cargo loading and unloading point to allocate the vehicles, thereby improving the operation efficiency of the automatic driving vehicles and improving the overall work efficiency.

[0130] Each of the embodiments in the present disclosure is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the difference from other embodiments.

[0131] The scope of protection of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various modifications and changes to the present disclosure without departing from the scope and spirit of the present disclosure. If these modifications and changes belong to the scope of the claims of the present disclosure and its equivalent technologies, the present disclosure also includes these modifications and changes.

Claims

1. A method for dispatching an autonomous driving vehicle, characterized in that: The following steps are involved: Obtain the cargo volume generated per unit time at each cargo loading and unloading point, as well as the maximum cargo volume carried by the vehicle in a single trip; Several cargo loading and unloading points that match the vehicle's maximum cargo capacity will be used as consolidation points; Obtain the real-time quantity of goods generated at each of the group ordering points; When the sum of the cargo volumes generated in real time by each of the group ordering points meets the vehicle dispatching task threshold condition, the current cargo volumes of each group ordering point are locked, and the vehicles are dispatched to each group ordering point in turn to load the cargo; Among them, the said several cargo loading and unloading points that match the maximum cargo carrying capacity of the vehicle as the order consolidation points include: Obtain the vehicle's single round trip operation time and the number of vehicles; when the sum of the real-time cargo volume generated by each of the aforementioned order-sharing points meets the vehicle dispatch task threshold condition Determine the maximum carrying capacity of all vehicles per unit time based on the number of vehicles and the single round trip operation time; The cargo volumes generated per unit time at each cargo loading and unloading point are sequentially added together. When the sum of the sums is less than or equal to the maximum carrying capacity of all vehicles per unit time, the added cargo loading and unloading points are selected as the consolidation points. The cargo loading and unloading points that match the maximum cargo capacity of the vehicle are used as the consolidation points and meet the following conditions: Where Y i is the volume of goods produced per hour at the i-place; N is the number of places where the order is placed; t is the time it takes for a vehicle to make a single round trip; is the number of vehicles; is the maximum carrying capacity of the vehicle; The vehicle dispatch task threshold condition is: Where, The sum of the cargo volumes to be allocated to vehicles at each consolidation point; , is a constant, Greater than / 2, ≤ .

2. The method for dispatching an autonomous driving vehicle according to claim 1, wherein: When the sum of the cargo volumes generated in real time by each of the group ordering points meets the vehicle dispatching task threshold condition, the current cargo volumes of each group ordering point are locked, and the vehicles are dispatched to each group ordering point in turn to load the cargo, including: When the vehicle starts, the cargo information of the order placement point is updated in real time until the vehicle arrives at the order placement point and the loading is completed; The real-time updated cargo information is continuously compared with the trigger threshold conditions, and new vehicles are dispatched when the trigger threshold conditions are met.

3. The method for dispatching an autonomous driving vehicle according to claim 2, wherein: The continuous comparison of the real-time updated cargo information with the trigger threshold conditions and the dispatch of new vehicles when the trigger threshold conditions are met includes: A second vehicle is added when the following conditions are met: When the following conditions are met, the Mth vehicle is added: Where, The sum of the cargo volumes to be allocated to vehicles at each consolidation point; , is a constant, Greater than / 2, ≤ ; j is the number of times the task loading quantity is locked.

4. The method for dispatching an autonomous driving vehicle according to claim 2, wherein: When the vehicle starts, the cargo information of the order placement point is updated in real time until the vehicle arrives at the order placement point and completes loading, including: When a vehicle is heading to a group order point but has not yet arrived, or when a vehicle arrives at a group order point but has not yet completed loading, the updated cargo information includes: for The loading quantity locked when the first vehicle is allocated to the order placement point; for The quantity of goods to be assigned to transport vehicles at the consolidation point; It is the sum of the quantity of goods to be assigned to vehicles at each group ordering point.

5. The method for dispatching an autonomous driving vehicle according to claim 1, wherein: The dispatching of the vehicles to the various order-matching points for loading the goods in sequence includes: Arrange the cargo quantities locked at each consolidation point in descending order; Control vehicles to run from the points with large cargo volumes to the points with small cargo volumes; When the cargo quantities at each order-collecting point are the same, the vehicle is controlled to run to the closest order-collecting point.

6. An autonomous driving vehicle dispatching system, characterized in that: include: Sensors installed at cargo loading and unloading points are used to detect cargo volume changes and real-time cargo volume; A logistics information management platform connected to sensors to count the real-time cargo volume at each loading and unloading point; A vehicle operation management platform connected to a logistics information management platform is used to execute the steps of the vehicle scheduling method as described in any one of claims 1 to 5.

7. A vehicle dispatching control device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the automatic driving vehicle scheduling method as described in any one of claims 1 to 5 are performed.

8. A computer-readable storage medium, characterized in that The computer program stored in the storage medium can be executed by one or more processors, and the computer program can be used to implement the steps of the autonomous driving vehicle scheduling method as described in any one of claims 1 to 5.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of the autonomous driving vehicle scheduling method as described in any one of claims 1 to 5 are implemented.

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