Unmanned vehicle processing method and device, electronic equipment and storage medium

Abstract

The present disclosure provides unmanned vehicle processing methods and devices, electronic equipment and storage media, and relates to the fields of artificial intelligence such as automatic driving, deep learning and big data processing. The method can include: for each station in any unmanned vehicle area, obtaining historical call sheet distribution information of the station, current call sheet distribution information of the station and current vehicle distribution information of the station, and determining the weight of the station according to the obtained information; in response to determining that there is an idle vehicle in the unmanned vehicle area, determining a target station according to the weight of each station, and dispatching the idle vehicle to the target station. The scheme of the present disclosure can improve the vehicle utilization rate and the single rate.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of artificial intelligence, in particular to an unmanned vehicle processing method and device, an electronic device and a storage medium in the fields of automatic driving, deep learning and big data processing. BACKGROUND

[0002] The current vehicle capacity scheduling is mainly for manned scenarios with drivers. The drivers usually choose the best place to wait according to their experience. With the continuous development of technology, unmanned vehicles will be used more and more widely in different occasions. Accordingly, the capacity scheduling of unmanned vehicles will be involved. SUMMARY

[0003] The present disclosure provides an unmanned vehicle processing method, device, electronic device and storage medium.

[0004] An unmanned vehicle processing method comprises:

[0005] For each station in any unmanned vehicle area, historical call distribution information of the station, current call distribution information of the station and current vehicle distribution information of the station are acquired respectively, and the weight of the station is determined according to the acquired information.

[0006] In response to determining that there is an idle vehicle in the unmanned vehicle area, a target station is determined according to the weight of each station, and the idle vehicle is dispatched to the target station.

[0007] An unmanned vehicle processing device comprises a weight acquisition module and a vehicle dispatching module.

[0008] The weight acquisition module is configured to acquire, for each station in any unmanned vehicle area, historical call distribution information of the station, current call distribution information of the station and current vehicle distribution information of the station respectively, and determine the weight of the station according to the acquired information.

[0009] The vehicle dispatching module is configured to determine a target station according to the weight of each station in response to determining that there is an idle vehicle in the unmanned vehicle area, and dispatch the idle vehicle to the target station.

[0010] An electronic device comprises:

[0011] at least one processor; and

[0012] a memory connected with the at least one processor in communication; wherein

[0013] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0014] A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method as described above.

[0015] A computer program product comprising computer programs / instructions which, when executed by a processor, implement the method as described above.

[0016] It should be understood that the contents described in this part are not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are used to better understand the present scheme and do not limit the present disclosure. Among them:

[0018] Figure 1 A flowchart of the unmanned vehicle processing method embodiment of the present disclosure is shown.

[0019] Figure 2 A schematic diagram of the component structure of the unmanned vehicle processing device embodiment 200 of the present disclosure is shown.

[0020] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0021] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, and should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0022] In addition, it should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it.

[0023] Figure 1 A flowchart of the unmanned vehicle processing method embodiment of the present disclosure is shown. As Figure 1The embodiments are shown below.

[0024] In step 101, for each station in any unmanned vehicle area, the historical order distribution information of the station, the current order distribution information of the station, and the current vehicle distribution information of the station are respectively obtained, and the weight of the station is determined according to the obtained information.

[0025] In step 102, in response to determining that there is an idle vehicle in the unmanned vehicle area, a target station is determined according to the weight of each station, and the idle vehicle is dispatched to the target station.

[0026] The above method embodiment can determine the weight of different stations by combining historical order distribution information, current order distribution information, and current vehicle distribution information, and can dynamically schedule the carrying capacity of unmanned vehicles according to the weight, so as to reasonably allocate limited carrying capacity and maximize the value of limited carrying capacity, thereby improving vehicle utilization, order completion rate, and relieving traffic congestion pressure. The order can also be referred to as an order.

[0027] In actual application, for a city, the area of unmanned vehicles can be divided according to the park, and each unmanned vehicle area can include multiple stations for passengers to get on and off. For each unmanned vehicle area, the method described in the present disclosure can be used for processing.

[0028] Among them, for each station in each unmanned vehicle area, the weight of the station can be obtained. Specifically, for each station, the historical order distribution information of the station, the current order distribution information of the station, and the current vehicle distribution information of the station can be obtained, and the weight of the station can be determined according to the obtained information.

[0029] Preferably, for each station, the weight of the station can be determined according to the obtained information in the following ways:

[0030] I) The weight of the station is calculated according to the obtained information according to a predetermined calculation method;

[0031] II) The obtained information is input into a pre-trained prediction model to obtain the output weight of the station.

[0032] The specific way of using the above method can be determined according to actual needs, which is very flexible and convenient.

[0033] In the first mode, preferably, the sum of the historical call distribution information and the current call distribution information can be calculated first to obtain a first calculation result, and then the difference between the first calculation result and the current vehicle distribution information can be calculated to obtain a second calculation result, and the second calculation result is taken as the weight of the station.

[0034] That is, the weight of the station = historical call distribution information + current call distribution information - current vehicle distribution information.

[0035] In the second mode, a training sample can be constructed according to historical data, and then a prediction model can be trained according to the training sample. Accordingly, when actually predicting, for each station, the obtained information can be taken as the input of the prediction model, so as to obtain the output weight of the station.

[0036] After obtaining the weights of the stations, the weights can be cached for use in dynamic scheduling of transport capacity, and the weights of the stations can be updated periodically. The specific period can be determined according to actual needs.

[0037] Regardless of the first mode or the second mode, for each station, the historical call distribution information, the current call distribution information, and the current vehicle distribution information of the station need to be obtained. The following describes the obtaining method of each information.

[0038] 1) Historical call distribution information

[0039] Preferably, for each station in the area of the unmanned vehicle, the following processing can be performed respectively: the number of pending call orders of the station in the same predetermined time period in the last N days is obtained respectively, N is a positive integer greater than one, the predetermined time period is a time period including the current time, and the last N days do not include the current day; the sum of the number of pending call orders in the last N days is obtained as the first statistical result of the station; the sum of the first statistical results of the stations is obtained as the first comprehensive statistical result; and the ratio of the first statistical result of the station to the first comprehensive statistical result is obtained as the historical call distribution information of the station.

[0040] The specific length of the predetermined time period can be determined according to actual needs. For example, it can be 30 minutes. Accordingly, assuming that the current time is 4:15 pm, the predetermined time period can be the time period from 4:00 pm to 4:30 pm.

[0041] The specific value of N can also be determined according to actual needs, such as 7 days or more. Each call order includes a starting point (origin) and a terminal point (destination).

[0042] For example, for a site a, the number of pending call orders of site a in the time period of 4:00-4:30 pm of each of the last 7 days can be obtained, which is the number of pending call orders with site a as the starting point, and then the sum of the number of pending call orders of the 7 days can be obtained as the first statistical result of site a. Assuming that the unmanned vehicle area includes site a, site b, site c and site d, after obtaining the first statistical result of the four sites, the sum of the four first statistical results can be calculated as the first comprehensive statistical result. Taking site a as an example, the ratio (or percentage, etc.) of the first statistical result of site a to the first comprehensive statistical result can be calculated as the historical call order distribution information of site a.

[0043] In the above processing mode, the historical call order distribution information of each site can be efficiently and accurately determined in combination with the historical call order situation of each site in the past days.

[0044] 2) Current call order distribution information

[0045] Preferably, for each site, the number of pending call orders of the site at the current time can be obtained as the second statistical result of the site, and the sum of the second statistical results of the sites can be obtained as the second comprehensive statistical result.

[0046] The ratio of the second statistical result of the site to the second comprehensive statistical result can be obtained as the current call order distribution information of the site.

[0047] Similarly, the number of pending call orders refers to the number of pending call orders with the site as the starting point.

[0048] For example, for site a, the number of pending call orders of site a at the current time can be obtained, such as 10, and can be taken as the second statistical result of site a. Assuming that the unmanned vehicle area includes site a, site b, site c and site d, after obtaining the second statistical result of the four sites, the sum of the four second statistical results can be calculated as the second comprehensive statistical result. Taking site a as an example, the ratio of the second statistical result of site a to the second comprehensive statistical result can be calculated as the current call order distribution information of site a.

[0049] In the above processing mode, the current call order distribution information of each site can be efficiently and accurately determined in combination with the call order situation of each site at the current time.

[0050] The current call order distribution information of each site can be efficiently and accurately determined in combination with the call order situation of each site at the current time.

[0051] 3) Current vehicle distribution information

[0052] Preferably, for each station, the target vehicle located in the predetermined area can be obtained

[0053] The number of vehicles in the predetermined area as the third statistical result of the station, and the number of vehicles in the autonomous vehicle area as the third comprehensive statistical result can be obtained

[0054] The ratio of the third statistical result of the station to the third comprehensive statistical result can be obtained as the current vehicle distribution information of the station.

[0055] The specific size of the predetermined area is not limited, and can be determined according to actual needs, for example, it can be a circular area with a radius of 1 km centered on the station.

[0056] 5Taking station a as an example, the number of target vehicles located in the predetermined area can be obtained as

[0057] The third statistical result of station a, and the number of vehicles in the entire autonomous vehicle area, i.e. the total number of all vehicles, can be obtained as the third comprehensive statistical result, and then the ratio of the third statistical result of station a to the third comprehensive statistical result can be obtained, and the obtained ratio is taken as the current vehicle distribution information of station a.

[0058] In the above processing mode, the current vehicle distribution information of each station can be efficiently and accurately determined according to the target vehicle corresponding to each station and the number of vehicles in the entire autonomous vehicle area.

[0059] In addition, preferably, the size of the predetermined area corresponding to any two different stations is the same, or the size of the predetermined area corresponding to any two different stations is different.

[0060] That is, the size of the predetermined area corresponding to different stations can be different, such as a circular area with a radius of 1 km centered on the station, or the size of the predetermined area corresponding to one or more stations can be different from that of other stations, for example, a station is located near a subway station, the passenger flow is large, and the vehicle speed is slow, so the size of the predetermined area corresponding to it can be set smaller, so that the weight of the station determined is larger, and more idle vehicles can be dispatched to the station to relieve the traffic congestion pressure of the station, etc.

[0061] In addition, preferably, for each station, the target vehicle can include: idle vehicles located in the predetermined area, and non-idle vehicles located in the predetermined area and ending at the station.

[0062] The idle vehicle refers to a vehicle currently in an idle state. The idle vehicle located in a predetermined area and a non-idle vehicle located in the predetermined area and having a terminal point being the station can be taken as a target vehicle. The non-idle vehicle usually ends the processing of a current call sheet soon and can also be taken as a target vehicle of the station for statistics, so that the current vehicle distribution information of the station can be obtained more comprehensively and accurately.

[0063] The weight of the station reflects the balance between the call sheet and the vehicle. The smaller the weight of the station, the more matched the call sheet and the vehicle of the current station, and too much vehicle does not need to be dispatched additionally. Conversely, the larger the weight of the station, the more unmatched the call sheet and the vehicle of the current station, and a certain vehicle needs to be dispatched additionally.

[0064] Correspondingly, the way of determining the target station according to the weight of each station can include determining the station with the highest weight as the target station.

[0065] Specifically, when the idle vehicle appears in the area of the unmanned vehicle, the station with the highest weight can be determined as the target station, and a dispatch sheet can be generated correspondingly. According to the dispatch sheet, the idle vehicle can be dispatched to the target station. Because the target station theoretically has the highest demand for vehicle, correspondingly, dispatching the idle vehicle to the target station can improve the efficiency of passengers and the success rate of the call sheet, and can improve the utilization rate of the vehicle. At the same time, through the way of dispatching the vehicle in advance during the peak period of using the vehicle, the traffic congestion pressure can be relieved.

[0066] Because the weight of each station is dynamically changing, the target station determined refers to the station with the highest weight at the current time.

[0067] Each idle vehicle appearing in the area of the unmanned vehicle can be processed according to the above way. In addition, it should be noted that if the idle vehicle to be dispatched is originally located in the target station, it does not need to be dispatched, such as not generating a dispatch sheet for it. Conversely, a dispatch sheet needs to be generated for the idle vehicle to be dispatched to the target station.

[0068] Preferably, in the process of the idle vehicle going to the target station, in response to determining that there is a to-be-processed call sheet meeting the following condition: the distance between the starting point of the to-be-processed call sheet and the idle vehicle is less than the distance between the idle vehicle and the target station, the idle vehicle is dispatched to process the to-be-processed call sheet.

[0069] That is to say, for a certain idle vehicle, assuming that a dispatch sheet is generated for it, the idle vehicle also goes to the target station according to the dispatch sheet. However, in the process of going to the target station, there is a to-be-processed call sheet with a shorter distance that needs to be processed. The idle vehicle can be dispatched to process the to-be-processed call sheet with the shorter distance, so as to further improve the utilization rate of the vehicle and the success rate of the call sheet.

[0070] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are expressed as a combination of a series of actions, but those skilled in the art should know that the disclosure is not limited by the order of the described actions, because according to the disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the disclosure.

[0071] In summary, the scheme described in the method embodiments of the disclosure can realize dynamic scheduling of the carrying capacity of unmanned vehicles, thereby improving vehicle utilization, single formation rate, and relieving traffic congestion pressure, and can be applied to automatic driving travel scenarios and automatic driving logistics scenarios and various unmanned scenarios, and has wide applicability.

[0072] The above is an introduction to the method embodiments, and the following further describes the scheme described in the disclosure through device embodiments.

[0073] Figure 2 The constituent structure of the unmanned vehicle processing device embodiment 200 of the disclosure is shown in FIG. 2. As shown in FIG. 2, it includes a weight acquisition module 201 and a vehicle scheduling module 202. Figure 2

[0074] The weight acquisition module 201 is configured to acquire, for each station in any unmanned vehicle region, historical order distribution information of the station, current order distribution information of the station, and current vehicle distribution information of the station, and determine a weight of the station according to the acquired information.

[0075] The vehicle scheduling module 202 is configured to determine a target station according to the weight of each station in response to determining that there is an idle vehicle in the unmanned vehicle region, and schedule the idle vehicle to the target station.

[0076] The scheme described in the above device embodiments can determine the weight of different stations in combination with historical order distribution information, current order distribution information, and current vehicle distribution information and other information. Accordingly, the carrying capacity of unmanned vehicles can be dynamically scheduled according to the weight, so that the limited carrying capacity can be reasonably allocated to maximize the value of the limited carrying capacity, thereby improving vehicle utilization, single formation rate, and relieving traffic congestion pressure.

[0077] ​The weight obtaining module 201 can obtain the weight of each station in each unmanned vehicle area respectively. Specifically, for each station, the historical order distribution information of the station, the current order distribution information of the station and the current vehicle distribution information of the station can be obtained respectively, and then the weight of the station can be determined according to the obtained information.

[0078] Preferably, for each station, the weight obtaining module 201 can determine the weight of the station according to the obtained information in the following ways:

[0079] I) calculating the weight of the station according to the obtained information in a predetermined calculation manner;

[0080] II) taking the obtained information as the input of a pre-trained prediction model to obtain the output weight of the station.

[0081] In the first way, preferably, the weight obtaining module 201 can first calculate the sum of the historical order distribution information and the current order distribution information to obtain a first calculation result, and then calculate the difference between the first calculation result and the current vehicle distribution information to obtain a second calculation result, and take the second calculation result as the weight of the station.

[0082] That is, the weight of the station = historical order distribution information + current order distribution information - current vehicle distribution information.

[0083] In the second way, the weight obtaining module 201 can construct a training sample according to historical data, and then train a prediction model according to the training sample. Accordingly, when actually predicting, for each station, the obtained information can be taken as the input of the prediction model to obtain the output weight of the station.

[0084] After obtaining the weights of the stations, the weights can be cached for use in dynamic scheduling of transport capacity, and the weights of the stations can be updated periodically.

[0085] Whether the first way or the second way is adopted, for each station, the historical order distribution information, the current order distribution information and the current vehicle distribution information of the station need to be obtained, and the obtaining methods of the information are described below.

[0086] Preferably, the weight obtaining module 201 can obtain, for each station in the unmanned vehicle area, the number of pending call sheets of the station in the same predetermined time period in the last N days, N being a positive integer greater than one, the predetermined time period being a time period including the current time, and the last N days not including the current day; obtain the sum of the number of pending call sheets in the last N days as the first statistical result of the station; obtain the sum of the first statistical results of the stations as the first comprehensive statistical result; and obtain the ratio of the first statistical result of the station to the first comprehensive statistical result as the historical call sheet distribution information of the station.

[0087] Preferably, for each station, the weight obtaining module 201 can obtain the number of pending call sheets of the station at the current time as the second statistical result of the station, obtain the sum of the second statistical results of the stations as the second comprehensive statistical result, and further obtain the ratio of the second statistical result of the station to the second comprehensive statistical result as the current call sheet distribution information of the station.

[0088] Preferably, for each station, the weight obtaining module 201 can obtain the number of target vehicles meeting the requirements located in the predetermined area as the third statistical result of the station, the predetermined area being a predetermined size area centered on the station, obtain the number of vehicles in the unmanned vehicle area as the third comprehensive statistical result, and further obtain the ratio of the third statistical result of the station to the third comprehensive statistical result as the current vehicle distribution information of the station.

[0089] The specific size of the predetermined area is not limited and can be determined according to actual needs. For example, the predetermined area can be a circular area with a radius of 1 km centered on the station.

[0090] In addition, preferably, the sizes of the predetermined areas corresponding to any two different stations are the same, or the sizes of the predetermined areas corresponding to any two different stations are different.

[0091] In addition, preferably, for each station, the target vehicles can include idle vehicles located in the predetermined area and non-idle vehicles located in the predetermined area and having the station as the terminal.

[0092] Further, when the vehicle dispatching module 202 determines that there is an idle vehicle in the unmanned vehicle area, the vehicle dispatching module 202 can determine a target station according to the weights of the stations and dispatch the idle vehicle to the target station.

[0093] Preferably, the vehicle dispatching module 202 can determine the station with the highest weight as the target station.

[0094] Preferably, the vehicle scheduling module 202 can also schedule the idle vehicle to process a to-be-processed call in the process of the idle vehicle going to the target station, in response to determining that there is a to-be-processed call meeting the following condition: the distance between the starting point of the to-be-processed call and the idle vehicle is less than the distance between the idle vehicle and the target station.

[0095] Figure 2 The specific working process of the device embodiment shown can refer to the related description in the foregoing method embodiment, and will not be described here.

[0096] In summary, the scheme described in the device embodiment of the present disclosure can realize dynamic scheduling of the carrying capacity of unmanned vehicles, thereby improving the utilization rate of vehicles, the single formation rate, and relieving the pressure of traffic congestion, and can be applied to various unmanned scenarios such as automatic driving travel scenarios and automatic driving logistics scenarios, and has wide applicability.

[0097] The scheme described in the present disclosure can be applied to the field of artificial intelligence, and particularly relates to the fields of automatic driving, deep learning, and big data processing. Artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of people, and includes both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc. Artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, and several other major directions.

[0098] The calls and vehicles in the embodiments of the present disclosure are not directed to a specific user, and cannot reflect the personal information of a specific user. In the technical scheme of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information meet the requirements of relevant laws and regulations, and do not violate public order and good customs.

[0099] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium, and a computer program product.

[0100] Figure 3A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, servers, blades, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0101] As shown, Figure 3 The device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 302 or a computer program loaded into a random access memory (RAM) 303 from a storage unit 308. Various programs and data required for the operation of the device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0102] Various components in the device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; the storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0103] The computing unit 301 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs various methods and processes described above, such as the methods described in the present disclosure. For example, in some embodiments, the methods described in the present disclosure can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded onto the RAM 303 and executed by the computing unit 301, one or more steps of the methods described in the present disclosure can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the methods described in the present disclosure by any other appropriate means, such as by means of firmware.

[0104] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0105] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0106] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0107] 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.

[0108] 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.

[0109] 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 arises 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.

[0110] It should be understood that the various forms of flow shown above can be used to reorder, add, or remove steps. For example, the steps described in the present disclosure can be performed in parallel, in series, or in a different order, without limitation herein, so long as the desired results of the technology described in the present disclosure are achieved.

[0111] The specific implementation described above does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A unmanned vehicle processing method, comprising: obtaining, for each station in a unmanned vehicle area, historical call distribution information of the station, current call distribution information of the station, and current vehicle distribution information of the station, determining a weight of the station according to the obtained information, wherein the distribution information is distribution information in the unmanned vehicle area; the current call distribution information of the station is obtained by obtaining a number of pending calls of the station at a current time as a second statistical result of the station, obtaining a sum of the second statistical result of each station as a second comprehensive statistical result, and obtaining a ratio of the second statistical result of the station to the second comprehensive statistical result as the current call distribution information of the station; in response to determining that there is an idle vehicle in the unmanned vehicle area, determining a station with the highest weight, determining the station as a target station, and dispatching the idle vehicle to the target station; further comprising: in the process of the idle vehicle going to the target station, in response to determining that there is a pending call that meets the following condition: a distance between a starting point of the pending call and the idle vehicle is less than a distance between the idle vehicle and the target station, dispatching the idle vehicle to process the pending call.

2. The method of claim 1, wherein, the determining of the weight of the station according to the obtained information comprises: calculating the weight of the station according to the obtained information according to a predetermined calculation method; or, taking the obtained information as an input of a pre-trained prediction model to obtain an output weight of the station.

3. The method of claim 2, wherein, the calculating of the weight of the station according to the obtained information according to a predetermined calculation method comprises: calculating a sum of the historical call distribution information and the current call distribution information to obtain a first calculation result; calculating a difference between the first calculation result and the current vehicle distribution information to obtain a second calculation result, and taking the second calculation result as the weight of the station.

4. The method of any one of claims 1-3, wherein, the obtaining of the historical call distribution information of the station comprises: for each station in the unmanned vehicle area, the following processing is performed respectively: obtaining a number of pending calls of the station in a same predetermined time period in the last N days, N being a positive integer greater than one, the predetermined time period being a time period including a current time, and the last N days not including a current day; obtaining a sum of the number of pending calls in the last N days as a first statistical result of the station; obtaining a sum of the first statistical result of each station as a first comprehensive statistical result; obtaining a ratio of the first statistical result of the station to the first comprehensive statistical result as the historical call distribution information of the station.

5. The method of any one of claims 1-3, wherein, the obtaining of the current vehicle distribution information of the station comprises: obtaining a number of target vehicles that meet a requirement in a predetermined area as a third statistical result of the station, the predetermined area being a predetermined size area centered on the station; obtaining a number of vehicles in the unmanned vehicle area as a third comprehensive statistical result; Obtaining a ratio of the third statistical result of the station and the third comprehensive statistical result as current vehicle distribution information of the station.

6. The method of claim 5, wherein, The predetermined area corresponding to any two different stations has the same size. Or, the predetermined area corresponding to any two different stations has different sizes.

7. The method of claim 5, wherein, The target vehicle includes an idle vehicle located in the predetermined area, and a non-idle vehicle located in the predetermined area and having the station as a terminal.

8. An unmanned vehicle handling apparatus comprising: A weight obtaining module and a vehicle scheduling module; The weight obtaining module is configured to obtain, for each station in the unmanned vehicle area, historical call distribution information of the station, current call distribution information of the station, and current vehicle distribution information of the station, determine a weight of the station according to the obtained information, and obtain the distribution information in the unmanned vehicle area; wherein obtaining the current call distribution information of the station includes: obtaining a number of to-be-processed call orders of the station at a current time as a second statistical result of the station, obtaining a sum of the second statistical results of all stations as a second comprehensive statistical result, and obtaining a ratio of the second statistical result of the station and the second comprehensive statistical result as the current call distribution information of the station. The vehicle scheduling module is configured to, in response to determining that an idle vehicle appears in the unmanned vehicle area, determine a station with the highest weight, determine the station with the highest weight as a target station, and schedule the idle vehicle to the target station. The vehicle scheduling module is further configured to, in the process of the idle vehicle going to the target station, in response to determining that there is a to-be-processed call order meeting the following condition: a distance between the to-be-processed call order and the idle vehicle is less than a distance between the idle vehicle and the target station, schedule the idle vehicle to process the to-be-processed call order.

9. The apparatus of claim 8, wherein, The weight obtaining module calculates the weight of the station according to the obtained information in a predetermined calculation manner. Or, the weight obtaining module takes the obtained information as an input of a pre-trained prediction model to obtain an output weight of the station.

10. The apparatus of claim 9, wherein, The weight obtaining module calculates a sum of the historical call distribution information and the current call distribution information to obtain a first calculation result, calculates a difference between the first calculation result and the current vehicle distribution information to obtain a second calculation result, and takes the second calculation result as the weight of the station.

11. The apparatus of any one of claims 8-10, wherein, The weight obtaining module obtains, for each station in the unmanned vehicle area, the following: the number of pending call sheets of the station in the same predetermined time period in the last N days, N being a positive integer greater than one, the predetermined time period being a time period including the current time, and the last N days not including the current day; the sum of the number of pending call sheets in the last N days as the first statistical result of the station; The weight obtaining module obtains the sum of the first statistical results of each station as the first comprehensive statistical result; The weight obtaining module obtains the ratio of the first statistical result of the station to the first comprehensive statistical result as the historical call sheet distribution information of the station.

12. The apparatus of any one of claims 8-10, wherein, The weight obtaining module obtains the number of target vehicles meeting the requirements located in a predetermined area as the third statistical result of the station, the predetermined area being a predetermined size area centered on the station, obtains the number of vehicles in the unmanned vehicle area as the third comprehensive statistical result, and obtains the ratio of the third statistical result of the station to the third comprehensive statistical result as the current vehicle distribution information of the station.

13. The apparatus of claim 12, wherein, The predetermined areas corresponding to any two different stations have the same size; Or, the predetermined areas corresponding to any two different stations have different sizes.

14. The apparatus of claim 12, wherein, The target vehicles include idle vehicles located in the predetermined area and non-idle vehicles located in the predetermined area and having the station as the terminal.

15. An electronic device, comprising: at least one processor; and a memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-7.

17. A computer program product comprising computer programs / instructions, which, when executed by a processor, implement the method of any one of claims 1-7.

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