CN116246480A

CN116246480A - Unmanned vehicle processing method and device, electronic equipment and storage medium

Info

Publication number: CN116246480A
Application number: CN202211610476.8A
Authority: CN
Inventors: 龚芷汀; 王成亮
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-12-12
Filing date: 2022-12-12
Publication date: 2023-06-09

Abstract

The disclosure provides a method and a device for processing an unmanned vehicle, electronic equipment and a storage medium, and relates to the artificial intelligence fields of automatic driving, deep learning, big data processing and the like. The method may include: for each station in any unmanned vehicle area, respectively acquiring historical call ticket distribution information of the station, current call ticket distribution information of the station and current vehicle distribution information of the station, and determining the weight of the station according to the acquired information; and in response to determining that the idle vehicle appears 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. By applying the scheme disclosed by the disclosure, the vehicle utilization rate, the single rate and the like can be improved.

Description

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

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to an unmanned vehicle processing method, an unmanned vehicle processing device, electronic equipment and a storage medium in the fields of automatic driving, deep learning, big data processing and the like.

Background

Current vehicle capacity schedules are mainly aimed at driver-driven scenes, and drivers often select the place where to get an order best to wait according to experience of practitioners and the like. With the continuous development of technology, unmanned vehicles can be widely applied in different occasions, and accordingly, the problem of capacity scheduling of the unmanned vehicles can be related.

Disclosure of Invention

The disclosure provides an unmanned vehicle processing method, an unmanned vehicle processing device, electronic equipment and a storage medium.

A method of unmanned vehicle handling, comprising:

for each station in any unmanned vehicle area, respectively acquiring historical call ticket distribution information of the station, current call ticket distribution information of the station and current vehicle distribution information of the station, and determining the weight of the station according to the acquired information;

and in response to determining that the idle vehicle appears 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.

An unmanned vehicle processing apparatus comprising: the weight acquisition module and the vehicle scheduling module;

the weight acquisition module is used for respectively acquiring historical call ticket distribution information of the stations, current call ticket distribution information of the stations and current vehicle distribution information of the stations for each station in any unmanned vehicle area, and determining the weight of the stations according to the acquired information;

the vehicle dispatching module is used for responding to the determination that the idle vehicle appears 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.

An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

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

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

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of an embodiment of a method of handling an unmanned vehicle in accordance with the present disclosure;

FIG. 2 is a schematic view showing the constitution of an embodiment 200 of a handling device for an unmanned vehicle according to the present disclosure;

fig. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In addition, it should be understood that the term "and/or" herein is merely one association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

Fig. 1 is a flowchart of an embodiment of a method of handling an unmanned vehicle according to the present disclosure. As shown in fig. 1, the following detailed implementation is included.

In step 101, for each station in any unmanned vehicle area, historical call ticket distribution information of the station, current call ticket distribution information of the station and current vehicle distribution information of the station are respectively acquired, and weight of the station is determined according to the acquired information.

In step 102, in response to determining that an idle vehicle is present in the unmanned vehicle region, a target station is determined according to the weights of the stations, and the idle vehicle is dispatched to the target station.

By adopting the scheme of the embodiment of the method, the weight of different stations can be determined by combining various information such as the historical call ticket distribution information, the current call ticket distribution information and the current vehicle distribution information, correspondingly, the capacity dynamic scheduling of the unmanned vehicle can be carried out according to the weight, so that the limited capacity can be reasonably distributed, the limited capacity can exert the maximum value, the vehicle utilization rate, the call ticket formation rate and the traffic jam pressure are improved. The call ticket may also be referred to as an order.

In practical application, for a city, the area division of the unmanned vehicles can be performed according to a park, and each unmanned vehicle area can comprise a plurality of stations for passengers to get on or off. For each unmanned vehicle region, processing may be performed in the manner described in this disclosure, respectively.

Wherein for each station within each unmanned vehicle area, its weight may be acquired separately. Specifically, for each site, the historical call ticket distribution information of the site, the current call ticket distribution information of the site and the current vehicle distribution information of the site can be acquired respectively, and the weight of the site is determined according to the acquired information.

Preferably, for each site, the method for determining the weight of the site according to the acquired information may include:

firstly) calculating the weight of the site according to the acquired information according to a predetermined calculation mode;

and secondly), taking the acquired information as input of a pre-trained prediction model to obtain the weight of the output site.

The specific mode can be determined according to actual needs, and the method is flexible and convenient.

In the first aspect, preferably, a sum of the historical call ticket distribution information and the current call ticket distribution information may be calculated first to obtain a first calculation result, and then a difference between the first calculation result and the current vehicle distribution information may be calculated to obtain a second calculation result, where the second calculation result is used as a weight of the station.

The method comprises the following steps: weight of site = history call ticket distribution information + current call ticket distribution information-current vehicle distribution information.

In the second mode), a training sample can be constructed according to the historical data, and then a prediction model can be obtained according to training of the training sample, accordingly, when prediction is actually performed, the obtained information can be used as input of the prediction model for each site, and accordingly the weight of the outputted site is obtained.

After the weight of each site is obtained, the weight of each site can be cached for dynamic scheduling of the transport capacity, the weight of each site can be periodically updated, and the specific period duration can be determined according to actual needs.

Whether in the first mode) or the second mode), for each site, it is necessary to acquire the history call ticket distribution information, the current call ticket distribution information, and the current vehicle distribution information of the site, and the acquisition modes of the respective information are described below.

1) Historical call ticket distribution information

Preferably, for each station in the unmanned vehicle area, the following processing may be performed separately: respectively acquiring the number of to-be-processed call tickets of the station in the same preset time period of N days, wherein N is a positive integer greater than one, the preset time period is a time period including the current time, and the N days do not include the current day; acquiring the sum of the number of the call tickets to be processed in the last N days as a first statistical result of the station; obtaining the sum of first statistical results of all sites as a first comprehensive statistical result; and acquiring the ratio of the first statistical result to the first comprehensive statistical result of the site as the historical call ticket distribution information of the site.

The specific duration of the predetermined time period may be according to actual needs, for example, may be 30 minutes, and accordingly, assuming that the current time is 4:15 minutes afternoon, the predetermined time period may be a time period of 4:00-4:30 pm.

The specific value of N may also be determined according to practical needs, such as 7 days or more. Each call ticket includes a start point (origin) and an end point (destination).

For example, for a certain station a, the number of calls to be processed of the station a in the period of 4:00-4:30 pm every day in the last 7 days can be obtained respectively, the number of calls to be processed of the station a refers to the number of calls to be processed of the station a, then the number of calls to be processed of the 7 days can be summed, the sum is obtained and is used as a first statistics result of the station a, and assuming that four stations of the station a, the station b, the station c and the station d are included in an unmanned vehicle area, after the first statistics results of the four stations are obtained respectively, the sum of the four first statistics results can be calculated and is used as a first comprehensive statistics result, the ratio (or percentage, etc.) of the first statistics result of the station a and the first comprehensive statistics result can be calculated and the obtained ratio is used as historical call distribution information of the station a.

In the processing mode, the historical call ticket distribution information of each site can be efficiently and accurately determined by combining the historical call ticket conditions of each site for a plurality of days.

2) Current call ticket distribution information

Preferably, for each station, the number of calls to be processed of the station at the current time can be acquired respectively, 5 is taken as the second statistical result of the station, and the sum of the second statistical results of the stations can be acquired as the first

And the second comprehensive statistical result can be further used for obtaining the ratio of the second statistical result to the second comprehensive statistical result of the site and taking the ratio as the current call ticket distribution information of the site.

Similarly, the number of calls to be processed refers to the number of calls to be processed starting from the site.

For example, for the station a, the number of calls to be processed of the station a at the current time, for example, 10, 0 and the number of calls to be processed of the station a can be obtained and used as the second statistical result of the station a, in addition, assuming that the unmanned vehicle area includes four stations of the station a, the station b, the station c and the station d in total, after the second statistical results of the four stations are respectively obtained, the sum of the four second statistical results can be calculated, the obtained sum is used as the second comprehensive statistical result, and still taking the station a as an example, then the ratio of the second statistical result of the station a to the second comprehensive statistical result can be calculated, and the obtained ratio is used as the current call distribution information of the station a.

5 in the processing mode, the call ticket condition of the current time of each site can be combined to efficiently and accurately determine

And outputting the current call ticket distribution information of each site.

3) Current vehicle profile information

Preferably, for each site, a satisfactory target located within a predetermined area can be acquired separately

The number of vehicles is taken as a third statistical result of the station, the predetermined area is a predetermined 0-size area centering on the station, and the number of vehicles in the unmanned vehicle area can be obtained as a third comprehensive statistical knot

And then, the ratio of the third statistical result to the third comprehensive statistical result of the station can be obtained and used as the current vehicle distribution information of the station.

The specific size of the predetermined area is not limited, and may be determined according to practical needs, for example, a circular area with a radius of 1 km centered on the station.

5 taking station a as an example, the number of target vehicles meeting the requirements in the preset area can be obtained as

And obtaining the ratio of the third statistical result of the station a to the third comprehensive statistical result, and taking the obtained ratio as the current vehicle distribution information of the station a.

In the processing mode, the current vehicle distribution information of each station can be efficiently and accurately determined according to the number of the target vehicles corresponding to each station and the number of vehicles in the whole unmanned vehicle.

In addition, preferably, the predetermined areas corresponding to any two different sites are the same in size, or the predetermined areas corresponding to any two different sites are different in size.

That is, the predetermined areas corresponding to different sites may be different in size, for example, circular areas with a radius of 1 km around the site, or the predetermined areas corresponding to one or some sites may be different from other sites, for example, a certain site is located near a subway entrance, the traffic flow is large, and the vehicle running speed is slow, so that the predetermined areas corresponding to the sites may be set to be smaller, so that the determined weight of the sites is larger, and further, more idle vehicles may be allocated to the sites, so as to relieve traffic jam pressure of the sites, and the like.

Further, preferably, the target vehicle may include, for each station: an idle vehicle located within the predetermined area, and a non-idle vehicle located within the predetermined area and ending at the station.

The idle vehicles refer to the vehicles in the idle state at present, and the idle vehicles in the preset area and the non-idle vehicles with the end point of the preset area being the stop can be used as target vehicles, and the non-idle vehicles usually end the processing of the current call ticket quickly, so that the idle vehicles can be used as the target vehicles of the stop to count, and the current vehicle distribution information of the stop can be acquired more comprehensively and accurately.

The weight of the station reflects the supply and demand balance relation between the call ticket and the vehicle, the smaller the weight of the station is, the more the call ticket and the capacity of the current station are matched, and the more capacity is not required to be additionally scheduled, otherwise, the larger the weight of the station is, the more the call ticket and the capacity of the current station are not matched, and the more capacity is required to be additionally scheduled.

Accordingly, the method for determining the target site according to the weight of each site may include: and determining the site with the highest weight, and taking the site with the highest weight as the target site.

Specifically, when an idle vehicle appears in the unmanned vehicle area, a station with the highest weight can be determined as a target station, a dispatch list can be correspondingly generated, the idle vehicle is dispatched to the target station according to the dispatch list, and accordingly, the idle vehicle is dispatched to the target station because the target station theoretically has the highest vehicle demand, the driving receiving efficiency and the single rate of passengers can be improved, the utilization rate of the vehicle can be improved, and meanwhile, the traffic jam pressure can be relieved in a mode of carrying out traffic capacity dispatch in advance in a peak period of use.

Because the weight of each station is dynamically changed, the determined target station is the station with the highest current time weight.

Each idle vehicle that occurs in the unmanned vehicle area may be processed in the manner described above. In addition, if the idle vehicle to be scheduled is originally located at the target station, it is not necessary to schedule the idle vehicle, such as generating a schedule for the idle vehicle, otherwise, generating a schedule for the idle vehicle, and scheduling the idle vehicle to the target station.

Preferably, in response to determining that there is a pending call ticket that meets the following conditions during the idle vehicle's journey to the destination site: and the distance between the starting point of the call ticket to be processed and the idle vehicle is smaller than the distance between the idle vehicle and the target station, and the idle vehicle is scheduled to process the call ticket to be processed.

That is, for a certain idle vehicle, assuming that a dispatch list is generated for the idle vehicle, the idle vehicle also goes to the target station according to the dispatch list, but in the process of going to the target station, a call list to be processed which is closer to the target station needs to be processed, the idle vehicle can be preferentially dispatched to process the call list to be processed which is closer to the target station, so that the vehicle utilization rate is further improved, the call list forming rate is further improved, and the like.

It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.

In a word, by adopting the scheme of the embodiment of the method disclosed by the invention, the capacity dynamic scheduling of the unmanned vehicle can be realized, so that the vehicle utilization rate and the single rate are improved, the traffic jam pressure and the like are relieved, the method can be suitable for various unmanned scenes such as an automatic driving travel scene, an automatic driving logistics scene and the like, and has wide applicability.

The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.

Fig. 2 is a schematic diagram of the composition and structure of an embodiment 200 of the handling device for an unmanned vehicle according to the present disclosure. As shown in fig. 2, includes: the weight acquisition module 201 and the vehicle scheduling module 202.

The weight obtaining module 201 is configured to obtain, for each station in any unmanned vehicle area, historical call ticket distribution information of the station, current call ticket distribution information of the station, and current vehicle distribution information of the station, and determine a weight of the station according to the obtained information.

The vehicle dispatching module 202 is configured to determine a target station according to weights of stations in response to determining that an idle vehicle appears in the unmanned vehicle area, and dispatch the idle vehicle to the target station.

By adopting the scheme of the embodiment of the device, the weight of different stations can be determined by combining various information such as the history call ticket distribution information, the current call ticket distribution information and the current vehicle distribution information, correspondingly, the capacity dynamic scheduling of the unmanned vehicle can be carried out according to the weight, so that the limited capacity can be reasonably distributed, the limited capacity can exert the maximum value, the vehicle utilization rate, the call ticket formation rate and the traffic jam pressure are improved.

The weight acquisition module 201 may acquire its weight separately for each station within each unmanned vehicle area. Specifically, for each site, the historical call ticket distribution information of the site, the current call ticket distribution information of the site and the current vehicle distribution information of the site can be acquired respectively, and then the weight of the site can be determined according to the acquired information.

Preferably, for each site, the manner in which the weight acquisition module 201 determines the weight of the site according to the acquired information may include:

In the first aspect, preferably, the weight obtaining module 201 may calculate a sum of the historical call ticket distribution information and the current call ticket distribution information to obtain a first calculation result, and then calculate a difference between the first calculation result and the current vehicle distribution information to obtain a second calculation result, and use the second calculation result as the weight of the station.

In the second mode), the weight obtaining module 201 may construct a training sample according to the historical data, and further may obtain a prediction model according to training of the training sample, and accordingly, when the prediction is actually performed, for each site, the obtained information may be used as input of the prediction model, so as to obtain the output weight of the site.

After the weights of the stations are obtained, the weights of the stations can be cached for dynamic scheduling of the capacity, and the weights of the stations can be updated periodically.

Preferably, for each station in the unmanned vehicle area, the weight acquisition module 201 may perform the following processing, respectively: respectively acquiring the number of to-be-processed call tickets of the station in the same preset time period of N days, wherein N is a positive integer greater than one, the preset time period is a time period including the current time, and the N days do not include the current day; acquiring the sum of the number of the call tickets to be processed in the last N days as a first statistical result of the station; obtaining the sum of first statistical results of all sites as a first comprehensive statistical result; and acquiring the ratio of the first statistical result to the first comprehensive statistical result of the site as the historical call ticket distribution information of the site.

Preferably, for each site, the weight obtaining module 201 may obtain the number of calls to be processed of the site at the current time, as a second statistical result of the site, and may obtain a sum of the second statistical results of the sites, as a second comprehensive statistical result, and further may obtain a ratio of the second statistical result of the site to the second comprehensive statistical result, as current call ticket distribution information of the site.

Preferably, for each station, the weight obtaining module 201 may obtain the number of target vehicles meeting the requirement in a predetermined area, as a third statistical result of the station, the predetermined area is a predetermined size area centered on the station, and may obtain the number of vehicles in the unmanned vehicle area, as a third comprehensive statistical result, and further may obtain a ratio of the third statistical result of the station to the third comprehensive statistical result, as current vehicle distribution information of the station.

Further, the vehicle dispatch module 202 may determine a target station according to the weight of each station and may dispatch an idle vehicle to the target station when it is determined that the idle vehicle is present in the unmanned vehicle region.

Preferably, the vehicle scheduling module 202 may determine the station with the highest weight, and take the station with the highest weight as the target station.

Preferably, the vehicle dispatch module 202 may also be responsive to determining that there is a pending call ticket that meets the following conditions during the course of the idle vehicle going to the target site: and the distance between the starting point of the call ticket to be processed and the idle vehicle is smaller than the distance between the idle vehicle and the target station, and the idle vehicle is scheduled to process the call ticket to be processed.

The specific workflow of the embodiment of the apparatus shown in fig. 2 may refer to the related description in the foregoing method embodiment, and will not be repeated.

In a word, by adopting the scheme of the embodiment of the disclosure, the capacity dynamic scheduling of the unmanned vehicle can be realized, so that the vehicle utilization rate, the single forming rate and the traffic jam pressure are improved, the method and the device are applicable to various unmanned scenes such as an automatic driving travel scene and an automatic driving logistics scene, and have wide applicability.

The scheme disclosed by the disclosure can be applied to the field of artificial intelligence, and particularly relates to the fields of automatic driving, deep learning, big data processing and the like. Artificial intelligence is the subject of studying certain thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) that make a computer simulate a person, and has technology at both hardware and software levels, and artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, etc., and artificial intelligence software technologies mainly include computer vision technologies, speech recognition technologies, natural language processing technologies, machine learning/deep learning, big data processing technologies, knowledge graph technologies, etc.

The call ticket, the vehicle, etc. in the embodiments of the present disclosure are not specific to a particular user, and cannot reflect personal information of a particular user. In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

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

Fig. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 3, the apparatus 300 includes a computing unit 301 that may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the device 300 may also be stored. The computing unit 301, the ROM302, and the RAM303 are connected to each other by a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

Various components in device 300 are connected to 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, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of 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 suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the methods described in this disclosure. For example, in some embodiments, the methods described in the present disclosure may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 300 via the ROM302 and/or the communication unit 309. One or more steps of the methods described in the present disclosure may be performed when the computer program is loaded into RAM303 and executed by computing unit 301. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the methods described in the present disclosure by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may 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 a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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.

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 a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically 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 may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A method of unmanned vehicle handling, comprising:

2. The method of claim 1, wherein the determining the weight of the site from the acquired information comprises:

according to a predetermined calculation mode, calculating the weight of the station according to the acquired information;

or taking the acquired information as input of a pre-trained prediction model to obtain the weight of the output site.

3. The method of claim 2, wherein said calculating the weight of the site from the acquired information in a predetermined calculation manner comprises:

calculating the sum of the historical call ticket distribution information and the current call ticket distribution information to obtain a first calculation result;

and calculating the 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. A method according to any one of claims 1 to 3, wherein the obtaining historical call ticket distribution information for the site comprises:

for each station in the unmanned vehicle area, the following processing is performed:

respectively acquiring the number of to-be-processed call tickets of the station in the same preset time period of the last N days, wherein N is a positive integer greater than one, the preset time period is a time period including the current time, and the last N days do not include the current day;

acquiring the sum of the number of the call tickets to be processed in the last N days as a first statistical result of the station;

obtaining the sum of first statistical results of all sites as a first comprehensive statistical result;

and acquiring the ratio of the first statistical result of the site to the first comprehensive statistical result as the historical call ticket distribution information of the site.

5. A method according to any one of claims 1 to 3, wherein obtaining current call ticket distribution information for the site comprises:

acquiring the number of call tickets to be processed of the station at the current time, and taking the number of call tickets to be processed of the station as a second statistical result of the station;

obtaining the sum of second statistical results of all sites as a second comprehensive statistical result;

and acquiring the ratio of the second statistical result of the station to the second comprehensive statistical result as the current call ticket distribution information of the station.

6. A method according to any one of claims 1 to 3, wherein obtaining current vehicle profile information for the station comprises:

acquiring the number of target vehicles meeting the requirements in a preset area, wherein the preset area is a preset size area taking the station as the center, and the preset area is used as a third statistical result of the station;

acquiring the number of vehicles in the unmanned vehicle area as a third comprehensive statistical result;

and acquiring 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.

7. The method of claim 6, wherein,

the size of the preset area corresponding to any two different sites is the same;

or the size of the preset area corresponding to any two different sites is different.

8. The method of claim 6, wherein,

the target vehicle includes: and the idle vehicles are positioned in the preset area, and the non-idle vehicles are positioned in the preset area and take the station as a destination.

9. The method according to claim 1 to 3, wherein,

the determining the target site according to the weight of each site comprises the following steps: and determining the site with the highest weight, and taking the site with the highest weight as the target site.

10. A method according to any one of claims 1 to 3, further comprising:

in response to determining that there is a pending call ticket that meets the following conditions during the arrival of the idle vehicle at the target site: and the distance between the starting point of the call ticket to be processed and the idle vehicle is smaller than the distance between the idle vehicle and the target station, and the idle vehicle is scheduled to process the call ticket to be processed.

11. An unmanned vehicle processing apparatus comprising: the weight acquisition module and the vehicle scheduling module;

12. The apparatus of claim 11, wherein,

the weight acquisition module calculates the weight of the site according to the acquired information according to a predetermined calculation mode;

or the weight acquisition module takes the acquired information as input of a pre-trained prediction model to acquire the weight of the outputted site.

13. The apparatus of claim 12, wherein,

the weight acquisition module calculates the sum of the historical call ticket distribution information and the current call ticket distribution information to obtain a first calculation result, calculates the 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.

14. The device according to any one of claims 11 to 13, wherein,

the weight acquisition module performs the following processing for each station in the unmanned vehicle area: respectively acquiring the number of to-be-processed call tickets of the station in the same preset time period of the last N days, wherein N is a positive integer greater than one, the preset time period is a time period including the current time, and the last N days do not include the current day; acquiring the sum of the number of the call tickets to be processed in the last N days as a first statistical result of the station; obtaining the sum of first statistical results of all sites as a first comprehensive statistical result; and acquiring the ratio of the first statistical result of the site to the first comprehensive statistical result as the historical call ticket distribution information of the site.

15. The device according to any one of claims 11 to 13, wherein,

the weight acquisition module acquires the number of the call tickets to be processed of the station at the current time, takes the number of the call tickets to be processed of the station as a second statistical result of the station, acquires the sum of the second statistical results of all the stations as a second comprehensive statistical result, and acquires the ratio of the second statistical result of the station to the second comprehensive statistical result as the current call ticket distribution information of the station.

16. The device according to any one of claims 11 to 13, wherein,

the weight acquisition module acquires the number of target vehicles meeting the requirements in a preset area as a third statistical result of the station, the preset area is an area with a preset size taking the station as a center, the number of vehicles in the unmanned vehicle area is acquired as a third comprehensive statistical result, and the ratio of the third statistical result of the station to the third comprehensive statistical result is acquired as the current vehicle distribution information of the station.

17. The apparatus of claim 16, wherein,

18. The apparatus of claim 16, wherein,

19. The device according to any one of claims 11 to 13, wherein,

and the vehicle scheduling module determines the station with the highest weight, and takes the station with the highest weight as the target station.

20. The device according to any one of claims 11 to 13, wherein,

the vehicle dispatch module is further configured to, in response to determining that there is a pending call ticket that meets the following conditions during the idle vehicle going to the destination site: and the distance between the starting point of the call ticket to be processed and the idle vehicle is smaller than the distance between the idle vehicle and the target station, and the idle vehicle is scheduled to process the call ticket to be processed.

21. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-10.

23. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the method of any of claims 1-10.