CN115526453A - Vehicle scheduling method, device, equipment, storage medium and computer program product - Google Patents

Abstract

The disclosure provides a vehicle scheduling method, a vehicle scheduling device, vehicle scheduling equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, in particular to intelligent traffic in the field of unmanned driving. The specific implementation scheme is as follows: acquiring a plurality of factor items influencing vehicle dispatching efficiency; combining the multiple factor items to obtain multiple first combinations; and determining efficiency statistical data of scheduling schemes corresponding to the first combinations respectively, and executing vehicle scheduling according to the scheduling scheme corresponding to the efficiency statistical data with optimal efficiency.

Description

Vehicle scheduling method, device, equipment, storage medium and computer program product

technical field

The present disclosure relates to the technical field of artificial intelligence, in particular to intelligent transportation in the field of unmanned driving.

Background technique

For the operation scenarios of self-driving vehicles, it is usually necessary to uniformly dispatch multiple vehicles in the scene through instructions, so that the self-driving vehicles can automatically run based on the scheduling and complete the work. Among them, for the specific scene of vehicle scheduling, there are often many factors that affect the overall scheduling efficiency, and then there is a need to find a better scheduling solution under the influence of multiple factors.

Contents of the invention

The disclosure provides a vehicle scheduling method, device, equipment, storage medium and computer program product.

According to an aspect of the present disclosure, a vehicle scheduling method is provided, including:

Obtain a plurality of factor items that affect the efficiency of vehicle dispatching; combine the plurality of factor items to obtain a plurality of first combinations; determine the efficiency statistics of the scheduling schemes corresponding to the plurality of first combinations, and use the most efficient The scheduling scheme corresponding to the optimal efficiency statistical data executes vehicle scheduling.

According to another aspect of the present disclosure, a vehicle dispatching device is provided, including:

Obtain a plurality of factor items that affect the efficiency of vehicle dispatching; combine the plurality of factor items to obtain a plurality of first combinations; determine the efficiency statistics of the scheduling schemes corresponding to the plurality of first combinations, and use the most efficient The scheduling scheme corresponding to the optimal efficiency statistical data executes vehicle scheduling.

According to another aspect of the present disclosure, an electronic device is provided, including:

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, and the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned method.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the above-mentioned method.

According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the above-mentioned method when executed by a processor.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood through the following description.

Description of drawings

The accompanying drawings are used to better understand the present solution, and do not constitute a limitation to the present disclosure. in:

Fig. 1 is a flow chart of a vehicle dispatching method shown in the present disclosure;

FIG. 2 is a flow chart of a method for combining multiple factor items to obtain multiple first combinations shown in the present disclosure;

FIG. 3 is a flow chart of a method for executing vehicle scheduling based on real-time simulation shown in the present disclosure;

FIG. 4 is a flowchart of a method for performing vehicle scheduling based on pre-simulation results shown in the present disclosure;

FIG. 5 is a flowchart of a method for scheduling efficiency statistics data of scheduling schemes respectively corresponding to multiple first combinations based on pre-simulation according to the present disclosure;

FIG. 6 is a flow chart of a method for real-time updating of pre-simulation results shown in the present disclosure;

FIG. 7 is a schematic diagram of determining an optimal scheduling scheme through a scheduling program and a simulation system shown in the present disclosure;

Fig. 8 is a block diagram of a vehicle dispatching device according to the present disclosure;

FIG. 9 shows a schematic block diagram of an example electronic device 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 they should be regarded as exemplary only. Accordingly, those 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 disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

An embodiment of the present disclosure provides a vehicle scheduling method, which can be applied to a scenario where multiple vehicles are uniformly scheduled.

For the operation scenarios of self-driving vehicles, it is usually necessary to uniformly dispatch multiple vehicles in the scene through instructions, so that the self-driving vehicles can automatically run based on the scheduling and complete the work. Among them, for the specific scene of vehicle scheduling, there are often many factors that affect the overall scheduling efficiency, and then there is a need to find a better scheduling solution under the influence of multiple factors.

In related technologies, for vehicle scheduling scenarios where multiple factors affect scheduling efficiency, the scheduling scheme relies on manual arrangement. Since the manual arrangement is too dependent on empirical values, and the complexity of the factors affecting the autonomous driving task, it is necessary to test the impact of different combinations on the efficiency of the autonomous driving task, which is an extremely time-consuming and labor-intensive process. Therefore, it often takes a lot of labor costs to manually generate a scheduling plan, which cannot meet the actual needs of optimal scheduling of autonomous vehicles.

In view of this, the present disclosure provides a vehicle scheduling method, which can automatically combine multiple factors that affect scheduling efficiency, and thereby determine the efficiency statistics of corresponding scheduling schemes in different combinations. On this basis, the dispatching plan with optimal efficiency can be screened and executed from a number of different feasible dispatching plans to ensure the operation efficiency in the vehicle dispatching scenario. Hereinafter in the present disclosure, for the convenience of description, the combination obtained by combining various factor items is referred to as the first combination.

Fig. 1 is a flow chart of a vehicle scheduling method shown in the present disclosure, as shown in Fig. 1 , including the following steps S101 to S103.

In step S101, a plurality of factor items affecting vehicle dispatching efficiency are obtained.

In the embodiments of the present disclosure, the factor item represents an exhaustive list of various factors that affect the efficiency of vehicle scheduling, for example, may include vehicle selection methods, priority relationships among vehicles, road environment, and/or the task phase of each vehicle.

In step S102, multiple factor items are combined to obtain multiple first combinations.

In step S103, the efficiency statistical data of the scheduling schemes corresponding to the plurality of first combinations are determined, and the vehicle scheduling is performed with the scheduling scheme corresponding to the efficiency statistical data with the best efficiency.

For example, the efficiency statistical data can be understood as quantitative data used to characterize the efficiency of vehicle dispatching, and the ranking of dispatching efficiency can be performed based on different quantitative indicators. For example, the amount of work within a specified time is used as a quantitative index, and the scheduling scheme with the largest amount of work within the specified time is taken as the scheduling scheme with the best scheduling efficiency. For another example, the operation time required to complete the specified amount of work is used as the quantitative index, and the scheduling scheme with the least time required to complete the specified amount of work is taken as the scheduling scheme with the best scheduling efficiency.

Through the method provided by the embodiments of the present disclosure, a vehicle scheduling scheme with optimal scheduling efficiency can be calculated by combining different factors affecting vehicle scheduling efficiency, so as to meet the actual business needs of the vehicle scheduling scene and improve business efficiency.

Generally, for each factor item, there are usually different possible situations corresponding to it. For example, for the priority relationship between vehicles, the priority relationship between vehicles may be priority order A, priority order B or priority order C. In an embodiment of the present disclosure, multiple possible situations corresponding to each factor item can be regarded as the factor sub-items of the factor item, and then by combining the factor sub-items of different factor items, combinations corresponding to different scheduling schemes can be obtained .

Fig. 2 is a flow chart of a method for combining multiple factor items to obtain multiple first combinations shown in the present disclosure. The execution methods of step S101 and step S103 are similar and will not be repeated here.

In step S202, the target factor sub-items that meet the current scheduling scenario in each of the multiple factor items are respectively determined.

In step S203, target factor sub-items belonging to different factor items are combined to obtain a plurality of first combinations.

In the embodiment of the present disclosure, each factor item includes a factor sub-item. For example, for the factor item "priority relationship between vehicles", its factor sub-items may be different priority orders among vehicles. As another example, if the task phases include "vehicle in loading area", "vehicle in loading", "vehicle leaving loading area", "vehicle carrying heavy load", "vehicle entering unloading area" or "vehicle leaving unloading area", for the factor item "task stage of each vehicle", its factor sub-item can be, for example, "vehicle D carries out heavy-duty transportation" or "vehicle E carries out loading". In addition, the factor sub-items of other factor items involved in the present disclosure are divided in a manner similar to the above-mentioned embodiment, and the present disclosure does not list them all here.

As an example, the present disclosure determines the target factor sub-items in each of the multiple factor items that meet the current scheduling scenario, and its purpose is to screen out factor sub-items that are not applicable to the current scheduling scenario. For ease of understanding, the following is an example illustrate. For example, for the material handling scene in the mine operation scene, if the road environment used for material handling only includes uphill roads and downhill roads, and there is no horizontal road, then for the factor item "road environment", the factor The sub-item "uphill road" and the factor sub-item "downhill road" are regarded as target factor sub-items, and will be selected in an alternative way when combining factor sub-items later. Correspondingly, the non-target factor sub-item "horizontal road" as the factor item "road environment" will not be selected in the subsequent combination of factor sub-items.

The method provided by the embodiments of the present disclosure, in the current scheduling scenario, only considers the factor sub-items that affect the scheduling efficiency of the current scheduling scenario, and does not consider the factor sub-items that have no impact or are irrelevant to the current scheduling scenario. The method can More efficiently provide scheduling solutions with optimal scheduling efficiency.

In this embodiment of the present disclosure, the efficiency statistical data of the scheduling scheme corresponding to the first combination may be obtained, for example, through a simulation task. In a feasible implementation manner, the efficiency statistical data of various scheduling schemes can be estimated in real time by creating simulation tasks in the current scheduling scenario. In another feasible implementation manner, the scheduling statistical data corresponding to all the combinations can be estimated in advance by creating simulation tasks, so that the pre-stored data can be called in real time when the scheduling scheme needs to be determined later. The present disclosure exemplifies the above two possible manners respectively below.

Fig. 3 is a flow chart of a method for executing vehicle scheduling based on real-time simulation shown in the present disclosure. As shown in Fig. 3, the execution of steps S301 to S303 in the embodiment of the present disclosure and steps S201 to S203 in Fig. 2 The method is similar and will not be repeated here.

In step S304, a simulation task of vehicle scheduling is executed for each of the multiple first combinations, and the efficiency statistical data of the scheduling schemes corresponding to the multiple first combinations are obtained, and the efficiency statistical data with the optimal efficiency The corresponding scheduling scheme performs vehicle scheduling.

The method provided by the embodiments of the present disclosure provides the efficiency statistical data of the scheduling schemes corresponding to different combinations through real-time simulation, and then performs vehicle scheduling with the scheduling scheme corresponding to the efficiency statistical data with the best efficiency. This method is suitable for The current scheduling scenario can realize efficient vehicle scheduling.

Fig. 4 is a flowchart of a method for executing vehicle scheduling based on pre-simulation results shown in the present disclosure. The execution methods are similar, and will not be repeated here.

In step S404, the efficiency statistical data of the dispatching schemes corresponding to the plurality of first combinations obtained based on the simulation tasks and stored in advance are acquired, and the dispatching scheme corresponding to the efficiency statistical data with the best efficiency is used to execute vehicle scheduling.

The method provided by the embodiments of the present disclosure can directly determine the dispatching scheme with optimal dispatching efficiency through the pre-simulation results to execute vehicle dispatching. Compared with the real-time simulation method, this method is more efficient and more suitable for actual needs. The implementation of the pre-simulation is described as an example below. In the present disclosure, for the convenience of description, all the combinations among the various factor subitems are referred to as the second combination. Wherein, it can be understood that the factor sub-items included in each second combination belong to different factor items, and the second combination includes the first combination.

FIG. 5 is a flow chart of a method for scheduling efficiency statistics data of scheduling schemes corresponding to multiple first combinations based on pre-simulation according to the present disclosure. As shown in FIG. 5 , it includes the following steps S501 and S502.

In step S501, the factor sub-items of each factor item are combined to obtain a plurality of second combinations.

In step S502, a simulation task of vehicle scheduling is executed for each second combination, so as to generate efficiency statistical data of scheduling schemes respectively corresponding to multiple second combinations.

In the embodiment of the present disclosure, the efficiency statistical data of the scheduling schemes respectively corresponding to the second combinations may be obtained through steps S501 and S502. Since the second combination includes the first combination, the obtained efficiency statistics data of the scheduling schemes respectively corresponding to the second combinations include efficiency statistics data of the scheduling schemes respectively corresponding to the first combinations. On this basis, for the current scheduling scenario, the efficiency statistical data of the scheduling schemes corresponding to the first combination can be directly used to determine the scheduling scheme with the best scheduling efficiency, and execute vehicle scheduling with the scheduling scheme with the optimal scheduling efficiency.

Since the factor items and/or factor sub-items that affect scheduling efficiency can be changed, in one embodiment, in order to meet the scheduling requirements of the current scheduling scenario, the factor items and/or factor sub-items of the scheduling scheme can be selected In the case of updating, the stored efficiency statistical data of the scheduling schemes respectively corresponding to the plurality of second combinations are updated.

Fig. 6 is a flow chart of a method for real-time updating of pre-simulation results shown in the present disclosure. As shown in Fig. 6, step S601 and step S602 in the embodiment of the present disclosure are the same as step S501 and step S502 in Fig. 5 The execution methods are similar, and will not be repeated here.

In step S603, in response to the factor item and/or the factor sub-item being updated, the stored efficiency statistical data of the dispatching schemes respectively corresponding to the plurality of second combinations are updated.

Through the method provided by the embodiments of the present disclosure, the stored pre-simulation results can be updated in time when the factor item and/or factor sub-item changes, and the method can ensure the correctness of the determined optimal scheduling scheme , so as to ensure the efficiency of vehicle scheduling.

In the foregoing embodiments, the scheduling scenario may include, for example, a mine operation scenario, and the simulation task may include, for example, a mine operation-based vehicle scheduling task. In the following, the present disclosure exemplifies the complete flow of vehicle scheduling in a mine operation scenario with reference to FIG. 7 .

Fig. 7 is a schematic diagram of determining an optimal scheduling scheme through a scheduler and a simulation system shown in the present disclosure.

For example, for a mine operation scenario, the vehicles currently to be operated may include, for example, vehicle D, vehicle E, and vehicle F, and the priority relationship among vehicles may include, for example, priority order A, priority order B, and priority order C. The road environment where each vehicle is located may be, for example, "vehicle D is on an uphill road", "vehicle E is on a downhill road", and "vehicle F is on a level road". Correspondingly, the task stages of each vehicle may be, for example, "vehicle D is carrying out heavy-duty transport", "vehicle E is carrying out loading" and "vehicle F is leaving the unloading area". Of course, in addition to the above-mentioned "priority relationship between vehicles", "road environment" and "task phase", other factor items may also be included, which are not listed in this disclosure.

Further, as shown in FIG. 7 , the above determined factor items and factor subitems can be input into the scheduling program to determine the scheduling schemes corresponding to different combinations. On this basis, the simulation system can obtain the efficiency statistical data of the scheduling schemes corresponding to different combinations through the simulation of each scheduling scheme, and then sort the efficiency statistics data of different scheduling schemes according to the pre-specified quantitative standards, and output different Efficiency ranking among scheduling schemes. Correspondingly, on the basis of determining the efficiency ranking, the dispatching scheme with the optimal dispatching efficiency can be selected to execute vehicle dispatching, so as to realize efficient and rapid vehicle dispatching.

The methods provided by the embodiments of the present disclosure can determine the most efficient dispatching scheme in the current dispatching scenario through real-time simulation or obtaining pre-simulation results, and then implement vehicle dispatching with optimal dispatching efficiency. Moreover, on the one hand, since the method can be completely separated from manual operation, it can improve the efficiency of finding the optimal scheduling solution and reduce the cost of manual use. On the other hand, since this method can generate the efficiency statistical data corresponding to different combinations through automatic simulation, it has the characteristics of small operation error and high accuracy compared with the manual entry method, and is more suitable for the actual situation of the vehicle dispatching scene Scheduling needs. In addition, since the method can realize real-time update of the pre-simulation results when the factor items and/or factor sub-items are updated, it has the characteristics of high iteration efficiency and can be used with less resources. Provide a scheduling plan with high accuracy.

Based on the same idea, an embodiment of the present disclosure also provides a vehicle scheduling device.

It can be understood that, in order to realize the above-mentioned functions, the vehicle dispatching device provided by the embodiments of the present disclosure includes corresponding hardware structures and/or software modules for performing various functions. Combining the modules and algorithm steps of the examples disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software drives hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the technical solutions of the embodiments of the present disclosure.

Fig. 8 is a block diagram of a vehicle dispatching device according to the present disclosure. Referring to FIG. 8 , the apparatus 800 includes an acquisition module 801 , a processing module 802 and a determination module 803 .

An acquisition module 801, configured to acquire multiple factor items that affect vehicle dispatching efficiency. The processing module 802 is configured to combine multiple factor items to obtain multiple first combinations. A determining module 803, configured to determine efficiency statistical data of the scheduling schemes respectively corresponding to the multiple first combinations. The processing module 802 is further configured to: perform vehicle scheduling with the scheduling scheme corresponding to the efficiency statistical data with optimal efficiency.

In one embodiment, each factor item in the plurality of factor items includes factor sub-items. The processing module 802 combines multiple factor items in the following manner to obtain multiple first combinations: separately determine the target factor sub-items in each of the multiple factor items that meet the current scheduling scenario. Combining target factor subitems belonging to different factor items respectively to obtain a plurality of first combinations.

In an implementation manner, the efficiency statistical data of the scheduling schemes respectively corresponding to the multiple first combinations are obtained based on simulation tasks.

In one embodiment, the processing module 802 obtains the efficiency statistical data of the scheduling schemes corresponding to the multiple first combinations based on the simulation tasks in the following manner: respectively execute the simulation task of vehicle scheduling for each of the multiple first combinations , to obtain the efficiency statistical data of the scheduling schemes respectively corresponding to the multiple first combinations.

In one embodiment, the processing module 802 obtains the efficiency statistical data of the scheduling schemes corresponding to the multiple first combinations based on the simulation tasks in the following manner: Obtain the scheduling schemes corresponding to the multiple first combinations obtained based on the simulation tasks and stored in advance efficiency statistics.

In one implementation manner, the processing module 802 obtains the efficiency statistical data of the scheduling schemes respectively corresponding to the multiple first combinations in the following manner: combine the factor sub-items of each factor item to obtain multiple second combinations. Wherein, the factor subitems contained in each second combination belong to different factor items respectively. A simulation task of vehicle scheduling is executed for each second combination, so as to generate efficiency statistical data of scheduling schemes respectively corresponding to multiple second combinations. Wherein, the second combination includes the first combination, and the efficiency statistical data of the scheduling schemes respectively corresponding to the multiple second combinations includes the efficiency statistical data of the scheduling schemes corresponding to the multiple first combinations.

In an implementation manner, the processing module 802 is further configured to: update the stored efficiency statistical data of the scheduling schemes respectively corresponding to the plurality of second combinations in response to the factor item and/or the factor sub-item being updated.

In one embodiment, the scheduling scenario includes a mine operation scenario, and the simulation task includes a mine operation-based vehicle scheduling task.

Regarding the apparatus in the foregoing embodiments, the specific manner in which each module executes operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

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

FIG. 9 shows a schematic block diagram of an example electronic device 900 that may be used to implement embodiments of the present disclosure. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 9 , the device 900 includes a computing unit 901 that can execute according to a computer program stored in a read-only memory (ROM) 902 or loaded from a storage unit 908 into a random-access memory (RAM) 903. Various appropriate actions and treatments. In the RAM 903, various programs and data necessary for the operation of the device 900 can also be stored. The computing unit 901 , ROM 902 , and RAM 903 are connected to each other through a bus 904 . An input/output (I/O) interface 905 is also connected to the bus 904 .

Multiple components in the device 900 are connected to the I/O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc. ; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.

The computing unit 901 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 901 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 executes various methods and processes described above, such as the vehicle scheduling method. For example, in some embodiments, the vehicle dispatch method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 908 . In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 900 via the ROM 902 and/or the communication unit 909 . When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the vehicle dispatching method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to execute the vehicle dispatching method in any other suitable manner (for example, by means of firmware).

Various implementations of the systems and techniques described herein above can 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 chips Implemented in a system of systems (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is 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 the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide for interaction with the user, the systems and techniques described herein 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 pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by 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.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present disclosure can be achieved, no limitation is imposed herein.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims (19)

1. A vehicle scheduling method, comprising:

acquiring a plurality of factor items influencing vehicle dispatching efficiency;

combining the multiple factor items to obtain multiple first combinations;

and determining efficiency statistical data of scheduling schemes corresponding to the first combinations respectively, and executing vehicle scheduling according to the scheduling scheme corresponding to the efficiency statistical data with optimal efficiency.

2. The method of claim 1, wherein each factor term of the plurality of factor terms comprises a factor sub-term;

the combining the plurality of factor items to obtain a plurality of first combinations comprises:

respectively determining a target factor sub-item which accords with the current scheduling scene in each factor item in a plurality of factor items;

and combining the target factor sub-items which respectively belong to different factor items to obtain a plurality of first combinations.

3. The method of claim 1 or 2, wherein the efficiency statistics of the scheduling schemes corresponding to the first combinations, respectively, are derived based on simulation tasks.

4. The method of claim 3, wherein the efficiency statistics of the scheduling schemes corresponding to the first combinations are obtained based on simulation tasks as follows:

and executing the simulation task of vehicle scheduling aiming at each first combination in the plurality of first combinations respectively to obtain the efficiency statistical data of the scheduling schemes corresponding to the plurality of first combinations respectively.

5. The method of claim 3, wherein the efficiency statistics of the scheduling schemes corresponding to the first combinations are obtained based on simulation tasks as follows:

and acquiring efficiency statistical data of scheduling schemes respectively corresponding to the plurality of first combinations, which are obtained based on the simulation tasks and stored in advance.

6. The method of claim 5, wherein the following is used to obtain the efficiency statistics of the scheduling schemes corresponding to the first combinations respectively:

combining the factor sub-items of each factor item to obtain a plurality of second combinations; wherein, each factor sub-item included in the second combination belongs to different factor items respectively;

executing simulation tasks of vehicle scheduling respectively aiming at each second combination to generate efficiency statistical data of scheduling schemes corresponding to a plurality of second combinations respectively;

the second combination comprises the first combination, and the efficiency statistical data of the scheduling schemes respectively corresponding to the second combinations comprise the efficiency statistical data of the scheduling schemes respectively corresponding to the first combinations.

7. The method of claim 6, further comprising:

updating the stored efficiency statistics of the scheduling schemes respectively corresponding to the plurality of second combinations in response to the factor item and/or the factor sub-item being updated.

8. The method of any one of claims 3 to 7, wherein the scheduling scenario comprises a mine operation scenario and the simulation task comprises a mine operation based vehicle scheduling task.

9. A vehicle dispatching device, comprising:

the system comprises an acquisition module, a scheduling module and a scheduling module, wherein the acquisition module is used for acquiring a plurality of factor items influencing the scheduling efficiency of the vehicle;

the processing module is used for combining the plurality of factor items to obtain a plurality of first combinations;

a determining module, configured to determine efficiency statistics of scheduling schemes corresponding to the plurality of first combinations, respectively;

the processing module is further configured to:

and executing vehicle scheduling according to the scheduling scheme corresponding to the efficiency statistical data with optimal efficiency.

10. The apparatus of claim 9, wherein each factor term of the plurality of factor terms comprises a factor sub-term;

the processing module combines the multiple factor items in the following way to obtain multiple first combinations:

respectively determining a target factor sub-item which accords with a current scheduling scene in each factor item in a plurality of factor items;

and combining the target factor sub-items which respectively belong to different factor items to obtain a plurality of first combinations.

11. The apparatus according to claim 9 or 10, wherein the efficiency statistics of the scheduling schemes respectively corresponding to the plurality of first combinations are derived based on simulation tasks.

12. The apparatus of claim 11, wherein the processing module obtains the efficiency statistics of the scheduling schemes corresponding to the first combinations based on the simulation task by:

and executing the simulation task of vehicle scheduling aiming at each first combination in the plurality of first combinations respectively to obtain the efficiency statistical data of the scheduling schemes corresponding to the plurality of first combinations respectively.

13. The apparatus of claim 11, wherein the processing module obtains the efficiency statistics of the scheduling schemes corresponding to the first combinations based on the simulation task by:

and acquiring efficiency statistical data of scheduling schemes respectively corresponding to the plurality of first combinations, which are obtained based on the simulation tasks and stored in advance.

14. The apparatus of claim 13, wherein the processing module obtains the efficiency statistics of the scheduling schemes corresponding to the first combinations as follows:

combining the factor sub-items of each factor item to obtain a plurality of second combinations; wherein, each factor sub-item included in the second combination belongs to different factor items respectively;

executing simulation tasks of vehicle scheduling respectively aiming at each second combination to generate efficiency statistical data of scheduling schemes corresponding to a plurality of second combinations respectively;

the second combination comprises the first combination, and the efficiency statistical data of the scheduling schemes respectively corresponding to the second combinations comprise the efficiency statistical data of the scheduling schemes respectively corresponding to the first combinations.

15. The apparatus of claim 14, the processing module further to:

updating the stored efficiency statistics of the scheduling schemes respectively corresponding to the plurality of second combinations in response to the factor item and/or the factor sub-item being updated.

16. The apparatus of any one of claims 11 to 15, wherein the scheduling scenario comprises a mine operation scenario and the simulation task comprises a mine operation based vehicle scheduling task.

17. 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 1-8.

18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.

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