WO2022095813A1 - 一种业务数据处理方法、设备以及可读存储介质 - Google Patents

一种业务数据处理方法、设备以及可读存储介质 Download PDF

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Publication number
WO2022095813A1
WO2022095813A1 PCT/CN2021/127848 CN2021127848W WO2022095813A1 WO 2022095813 A1 WO2022095813 A1 WO 2022095813A1 CN 2021127848 W CN2021127848 W CN 2021127848W WO 2022095813 A1 WO2022095813 A1 WO 2022095813A1
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Prior art keywords
driving
predicted
target
remote control
network
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PCT/CN2021/127848
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English (en)
French (fr)
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雷艺学
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腾讯科技（深圳）有限公司
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Publication of WO2022095813A1 publication Critical patent/WO2022095813A1/zh
Priority to US17/978,853 priority Critical patent/US20230045979A1/en

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0011—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement
    • G05D1/0022—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement characterised by the communication link
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
    • G05D1/0282—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal generated in a local control room
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0285—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using signals transmitted via a public communication network, e.g. GSM network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control

Definitions

  • the present application relates to the field of computer technologies, and in particular, to a business data processing method, device, and readable storage medium.
  • online shopping has played an indispensable role in people's daily life, and online shopping behaviors such as ordering and shopping are bound to generate demand for item delivery.
  • Embodiments of the present application provide a business data processing method, device, and readable storage medium, which can reduce logistics costs.
  • the embodiments of the present application provide a service data processing method, and the method is executed by a computer device, including:
  • the remote control driving request includes the business service information of the target item;
  • Unmanned vehicles refer to tools used to transport target items.
  • an embodiment of the present application provides a service data processing apparatus, and the apparatus is deployed on computer equipment, including:
  • the request acquisition module is used to obtain the remote control driving request for the target item;
  • the remote control driving request includes the business service information of the target item;
  • a driving data determination module configured to determine the predicted driving data associated with the target item according to the business service information
  • the instruction generation module is used to generate remote control driving instructions according to the predicted driving data
  • the instruction sending module is used to send the remote control driving instruction to the unmanned vehicle, so that the unmanned vehicle can perform automatic driving through the remote control driving instruction.
  • the unmanned vehicle refers to a tool used for transporting target items.
  • An aspect of the embodiments of the present application provides a computer device, including: a processor and a memory;
  • the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to execute the method in the embodiments of the present application.
  • An aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the method in the embodiments of the present application is executed.
  • a computer program product or computer program including computer instructions stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in one aspect of the embodiments of the present application.
  • remote driving technology is introduced in the logistics distribution service of items, which can control unmanned vehicles to transport items, realize unmanned logistics distribution of items, and save a lot of labor costs.
  • a remote control driving request for the target item is obtained, and the predicted driving data associated with the target item is determined according to the business service information in the remote control driving request, so as to generate a remote control driving instruction according to the predicted driving data.
  • this application can provide unmanned logistics distribution services for items based on remote driving technology, integrate unmanned logistics distribution with unmanned driving, and realize personalized smart logistics based on remote driving technology. The whole process is automated without manual participation. , which can save logistics costs.
  • FIG. 1 is a network architecture diagram provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of a scenario provided by an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a business data processing method provided by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a scenario for allocating network communication resources provided by an embodiment of the present application.
  • FIG. 5 is a scene architecture diagram of a remote driving provided by an embodiment of the present application.
  • FIG. 6 is a functional architecture diagram of an unmanned logistics distribution service system provided by an embodiment of the present application.
  • FIG. 7 is a flow diagram of an unmanned logistics distribution service provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a service data processing apparatus provided by an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
  • Artificial Intelligence is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence.
  • Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making.
  • Artificial intelligence technology is a comprehensive discipline, involving a wide range of fields, including both hardware-level technology and software-level technology.
  • the basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • artificial intelligence technology has been researched and applied in many fields, such as common smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned driving, autonomous driving, drones It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important value.
  • the solutions provided in the embodiments of the present application belong to the automatic driving technology under the field of artificial intelligence.
  • Autonomous driving technology usually includes high-precision maps, environmental perception, behavioral decision-making, path planning, motion control and other technologies.
  • Self-driving technology has a wide range of application prospects.
  • FIG. 1 is a network architecture diagram provided by an embodiment of the present application.
  • the network architecture may include a user terminal cluster, a server portal, a business service background, a remote control platform, and a driver cluster.
  • the user terminal cluster may include one or more user terminals.
  • the multiple user terminals may include user terminals 100a, user terminals 100b, user terminals 100c, . . . , user terminals 100n; as shown in FIG.
  • the user terminals 100n may be respectively connected to the server portal via a network, so that each user terminal can perform data interaction with the server portal through the network connection.
  • a driver-end cluster may include one or more driver-ends, and the number of driver-ends will not be limited here.
  • the plurality of driving terminals may include a driving terminal 1000a, a driving terminal 1000b, ... a driving terminal 1000n; as shown in FIG. Network connection, so that each driver can exchange data with the remote control platform through the network connection.
  • each user terminal shown in FIG. 1 can be installed with a target application, and when the target application runs in each user terminal, data interaction can be performed with the server portal shown in FIG. 1 respectively. , so that the server portal can receive service data from each user terminal.
  • the target application may include an application with a function of displaying data information such as text, image, audio, and video.
  • the application can be an ordering application, which can be used by the user to order food; the application can also be a shopping application, which can be used by the user to purchase items.
  • the server portal in this application can obtain business data according to these applications.
  • the business data can be the user's logistics distribution requirement information for an item.
  • the user terminal can send the logistics distribution requirements for the clothing to the server portal; then, the server portal can obtain the distribution destination address information of the clothing, clothing items according to the logistics distribution requirements.
  • Attribute information volume, weight and other attributes, namely item attributes), user information, etc. (for example, user name, user contact information, etc.), then the delivery destination address information, clothing attribute information, user information and other information can be used as the business data.
  • the server portal can generate a transportation service request according to the service data, and send the transportation service request and the service data to the service backend together with the service data.
  • the transportation service request includes these service data.
  • the business service background can generate business service information according to the business data, and generate a remote driving request including the business service information; then, the business service background can send the remote driving request to the remote control platform, and the remote control platform can The business service information in the remote driving request, determine the predicted driving data for the clothing, including the predicted driving route and the predicted driving time, and according to the service level (Service-Level Agreement, SLA) parameters agreed in the network configuration rules (which can include Network coverage and network connection quality), determine the network coverage and network connection quality corresponding to the predicted driving route and predicted driving time; then, the remote control platform can be based on the network coverage and network connection quality.
  • SLA Service-Level Agreement
  • the driver terminal allocates corresponding network communication resources to the driver terminal 100b).
  • the remote control platform can instruct the network base station according to the network coverage and network connection quality, and allocate network communication resources for the target driver to satisfy the network coverage and network connection quality. In this way, when the target driving terminal is driving on the predicted driving route in the predicted driving time, it is within the coverage of the network and is in a network connection state.
  • the remote control platform can generate a remote control driving instruction, and send the remote control driving instruction to the target driver. Because the target driver has been allocated network communication resources, the target driver can receive the remote control driving instructions, and perform automatic driving through the remote control driving instructions, so as to carry out the logistics and transportation of the clothing.
  • the remote control platform can obtain the current position and status of the target driver on time (for example, every 30 minutes) during the logistics and transportation of the clothing, generate a status position report and send it to the business service background, while the business service The background can send the state location report to the server portal, and the server portal can return the state location report to the user terminal. Then, the user terminal can output the status and position report of the target driver in the display interface, and the user can check the current position and status of the clothes purchased by himself.
  • one user terminal may be selected as a target user terminal among multiple user terminals, and the user terminal may include: a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart TV, a smart speaker, a desktop computer, a smart Smart terminals that carry data processing functions (eg, text data display function, video data playback function, music data playback function) such as watches and in-vehicle devices, but are not limited thereto.
  • the user terminal 100a shown in FIG. 1 may be used as the target user terminal, and the target user terminal may be integrated with the above-mentioned target application. In this case, the target user terminal may use the target application and the server portal data exchange between them.
  • the server portal can obtain the delivery destination address information of the necklace, user information (for example, user name, contact information, etc.) and the necklace's shipping address information (that is, the necklace's delivery origin address information) can be obtained. ; Then, the server portal can generate a transportation service request for the necklace, and send the transportation service request and receiving address information, user information, shipping address information, etc.
  • a target application such as a shopping application
  • the server portal can generate a transportation service request for the necklace, and send the transportation service request and receiving address information, user information, shipping address information, etc.
  • the business service backend can The receiving address information, user information, shipping address information and other information carried in the business request, determine the expected transportation time (expected transportation time range), and generate business service information; the business service background can generate a remote driving request including the business service information , and send the remote driving request to the remote control platform; then, the remote control platform can determine the predicted driving route and predicted driving time for the necklace according to the remote driving request, and according to the SLA parameters agreed in the network configuration rules, Determine the quality of service parameters (including network coverage and network connection quality) corresponding to the predicted travel route and the predicted travel time; then, the remote control platform can be used to transport the necklace according to the network coverage and network connection quality.
  • the remote control platform can generate remote control driving instructions and send the remote control driving instructions to the target driver. Because the target driver has been allocated network communication resources, the target driver The terminal can receive the remote control driving instruction, and perform automatic driving through the remote control driving instruction, so as to carry out the logistics distribution of the necklace.
  • the business service background shown in FIG. 1 can be integrated into the remote control platform in the form of functional modules. That is to say, the remote control platform can receive data from the server portal (for example, transportation service request and receiving address information, user information, shipping address information, item attribute information, etc.), and determine the expected transportation time, so that it can Determine the predicted travel route and predicted travel time for the item (for example, the above necklace), and determine the service quality parameters corresponding to the predicted travel route and the predicted travel time according to the SLA parameters agreed in the network configuration rules, and according to the service quality parameter to generate a remote driving request.
  • the server portal for example, transportation service request and receiving address information, user information, shipping address information, item attribute information, etc.
  • the expected transportation time so that it can Determine the predicted travel route and predicted travel time for the item (for example, the above necklace)
  • the service quality parameters corresponding to the predicted travel route and the predicted travel time according to the SLA parameters agreed in the network configuration rules, and according to the service quality parameter to generate a remote driving request.
  • server portal shown in FIG. 1 can also be integrated into the user terminal in the form of functional modules.
  • the methods provided in the embodiments of the present application may be executed by a computer device, and the computer device includes but is not limited to a user terminal or a remote control platform.
  • the remote control platform may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, Cloud servers for basic cloud computing services such as cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.
  • the user terminal and the remote control platform may be directly or indirectly connected through wired or wireless communication, which is not limited in this application.
  • FIG. 2 is a schematic diagram of a scenario provided by an embodiment of the present application.
  • the server portal shown in FIG. 2 may be the server portal shown in FIG. 1, and the business service background shown in FIG. 2 may be the business service background shown in FIG. 1.
  • the user terminal M may be any user terminal selected in the user terminal cluster of the embodiment corresponding to FIG. 1, for example, the user terminal may be the above-mentioned user terminal 100b; and the driver's terminal E shown in FIG. 2 may be Any driver terminal selected from the driver terminal cluster in the embodiment corresponding to FIG. 1 , for example, the driver terminal may be the above-mentioned user terminal 1000b.
  • user M uses a shopping application to initiate a shopping request for necklaces.
  • user terminal M can send the shopping request of user M to the server portal ;
  • the server portal can generate a transportation service request, and associate the transportation service request with the item attributes, driving address information (receiving address information, shipping address information), user information (receiving user's name, contact information, etc.) and other information will be sent to the business service background together.
  • the business service background can determine the expected transportation time, and generate business service information that includes the driving address information and the expected transportation time; then, the business service background can determine the expected transportation time range corresponding to the driving address information, and generate information including item attributes, travel Address information (receiving address information, shipping address information), user information (receiving user's name, contact information, etc.), and business service information for information such as transportation expected time ranges.
  • the business service background can generate a remote driving request according to the business service information, and send the remote driving request to the remote control platform;
  • the remote control platform can determine the predicted driving route and prediction according to the business service information in the remote driving request Travel time, and determine the network connection range and network connection quality corresponding to the predicted travel route and predicted travel time according to the SLA parameters agreed by the network configuration rules between the business service background and the remote control platform; for example, the remote control platform can be based on
  • the network configuration rule obtains the service quality parameters corresponding to the predicted driving route (which may include network coverage and network connection quality). Obtain the network coverage and network connection quality agreed to by city R in the network configuration rules. At the same time, obtain the network coverage and network connection quality agreed to by city U in the network configuration rules.
  • the remote control platform can also obtain the service quality parameters corresponding to the predicted travel time (which may include network coverage and network connection quality) according to network configuration rules.
  • the predicted travel time is 13:00-14:30
  • the The control platform can match the predicted travel time with the set of agreed time periods included in the network configuration rules, determine the time range to which the predicted travel time belongs to the agreed time period 13:00-15:00, and the service server can obtain the network configuration rules
  • the network coverage and network connection quality agreed in the time range of 13:00-15:00, and the network coverage and network connection quality corresponding to the time range of 13:00-15:00 are determined as the predicted travel time QoS parameters corresponding to 13:00-14:30.
  • the remote control platform may allocate network communication resources corresponding to the quality of service parameters to the driver terminal 1000b according to the network connection range and network connection quality.
  • the service quality parameter can be selected according to the best choice ( For example, the one with larger network coverage and higher network connection quality) can be selected as the final service quality parameter.
  • the remote control platform can generate a remote control driving command according to the predicted driving route and the predicted driving time, and send the remote control driving command to the driving terminal 1000b; it should be understood that the driving terminal 1000b has been allocated with network communication resources , then the driving terminal 1000b can receive the remote control driving instruction, and perform automatic driving by giving the remote control driving instruction, so as to perform unmanned logistics distribution of the necklace.
  • the remote control platform can obtain the location and status of the driving end 1000b on time (for example, every 10 minutes) (for example, the smooth traffic Congestion status, etc.), and generate a position status report and send it to the business service background, the business service background can forward the position status report to the server portal, the server portal can forward the position status report to the user terminal M, and the user terminal M can The position and status in the position status report (that is, the current position and current status of the necklace) are displayed on the display interface, and the user M can view the position and status of the necklace.
  • time for example, every 10 minutes
  • FIG. 3 is a schematic flowchart of a service data processing method provided by an embodiment of the present application.
  • the method can be implemented by a user terminal (for example, the user terminal shown in FIG. 1 and FIG. 2 above), a server portal (for example, the server portal shown in FIG. 1 and FIG. 2 above), a remote control platform (such as the above-mentioned FIG. 1 )
  • the remote control platform in the corresponding embodiment) and the business service background (such as the business service background in the embodiment corresponding to FIG. 1 ) are jointly executed.
  • the service data processing method may include at least the following S101-S103:
  • the server portal can obtain the user's receiving address information (that is, the delivery destination address information of the necklace), user information (such as the receiving user's user name, contact information, etc.) and the necklace's information according to the demand.
  • the user's receiving address information that is, the delivery destination address information of the necklace
  • user information such as the receiving user's user name, contact information, etc.
  • Shipping address information that is, the address information of the delivery origin of the necklace
  • item attributes and other information that is, the server portal can use the item as a target item, and generate a transport service request for the target item, and the transport service request It is sent to the business service background together with the receiving address information, shipping address information, item attributes and user information.
  • the business service background can generate business service information according to the transportation business request, and generate a remote driving request according to the business service information.
  • the specific method for the business service background to generate the remote driving request may be as follows: the business service background may first obtain the transportation business request for the target item (that is, the transportation request generated by the user's shopping or delivery needs), and then obtain the transportation business request.
  • the item attributes and driving address information of the target item in the request (which may include the receiving address information and shipping address information of the item); the business service background can obtain the initial transportation time range corresponding to the driving address information according to the business configuration rules, according to the driving address information,
  • the initial shipping time frame and item attributes can generate business service information for the target item; subsequently, a remote control driving request can be generated that includes the business service information.
  • the item attribute may refer to the volume attribute (item volume), weight attribute (item weight), quantity attribute (item quantity), etc. of the target item;
  • there may be transportation pressure on the transportation vehicles, driving roads, etc. when the items are larger in size, the weight of the items is larger, or the number of items is large, the timeliness requirements for the transportation of the items can be appropriately relaxed, that is to say , on the basis of the initial transportation time range, increase the transportation waiting time range to obtain the expected transportation time range, and generate business service information according to the expected transportation time range; the following will take the item attributes including the item volume as an example to analyze the driving conditions according to the driving conditions.
  • the range of the waiting time for transportation corresponding to the target item can be determined according to the volume range to which the item volume belongs; then, the initial The transportation time range and the transportation waiting time range are added together to obtain the expected transportation time range of the target item; business service information for the target item can be generated according to the driving address information, item attributes and the expected transportation time range.
  • the business configuration rule can be configured for the policy negotiation between the server portal and the business service background regarding the business of the target item, and the significance of the business configuration rule is to support the authentication between the server portal and the business service background. , billing and configuration of service content and service quality of target items.
  • Service configuration rules may include, but are not limited to, the following: service classification, location services, big data services, billing services, and authentication services.
  • Business classification business classification can be applied to separate and independently deployed business service backgrounds. That is to say, the business service backend can be divided into business service backend A and business service backend B.
  • business service backend A is mainly toC (personal-oriented) business (small volume, small weight, and small number of items). business) to provide services;
  • business service background B mainly provides services for toB (enterprise-oriented) business (logistics business with large items or heavy items or a large number of items).
  • Location service location service is mainly used to extract location information, that is to say, after receiving the transportation service request from the server entrance, the business service background can extract the information related to the location (for example, the receiving address information, The delivery address information is extracted), and business service information is generated from the extracted location information.
  • Big data service big data service is mainly used for analysis, which can analyze the expected transportation time (initial transportation time range) required to transport the target item from the shipping address information to the receiving address information, and the probability that the expected transportation time can satisfy the customer, etc. .
  • Billing service the billing service is mainly used to interact with the business service background, and execute the billing function according to the specific billing policy of the unmanned logistics service.
  • the business service background is the logistics service background corresponding to the express company, and the logistics service background has a specific billing strategy. If the logistics service of the express company is to be used for transportation, the billing strategy of the corresponding logistics service background needs to be adopted. to be billed.
  • authentication service is mainly used to authenticate the business service background to avoid forgery and denial.
  • the server portal can classify the business of the target item according to the item attribute.
  • the business of the target item is divided into toC business; then, the server portal can generate a transportation business request, and send the transportation business request to the toC service background (which has passed the authentication); then, the toC service background can obtain the business configuration rules according to the business configuration rules.
  • the business service background can extract location information, determine the expected transportation time, calculate the transportation cost according to the business configuration rules, and generate business service information from the location information, expected transportation time, and calculate the transportation cost; then, the toC business service background can be based on the business service.
  • the information generates a remote control driving request, and sends the remote control driving request to the service server.
  • the server portal can classify the business of the target item into toB services; then, the server portal can also generate a transportation service request, and Send the transportation service request to the toB business service backend; then, the toB business service backend can also perform location extraction, determine expected transportation time, perform billing and other functions according to business configuration rules, and generate information including location information, expected transportation time and transportation. Fees for business service information.
  • the business service background can generate prompt information about the relaxation of the time limit requirement (prompt information about the expected transportation time extension), and display the time limit request relaxation prompt information on the display interface of the user terminal to remind the user that the time limit requirement can be relaxed (extended time limit).
  • Expected transportation time if the user decides to relax the time limit requirement, the user can be given corresponding tariff incentives (that is, reduce part of the transportation cost), thus, the updated expected transportation time (expected transportation time range) and updated
  • the updated transportation cost can be generated, so that business service information including location information (driving address information), a transportation expected time range, and the updated transportation cost can be generated.
  • the business service background can also be deployed uniformly, that is, business classification is no longer based on item attributes, and the server portal generates transportation after receiving the user's shopping demand.
  • S102 Determine predicted travel data associated with the target item according to the business service information.
  • the remote control platform can receive the remote driving request from the business service background, and determine the predicted driving data associated with the target item according to the business service information in the remote driving request, and the predicted driving data may include the predicted driving route and Predict travel time.
  • the specific method for determining the predicted travel route associated with the target item according to the business service information may be as follows: firstly, the travel address information and the expected transportation time range in the business service information may be obtained; then, The initial predicted travel route of the target item can be determined according to the travel address information; then, the predicted travel time range corresponding to the initial predicted travel route can be determined. If the predicted travel time range is within the expected transportation time range, the initial predicted travel route is determined as The predicted travel route corresponding to the target item.
  • the remote control platform predicts the travel route of the target item (that is, obtains the predicted travel route)
  • the travel time of the predicted travel route should meet the timeliness requirement (that is, the expected time range of transportation) in the business service information; if the business
  • the initial predicted travel route determined by the server includes an initial predicted travel route 1 and an initial predicted travel route 2.
  • the predicted travel time range of the initial predicted travel route 1 is 11:00-12:00
  • the predicted travel time range of the initial predicted travel route 2 is 11:00-15:00, because the predicted travel time range 11:00-15:00 is not within the expected transportation time range of 10:00-14:00
  • the initial predicted travel route 2 can be determined as not meeting the timeliness requirements
  • the initial predicted travel route 2 can be eliminated, and the initial predicted travel route 1 can be used as the final predicted travel route.
  • the one with the smallest predicted travel time range may be selected from the multiple initial predicted travel routes as the final predicted travel route of the target item.
  • the initial predicted travel route determined by the remote control platform includes an initial predicted travel route 1 and an initial predicted travel route 2, the predicted travel time range of the initial predicted travel route 1 is 11:00-12:00, and the predicted travel time of the initial predicted travel route 2 is The travel time range is 11:00-12:30, because the predicted travel time ranges of 11:00-12:00 and 11:00-12:30 are both in the expected transportation time range of 10:00-14:00, you can use Both the initial predicted travel route 1 and the initial predicted travel route 2 are determined to meet the timeliness requirements; but because the predicted travel time range of 11:00-12:00 is smaller than the predicted travel time range of 11:00-12:30, the initial predicted travel time can be Route 1 is used as the final predicted driving route.
  • the specific method for determining the predicted travel time associated with the target item according to the business service information may be as follows: firstly, the expected transportation time range in the business service information may be obtained; Obtain the congested travel time period within the time period; wherein, the congested travel time period refers to the time period in which the probability of occurrence of travel congestion is greater than the probability threshold; then, the congested travel time period in the agreed travel time period can be deleted to obtain candidate travel time period; then, the predicted travel time period of the target item can be selected from the candidate travel time periods, and the travel time composed of the predicted travel time period is determined as the predicted travel time of the target item; the predicted travel time is within the expected transportation time range.
  • the morning and evening peak periods can be avoided, that is, the time period that is likely to cause traffic congestion can be avoided, thereby reducing traffic congestion.
  • the remote control platform determines the predicted driving data associated with the target item according to the business service information
  • it can also be: acquiring the driving address information in the business service information and Expected time range of transportation; from the network configuration rules, obtain the network coverage and network connection quality associated with the driving address information and the expected transportation time range; The network coverage and network connection quality associated with the expected time range of transportation are used to schedule the transportation of the target items to obtain the predicted driving data.
  • the predicted travel data may include a predicted travel route and a predicted travel time.
  • the present application formulates a network configuration rule, in which the network coverage is allocated for the driving area and the driving time period respectively, and the network connection quality is agreed;
  • the network coverage and network connection quality corresponding to the area can also be determined, and the coverage and network connection quality corresponding to each driving time period can also be determined.
  • a network configuration rule in which the network coverage is allocated for the driving area and the driving time period respectively, and the network connection quality is agreed;
  • the network coverage and network connection quality corresponding to the area can also be determined, and the coverage and network connection quality corresponding to each driving time period can also be determined.
  • the coverage and network connection quality corresponding to each driving time period can also be determined.
  • the present application when determining the predicted travel route of the target article, can first determine the initial predicted travel route according to the travel start point and travel end point, and then determine the time to meet the expected transportation time in these initial predicted travel routes.
  • the route within the range is used as a candidate driving route; further, a target transportation safety level can be determined according to the item attribute of the target item. For example, if the item attribute is that the item is bulky and the item is heavy, it is easy to cause pressure on the transportation road.
  • the volume and the weight of the item determine a target transportation safety level (for example, 8); further, the transportation safety level of the target item in these candidate travel routes can be determined, and a candidate travel route that satisfies the target transportation safety level can be determined; for example,
  • the candidate travel route includes candidate travel route 1 and candidate travel route 2.
  • the safety level of transporting the target item in the candidate travel route 1 is 9, and the safety level of transporting the target item in the candidate travel route 2 is 9, so the target If the transportation safety levels of the items in the candidate travel route 1 and the candidate travel route 2 both meet the target transportation security level 8, the candidate travel route 1 and the candidate travel route 2 can both be used as routes for transporting the target item.
  • the final predicted travel route of the target item can be determined according to the network coverage and network connection quality corresponding to the candidate travel route (for example, a candidate travel route with a larger network coverage and higher network connection quality can be selected as the final prediction). driving route).
  • the final predicted driving route can be determined according to the network coverage and network connection quality corresponding to the candidate driving route, and If the transportation safety levels of the target items in the candidate driving routes are inconsistent and all meet the target transportation security level, the optimal selection can be made according to the transportation security level (for example, select the one with a higher transportation security level), and no longer consider the candidate driving routes. Corresponding network coverage and network connection quality.
  • the above description is to first determine the candidate travel route according to the expected transportation time range, and then determine the final travel route according to the target transportation level and the network coverage and network connection quality corresponding to the candidate travel route.
  • the predicted driving route can also be generated by comprehensively considering the expected time range of transportation, item attributes, network coverage and network connection quality. That is, the starting point and the ending point of the route can be determined according to the driving address information, so that the driving area between the starting point and the ending point of the route can be obtained, and the transportation safety level of the target item in these driving areas can be obtained according to the network coverage and network connection quality.
  • the network coverage of these target driving areas is large, the quality level of network connection quality is high, and the transportation safety level of the target items in these target driving areas all meet the target transportation security level; then, you can A final predicted travel route is generated based on the transportation expected time frame and these target travel areas.
  • target travel time periods are determined.
  • the network coverage of these target travel time periods is large, the quality level of network connection quality is high, and the transportation safety level of the target items in these target time periods is satisfied.
  • a target transportation safety level; then, among these target travel time periods, a predicted travel time period may be selected for making up a predicted travel time that satisfies the expected time frame of transportation.
  • this application can jointly determine the driving route and driving time of the target item according to the item volume, item weight, transportation safety, destination, and network coverage and network connection quality of the target item configured according to the network configuration rules. Carry out transportation scheduling. For example, if the item volume, item weight, and transportation safety of item A and item B are not much different, and the destination is the same, then item A and item B can be aggregated in a certain area, so that unified transportation can be carried out, and then Transportation resources can be saved.
  • the above-mentioned business service background can be integrated and deployed in the remote control platform in the form of functional modules, so that the remote control platform can also perform the functions performed by the business service background.
  • S103 Generate a remote control driving instruction according to the predicted driving data, and send the remote control driving instruction to the unmanned vehicle, so that the unmanned vehicle can perform automatic driving through the remote control driving instruction.
  • the service quality parameters corresponding to the predicted driving data can also be obtained according to the network configuration rules, and the service quality can be allocated to the unmanned vehicle The network communication resource indicated by the parameter.
  • sending the remote control driving instructions to the unmanned vehicle may actually be sending the remote control driving instructions to the unmanned vehicle to which the network communication resources are allocated.
  • the network supports remote control of unmanned vehicles for driving, it can also make reasonable use of network communication resources, increase the reliability of unmanned driving, and realize the provision of unmanned vehicles based on remote driving technology and network communication. People logistics and distribution services.
  • the unmanned vehicle may refer to any driving terminal in the driving terminal cluster shown in the above-mentioned embodiment corresponding to FIG. 1 , for example, the unmanned vehicle may be the driving terminal 100a.
  • the remote control platform can obtain the service quality parameters corresponding to the predicted driving data according to the network configuration rules.
  • the quality of service parameters may include network coverage and network connection quality.
  • the specific method for obtaining the QoS parameters corresponding to the predicted driving data according to the network configuration rules may be as follows: firstly, the QoS parameters may be obtained from the network configuration rules according to the predicted driving data; wherein, the QoS parameters include the agreed target network coverage and The agreed target network connection quality; then, according to the target network coverage and the target base station indicated by the target network connection quality, the unmanned vehicle can be allocated network communication resources to meet the target network coverage and target network connection quality. ; the target base station is located in the area traversed based on the predicted driving data.
  • the specific method for obtaining the QoS parameters corresponding to the predicted driving route according to the network configuration rule may be as follows: firstly, the set of agreed driving areas in the network configuration rule may be obtained; The set includes at least two area information, and the respectively agreed network coverage and network connection quality for each area information; then, at least one predicted travel area included in the predicted travel route can be obtained, and the at least one predicted travel area is associated with the agreed The set of travel areas is matched, and the target area information is determined from at least two area information; wherein, the target area information covers the predicted travel area; then, the network coverage agreed for the target area information can be determined as the target network of the predicted travel area Coverage, the network connection quality agreed for the target area information is determined as the target network connection quality of the predicted driving area.
  • the specific method for obtaining the quality of service parameter corresponding to the predicted travel time according to the network configuration rule may be as follows: firstly, the set of agreed time periods in the network configuration rule may be obtained; The set includes at least two appointment time periods, and the respectively agreed network coverage and network connection quality for each appointment time period; then, the predicted travel time can be matched with the set of appointment time periods, and the at least two appointment time periods determine the target appointment time period in The network connection quality agreed upon by the segment is determined as the target network connection quality for the predicted travel time.
  • network-related policy configuration is performed between the network deployer (eg, operator deployer, network base station) and the remote control platform, that is, network configuration rules are formulated between the two parties. It lies in the service level parameters (Service-Level Agreement, SLA) suitable for unmanned logistics distribution services that can be provided by the agreed network (for example, the 5th generation mobile networks (5G) network).
  • SLA Service-Level Agreement
  • Network configuration rules include but are not limited to the following 3 rules:
  • the remote control mode may include a wire-by-wire mode and a route guidance mode.
  • the unmanned vehicle can be installed with a network communication module, and the service server (network remote control platform) can directly act on the driving control module of the unmanned vehicle through the wire control of the driving instructions for the implementation process to control the unmanned logistics distribution service.
  • the control-by-wire mode is suitable for roads with complex road conditions, that is to say, if the road conditions of the predicted driving route are complex, the remote control mode of the unmanned vehicle can be determined as the control-by-wire mode;
  • the control mode is also suitable for scenarios when the autonomous driving function level of the unmanned vehicle is low.
  • the remote control mode of the unmanned vehicle can be determined as the route guidance mode.
  • the remote control platform can determine the predicted driving route according to the driving address information in the business service information, and then obtain the predicted driving area included in the predicted driving route, and compare the predicted driving area with the parameters in the network configuration rules. Compare the driving areas, take the network coverage corresponding to the driving area covering the predicted driving area in the network configuration rule as the target network coverage of the predicted driving area, and use the network configuration rule covering the driving area of the predicted driving area. The corresponding network connection quality is used as the target network connection quality of the predicted driving area. By determining the target network coverage and target network connection quality of each predicted driving area, the target network coverage and target network connection quality of the entire predicted driving route can be determined.
  • the remote control platform can also determine the predicted travel time according to the expected time range of transportation in the business service information. Then, the predicted travel time can be compared with the agreed time period in the network configuration rules, and the network configuration rules include The network coverage corresponding to the agreed time period of the predicted travel time is taken as the target network coverage of the predicted travel time, and the network connection quality corresponding to the reserved time period of the predicted travel time is included in the network configuration rule as the target network coverage of the predicted travel time. Target network connection quality to predict travel time.
  • the predicted travel time is 12:00-14:00
  • the predicted travel time (the predicted travel time is a combination of the predicted travel time period 12:00-13:00 and the predicted travel time period 13:30-14:00) composition)
  • the predicted travel time 12:00-14:00 is in the travel time period 11:00-14:00 in the network configuration rule
  • the agreed network coverage and network connection quality, and the agreed network coverage of the agreed time period 11:00-14:00 is used as the target network coverage corresponding to the predicted travel time 12:00-14:00.
  • the network connection quality agreed in the travel time period 11:00-14:00 is taken as the target network connection quality corresponding to the predicted travel time 12:00-14:00.
  • the remote control driving command may include a first driving command and a second driving command.
  • the specific method for the remote control platform to generate the remote control driving instructions according to the predicted driving data can be as follows: firstly, the road condition complexity corresponding to the predicted driving data can be obtained, and then the remote control mode of the unmanned vehicle can be determined according to the network configuration rules and the road condition complexity.
  • the first driving command can be generated according to the predicted driving data; wherein, the first driving command is used to provide the driving direction and driving speed for the unmanned vehicle; and if the remote control If the mode is the route guidance mode, the second driving instruction can be generated according to the predicted driving data; wherein, the second driving instruction is used to provide driving route information for the unmanned vehicle, and the driving route information is used to guide the unmanned vehicle to analyze Get driving direction and driving speed.
  • the remote control modes configured in the network configuration rules can be determined to be used for transporting the target item.
  • the remote control mode of the unmanned vehicle different remote control modes can generate different remote control driving instructions.
  • the agreed remote control mode in the network configuration rules may be obtained first; Route guidance mode; if the complexity of the road condition is greater than the complexity threshold, the remote control mode of the unmanned vehicle is determined as the control-by-wire mode; if the complexity of the road condition is less than or equal to the complexity threshold, the automatic control mode of the unmanned vehicle is obtained.
  • the remote control mode of the unmanned vehicle will be determined as the wire-controlled mode; if the automatic driving function level meets the unmanned driving conditions, the unmanned traffic The remote control mode of the tool is determined to be the route guidance mode.
  • the quality of the network connection of the unmanned vehicle can be dynamically detected. That is, when the remote control mode of the unmanned vehicle is the wire control mode, the network connection quality of the unmanned vehicle can be detected. If the quality level corresponding to the network connection quality of the unmanned vehicle is lower than the level threshold, Then, a driving stop instruction can be generated, and the driving stop instruction can be sent to the unmanned vehicle, so that the unmanned vehicle can switch from the driving state to the driving stop state through the driving stop instruction. That is to say, during the transportation of the target item by the unmanned vehicle, if it is detected that the quality of the network connection is degraded, the unmanned vehicle can be parked nearby to avoid accidents.
  • the remote control mode of the unmanned vehicle is the wire control mode
  • the network connection quality of the unmanned vehicle is detected.
  • the quality level corresponding to the quality of the network connection is lower than the level threshold, and the autonomous driving function level of the unmanned vehicle meets the unmanned driving conditions, a mode switching instruction can be generated, and the mode switching instruction can be sent to the unmanned traffic The tool switches from the wire control mode to the route guidance mode through this mode switching instruction.
  • the mode switching strategy can be executed for the unmanned vehicle that meets the unmanned conditions, so that the unmanned traffic Instead of relying on network communication resources to receive control commands including driving directions, the tool can avoid accidents by relying on routes for autonomous driving.
  • the above service configuration rules stipulate that different charging policies are configured.
  • the network configuration rules also stipulate that different resource charging policies are configured, and different driving areas or driving time periods correspond to different networks. Coverage and network connection quality, and different network coverage and network connection quality also correspond to different resource prices. Therefore, in this application, the final transportation cost of the target item can be determined according to the charging policy in the service configuration rule and the charging policy in the network configuration rule.
  • the specific method for determining the final transportation cost of the target item may be as follows: firstly, the first consumption virtual asset (transportation cost) used for transporting the target item in the business service information may be obtained, and then, the service quality in the network configuration rule may be obtained.
  • the agreed virtual asset agreed by the parameters, and the agreed virtual asset is used as the second consumption virtual asset for transporting the target item; then, the target consumption for transporting the target item can be determined according to the first consumption virtual asset and the second consumption virtual asset virtual assets.
  • the quality evaluation of the transportation process of the unmanned vehicle can be carried out, and the entire transportation process of the unmanned vehicle can be evaluated according to the evaluation result.
  • Optimize the unmanned logistics service for example, optimize the SLA parameters agreed in the network configuration rules, optimize the business configuration rules, etc.
  • the specific method for evaluating the quality of the transportation process of the unmanned vehicle to generate the evaluation result may be: if the unmanned vehicle completes the transportation of the target item, the unmanned vehicle and the target item can be obtained.
  • the associated data statistics report wherein, the data statistics report includes the network coverage and network connection quality collected by the unmanned vehicle during transportation; then, the data statistics report can be sent to the user terminal, so that the user terminal Through the network coverage and network connection quality collected during transportation, a driving quality score is generated, and an evaluation result can be generated according to the driving quality score.
  • remote driving technology is introduced in the logistics distribution service of items, which can control unmanned vehicles to transport items, realize unmanned logistics distribution of items, and save a lot of labor costs.
  • a remote control driving request for the target item is obtained, and the predicted driving data associated with the target item is determined according to the business service information in the remote control driving request, so as to generate a remote control driving instruction according to the predicted driving data.
  • this application can provide unmanned logistics distribution services for items based on remote driving technology, integrate unmanned logistics distribution with unmanned driving, and realize personalized smart logistics based on remote driving technology. The whole process is automated without manual participation. , which can save logistics costs
  • network configuration rules are set, and different items have different predicted driving data.
  • different network communication resources can be configured for the unmanned vehicle, so that the autonomous vehicle can be configured with different network communication resources.
  • the unmanned vehicle is within the network coverage on the driving route of the driving starting point and the end point, and is connected to the network, which can support the remote control of the unmanned vehicle for driving through the network; at the same time, it can also be Reasonable utilization of communication resources; it can be seen that this application can provide unmanned logistics distribution services for items based on remote driving technology and network communication, and integrate unmanned logistics distribution and unmanned driving to realize personalized wisdom based on remote driving technology. Logistics, the whole process is automated, without manual participation, which can save logistics costs. It can be seen that when the network communication is a 5G network, the application can provide unmanned logistics distribution services for items based on the remote driving technology supported by 5G, and realize personalized smart logistics based on the 5G system.
  • FIG. 4 is a schematic diagram of a scenario for allocating network communication resources provided by an embodiment of the present application.
  • the predicted travel route from the driving point M to the driving destination N includes three predicted travel areas, namely, a predicted travel area 40a, a predicted travel area 40b, and a predicted travel area 40c.
  • a plurality of agreed driving areas in the network configuration rule may be acquired, and a predetermined driving area covering the predicted driving area 40a, a predetermined driving area covering the predicted driving area 40b may be acquired from the plurality of agreed driving areas, and the prediction The reserved travel area of the travel area 40c.
  • the reserved travel area covering the predicted travel area 40a is the reserved travel area 40A
  • the reserved travel area covering the predicted travel area 40b is the reserved travel area 40B
  • the reserved travel area covering the predicted travel area 40c is the reserved travel area Travel area 40C.
  • the network coverage area (the network coverage area 1 shown in FIG. 4 ) that is configured for the agreed driving area 40A in the network configuration rule can be obtained, then the network coverage area 1 can be used as the network coverage of the predicted driving area 40A
  • the network coverage (network coverage 2 shown in FIG. 4 ) configured for the agreed driving area 40B can be obtained, then the network coverage 2 can be used as the network coverage of the predicted driving area 40b; the same
  • the network coverage configured for the agreed driving area 40C can be obtained (the network coverage 3 shown in FIG. 4 ), then the network coverage 3 can be used as the network coverage of the predicted driving area 40c.
  • the total network coverage of the predicted driving route from the driving start point M to the driving end point N can be determined (as shown in FIG. 4 , including network coverage 1, network coverage 2, and network coverage 3).
  • the network configuration rule is also configured with network connection quality for each agreed driving area.
  • the corresponding network connection quality is the network connection quality of the agreed driving area 40A.
  • the network communication resources allocated by the unmanned vehicle are also the network connection quality corresponding to the network coverage area 1; when the unmanned vehicle is in the network coverage area 2, the corresponding network connection quality is that of the agreed driving area 40B.
  • Network connection quality the network communication resources allocated by the unmanned vehicle are also corresponding to the network connection quality and network coverage 2; when the unmanned vehicle is in the network coverage 3, the corresponding network connection quality is the agreed driving
  • the network connection quality of the area 40C and the network communication resources allocated to the driverless vehicle are also corresponding to the network connection quality and network coverage 3 .
  • FIG. 5 is an architecture diagram of a remote driving scenario provided by an embodiment of the present application. As shown in Figure 5, the scenario architecture is illustrated by taking the network as a 5G network as an example.
  • the scenario architecture diagram may include: the operator's 5G network, the remote control center (remote control platform), the 5G base station, and various unmanned vehicles (drivers).
  • the operator's 5G network can provide a reliable 5G network connection for the remote control center of remote driving, and this reliable 5G network connection needs to rely on the communication provided by the 5G network with a hard quality of service (Quality of Service, QoS) guarantee.
  • Data (Protocol Data Unit, PDU) session is provided. That is to say, the remote control center can control the unmanned vehicle for automatic driving. In this process, it is mainly provided by the operator's 5G network with hard QoS guarantee.
  • the PDU session is used to provide a reliable 5G connection for the remote control center. Through this 5G connection, the 5G base station can be instructed to allocate network communication resources to the unmanned vehicles. Therefore, the remote control center can control the unmanned vehicles that are allocated with network communication resources.
  • the vehicle is driving autonomously.
  • the 5G network in the above scenario architecture diagram is described by taking operator deployment as an example, but the 5G network can also be deployed by other deploying parties, which is not limited in this application.
  • FIG. 6 is a functional architecture diagram of an unmanned logistics distribution service system provided by an embodiment of the present application.
  • the functional architecture can include user application module, server portal, network deployer strategy and service platform, 5G remote control platform, toC service background and toB service background.
  • the interfaces between the various modules shown in FIG. 6 are connected in a bus manner. It should be understood that the various modules shown in FIG. 6 may be supported by a Service Based Architecture (SBA) interface.
  • SBA Service Based Architecture
  • the user application module can be any application that will generate logistics distribution requirements, for example, can be a shopping application, an ordering application, and the like.
  • the user application module can be used by the user to initiate a logistics distribution requirement for any item.
  • the server entry module is mainly used to receive the service request of the user application, and then the server entry module can initiate the service request to the toC service background or the toB service background. It should be understood that the server entry module can classify the service requests into toC-type services or toB-type services, and distribute and connect to the toC or toB service background.
  • the server portal For the specific method for classifying service requests by the server portal, reference may be made to the description of the service classification by the server portal in S101 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • server portals are deployed separately, that is, one server portal corresponds to one business service background, after receiving the service request, the server portal does not need to be classified, but only needs to be sent to the corresponding service background.
  • toC and toB service backend is a general term for services other than 5G remote control platforms, including location services, big data services, billing services, authentication services, etc.
  • the ToC service background mainly provides toC-type business services, and mainly receives toC-type business requests generated by the server entrance;
  • the toB service background mainly provides toB-type business services, and mainly accepts toB-type business requests generated by the server entrance.
  • the following will explain the various services provided by the toC and toB service backgrounds:
  • the location service is to extract the information related to the location according to the received toC or toB service request from the server entrance, and generate data that can be used by the 5G remote control platform to initiate an unmanned logistics delivery request.
  • Big data service can analyze the expected delivery time of the logistics distribution service and the probability of being able to meet customers.
  • Billing service The billing service is to perform the billing function according to a specific billing policy.
  • Authentication service is to authenticate the toC server or the toB server to avoid forgery and denial.
  • toB service background or the toC service background can be integrated and deployed in the 5G remote control platform.
  • the 5G remote control platform which is also the remote control center, is mainly used for policy services and network quality monitoring services.
  • policy service and network quality monitoring service are described below:
  • the network-related policy service configuration between the 5G remote control platform and the network deployment service platform can be configured through the connection between the application function module (Application Function, AF) and the network exposure function management module (Network Exposure Function, NEF). Interface to achieve, that is to say, AF can be used as a part of the 5G remote control platform functional modules to interact with the NEF in the network deployment service platform.
  • Application Function Application Function
  • NEF Network Exposure Function
  • the specific method for specifying SLA parameters may refer to the above-mentioned embodiment corresponding to FIG. 3 .
  • the description of the agreed configuration of the network configuration rule in the foregoing steps will not be repeated here.
  • This network quality monitoring service can dynamically detect the quality of the 5G network connection. When the quality of the 5G network connection drops, corresponding measures will be taken on the cloud and the driverless vehicle side. For example, when the quality of the network connection of the driverless vehicle is degraded, the driverless vehicle can be parked nearby according to a pre-configured policy.
  • FIG. 7 is a flow interaction diagram of an unmanned logistics distribution provided by an embodiment of the present application. As shown in Figure 7, the process can include:
  • the user application module sends the unmanned logistics distribution requirement to the server entrance.
  • the user can purchase any item in the user application of the user terminal.
  • the user application module can send the unmanned logistics distribution request initiated by the user to the server portal.
  • the server portal sends a transportation service request to the service background.
  • the server entrance can process the unmanned logistics distribution requirements, generate a transportation service request and send it to the service background, and can also send the item attributes (for example, item volume, item weight, etc.), receiving address information, sending The delivery address information, receiving user information, etc. are sent to the service background.
  • the service backend can include toC service backend and toB service backend
  • the server entrance can classify transportation business requests. If the transportation business request is of the toC category, the transportation business request will be distributed to the toC service backend. If the transportation business request is toB class, distribute the transportation business request to the toB service background.
  • the service background generates a remote driving request.
  • the service backend can generate business service information including item attributes (for example, item volume, item weight, etc.), receiving address information, shipping address information, receiving user information, and expected transportation time range, and then can generate business service information including business Remote driving requests for service information.
  • item attributes for example, item volume, item weight, etc.
  • business service information including business Remote driving requests for service information.
  • the service background sends a remote driving request to the 5G remote control platform.
  • the service background can send a remote driving request to the 5G remote control platform, and also send the business service information to the 5G remote control platform.
  • the 5G remote control platform determines predicted driving data according to the business service information, and determines SLA parameters corresponding to the predicted driving data.
  • the 5G remote control platform can determine the predicted travel data of the target item (which may include the predicted travel route and the predicted travel time) according to the business service information, and determine the SLA parameters corresponding to the predicted travel route and the predicted travel time through network configuration rules (Including network coverage, network connection quality, remote control mode, etc.).
  • the predicted travel data of the target item (which may include the predicted travel route and the predicted travel time) is determined according to the business service information, and the specific SLA parameters corresponding to the predicted travel route and the predicted travel time are determined through the network configuration rules.
  • the method please refer to the relevant description in the embodiment corresponding to FIG. 3 above. The description of the remote control platform determining the predicted driving data and determining the service quality parameter corresponding to the predicted driving data will not be repeated here.
  • the 5G remote control platform generates remote control instructions and sends them to the unmanned vehicle.
  • the 5G remote control platform sends a transportation completion notification to the service background.
  • a transportation completion notification may be generated, and the transportation completion notification may be sent to the service background.
  • the service background generates a driving quality score.
  • the service backend can generate a driving quality score for this unmanned logistics distribution service for quality assessment.
  • the service background sends a transportation completion notification to the server portal.
  • the server portal sends a transport completion notification to the user application module.
  • the 5G remote driving technology is introduced in the logistics distribution service of items, which can control the unmanned vehicle to transport the items, realize the unmanned logistics distribution of the items, and save a lot of labor costs; and
  • network configuration rules are set. Different items have different predicted driving data.
  • different network communication resources can be configured for unmanned vehicles, so as to realize The driverless vehicle is within the network coverage on the driving route from the driving start point and the end point, and is connected to the network, which can support the remote control of the driverless vehicle for driving through the network; at the same time, it can also communicate with the network.
  • this application can provide unmanned logistics distribution services for items based on 5G remote driving technology, integrate unmanned logistics distribution and unmanned driving, and realize personalized smart logistics based on 5G system, with full automation It can be carried out without manual participation, which can save logistics costs.
  • FIG. 8 is a schematic structural diagram of a service data processing apparatus provided by an embodiment of the present application.
  • the service data processing apparatus may be a computer program (including program code) running in computer equipment, for example, the service data processing apparatus is an application software; the service data processing apparatus may be used to execute the method shown in FIG. 3 .
  • the service data processing apparatus 1 may include: a request acquisition module 11 , a driving data determination module 12 , an instruction generation module 14 and an instruction transmission module 15 .
  • the request obtaining module 11 is used to obtain a remote control driving request for the target item; the remote control driving request includes the business service information of the target item;
  • a driving data determining module 12 configured to determine predicted driving data associated with the target item according to the business service information
  • an instruction generation module 14 configured to generate a remote control driving instruction according to the predicted driving data
  • the instruction sending module 15 is used to send the remote control driving instruction to the unmanned vehicle, so that the unmanned vehicle can perform automatic driving through the remote control driving instruction, and the unmanned vehicle refers to a tool for transporting target items .
  • the specific implementations of the request acquisition module 11 , the driving data determination module 12 , the instruction generation module 14 and the instruction sending module 15 may refer to the descriptions of S101 to S103 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the device further includes a resource allocation module 13, configured to obtain a quality of service parameter corresponding to the predicted driving data according to a network configuration rule, and allocate the network communication indicated by the quality of service parameter to the unmanned vehicle resource;
  • the instruction sending module 15 is specifically configured to send a remote control driving instruction to the unmanned vehicle to which the network communication resource is allocated.
  • the quality of service parameters include network coverage and network connection quality
  • the resource allocation module 13 may include: a service parameter acquisition unit 131 and a resource allocation unit 132 .
  • the service parameter obtaining unit 131 is configured to obtain the service quality parameter from the network configuration rule according to the predicted driving data; the service quality parameter includes the agreed target network coverage and the agreed target network connection quality;
  • the resource allocation unit 132 is used to allocate network communication resources for the unmanned vehicle to meet the target network coverage and the target network connection quality according to the target base station indicated by the target network coverage and the target network connection quality; the target base station is located at Area traveled based on predicted driving data.
  • the specific implementation of the service parameter acquisition unit 131 and the resource allocation unit 132 may refer to the relevant description in the embodiment corresponding to FIG. 3 above, which will not be repeated here.
  • the predicted driving data includes a predicted driving route
  • the service parameter obtaining unit 131 may include: an area set obtaining subunit 1311 , a target area obtaining subunit 1312 , and a first parameter determining subunit 1313 .
  • the area set obtaining subunit 1311 is used to obtain a set of agreed driving areas in the network configuration rule; the set of agreed driving areas includes at least two area information, and the respectively agreed network coverage and network connection quality for each area information;
  • the target area acquisition subunit 1312 is configured to acquire at least one predicted travel area included in the predicted travel route, match the at least one predicted travel area with the set of agreed travel areas, and determine the target area information in at least two area information; the target area information Cover the predicted driving area;
  • the first parameter determination sub-unit 1313 is used to determine the network coverage agreed by the target area information as the target network coverage of the predicted driving area, and the network connection quality agreed by the target area information as the target of the predicted driving area. Internet connection quality.
  • the specific implementation of the area set acquisition subunit 1311 , the target area acquisition subunit 1312 , and the first parameter determination subunit 1313 can be found in the relevant descriptions in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the predicted travel data includes predicted travel time
  • the service parameter acquisition unit 131 may include: a time set acquisition subunit 1314 , a target time determination subunit 1315 , and a second parameter determination subunit 1316 .
  • the time set acquisition subunit 1314 is used to acquire the set of appointment time periods in the network configuration rule; the set of appointment time periods includes at least two appointment time periods, and the respectively agreed network coverage and network connection quality for each appointment time period ;
  • target time determination subunit 1315 configured to match the predicted travel time with the set of appointment time periods, and determine the target appointment time period in at least two appointment time periods; the predicted travel time is in the target appointment time period;
  • the second parameter determination subunit 1316 is configured to determine the network coverage agreed for the target appointment time period as the target network coverage of the predicted travel time, and determine the network connection quality agreed for the target appointment time period as the predicted travel time target network connection quality.
  • time set acquisition subunit 1314 the target time determination subunit 1315, and the second parameter determination subunit 1316 can refer to the relevant description in the embodiment corresponding to FIG. 3, and will not be repeated here.
  • the remote control driving command includes a first driving command and a second driving command
  • the instruction generation module 14 may include: a mode determination unit 141 , an instruction generation unit 142 and an instruction generation unit 143 .
  • the mode determination unit 141 is configured to acquire the road condition complexity corresponding to the predicted driving data, and determine the remote control mode of the unmanned vehicle according to the network configuration rule and the road condition complexity;
  • an instruction generation unit 142 configured to generate a first driving instruction according to the predicted driving data if the remote control mode is the wire-controlled mode; the first driving instruction is used to provide a driving direction and a driving speed for the unmanned vehicle;
  • the instruction generation unit 143 is further configured to generate a second driving instruction according to the predicted driving data if the remote control mode is the route guidance mode; the second driving instruction is used to provide driving route information for the unmanned vehicle, and the driving route information is used for Guide the unmanned vehicle to analyze the driving direction and driving speed.
  • mode determination unit 141 the instruction generation unit 142 , and the instruction generation unit 143 may refer to the description in S103 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the mode determination unit 141 may include: a mode acquisition subunit 1411 and a mode determination subunit 1412 .
  • the mode acquisition subunit 1411 is used to acquire the agreed remote control mode in the network configuration rule; the agreed remote control mode includes the wire control mode and the route guidance mode;
  • a mode determination subunit 1412 configured to determine the remote control mode of the unmanned vehicle as the control-by-wire mode if the complexity of the road condition is greater than the complexity threshold;
  • the mode determination subunit 1412 is further configured to obtain the automatic driving function level of the unmanned vehicle if the complexity of the road condition is less than or equal to the complexity threshold, and if the automatic driving function level does not meet the unmanned conditions, then The remote control mode of the vehicle is determined as the wire control mode;
  • the mode determination subunit 1412 is further configured to determine the remote control mode of the unmanned vehicle as the route guidance mode if the automatic driving function level satisfies the unmanned driving condition.
  • mode acquisition subunit 1411 and the mode determination subunit 1412 can refer to the description in S103 in the embodiment corresponding to FIG. 3 above, and will not be repeated here.
  • the apparatus 1 may further include: a network quality detection module 16 and a driving stop module 17 .
  • the network quality detection module 16 is used for detecting the network connection quality of the unmanned vehicle when the remote control mode of the unmanned vehicle is the wire control mode;
  • the driving stop module 17 is configured to generate a driving stop instruction if the quality level corresponding to the network connection quality of the unmanned vehicle is lower than the level threshold, and send the driving stop instruction to the unmanned vehicle, so that the unmanned traffic The tool switches from the drive state to the drive stop state by the drive stop command.
  • the request obtaining module 11 may include: a service request obtaining unit 111 , a service information generating unit 112 and a request generating unit 113 .
  • the service request obtaining unit 111 is configured to obtain a transportation service request for the target item, and obtain the item attribute and driving address information of the target item in the transportation service request;
  • the business information generating unit 112 is configured to obtain the initial transportation time range corresponding to the driving address information according to the business configuration rule, and generate business service information for the target item according to the driving address information, the initial transportation time range and the item attribute;
  • the request generating unit 113 is configured to generate a remote control driving request including business service information.
  • the specific implementations of the service request obtaining unit 111 , the service information generating unit 112 and the request generating unit 113 may refer to the description in S101 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • item attributes include item volume
  • the service information generation unit 112 may include: a range determination subunit 1121 and a service information generation subunit 1122 .
  • the range determination subunit 1121 is used to determine the transport waiting time range corresponding to the target item according to the volume range to which the item volume belongs;
  • the range determination sub-unit 1121 is further configured to add the initial transportation time range and the transportation waiting time range to obtain the expected time range of transportation of the target item;
  • the business information generating subunit 1122 is configured to generate business service information for the target article according to the travel address information, the article attribute and the expected transportation time range.
  • the specific implementation manner of the range determination subunit 1121 and the service information generation subunit 1122 may refer to the description in S101 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the predicted driving data includes a predicted driving route
  • the driving data determination module 12 may include: an information acquisition unit 121 and a route determination unit 122 .
  • the information acquisition unit 121 is used to acquire the travel address information and the expected transportation time range in the business service information;
  • a route determination unit 122 configured to determine an initial predicted travel route of the target item according to the travel address information
  • the route determination unit 122 is further configured to determine the predicted travel time range corresponding to the initial predicted travel route. If the predicted travel time range is within the expected transportation time range, the initial predicted travel route is determined as the predicted travel route corresponding to the target item.
  • the specific implementation of the information acquisition unit 121 and the route determination unit 122 can be referred to the description in S102 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the predicted driving behavior data includes predicted driving time
  • the driving data determination module 12 may include: a desired range acquisition unit 123 , a time period acquisition unit 124 , a time period deletion unit 125 , and a time period selection unit 126 .
  • an expected range obtaining unit 123 configured to obtain the expected time range of transportation in the business service information
  • the time period obtaining unit 124 is configured to obtain the congested travel time period within the agreed travel time period; the congested travel time period refers to the time period in which the probability of occurrence of travel congestion is greater than the probability threshold;
  • a time period deletion unit 125 configured to delete the congested travel period in the agreed travel period to obtain a candidate travel period
  • the time period selection unit 126 is configured to select the predicted travel time period of the target item from the candidate travel time periods, and determine the travel time composed of the predicted travel time period as the predicted travel time of the target item; the predicted travel time is within the expected transportation time range .
  • the specific implementation of the desired range acquisition unit 123 , the time period acquisition unit 124 , the time period deletion unit 125 and the time period selection unit 126 may refer to the description in S102 in the embodiment corresponding to FIG. 3 above, which will not be repeated here. Repeat.
  • the driving data determination module 12 may further include: a range acquisition unit 127 , a data acquisition unit 128 and a transportation scheduling unit 129 .
  • a range acquisition unit 127 configured to acquire the travel address information and the expected transportation time range in the business service information
  • a data acquisition unit 128, configured to acquire, from the network configuration rules, the network coverage and network connection quality associated with the driving address information and the expected time range of transportation;
  • the transportation scheduling unit 129 is used to schedule the transportation of the target item according to the item attribute, the driving address information, the expected transportation time range, the network coverage associated with the driving address information and the expected transportation time range, and the quality of the network connection to obtain the predicted travel data.
  • the specific implementations of the range acquisition unit 127 , the data acquisition unit 128 , and the transportation scheduling unit 129 may refer to the description in S102 in the embodiment corresponding to FIG. 3 , which will not be repeated here.
  • the apparatus 1 may further include: a statistical report obtaining module 18 and a report sending module 19 .
  • the statistical report obtaining module 18 is configured to obtain a statistical report of data associated with the unmanned vehicle and the target item if the unmanned vehicle completes the transportation of the target item; The network coverage and the quality of the network connection as reported in the statistics;
  • the report sending module 19 is configured to send the data statistics report to the user terminal, so that the user terminal can generate a driving quality score according to the network coverage and network connection quality collected during transportation.
  • the specific implementation manner of the statistical report obtaining module 18 and the report sending module 19 may refer to the description in S103 in the embodiment corresponding to FIG. 3 above, which will not be repeated here.
  • the business service information includes the first consumption virtual asset used for transporting the target item
  • the apparatus 1 may further include: an asset acquisition module 20 and a target asset determination module 21 .
  • the asset acquisition module 20 is configured to acquire the contracted virtual assets stipulated in the network configuration rules for the service quality parameters, and use the contracted virtual assets as the second consumption virtual assets used for transporting the target item;
  • the target asset determination module 21 is configured to determine the target consumption virtual asset for transporting the target item according to the first consumption virtual asset and the second consumption virtual asset.
  • the specific implementation of the asset acquisition module 20 and the target asset determination module 21 may refer to the description in S13 in the above-mentioned embodiment corresponding to FIG. 3 , which will not be repeated here.
  • remote driving technology is introduced in the logistics distribution service of items, which can control unmanned vehicles to transport items, realize unmanned logistics distribution of items, and save a lot of labor costs.
  • a remote control driving request for the target item is obtained, and the predicted driving data associated with the target item is determined according to the business service information in the remote control driving request, so as to generate a remote control driving instruction according to the predicted driving data.
  • this application can provide unmanned logistics distribution services for items based on remote driving technology, integrate unmanned logistics distribution with unmanned driving, and realize personalized smart logistics based on remote driving technology. The whole process is automated without manual participation. , which can save logistics costs.
  • FIG. 9 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
  • the apparatus 1 in the embodiment corresponding to FIG. 8 can be applied to the above computer device 1000.
  • the above computer device 1000 may include: a processor 1001, a network interface 1004 and a memory 1005.
  • the above computer device 1000 also Including: a user interface 1003, and at least one communication bus 1002.
  • the communication bus 1002 is used to realize the connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may include a standard wired interface, a wireless interface (eg, a WI-FI interface).
  • the memory 1005 may be high-speed RAM memory or non-volatile memory, such as at least one disk memory.
  • the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001 .
  • the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.
  • the network interface 1004 can provide a network communication function;
  • the user interface 1003 is mainly used to provide an input interface for the user; and
  • the processor 1001 can be used to call the device control application stored in the memory 1005 program to achieve:
  • the remote control driving request includes the business service information of the target item;
  • Unmanned vehicles refer to tools used to transport target items.
  • the computer device 1000 described in the embodiment of the present application can execute the description of the service data processing method in the foregoing embodiment corresponding to FIG. 3 , and can also execute the service data processing apparatus in the foregoing embodiment corresponding to FIG. 8 .
  • the description of 1 will not be repeated here.
  • the description of the beneficial effects of using the same method will not be repeated.
  • the embodiment of the present application further provides a computer-readable storage medium, and the computer program executed by the computer device 1000 for data processing mentioned above is stored in the computer-readable storage medium, and
  • the above computer program includes program instructions, and when the above processor executes the above program instructions, the above description of the above service data processing method in the embodiment corresponding to FIG. 3 can be executed.
  • the description of the beneficial effects of using the same method will not be repeated.
  • the above-mentioned computer-readable storage medium may be the service data processing apparatus provided in any of the foregoing embodiments or an internal storage unit of the above-mentioned computer device, such as a hard disk or a memory of the computer device.
  • the computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (smart media card, SMC), a secure digital (secure digital, SD) card equipped on the computer device, Flash card (flash card), etc.
  • the computer-readable storage medium may also include both an internal storage unit of the computer device and an external storage device.
  • the computer-readable storage medium is used to store the computer program and other programs and data required by the computer device.
  • the computer-readable storage medium can also be used to temporarily store data that has been or will be output.
  • a computer program product or computer program including computer instructions stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in one aspect of the embodiments of the present application.
  • each process and/or the schematic structural diagrams of the method flowcharts and/or structural schematic diagrams can be implemented by computer program instructions. or blocks, and combinations of processes and/or blocks in flowcharts and/or block diagrams.
  • These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce a function
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions
  • the apparatus implements the functions specified in one or more of the flowcharts and/or one or more blocks of the structural diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the block or blocks of the flowchart and/or structural representation.

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Abstract

本申请实施例公开了一种业务数据处理方法、设备及可读存储介质，本申请属于计算机技术领域，方法包括：获取针对目标物品的远程操控驾驶请求；根据业务服务信息确定与目标物品相关联的预测行驶数据；根据预测行驶数据生成远程操控驾驶指令，将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶。采用本申请，可以减少物流成本。

Description

一种业务数据处理方法、设备以及可读存储介质
本申请要求于2020年11月6日提交中国专利局、申请号202011233895.5、申请名称为“一种业务数据处理方法、设备及可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及计算机技术领域，尤其涉及一种业务数据处理方法、设备以及可读存储介质。
背景技术
目前，网购在人们日常生活中已扮演了不可或缺的角色，而点餐、购物等网购行为势必会产生物品配送需求。
对于物品的配送，目前主要采用人工进行配送，由于物品配送需求量大，所需的配送人员数量也多，增加了人力成本，导致物流成本急剧上升。
发明内容
本申请实施例提供一种业务数据处理方法、设备以及可读存储介质，可以减少物流成本。
本申请实施例一方面提供了一种业务数据处理方法，该方法由计算机设备执行，包括：
获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息；
根据业务服务信息确定与目标物品相关联的预测行驶数据；
根据预测行驶数据生成远程操控驾驶指令；
将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶,无人驾驶交通工具是指用于运输目标物品的工具。
本申请实施例一方面提供了一种业务数据处理装置，该装置部署在计算机设备上，包括：
请求获取模块，用于获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息；
行驶数据确定模块，用于根据业务服务信息确定与目标物品相关联的预测行驶数据；
指令生成模块，用于根据预测行驶数据生成远程操控驾驶指令；
指令发送模块，用于将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶，无人驾驶交通工具是指用于运输目标物品的工具。
本申请实施例一方面提供了一种计算机设备，包括：处理器和存储器；
存储器存储有计算机程序，计算机程序被处理器执行时，使得处理器执行本申请实施例中的方法。
本申请实施例一方面提供了一种计算机可读存储介质，计算机可读存储介质存储有计算机程序，计算机程序包括程序指令，程序指令当被处理器执行时，执行本申请实施例中的方法。
本申请的一个方面，提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行本申请实施例中一方面提供的方法。
在本申请实施例中，在物品的物流配送服务中，引入了远程驾驶技术，可以控制无人驾驶交通工具运输物品，实现了物品的无人物流配送，从而可以节约大量的人力成本。以物品是目标物品为例，获取针对目标物品的远程操控驾驶请求，根据远程操控驾驶请求中的业务服务信息确定与目标物品相关联的预测行驶数据，以根据预测行驶数据生成远程操控驾驶指令。将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶。可以看出，本申请可以基于远程驾驶技术为物品提供无人物流配送服务，将无人物流配送与无人驾驶进行融合，实现基于远程驾驶技术的个性化智慧物流，全程自动化进行，无需人工参与，可以节约物流成本。
附图说明
图1是本申请实施例提供的一种网络架构图；
图2是本申请实施例提供的一种场景示意图；
图3是本申请实施例提供的一种业务数据处理方法的流程示意图；
图4是本申请实施例提供的一种分配网络通信资源的场景示意图；
图5是本申请实施例提供的一种远程驾驶的场景架构图；
图6是本申请实施例提供的一种无人物流配送服务系统的功能架构图；
图7是本申请实施例提供的一种无人物流配送服务的流程交互图；
图8是本申请实施例提供的一种业务数据处理装置的结构示意图；
图9是本申请实施例提供的一种计算机设备的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。
人工智能(Artificial Intelligence,AI)是利用数字计算机或者数字计算机控制的机器模拟、延伸和扩展人的智能，感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。换句话说，人工智能是计算机科学的一个综合技术，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器。人工智能也就是研究各种智能机器的设计原理与实现方法，使机器具有感知、推理与决策的功能。
人工智能技术是一门综合学科，涉及领域广泛，既有硬件层面的技术也有软件层面的技术。人工智能基础技术一般包括如传感器、专用人工智能芯片、云计算、分布式存储、大数据处理技术、操作/交互系统、机电一体化等技术。人工智能软件技术主要包括计算机视觉技术、语音处理技术、自然语言处理技术以及机器学习/深度学习等几大方向。
随着人工智能技术研究和进步，人工智能技术在多个领域展开研究和应用，例如常见的智能家居、智能穿戴设备、虚拟助理、智能音箱、智能营销、无人驾驶、自动驾驶、无人机、机器人、智能医疗、智能客服等，相信随着技术的发展，人工智能技术将在更多的领域得到应用，并发挥越来越重要的价值。
本申请实施例提供的方案属于人工智能领域下属的自动驾驶技术。
自动驾驶技术通常包括高精地图、环境感知、行为决策、路径规划、运动控制等技术，自定驾驶技术有着广泛的应用前景。
请参见图1，图1是本申请实施例提供的一种网络架构图。如图1所示，如图1所示，该网络架构可以包括用户终端集群、服务端入口、业务服务后台、远程操控平台以及驾驶端集群，用户终端集群可以包括一个或者多个用户终端，这里将不对用户终端的数量进行限制。如图1所示，多个用户终端可以包括用户终端100a、用户终端100b、用户终端100c、…、用户终端100n；如图1所示，用户终端100a、用户终端100b、用户终端100c、…、用户终端100n可以分别与服务端入口进行网络连接，以便于每个用户终端可以通过该网络连接与服务端入口之间进行数据交互。
驾驶端集群可以包括一个或多个驾驶端，这里将不对驾驶端的数量进行限制。如图1所示，多个驾驶端可以包括驾驶端1000a、驾驶端1000b、…驾驶端1000n；如图1所示，驾驶端1000a、驾驶端1000b、…驾驶端1000n可以分别与远程操控平台进行网络连接，以便于每个驾驶端可以通过该网络连接与远程操控平台进行数据交互。
可以理解的是，如图1所示的每个用户终端均可以安装有目标应用，当该目标应用运行于各用户终端中时，可以分别与图1所示的服务端入口之间进行数据交互，使得服务端入口可以接收来自于每个用户终端的业务数据。其中，该目标应用可以包括具有显示文字、图像、音频以及视频等数据信息功能的应用。如，应用可以为点餐应用，该点餐应用可以用于用户进行点餐；应用也可以为购物应用，该购物应用可以用于用户进行购买物品。
应当理解，本申请中的服务端入口可以根据这些应用获取到业务数据，如，该业务数据可以为用户对于一个物品的物流配送需求信息。例如，用户购买了一件衣物，用户终端可以向服务端入口发送针对这件衣物的物流配送需求；随后，该服务端入口可以根据该物流配送需求获取这件衣物的配送目的地地址信息、衣物的属性信息(体积、重量等属性，即物品属性)、用户信息等(例如，用户姓名、用户联系方式等)，则配送目的地地址信息、衣物的属性信息、用户信息等信息均可以作为该业务数据。
随后，服务端入口可以根据这些业务数据，生成运输业务请求，并将该运输业务请求与这些业务数据一并发送至业务服务后台，通常情况下，运输业务请求中包括这些业务数据。随后，业务服务后台可以根据这些业务数据，生成业务服务信息，并生成包含业务服务信息的远程驾驶请求；随后，业务服务后台可以将该远程驾驶请求发送至远程操控平台，远程操控平台可以根据该远程驾驶请求中的业务服务信息，确定针对该衣物的预测行驶数据，包括预测行驶路线以及预测行驶时间，并按照网络配置规则中所约定的服务等级 (Service-Level Agreement，SLA)参数(可以包括网络覆盖范围以及网络连接质量)，确定出预测行驶路线以及预测行驶时间对应的网络覆盖范围以及网络连接质量；随后，远程操控平台可以根据网络覆盖范围以及网络连接质量，为目标驾驶端(驾驶端集群中的任一驾驶端，例如，该驾驶端为驾驶端100b)分配对应的网络通信资源。例如，远程操控平台可以根据该网络覆盖范围与网络连接质量指示网络基站，为目标驾驶端分配用于满足该网络覆盖范围与网络连接质量的网络通信资源。由此可以使得目标驾驶端在预测行驶时间中行驶在预测行驶路线时，是处于网络覆盖范围内且处于网络连接状态。
随后，远程操控平台可以生成远程操控驾驶指令，并将该远程操控驾驶指令发送至该目标驾驶端，因为该目标驾驶端已被分配有网络通信资源，所以该目标驾驶端可以接收到该远程操控驾驶指令，并通过该远程操控驾驶指令进行自动驾驶，从而进行对该衣物的物流运输。
可以理解的是，远程操控平台可以在对该衣物的物流运输过程中，按时(例如，每30分钟)获取目标驾驶端的当前位置以及状态，生成状态位置报告并发送至业务服务后台，而业务服务后台可以将该状态位置报告发送至服务端入口，服务端入口可以将该状态位置报告返回至用户终端。随后，用户终端可以在显示界面中，输出该目标驾驶端的状态位置报告，则用户可以查看到自己所购买的衣物的当前位置以及状态。
本申请实施例可以在多个用户终端中选择一个用户终端作为目标用户终端，该用户终端可以包括：智能手机、平板电脑、笔记本电脑、桌上型电脑、智能电视、智能音箱、台式计算机、智能手表、车载设备等携带数据处理功能(例如，文本数据显示功能、视频数据播放功能、音乐数据播放功能)的智能终端，但并不局限于此。例如，本申请实施例可以将图1所示的用户终端100a作为该目标用户终端，该目标用户终端中可以集成有上述目标应用，此时，该目标用户终端可以通过该目标应用与服务端入口之间进行数据交互。
如，用户在使用用户终端中的目标应用(如购物应用)时，用户在该购物应用中发起一个对项链的购物需求，用户终端可以将该购物需求发送至服务端入口；随后，服务端入口可以获取该用户的接收地址信息(即项链的配送目的地地址信息)、用户信息(例如，用户姓名、联系方式等信息)以及项链的发货地址信息(即项链的配送起始地地址信息)；随后，服务端入口可以生成针对该项链的运输业务请求，并将该运输业务请求与接收地址信息、用户信息、发货地址信息等发送至业务服务后台；随后，业务服务后台可以根据该运输业务请求中携带的接收地址信息、用户信息、发货地址信息等信息，确定预期运输时间(运输期望时间范围)，并生成业务服务信息；业务服务后台可以生成包括该业务服务信息的远程驾驶请求，并将该远程驾驶请求发送至远程操控平台；随后，远程操控平台可以根据该远程驾驶请求，确定针对该项链的预测行驶路线与预测行驶时间，并根据网络配置规则中所约定的SLA参数，确定该预测行驶路线与预测行驶时间对应的服务质量参数(包括网络覆盖范围以及网络连接质量)；随后，远程操控平台可以根据网络覆盖范围以及网络连接质量，为用于运输该项链的目标驾驶端分配对应的网络通信资源；随后，远程操控平台可以生成远程操控驾驶指令，并将该远程操控驾驶指令发送至该目标驾驶端，因为该目标驾驶端已被分配有网络通信资源，所以该目标驾驶端可以接收到该远程操控驾驶指令，并通过该远程操控驾驶指令进行自动驾驶，从而进行对该项链的物流配送。
在一种可能的实现方式中，可以理解的是，如图1所示的业务服务后台可以功能模块的形式集成于远程操控平台中。也就是说，远程操控平台可以接收来自于服务端入口的数据(例如，运输业务请求与接收地址信息、用户信息、发货地址信息、物品属性信息等数 据)，并确定预期运输时间，从而可以确定针对物品(例如，上述项链)的预测行驶路线与预测行驶时间，并根据网络配置规则中所约定的SLA参数，确定该预测行驶路线与预测行驶时间对应的服务质量参数，并根据该服务质量参数生成远程驾驶请求。
在一种可能的实现方式中，可以理解的是，如图1所示的服务端入口也可以功能模块的形式集成于用户终端中。
可以理解的是，本申请实施例提供的方法可以由计算机设备执行，计算机设备包括但不限于用户终端或远程操控平台。其中，远程操控平台可以是独立的物理服务器，也可以是多个物理服务器构成的服务器集群或者分布式系统，还可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、CDN、以及大数据和人工智能平台等基础云计算服务的云服务器。
其中，用户终端以及远程操控平台可以通过有线或无线通信方式进行直接或间接地连接，本申请在此不做限制。
为便于理解，请参见图2，图2是本申请实施例提供的一种场景示意图。其中，如图2所示的服务端入口可以为上述图1所示的服务端入口，如图2所示的业务服务后台可以为上述图1所示的业务服务后台，如图2所示的用户终端M可以为在上述图1所对应实施例的用户终端集群中所选取的任意一个用户终端，比如，该用户终端可以为上述用户终端100b；且如图2所示的驾驶端E可以为在上述图1所对应实施例的驾驶端集群中所选取的任意一个驾驶端，比如，该驾驶端可以为上述用户终端1000b。
如图2所示，用户M使用购物应用发起对项链的购物需求，在用户M确认收货地址信息以及项链的价格等信息后，用户终端M可以将该用户M的购物需求发送至服务端入口；随后，服务端入口可以生成运输业务请求，并将该运输业务请求与物品属性、行驶地址信息(接收地址信息、发货地址信息)、用户信息(收货用户的姓名、联系方式等信息)等信息一并发送至业务服务后台。随后，业务服务后台可以确定预期运输时间，并生成包含行驶地址信息与预期运输时间的业务服务信息；随后，业务服务后台可以确定行驶地址信息对应的运输期望时间范围，并生成包括物品属性、行驶地址信息(接收地址信息、发货地址信息)、用户信息(收货用户的姓名、联系方式等信息)以及运输期望时间范围等信息的业务服务信息。
随后，业务服务后台可以根据该业务服务信息，生成远程驾驶请求，并将该远程驾驶请求发送至远程操控平台；远程操控平台可以根据该远程驾驶请求中的业务服务信息，确定预测行驶路线与预测行驶时间，并根据业务服务后台与远程操控平台之间的网络配置规则所约定的SLA参数，确定预测行驶路线与预测行驶时间分别对应的网络连接范围与网络连接质量；例如，远程操控平台可以根据网络配置规则获取到该预测行驶路线对应的服务质量参数(可以包括网络覆盖范围以及网络连接质量)，例如，预测行驶路线为R市-U市-A省aa市aaa区aaaa街道，业务服务器可以获取网络配置规则中为R市所约定配置的网络覆盖范围以及网络连接质量，同时获取到网络配置规则中为U市所约定配置的网络覆盖范围以及网络连接质量，还可以获取到网络配置规则中为A省aa市aaa区aaaa街道所约定配置的网络覆盖范围以及网络连接质量。同理，远程操控平台也可以根据网络配置规则获取到该预测行驶时间对应的服务质量参数(可以包括网络覆盖范围以及网络连接质量)，例如，预测行驶时间为13:00-14:30，远程操控平台可以将该预测行驶时间与网络配置规则中包括的约定时间段集合进行匹配，确定该预测行驶时间所属的时间范围为约定时间段 13:00-15:00，业务服务器可以获取网络配置规则中为时间范围13:00-15:00所约定配置的网络覆盖范围以及网络连接质量，并将该时间范围13:00-15:00对应的网络覆盖范围以及网络连接质量确定为该预测行驶时间13:00-14:30对应的服务质量参数。
在一些情况下，远程操控平台可以根据该网络连接范围与网络连接质量，为驾驶端1000b分配服务质量参数对应的网络通信资源。其中，应当理解，若驾驶端1000b行驶至预测行驶路线中的某一区域时，该行驶时间对应的服务质量参数与该区域对应的服务质量参数不一致，则可以在服务质量参数中进行择优选择(例如，可以选择网络覆盖范围较大且网络连接质量较高的)，作为最终的服务质量参数。
随后，远程操控平台可以根据该预测行驶路线以及预测行驶时间，生成远程操控驾驶指令，并将该远程操控驾驶指令发送至该驾驶端1000b；应当理解，该驾驶端1000b已被分配有网络通信资源，则该驾驶端1000b可以接收到该远程操控驾驶指令，并通过给远程操控驾驶指令进行自动驾驶，以进行对该项链的无人物流配送。
可以理解的是，在驾驶端1000b对项链进行物流配送的过程中，远程操控平台可以按时(例如，每隔10分钟)获取到驾驶端1000b的所处位置以及状态(例如，交通顺利状态、交通拥堵状态等)，并生成位置状态报告发送至业务服务后台，业务服务后台可以将该位置状态报告转发至服务端入口，服务端入口可以将该位置状态报告转发至用户终端M，用户终端M可以在显示界面中显示该位置状态报告中的位置以及状态(也就是项链的当前位置以及当前状态)，用户M可以查看到项链的位置以及状态。
为便于理解，请参见图3，图3是本申请实施例提供的一种业务数据处理方法的流程示意图。该方法可以由用户终端(例如，上述图1、图2所示的用户终端)、服务端入口(例如，上述图1、图2所示的服务端入口)、远程操控平台(如上述图1所对应实施例中的远程操控平台)以及业务服务后台(如上述图1所对应实施例中的业务服务后台)共同执行。其中，该业务数据处理方法至少可以包括以下S101-S103：
S101，获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息。
本申请中，用户在使用用户终端中的目标应用(如购物应用)时，用户在该目标应用中可以发起针对某个物品的购物或配送需求，用户终端可以将该购物或配送需求发送至服务端入口，随后，服务端入口可以根据该需求，获取该用户的接收地址信息(即项链的配送目的地地址信息)、用户信息(例如，接收用户的用户姓名、联系方式等信息)以及项链的发货地址信息(即项链的配送起始地地址信息)、物品属性等信息；随后，服务端入口可以将该物品作为目标物品，并生成针对该目标物品的运输业务请求，将该运输业务请求与该接收地址信息、发货地址信息、物品属性以及用户信息一并发送至业务服务后台。
随后，业务服务后台可以根据该运输业务请求，生成业务服务信息，并根据该业务服务信息生成远程驾驶请求。其中，业务服务后台生成远程驾驶请求的具体方法可以为，业务服务后台可以先获取针对目标物品的运输业务请求(即由用户的购物或配送需求所产生的运输请求)，随后，可以获取运输业务请求中目标物品的物品属性以及行驶地址信息(可以包括物品的接收地址信息以及发货地址信息)；业务服务后台可以按照业务配置规则获取行驶地址信息对应的初始运输时间范围，根据行驶地址信息、初始运输时间范围以及物品属性可以生成针对目标物品的业务服务信息；随后，可以生成包括该业务服务信息的远程操控驾驶请求。
其中，物品属性可以是指目标物品的体积属性(物品体积)、重量属性(物品重量)、数量属性(物品数量)等；应当理解，若物品体积大或物品重量大或物品数量多，则在对目标物品的运输过程中，可能对运输车辆、行驶道路等方面运输压力，则在物品体积较大或物品重量较大或物品数量较多时，可以适当放宽对物品运输的时效要求，也就是说，在初始运输时间范围的基础上，再增加运输等待时间范围，得到运输期望时间范围，并根据运输期望时间范围来生成业务服务信息；以下将以物品属性包括物品体积为例，来对根据行驶地址信息、初始运输时间范围以及物品属性生成针对目标物品的业务服务信息的具体实现方式进行说明，首先，可以根据物品体积所属的体积范围确定目标物品对应的运输等待时间范围；随后，可以将初始运输时间范围与运输等待时间范围进行相加处理，得到目标物品的运输期望时间范围；根据行驶地址信息、物品属性以及运输期望时间范围，可以生成针对目标物品的业务服务信息。
其中，应当理解，业务配置规则可以为服务端入口与业务服务后台之间的对目标物品的业务相关的策略协商配置，该业务配置规则的意义在于支持服务端入口与业务服务后台之间的认证、计费和对目标物品的服务内容及服务质量等配置。
业务配置规则可以包括但不限于以下内容：业务分类、位置服务、大数据服务、计费服务以及认证服务。
业务分类，业务分类可以适用于分开独立部署的业务服务后台。也就是说，业务服务后台可以被分为业务服务后台A与业务服务后台B，其中，业务服务后台A主要为toC(面向个人)类业务(物品体积小、物品重量小、物品数量少的物流业务)提供服务；业务服务后台B主要为toB(面向企业)类业务(物品体积大或物品重量大或物品数量多的物流业务)提供服务。
位置服务，位置服务主要用于提取位置信息，也就是说，在接收到服务端入口的运输业务请求后，业务服务后台可以将其中涉及到位置的信息提取出来(例如，可以将接收地址信息、发货地址信息均提取出来)，并由提取出来的位置信息生成业务服务信息。
大数据服务，大数据服务主要用于进行分析，可以分析将目标物品从发货地址信息运输至接收地址信息需要的预期运输时间(初始运输时间范围)，以及预期运输时间能够满足客户的概率等。
计费服务，计费服务主要用于与业务服务后台进行交互，根据无人物流服务特定的计费策略，执行计费功能。例如，业务服务后台为快递公司对应的物流服务后台，该物流服务后台具有特定的计费策略，若要采用该快递公司的物流服务进行运输，则需要采用其对应的物流服务后台的计费策略来进行计费。
认证服务，认证服务主要用于认证业务服务后台，避免伪造和抵赖。
应当理解，服务端入口在接收到来自用户针对目标物品的购物需求后，可以按照物品属性对目标物品的业务进行业务分类，若目标物品具有体积小或重量小或数量少的属性，则可以将目标物品的业务分为toC类业务；随后，服务端入口可以生成运输业务请求，并将该运输业务请求发送至toC服务后台(已通过认证)；随后，toC服务后台可以按照业务配置规则，获取到与该目标物品相关联的位置信息(例如，接收地址信息、发货地址信息等)，并按照业务配置规则计算出预期运输时间，同时按照业务配置规则执行计费功能，也就是说，toC业务服务后台可以按照业务配置规则提取位置信息、确定预期运输时间、计 算运输费用，并由位置信息、预期运输时间以及计算运输费用等生成业务服务信息；随后，toC业务服务后台可以根据该业务服务信息生成远程操控驾驶请求，并将该远程操控驾驶请求发送至业务服务器。
同理，应当理解，若目标物品具有体积大或重量大或数量多的属性，则服务端入口可以将目标物品的业务分为toB类业务；随后，服务端入口也可以生成运输业务请求，并将该运输业务请求发送至toB业务服务后台；随后，toB业务服务后台也可以按照业务配置规则执行位置提取、确定预期运输时间、执行计费等功能，并生成包括位置信息、预期运输时间以及运输费用的业务服务信息。
应当理解，具有体积大或重量大或数量多的物品属性的物品，在运输时易对车辆、道路以及信息网络资源造成运输压力，可能需要排队等待运输或延迟运输，在出现需要排队等待或延迟运输的情况时，业务服务后台可以生成时效要求放宽提示信息(预期运输时间延长的提示信息)，并在用户终端的显示界面中显示该时效要求放宽提示信息，以提示用户可以放宽时效要求(延长预期运输时间)，若用户确定放宽时效要求，则可以给予用户对应的资费方面的鼓励(也就是减少部分运输费用)，由此，可以得到更新后的预期运输时间(运输期望时间范围)以及更新后的运输费用，从而可以生成包括位置信息(行驶地址信息)、运输期望时间范围以及更新后的运输费用的业务服务信息。
在一种可能的实现方式中，可以理解的是，业务服务后台也可以进行统一部署，也就是说，不再按照物品属性进行业务分类，服务端入口在接收到用户的购物需求后，生成运输业务请求，并采用统一的业务服务后台进行服务。
S102，根据业务服务信息确定与目标物品相关联的预测行驶数据。
本申请中，远程操控平台可以接收来自于业务服务后台的远程驾驶请求，并根据远程驾驶请求中的业务服务信息确定与目标物品相关联的预测行驶数据，而预测行驶数据可以包括预测行驶路线与预测行驶时间。当预测行驶数据包括预测行驶路线时，对于根据业务服务信息确定与目标物品相关联的预测行驶路线的具体方法可以为，可以先获取业务服务信息中的行驶地址信息以及运输期望时间范围；随后，可以根据行驶地址信息，确定目标物品的初始预测行驶路线；随后，可以确定初始预测行驶路线对应的预测行驶时间范围，若预测行驶时间范围处于运输期望时间范围中，则将初始预测行驶路线确定为目标物品对应的预测行驶路线。
应当理解，远程操控平台在预测目标物品的行驶路线(即得到预测行驶路线)时，所预测的行驶路线的行驶时间应该满足业务服务信息中的时效要求(也就是运输期望时间范围)；若业务服务器确定的初始预测行驶路线包括初始预测行驶路线1与初始预测行驶路线2，初始预测行驶路线1的预测行驶时间范围为11:00-12:00，初始预测行驶路线2的预测行驶时间范围为11:00-15:00，因为预测行驶时间范围11:00-15:00未处于运输期望时间范围10：00-14:00中，则可以将初始预测行驶路线2确定为不满足时效要求，可以将初始预测行驶路线2淘汰，并将初始预测行驶路线1作为最终的预测行驶路线。
应当理解，若远程操控平台确定的多条初始预测行驶路线均满足时效要求，则可以在多条初始预测行驶路线中，选择预测行驶时间范围最小的一条，作为目标物品最终的预测行驶路线。例如，远程操控平台确定的初始预测行驶路线包括初始预测行驶路线1与初始预测行驶路线2，初始预测行驶路线1的预测行驶时间范围为11:00-12:00，初始预测行驶路线2的预测行驶时间范围为11:00-12:30，因为预测行驶时间范围11:00-12:00与 11:00-12:30均处于运输期望时间范围10：00-14:00中，则可以将初始预测行驶路线1与初始预测行驶路线2均确定为满足时效要求；但因为预测行驶时间范围11:00-12:00小于预测行驶时间范围11:00-12:30，则可以将初始预测行驶路线1作为最终的预测行驶路线。
当预测行驶数据包括预测行驶时间时，对于根据业务服务信息确定与目标物品相关联的预测行驶时间的具体方法可以为，可以先获取业务服务信息中的运输期望时间范围；随后，可以在约定行驶时间段内获取拥堵行驶时间段；其中，该拥堵行驶时间段是指行驶拥堵的发生概率大于概率阈值的时间段；随后，可以将约定行驶时间段中的拥堵行驶时间段进行删除，得到候选行驶时间段；随后，可以在候选行驶时间段中选择目标物品的预测行驶时间段，并将预测行驶时间段组成的行驶时间确定为目标物品的预测行驶时间；预测行驶时间处于运输期望时间范围中。
应当理解，在选择运输目标物品的行驶时间段时，可以避开早晚高峰期，也就是说可以避开易造成交通拥堵的时间段，从而可以减少交通拥堵。
在一种可能的实现方式中，可以理解的是，对于远程操控平台根据业务服务信息确定与目标物品相关联的预测行驶数据的具体方法，还可以为，获取业务服务信息中的行驶地址信息以及运输期望时间范围；从网络配置规则中，获取与行驶地址信息以及运输期望时间范围相关联的网络覆盖范围以及网络连接质量；根据物品属性、行驶地址信息、运输期望时间范围、与行驶地址信息以及运输期望时间范围相关联的网络覆盖范围以及网络连接质量，对目标物品进行运输调度，得到预测行驶数据。其中，预测行驶数据可以包括预测行驶路线与预测行驶时间。应当理解，本申请制定了一种网络配置规则，在该网络配置规则中为行驶区域以及行驶时间段分别分配了网络覆盖范围并约定了网络连接质量；所以通过该网络配置规则可以确定每个行驶区域对应的网络覆盖范围以及网络连接质量，也可以确定每个行驶时间段对应的覆盖范围以及网络连接质量，由此，通过该网络配置规则可以确定预测行驶数据(包括预测行驶路线或预测行驶时间)相关联的网络覆盖范围与网络连接质量。其中，对于网络配置规则所约定配置的具体内容，以及对于根据网络配置规则确定候选预测行驶数据对应的网络覆盖范围以及网络连接质量的具体方式，可以参见后续的描述。
也就是说，可以理解的是，本申请在确定目标物品的预测行驶路线时，可以先根据行驶起点与行驶终点确定出初始预测行驶路线，再在这些初始预测行驶路线中确定出满足运输期望时间范围的路线，作为候选行驶路线；进一步地，可以根据目标物品的物品属性确定一个目标运输安全等级，例如，物品属性为物品体积大且物品重量大，则容易对运输道路构成压力，可以根据物品体积与物品重量确定一个目标运输安全等级(例如，8)；进一步地，可以确定目标物品在这些候选行驶路线中的运输安全等级，并确定出满足该目标运输安全等级的候选行驶路线；例如，候选行驶路线包括候选行驶路线1与候选行驶路线2，在该候选行驶路线1中运输该目标物品的安全等级为9，而在候选行驶路线2中运输该目标物品的安全等级为9，所以目标物品在候选行驶路线1与候选行驶路线2中的运输安全等级均满足目标运输安全等级8，则该候选行驶路线1与候选行驶路线2均可以作为用于运输目标物品的路线。进一步地，可以根据候选行驶路线对应的网络覆盖范围与网络连接质量，确定目标物品最终的预测行驶路线(例如，可以选择网络覆盖范围较大且网络连接质量较高的候选行驶路线作为最终的预测行驶路线)。
可以理解的是，在目标物品在候选行驶路线中的运输安全等级一致且均满足目标运输安全等级时，可以根据候选行驶路线对应的网络覆盖范围与网络连接质量来确定最终的预 测行驶路线，而若目标物品在候选行驶路线中的运输安全等级不一致且均满足目标运输安全等级时，则可以根据运输安全等级来进行择优选择(例如，选择运输安全等级更高的)，不再考虑候选行驶路线对应的网络覆盖范围与网络连接质量。
在一种可能的实现方式中，可以理解的是，以上描述的为先根据运输期望时间范围确定候选行驶路线，再根据目标运输等级以及候选行驶路线对应的网络覆盖范围与网络连接质量来确定最终的预测行驶路线，还可以采用综合考虑运输期望时间范围、物品属性、网络覆盖范围与网络连接质量的方式来生成预测行驶路线的方式。即，可以根据行驶地址信息确定路线起点与路线终点，从而可以获取处于路线起点与路线终点之间的行驶区域，并根据网络覆盖范围与网络连接质量、目标物品在这些行驶区域中的运输安全等级来选择出目标行驶区域，这些目标行驶区域的网络覆盖范围较大且网络连接质量的质量等级较高，且目标物品在这些目标行驶区域中的运输安全等级均满足目标运输安全等级；随后，可以根据运输期望时间范围以及这些目标行驶区域来生成最终的预测行驶路线。
可以理解的是，本申请在选择运输目标物品的行驶时间段时，可以避开早晚高峰期，也就是说可以避开易造成交通拥堵的时间段，从而可以减少交通拥堵；随后，可以在剩下的行驶时间段中，确定出目标行驶时间段，这些目标行驶时间段的网络覆盖范围较大且网络连接质量的质量等级较高，且目标物品在这些目标时间段中的运输安全等级均满足目标运输安全等级；随后，可以在这些目标行驶时间段中选择出用于组成满足运输期望时间范围的预测行驶时间的预测行驶时间段。
应当理解，本申请可以根据目标物品的物品体积、物品重量、运输安全性、目的地，以及网络配置规则所约定配置的网络覆盖范围与网络连接质量，来共同对目标物品的行驶路线与行驶时间进行运输调度。例如，物品A与物品B的物品体积、物品重量、运输安全性均相差不大，且目的地相同，则可以将物品A与物品B在某一区域进行汇聚处理，从而可以进行统一运输，进而可以节约运输资源。
在一种可能的实现方式中，上述业务服务后台可以功能模块的形式集成部署于远程操控平台中，从而远程操控平台也可以执行业务服务后台所执行的功能。
S103，根据预测行驶数据生成远程操控驾驶指令，将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶，无人驾驶交通工具是指用于运输目标物品的工具。
在一种可能的实现方式中，根据业务服务信息确定与目标物品相关联的预测行驶数据之后，还可以根据网络配置规则获取预测行驶数据对应的服务质量参数，为无人驾驶交通工具分配服务质量参数所指示的网络通信资源。这样，将远程操控驾驶指令发送至无人驾驶交通工具实际上可以是将远程操控驾驶指令发送至被分配网络通信资源的无人驾驶交通工具。从而可以实现在网络支持远程操控无人驾驶交通工具进行驾驶的同时，还可以做到对网络通信资源的合理利用，增加无人驾驶的可靠性，实现基于远程驾驶技术和网络通信为物品提供无人物流配送服务。
本申请中，无人驾驶交通工具可以是指上述图1所对应实施例中所示的驾驶端集群中的任一驾驶端，如，该无人驾驶交通工具可以为驾驶端100a。远程操控平台可以根据网络配置规则获取预测行驶数据对应的服务质量参数。其中，服务质量参数可以包括网络覆盖范围与网络连接质量。对于根据网络配置规则获取预测行驶数据对应的服务质量参数的具体方法可以为，可以先根据预测行驶数据从网络配置规则中获取服务质量参数；其中，服 务质量参数包括已约定的目标网络覆盖范围以及已约定的目标网络连接质量；随后，可以根据目标网络覆盖范围与目标网络连接质量指示的目标基站，为无人驾驶交通工具分配用于满足目标网络覆盖范围，以及目标网络连接质量的网络通信资源；目标基站位于基于预测行驶数据所行经的区域。
其中，当预测行驶数据为预测行驶路线时，对于根据网络配置规则获取预测行驶路线对应的服务质量参数的具体方法可以为，可以先获取网络配置规则中的约定行驶区域集合；其中，约定行驶区域集合中包括至少两个区域信息，以及为每个区域信息分别约定的网络覆盖范围以及网络连接质量；随后，可以获取预测行驶路线包括的至少一个预测行驶区域，并将至少一个预测行驶区域与约定行驶区域集合进行匹配，在至少两个区域信息中确定目标区域信息；其中，目标区域信息覆盖预测行驶区域；随后，可以将为目标区域信息所约定的网络覆盖范围确定为预测行驶区域的目标网络覆盖范围，将为目标区域信息所约定的网络连接质量确定为预测行驶区域的目标网络连接质量。
其中，当预测行驶数据为预测行驶时间时，对于根据网络配置规则获取预测行驶时间对应的服务质量参数的具体方法可以为，可以先获取网络配置规则中的约定时间段集合；其中，约定时间段集合中包括至少两个约定时间段，以及为每个约定时间段分别约定的网络覆盖范围以及网络连接质量；随后，可以将预测行驶时间与约定时间段集合进行匹配，在至少两个约定时间段中确定目标约定时间段；其中，预测行驶时间处于目标约定时间段中；随后，可以将为目标约定时间段所约定的网络覆盖范围确定为预测行驶时间的目标网络覆盖范围，将为目标约定时间段所约定的网络连接质量确定为预测行驶时间的目标网络连接质量。
应当理解，本申请中，在网络部署方(例如，运营商部署方、网络基站)与远程操控平台之间进行了网络相关的策略配置，也就是在双方之间制定了网络配置规则，其意义在于约定网络(例如，第五代移动通信(5th generation mobile networks，5G)网络)能够提供的适合于无人物流配送服务的服务等级参数(Service-Level Agreement，SLA)。网络配置规则包括但不限于以下3种规则：
1、为行驶区域分配网络覆盖范围并约定网络连接质量。例如，对于S市，S市的S1区较为繁华，道路中的行经车辆较多，所以需要保证S1区的网络连接质量较好，从而可以使得用于运输该目标物品的无人驾驶交通工具网络连接信号强，可以快速准确的接收远程操控指令，所以为S市的S1区约定配置的网络覆盖范围为整个S1区，且约定配置的网络连接质量等级较高；而对于S市的S2区，S2区的道路少，行经车辆也很少，无需保证极好的网络连接质量，所以为S市的S2区所约定配置的网络覆盖范围为整个S2区，但约定配置的网络连接质量等级不如S1区的等级。
2、为行驶时间段分配网络覆盖范围并约定网络连接质量。应当理解，无人驾驶交通工具所处的行驶区域、道路等不同的道路状况会因时间段的不同而不同，例如，当处于上下班早高峰这个时间段时，A区域会产生交通拥堵状况，而未处于上下班早高峰这个时间段时，A区域并不会产生交通拥堵状况。所以需要为不同的时间段配置不同的网络覆盖范围并约定不同的网络连接质量，在易造成交通拥堵、车辆较多的时间段，配置等级较高的网络连接质量。
3、为无人驾驶交通工具配置远程操控模式。远程操控模式可以包括线控模式与路线引导模式。无人驾驶交通工具可以安装网络通信模块，而业务服务器(网络远程操控平台) 可以把实施程操控无人物流配送服务的驾驶指令，通过线控直接作用至无人驾驶交通工具的行驶控制模块。应当理解，线控模式适用于路况复杂度较大的道路，也就是说，若预测行驶路线的路况复杂度较大，则可以将无人驾驶交通工具的远程操控模式确定为线控模式；线控模式也适用于无人驾驶交通工具的自动驾驶功能等级较低时的场景。
而若预测行驶路线的路况复杂度较小，且无人驾驶交通工具的自动驾驶功能等级较高时，可以将无人驾驶交通工具的远程操控模式确定为路线引导模式。
应当理解，远程操控平台根据业务服务信息中的行驶地址信息，可以确定预测行驶路线，随后，可以获取该预测行驶路线中所包括的预测行驶区域，并将该预测行驶区域与网络配置规则中的行驶区域进行对比，将网络配置规则中覆盖该预测行驶区域的行驶区域所对应的网络覆盖范围，作为该预测行驶区域的目标网络覆盖范围，并将网络配置规则中覆盖该预测行驶区域的行驶区域所对应的网络连接质量，作为该预测行驶区域的目标网络连接质量。通过确定每个预测行驶区域的目标网络覆盖范围与目标网络连接质量，就可以确定出整条预测行驶路线的目标网络覆盖范围与目标网络连接质量。
而同时，远程操控平台根据业务服务信息中的运输期望时间范围，也可以确定预测行驶时间，随后，可以将该预测行驶时间与网络配置规则中的约定时间段进行对比，将网络配置规则中包含该预测行驶时间的约定时间段所对应的网络覆盖范围，作为该预测行驶时间的目标网络覆盖范围，并将网络配置规则中包含该预测行驶时间的约定时间段所对应的网络连接质量，作为该预测行驶时间的目标网络连接质量。例如，预测行驶时间为12:00-14:00，该预测行驶时间(该预测行驶时间是由预测行驶时间段12:00-13:00，以及预测行驶时间段13:30-14:00共同组成)，该预测行驶时间12:00-14:00处于网络配置规则中的行驶时间段11:00-14:00中，则可以获取网络配置规则中为该时间段11:00-14:00所约定的网络覆盖范围与网络连接质量，并将该约定时间段11:00-14:00所约定的网络覆盖范围作为该预测行驶时间12:00-14:00对应的目标网络覆盖范围，将该行驶时间段11:00-14:00所约定的网络连接质量作为该预测行驶时间12:00-14:00对应的目标网络连接质量。
本申请中，远程操控驾驶指令可以包括第一驾驶指令与第二驾驶指令。对于远程操控平台根据预测行驶数据生成远程操控驾驶指令的具体方法可以为，可以先获取预测行驶数据对应的路况复杂度，并根据网络配置规则以及路况复杂度确定无人驾驶交通工具的远程操控模式；应当理解，若远程操控模式为线控模式，则可以根据预测行驶数据生成第一驾驶指令；其中，第一驾驶指令用于为无人驾驶交通工具提供驾驶方向以及驾驶速度；而若远程操控模式为路线引导模式，则可以根据预测行驶数据生成第二驾驶指令；其中，第二驾驶指令用于为无人驾驶交通工具提供行驶路线信息，该行驶路线信息用于指引无人驾驶交通工具分析得到驾驶方向以及驾驶速度。
应当理解，因为网络配置规则中配置有无人驾驶交通工具的远程操控模式(包括线控模式与路线引导模式)，可以根据网络配置规则中所约定配置的远程操控模式，确定用于运输目标物品的无人驾驶交通工具的远程操控模式，不同的远程操控模式可以生成不同的远程操控驾驶指令。而对于根据网络配置规则以及路况复杂度确定无人驾驶交通工具的远程操控模式的具体方法可以为，可以先获取网络配置规则中的约定远程操控模式；其中，约定远程操控模式包括线控模式以及路线引导模式；若路况复杂度大于复杂度阈值，则将无人驾驶交通工具的远程操控模式确定为线控模式；若路况复杂度小于或等于复杂度阈值，则获取无人驾驶交通工具的自动驾驶功能等级，若自动驾驶功能等级不满足无人驾驶条件， 则将无人驾驶交通工具的远程操控模式确定为线控模式；若自动驾驶功能等级满足无人驾驶条件，则将无人驾驶交通工具的远程操控模式确定为路线引导模式。
在一种可能的实现方式中，可以理解的是，在无人驾驶交通工具对目标物品的运输过程中，可以对无人驾驶交通工具的网络连接质量进行动态检测。即在无人驾驶交通工具的远程操控模式为线控模式时，可以对无人驾驶交通工具的网络连接质量进行检测，若无人驾驶交通工具的网络连接质量对应的质量等级低于等级阈值，则可以生成驾驶停止指令，并将该驾驶停止指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过驾驶停止指令，从驾驶状态切换为驾驶停止状态。也就是说，在无人驾驶交通工具对目标物品的运输过程中，若检测到网络连接质量下降，则可以对无人驾驶交通工具执行就近停靠，避免发生事故。
在一种可能的实现方式中，可以理解的是，若在无人驾驶交通工具的远程操控模式为线控模式时，对无人驾驶交通工具的网络连接质量进行检测，若无人驾驶交通工具的网络连接质量对应的质量等级低于等级阈值，且该无人驾驶交通工具的自动驾驶功能等级满足无人驾驶条件时，可以生成模式切换指令，并将该模式切换指令发送至无人驾驶交通工具通过该模式切换指令，从线控模式切换为路线引导模式。应当理解，在无人驾驶交通工具对目标物品的运输过程中，若检测到网络连接质量下降，则可以对满足无人驾驶条件的无人驾驶交通工具执行模式切换策略，以使无人驾驶交通工具依赖路线进行自动驾驶，而不用再依靠网络通信资源来接收包括驾驶方向的控制指令，从而可以避免发生事故。
可以理解的是，上述业务配置规则中约定配置有不同的计费策略，同理，网络配置规则中也约定配置有不同的资源计费策略，不同的行驶区域或行驶时间段对应有不同的网络覆盖范围与网络连接质量，而不同的网络覆盖范围与网络连接质量也对应有不同的资源价格。所以本申请中，可以根据业务配置规则中的计费策略与网络配置规则中的计费策略，确定出该目标物品的最终的运输费用。对于确定目标物品的最终的运输费用的具体方法可以为，可以先获取业务服务信息中用于运输该目标物品的第一消耗虚拟资产(运输费用)，随后，可以获取网络配置规则中为服务质量参数约定的约定虚拟资产，并将约定虚拟资产作为用于运输目标物品的第二消耗虚拟资产；随后，可以根据第一消耗虚拟资产与第二消耗虚拟资产，确定用于运输目标物品的目标消耗虚拟资产。
在一种可能的实现方式中，可以理解的是，在无人驾驶交通工具完成对目标物品的运输后，可以对本次无人驾驶交通工具的运输过程进行质量评估，并根据评估结果对整个无人驾驶物流服务进行优化(例如，对网络配置规则中所约定的SLA参数进行优化、对业务配置规则进行优化等)。其中，对于对本次无人驾驶交通工具的运输过程进行质量评估生成评估结果的具体方法可以为，若无人驾驶交通工具对目标物品运输完成，则可以获取与无人驾驶交通工具和目标物品相关联的数据统计报告；其中，数据统计报告包括无人驾驶交通工具在运输过程中所统计到的网络覆盖范围以及网络连接质量；随后，可以将数据统计报告发送至用户终端，以使用户终端通过在运输过程中所统计到的网络覆盖范围以及网络连接质量，生成驾驶质量评分，根据该驾驶质量评分可以生成评估结果。
在本申请实施例中，在物品的物流配送服务中，引入了远程驾驶技术，可以控制无人驾驶交通工具运输物品，实现了物品的无人物流配送，从而可以节约大量的人力成本。以物品是目标物品为例，获取针对目标物品的远程操控驾驶请求，根据远程操控驾驶请求中的业务服务信息确定与目标物品相关联的预测行驶数据，以根据预测行驶数据生成远程操控驾驶指令。将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过 远程操控驾驶指令进行自动驾驶。可以看出，本申请可以基于远程驾驶技术为物品提供无人物流配送服务，将无人物流配送与无人驾驶进行融合，实现基于远程驾驶技术的个性化智慧物流，全程自动化进行，无需人工参与，可以节约物流成本
且在此过程中，设定了网络配置规则，不同的物品对应的预测行驶数据不同，则根据网络配置规则以及不同的预测行驶数据可以为无人驾驶交通工具配置不同的网络通信资源，从而可以实现无人驾驶交通工具在驾驶起点与终点的行驶路线上均处于网络覆盖范围内，且连接有网络，进而可以支持通过网络远程操控无人驾驶交通工具进行驾驶；同时，还可以做到对网络通信资源的合理利用；可以看出，本申请可以基于远程驾驶技术和网络通信为物品提供无人物流配送服务，将无人物流配送与无人驾驶进行融合，实现基于远程驾驶技术的个性化智慧物流，全程自动化进行，无需人工参与，可以节约物流成本。可见，网络通信为5G网络时，本申请可以基于由5G支持的远程驾驶技术为物品提供无人物流配送服务，实现基于5G系统的个性化智慧物流。
为便于理解，请参见图4，图4是本申请实施例提供的一种分配网络通信资源的场景示意图。如图4所示，在从驾驶地点M至驾驶终点N的预测行驶路线中，包括有三个预测行驶区域，分别为预测行驶区域40a、预测行驶区域40b以及预测行驶区域40c。随后，可以获取网络配置规则中的多个约定行驶区域，并可以在这多个约定行驶区域中获取到覆盖该预测行驶区域40a的约定行驶区域，覆盖该预测行驶区域40b的约定行驶区域，预测行驶区域40c的约定行驶区域。如图4所示，覆盖该预测行驶区域40a的约定行驶区域为约定行驶区域40A，覆盖该预测行驶区域40b的约定行驶区域为约定行驶区域40B，覆盖该预测行驶区域40c的约定行驶区域为约定行驶区域40C。
另外，可以获取该网络配置规则中，为约定行驶区域40A所约定配置的网络覆盖范围(如图4所示的网络覆盖范围1)，则该网络覆盖范围1可以作为预测行驶区域40a的网络覆盖范围；同理，可以获取为约定行驶区域40B所约定配置的网络覆盖范围(如图4所示的网络覆盖范围2)，则该网络覆盖范围2可以作为预测行驶区域40b的网络覆盖范围；同理，可以获取为约定行驶区域40C所约定配置的网络覆盖范围(如图4所示的网络覆盖范围3)，则该网络覆盖范围3可以作为预测行驶区域40c的网络覆盖范围。由此，可以确定出从驾驶起点M至驾驶终点N的预测行驶路线的总的网络覆盖范围(如图4所示，包括网络覆盖范围1、网络覆盖范围2以及网络覆盖范围3)。
应当理解，网络配置规则中也为每个约定行驶区域约定配置有网络连接质量，例如，当无人驾驶交通工具处于网络覆盖范围1中，对应的网络连接质量为约定行驶区域40A的网络连接质量，无人驾驶交通工具被分配的网络通信资源也为该网络连接质量与网络覆盖范围1相对应；当无人驾驶交通工具处于网络覆盖范围2中，对应的网络连接质量为约定行驶区域40B的网络连接质量，无人驾驶交通工具被分配的网络通信资源也为该网络连接质量与网络覆盖范围2相对应；当无人驾驶交通工具处于网络覆盖范围3中，对应的网络连接质量为约定行驶区域40C的网络连接质量，无人驾驶交通工具被分配的网络通信资源也为该网络连接质量与网络覆盖范围3相对应。
请参见图5，图5是本申请实施例提供的一种远程驾驶的场景架构图。如图5所示，该场景架构以网络为5G网络为例进行说明。
该场景架构图中可以包括：运营商5G网络，远程控制中心(远程操控平台)，5G基站以及各个无人驾驶车辆(驾驶端)。其中，运营商5G网络可以为远程驾驶的远程控制 中心提供可靠的5G网络连接，而这个可靠的5G网络连接需要借助5G网络所提供的具有硬服务质量((Quality of Service，QoS)保证的通信数据(Protocol Data Unit，PDU)会话来提供。也就是说，远程控制中心可以控制无人驾驶车辆进行自动驾驶，在此过程中，主要通过运营商5G网络所提供的，具有硬的QoS保证的PDU会话来为远程控制中心提供可靠的5G连接，通过该5G连接，可以指示5G基站会无人驾驶车辆分配网络通信资源，由此，远程控制中心可以控制被分配有网络通信资源的无人驾驶车辆进行自动驾驶。
在一种可能的实现方式中，可以理解的是，以上场景架构图中的5G网络是以运营商部署为例进行说明，但5G网络也可以其他部署方进行部署，本申请不做限制。
在一种可能的实现方式中，请参见图6，图6是本申请实施例提供的一种无人物流配送服务系统的功能架构图。如图6所示，该功能架构中可以包括用户应用模块，服务端入口，网络部署方策略和服务平台，5G远程操控平台，toC服务后台以及toB服务后台。如图6所示的各个模块之间的接口以总线方式相连接，应当理解，如图6所示的各个模块之间可以采用服务化(Service Based Architecture，SBA)接口来支持。以下将对如图6所示的各个模块对应的功能进行阐述：
用户应用模块，该用户应用模块可以为任一会产生物流配送需求的应用，例如，可以为购物应用、点餐应用等。该用户应用模块可以用于用户发起针对任一物品的物流配送需求。
服务端入口模块，该服务端入口模块主要用于接收用户应用的业务请求，随后，该服务端入口模块可以向toC服务后台或toB服务后台发起该业务请求。应当理解，服务端入口模块可以将业务请求分类为toC类业务或toB类业务，并分发对接到toC或toB服务后台。其中，对于服务端入口将业务请求分类的具体方法，可以参见上述图3所对应实施例中S101中对于服务端入口进行业务分类的描述，这里将不再进行赘述。
应当理解，若服务端入口是分开部署的，即一个服务端入口对应一个业务服务后台，则服务端入口在接收到业务请求后，无需再进行分类，只需发送至对应的服务后台即可。
toC与toB服务后台，toC与toB服务后台是除了5G远程操控平台以外的服务的总称，包括位置服务、大数据服务、计费服务、认证服务等。ToC服务后台主要提供toC类业务的服务，主要接收服务端入口产生的toC类业务请求；toB服务后台主要提供toB类业务的服务，主要接受服务端入口产生的toB类业务请求。为便于理解，以下将对toC与toB服务后台提供的各种服务进行阐述：
位置服务：位置服务也就是根据接收到的来自服务端入口的toC或toB业务请求，将其中涉及位置的信息提取出来，并生成5G远程操控平台可以使用的数据，发起无人物流配送请求。
大数据服务，大数据服务可以分析该物流配送服务的预期配送时间及预期能够满足客户的概率等。
计费服务：计费服务也就是根据特定的计费策略，执行计费功能。
认证服务：认证服务也就是认证toC服务端或toB服务端，避免伪造和抵赖。
应当理解，该toB服务后台或toC服务后台可以集成部署于5G远程操控平台中。
5G远程操控平台，该5G远程操控平台也就是远程控制中心，主要用于进行策略服务以及网络质量监测服务。为便于理解，以下将对策略服务以及网络质量监测服务进行阐述：
策略服务：与网络部署服务平台(例如，运营商服务平台)进行交互，双方约定一定的策略，例如，对与网络部署服务平台提供的5G远程驾驶连接的SLA参数进行约定等。其中，在5G远程操控平台与网络部署服务平台之间进行的网络相关的策略服务配置，可以通过应用功能模块(Application Function，AF)与网络开放功能管理模块(Network Exposure Function，NEF)之间的接口来实现，也就是说，AF可以作为5G远程操控平台的一部分功能模块，与网络部署服务平台中的NEF进行交互。
其中，具体约定SLA参数的方法可以参见上述图3所对应实施例，前述步骤中对网络配置规则的约定配置的描述，这里将不再进行赘述。
网络质量监测服务：该网络质量监测服务可以动态检测5G网络连接质量，当5G网络连接质量下降时，在云端与无人驾驶交通工具端分别采取相应的措施。例如，当无人驾驶交通工具端出现网络连接质量下降时，可以根据预先配置好的策略对无人驾驶交通工具执行就近停靠。
在一种可能的实现方式中，请参见图7，图7是本申请实施例提供的一种无人物流配送的流程交互图。如图7所示，该流程可以包括：
S301，用户应用模块向服务端入口发送无人物流配送需求。
本申请中，用户可以在用户终端的用户应用中购买任一物品，当用户选择无人物力配送后，用户应用模块可以向服务端入口发送该用户发起的无人物流配送需求。
S302，服务端入口向服务后台发送运输业务请求。
本申请中，服务端入口可以将无人物流配送需求进行处理加工，生成运输业务请求并发送至服务后台，同时也可以将物品属性(例如，物品体积、物品重量等)、接收地址信息、发货地址信息、收货用户信息等发送至服务后台。其中，服务后台可以包括toC服务后台与toB服务后台，服务端入口可以将运输业务请求进行分类，若运输业务请求为toC类，则将该运输业务请求分发至toC服务后台，若运输业务请求为toB类，则将该运输业务请求分发至toB服务后台。
S303，服务后台生成远程驾驶请求。
本申请中，服务后台可以生成包括物品属性(例如，物品体积、物品重量等)、接收地址信息、发货地址信息、收货用户信息、运输期望时间范围的业务服务信息，随后可以生成包括业务服务信息的远程驾驶请求。对于服务后台生成远程驾驶请求的具体方法，可以参见上述图3所对应实施例中S101中生成远程驾驶请求的描述，这里将不再进行赘述。
S304，服务后台发送远程驾驶请求至5G远程操控平台。
本申请中，服务后台可以发送远程驾驶请求发送至5G远程操控平台，并将业务服务信息一并发送至5G远程操控平台。
S305，5G远程操控平台根据业务服务信息确定预测行驶数据，并确定预测行驶数据对应的SLA参数。
本申请中，5G远程操控平台可以根据业务服务信息确定目标物品的预测行驶数据(可以包括预测行驶路线与预测行驶时间)，并通过网络配置规则确定该预测行驶路线与预测行驶时间对应的SLA参数(包括网络覆盖范围、网络连接质量、远程操控模式等)。其中，对于5G远程操控平台根据业务服务信息确定目标物品的预测行驶数据(可以包括预测行驶路线与预测行驶时间)，并通过网络配置规则确定该预测行驶路线与预测行驶时间对应的SLA参数的具体方法，可以参见上述图3所对应实施例中相关描述，对于远程操控平台确定预测行驶数据，并确定预测行驶数据对应的服务质量参数的描述，这里将不再进行赘述。
S306，5G远程操控平台生成远程操控指令并发送至无人驾驶交通工具。
本申请中，对于5G远程操控平台生成远程操控指令的具体实现方式，可以参见上述图3所对应实施例中S103中业务服务器生成远程操控指令的描述，这里将不再进行赘述。
S307，5G远程操控平台向服务后台发送运输完成通知。
本申请中，在无人驾驶交通工具对目标物品运输完成后，可以生成运输完成通知，并将该运输完成通知发送至服务后台。
S308，服务后台生成驾驶质量评分。
本申请中，服务后台在运输完成后，可以对本次无人物流配送服务生成驾驶质量评分，以进行质量评估。
S309，服务后台发送运输完成通知至服务端入口。
S310，服务端入口发送运输完成通知至用户应用模块。
在本申请实施例中，在物品的物流配送服务中，引入了5G远程驾驶技术，可以控制无人驾驶交通工具运输物品，实现了物品的无人物流配送，从而可以节约大量的人力成本；且在此过程中，设定了网络配置规则，不同的物品对应的预测行驶数据不同，则根据网络配置规则以及不同的预测行驶数据可以为无人驾驶交通工具配置不同的网络通信资源，从而可以实现无人驾驶交通工具在驾驶起点与终点的行驶路线上均处于网络覆盖范围内，且连接有网络，进而可以支持通过网络远程操控无人驾驶交通工具进行驾驶；同时，还可以做到对网络通信资源的合理利用；可以看出，本申请可以基于5G远程驾驶技术为物品提供无人物流配送服务，将无人物流配送与无人驾驶进行融合，实现基于5G系统的个性化智慧物流，全程自动化进行，无需人工参与，可以节约物流成本。
在一种可能的实现方式中，请参见图8，图8是本申请实施例提供的一种业务数据处理装置的结构示意图。业务数据处理装置可以是运行于计算机设备中的一个计算机程序(包括程序代码)，例如该业务数据处理装置为一个应用软件；该业务数据处理装置可以用于执行图3所示的方法。如图8所示，业务数据处理装置1可以包括：请求获取模块11、行驶数据确定模块12、指令生成模块14以及指令发送模块15。
请求获取模块11，用于获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息；
行驶数据确定模块12，用于根据业务服务信息确定与目标物品相关联的预测行驶数据；
指令生成模块14，用于根据预测行驶数据生成远程操控驾驶指令；
指令发送模块15，用于将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶，无人驾驶交通工具是指用于运输目标物品的工具。
其中，请求获取模块11、行驶数据确定模块12、指令生成模块14以及指令发送模块15的具体实现方式，可以参见上述图3所对应实施例中S101-S103的描述，这里将不再进行赘述。
在一种可能的实现方式中，所述装置还包括资源分配模块13，用于根据网络配置规则获取预测行驶数据对应的服务质量参数，为无人驾驶交通工具分配服务质量参数所指示的网络通信资源；
所述指令发送模块15具体用于将远程操控驾驶指令发送至被分配有所述网络通信资源的无人驾驶交通工具。
其中，服务质量参数包括网络覆盖范围与网络连接质量；
请参见图8，资源分配模块13可以包括：服务参数获取单元131以及资源分配单元132。
服务参数获取单元131，用于根据预测行驶数据从网络配置规则中获取服务质量参数；服务质量参数包括已约定的目标网络覆盖范围以及已约定的目标网络连接质量；
资源分配单元132，用于根据目标网络覆盖范围与目标网络连接质量指示的目标基站，为无人驾驶交通工具分配用于满足目标网络覆盖范围，以及目标网络连接质量的网络通信资源；目标基站位于基于预测行驶数据所行经的区域。
其中，服务参数获取单元131以及资源分配单元132的具体实现方式，可以参见上述图3所对应实施例中相关的描述，这里将不再进行赘述。
其中，预测行驶数据包括预测行驶路线；
请参见图8，服务参数获取单元131可以包括：区域集合获取子单元1311、目标区域获取子单元1312以及第一参数确定子单元1313。
区域集合获取子单元1311，用于获取网络配置规则中的约定行驶区域集合；约定行驶区域集合中包括至少两个区域信息，以及为每个区域信息分别约定的网络覆盖范围以及网络连接质量；
目标区域获取子单元1312，用于获取预测行驶路线包括的至少一个预测行驶区域，将至少一个预测行驶区域与约定行驶区域集合进行匹配，在至少两个区域信息中确定目标区域信息；目标区域信息覆盖预测行驶区域；
第一参数确定子单元1313，用于将为目标区域信息所约定的网络覆盖范围确定为预测行驶区域的目标网络覆盖范围，将为目标区域信息所约定的网络连接质量确定为预测行驶区域的目标网络连接质量。
其中，区域集合获取子单元1311、目标区域获取子单元1312以及第一参数确定子单元1313的具体实现方式，可以参见上述图3所对应实施例中相关的描述，这里将不再进行赘述。
其中，预测行驶数据包括预测行驶时间；
请参见图8，服务参数获取单元131可以包括：时间集合获取子单元1314、目标时间确定子单元1315以及第二参数确定子单元1316。
时间集合获取子单元1314，用于获取网络配置规则中的约定时间段集合；约定时间段集合中包括至少两个约定时间段，以及为每个约定时间段分别约定的网络覆盖范围以及网络连接质量；
目标时间确定子单元1315，用于将预测行驶时间与约定时间段集合进行匹配，在至少两个约定时间段中确定目标约定时间段；预测行驶时间处于目标约定时间段中；
第二参数确定子单元1316，用于将为目标约定时间段所约定的网络覆盖范围确定为预测行驶时间的目标网络覆盖范围，将为目标约定时间段所约定的网络连接质量确定为预测行驶时间的目标网络连接质量。
其中，时间集合获取子单元1314、目标时间确定子单元1315以及第二参数确定子单元1316的具体实现方式，可以参见上述图3所对应实施例中相关的描述，这里将不再进行赘述。
其中，远程操控驾驶指令包括第一驾驶指令与第二驾驶指令；
请参见图8，指令生成模块14可以包括：模式确定单元141、指令生成单元142以及指令生成单元143。
模式确定单元141，用于获取预测行驶数据对应的路况复杂度，根据网络配置规则以及路况复杂度确定无人驾驶交通工具的远程操控模式；
指令生成单元142，用于若远程操控模式为线控模式，则根据预测行驶数据生成第一驾驶指令；第一驾驶指令用于为无人驾驶交通工具提供驾驶方向以及驾驶速度；
指令生成单元143，还用于若远程操控模式为路线引导模式，则根据预测行驶数据生成第二驾驶指令；第二驾驶指令用于为无人驾驶交通工具提供行驶路线信息，行驶路线信息用于指引无人驾驶交通工具分析得到驾驶方向以及驾驶速度。
其中，模式确定单元141、指令生成单元142以及指令生成单元143的具体实现方式，可以参见上述图3所对应实施例中S103中的描述，这里将不再进行赘述。
请参见图8，模式确定单元141可以包括：模式获取子单元1411以及模式确定子单元1412。
模式获取子单元1411，用于获取网络配置规则中的约定远程操控模式；约定远程操控模式包括线控模式以及路线引导模式；
模式确定子单元1412，用于若路况复杂度大于复杂度阈值，则将无人驾驶交通工具的远程操控模式确定为线控模式；
模式确定子单元1412，还用于若路况复杂度小于或等于复杂度阈值，则获取无人驾驶交通工具的自动驾驶功能等级，若自动驾驶功能等级不满足无人驾驶条件，则将无人驾驶交通工具的远程操控模式确定为线控模式；
模式确定子单元1412，还用于若自动驾驶功能等级满足无人驾驶条件，则将无人驾驶交通工具的远程操控模式确定为路线引导模式。
其中，模式获取子单元1411以及模式确定子单元1412的具体实现方式，可以参见上述图3所对应实施例中S103中的描述，这里将不再进行赘述。
请参见图8，该装置1还可以包括：网络质量检测模块16以及驾驶停止模块17。
网络质量检测模块16，用于在无人驾驶交通工具的远程操控模式为线控模式时，对无人驾驶交通工具的网络连接质量进行检测；
驾驶停止模块17，用于若无人驾驶交通工具的网络连接质量对应的质量等级低于等级阈值，则生成驾驶停止指令，将驾驶停止指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过驾驶停止指令，从驾驶状态切换为驾驶停止状态。
其中，网络质量检测模块16以及驾驶停止模块17的具体实现方式，可以参见上述图3所对应实施例中S103中的描述，这里将不再进行赘述。
请参见图8，请求获取模块11可以包括：业务请求获取单元111、业务信息生成单元112以及请求生成单元113。
业务请求获取单元111，用于获取针对目标物品的运输业务请求，获取运输业务请求中目标物品的物品属性以及行驶地址信息；
业务信息生成单元112，用于按照业务配置规则获取行驶地址信息对应的初始运输时间范围，根据行驶地址信息、初始运输时间范围以及物品属性生成针对目标物品的业务服务信息；
请求生成单元113，用于生成包括业务服务信息的远程操控驾驶请求。
其中，业务请求获取单元111、业务信息生成单元112以及请求生成单元113的具体实现方式，可以参见上述图3所对应实施例中S101中的描述，这里将不再进行赘述。
其中，物品属性包括物品体积；
请参见图8，业务信息生成单元112可以包括：范围确定子单元1121以及业务信息生成子单元1122。
范围确定子单元1121，用于根据物品体积所属的体积范围确定目标物品对应的运输等待时间范围；
范围确定子单元1121，还用于将初始运输时间范围与运输等待时间范围进行相加处理，得到目标物品的运输期望时间范围；
业务信息生成子单元1122，用于根据行驶地址信息、物品属性以及运输期望时间范围，生成针对目标物品的业务服务信息。
其中，范围确定子单元1121以及业务信息生成子单元1122的具体实现方式，可以参见上述图3所对应实施例中S101中的描述，这里将不再进行赘述。
其中，预测行驶数据包括预测行驶路线；
请参见图8，行驶数据确定模块12可以包括：信息获取单元121以及路线确定单元122。
信息获取单元121，用于获取业务服务信息中的行驶地址信息以及运输期望时间范围；
路线确定单元122，用于根据行驶地址信息，确定目标物品的初始预测行驶路线；
路线确定单元122，还用于确定初始预测行驶路线对应的预测行驶时间范围，若预测行驶时间范围处于运输期望时间范围中，则将初始预测行驶路线确定为目标物品对应的预测行驶路线。
其中，信息获取单元121以及路线确定单元122的具体实现方式，可以参见上述图3所对应实施例中S102中的描述，这里将不再进行赘述。
其中，预测驾驶行为数据包括预测行驶时间；
请参见图8，行驶数据确定模块12可以包括：期望范围获取单元123、时间段获取单元124、时间段删除单元125以及时间段选择单元126。
期望范围获取单元123，用于获取业务服务信息中的运输期望时间范围；
时间段获取单元124，用于在约定行驶时间段内获取拥堵行驶时间段；拥堵行驶时间段是指行驶拥堵的发生概率大于概率阈值的时间段；
时间段删除单元125，用于将约定行驶时间段中的拥堵行驶时间段进行删除，得到候选行驶时间段；
时间段选择单元126，用于在候选行驶时间段中选择目标物品的预测行驶时间段，将预测行驶时间段组成的行驶时间确定为目标物品的预测行驶时间；预测行驶时间处于运输期望时间范围中。
其中，期望范围获取单元123、时间段获取单元124、时间段删除单元125以及时间段选择单元126的具体实现方式，可以参见上述图3所对应实施例中S102中的描述，这里将不再进行赘述。
请参见图8，行驶数据确定模块12还可以包括：范围获取单元127、数据获取单元128以及运输调度单元129。
范围获取单元127，用于获取业务服务信息中的行驶地址信息以及运输期望时间范围；
数据获取单元128，用于从网络配置规则中，获取与行驶地址信息以及运输期望时间范围相关联的网络覆盖范围以及网络连接质量；
运输调度单元129，用于根据物品属性、行驶地址信息、运输期望时间范围、与行驶地址信息以及运输期望时间范围相关联的网络覆盖范围以及网络连接质量，对目标物品进行运输调度，得到预测行驶数据。
其中，范围获取单元127、数据获取单元128以及运输调度单元129的具体实现方式，可以参见上述图3所对应实施例中S102中的描述，这里将不再进行赘述。
请参见图8，该装置1还可以包括：统计报告获取模块18以及报告发送模块19。
统计报告获取模块18，用于若无人驾驶交通工具对目标物品运输完成，则获取与无人驾驶交通工具和目标物品相关联的数据统计报告；数据统计报告包括无人驾驶交通工具在运输过程中所统计到的网络覆盖范围以及网络连接质量；
报告发送模块19，用于将数据统计报告发送至用户终端，以使用户终端通过在运输过程中所统计到的网络覆盖范围以及网络连接质量，生成驾驶质量评分。
其中，统计报告获取模块18以及报告发送模块19的具体实现方式，可以参见上述图3所对应实施例中S103中的描述，这里将不再进行赘述。
其中，业务服务信息包括用于运输目标物品的第一消耗虚拟资产；
请参见图8，该装置1还可以包括：资产获取模块20以及目标资产确定模块21。
资产获取模块20，用于获取网络配置规则中为服务质量参数约定的约定虚拟资产，将约定虚拟资产作为用于运输目标物品的第二消耗虚拟资产；
目标资产确定模块21，用于根据第一消耗虚拟资产与第二消耗虚拟资产，确定用于运输目标物品的目标消耗虚拟资产。
其中，资产获取模块20以及目标资产确定模块21的具体实现方式，可以参见上述图3所对应实施例中S13中的描述，这里将不再进行赘述。
在本申请实施例中，在物品的物流配送服务中，引入了远程驾驶技术，可以控制无人驾驶交通工具运输物品，实现了物品的无人物流配送，从而可以节约大量的人力成本。以物品是目标物品为例，获取针对目标物品的远程操控驾驶请求，根据远程操控驾驶请求中的业务服务信息确定与目标物品相关联的预测行驶数据，以根据预测行驶数据生成远程操控驾驶指令。将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶。可以看出，本申请可以基于远程驾驶技术为物品提供无人物流配送服务，将无人物流配送与无人驾驶进行融合，实现基于远程驾驶技术的个性化智慧物流，全程自动化进行，无需人工参与，可以节约物流成本。
进一步地，请参见图9，图9是本申请实施例提供的一种计算机设备的结构示意图。如图9所示，上述图8所对应实施例中的装置1可以应用于上述计算机设备1000，上述计算机设备1000可以包括：处理器1001，网络接口1004和存储器1005，此外，上述计算机设备1000还包括：用户接口1003，和至少一个通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。其中，用户接口1003可以包括显示屏(Display)、键盘(Keyboard)，可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器1005还可以是至少一个位于远离前述处理器1001的存储装置。如图9所示，作为一种计算机可读存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及设备控制应用程序。
在图9所示的计算机设备1000中，网络接口1004可提供网络通讯功能；而用户接口1003主要用于为用户提供输入的接口；而处理器1001可以用于调用存储器1005中存储的设备控制应用程序，以实现：
获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息；
根据业务服务信息确定与目标物品相关联的预测行驶数据；
根据预测行驶数据生成远程操控驾驶指令；
将远程操控驾驶指令发送至无人驾驶交通工具，以使无人驾驶交通工具通过远程操控驾驶指令进行自动驾驶,无人驾驶交通工具是指用于运输目标物品的工具。
应当理解，本申请实施例中所描述的计算机设备1000可执行前文图3所对应实施例中对该业务数据处理方法的描述，也可执行前文图8所对应实施例中对该业务数据处理装置1的描述，在此不再赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。
此外，这里需要指出的是：本申请实施例还提供了一种计算机可读存储介质，且上述计算机可读存储介质中存储有前文提及的数据处理的计算机设备1000所执行的计算机程序，且上述计算机程序包括程序指令，当上述处理器执行上述程序指令时，能够执行前文图3所对应实施例中对上述业务数据处理方法的描述，因此，这里将不再进行赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。对于本申请所涉及的计算机可读存储介质实施例中未披露的技术细节，请参照本申请方法实施例的描述。
上述计算机可读存储介质可以是前述任一实施例提供的业务数据处理装置或者上述计算机设备的内部存储单元，例如计算机设备的硬盘或内存。该计算机可读存储介质也可以是该计算机设备的外部存储设备，例如该计算机设备上配备的插接式硬盘，智能存储卡(smart media card，SMC)，安全数字(secure digital，SD)卡，闪存卡(flash card)等。进一步地，该计算机可读存储介质还可以既包括该计算机设备的内部存储单元也包括外部存储设备。该计算机可读存储介质用于存储该计算机程序以及该计算机设备所需的其他程序和数据。该计算机可读存储介质还可以用于暂时地存储已经输出或者将要输出的数据。
本申请的一个方面，提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行本申请实施例中一方面提供的方法。
本申请实施例的说明书和权利要求书及附图中的术语“第一”、“第二”等是用于区别不同对象，而非用于描述特定顺序。此外，术语“包括”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、装置、产品或设备没有限定于已列出的步骤或模块，而是可选地还包括没有列出的步骤或模块，或可选地还包括对于这些过程、方法、装置、产品或设备固有的其他步骤单元。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
本申请实施例提供的方法及相关装置是参照本申请实施例提供的方法流程图和/或结构示意图来描述的，具体可由计算机程序指令实现方法流程图和/或结构示意图的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。这些计算机程序指令可提供到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现 在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能的装置。这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能。这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或结构示意一个方框或多个方框中指定的功能的步骤。
以上所揭露的仅为本申请较佳实施例而已，当然不能以此来限定本申请之权利范围，因此依本申请权利要求所作的等同变化，仍属本申请所涵盖的范围。

Claims (19)

  1. 一种业务数据处理方法，所述方法由计算机设备执行，包括：
    获取针对目标物品的远程操控驾驶请求；所述远程操控驾驶请求包括所述目标物品的业务服务信息；
    根据所述业务服务信息确定与所述目标物品相关联的预测行驶数据；
    根据所述预测行驶数据生成远程操控驾驶指令；
    将所述远程操控驾驶指令发送至无人驾驶交通工具，以使所述无人驾驶交通工具通过所述远程操控驾驶指令进行自动驾驶,所述无人驾驶交通工具是指用于运输所述目标物品的工具。
  2. 根据权利要求1所述的方法，所述根据所述业务服务信息确定与所述目标物品相关联的预测行驶数据之后，所述方法还包括：
    根据网络配置规则获取所述预测行驶数据对应的服务质量参数，为所述无人驾驶交通工具分配所述服务质量参数所指示的网络通信资源；
    所述将所述远程操控驾驶指令发送至无人驾驶交通工具，包括：
    将所述远程操控驾驶指令发送至被分配有所述网络通信资源的所述无人驾驶交通工具。
  3. 根据权利要求2所述的方法，所述服务质量参数包括网络覆盖范围与网络连接质量；
    所述根据网络配置规则获取所述预测行驶数据对应的服务质量参数，为所述无人驾驶交通工具分配所述服务质量参数所指示的网络通信资源，包括：
    根据所述预测行驶数据从网络配置规则中获取服务质量参数；所述服务质量参数包括已约定的目标网络覆盖范围以及已约定的目标网络连接质量；
    根据所述目标网络覆盖范围与所述目标网络连接质量指示的目标基站，为所述无人驾驶交通工具分配用于满足所述目标网络覆盖范围，以及所述目标网络连接质量的网络通信资源；所述目标基站位于基于所述预测行驶数据所行经的区域。
  4. 根据权利要求3所述的方法，所述预测行驶数据包括预测行驶路线；
    所述根据所述预测行驶数据从网络配置规则中获取服务质量参数，包括：
    获取所述网络配置规则中的约定行驶区域集合；所述约定行驶区域集合中包括至少两个区域信息，以及为每个区域信息分别约定的网络覆盖范围以及网络连接质量；
    获取所述预测行驶路线包括的至少一个预测行驶区域；
    将所述至少一个预测行驶区域与所述约定行驶区域集合进行匹配，在所述至少两个区域信息中确定目标区域信息；所述目标区域信息覆盖所述预测行驶区域；
    将为所述目标区域信息所约定的网络覆盖范围确定为所述预测行驶区域的目标网络覆盖范围，将为所述目标区域信息所约定的网络连接质量确定为所述预测行驶区域的目标网络连接质量。
  5. 根据权利要求3所述的方法，所述预测行驶数据包括预测行驶时间；
    所述根据所述预测行驶数据从网络配置规则中获取服务质量参数，包括：
    获取所述网络配置规则中的约定时间段集合；所述约定时间段集合中包括至少两个约定时间段，以及为每个约定时间段分别约定的网络覆盖范围以及网络连接质量；
    将所述预测行驶时间与所述约定时间段集合进行匹配，在所述至少两个约定时间段中确定目标约定时间段；所述预测行驶时间处于所述目标约定时间段中；
    将为所述目标约定时间段所约定的网络覆盖范围确定为所述预测行驶时间的目标网络覆盖范围，将为所述目标约定时间段所约定的网络连接质量确定为所述预测行驶时间的目标网络连接质量。
  6. 根据权利要求2所述的方法，所述远程操控驾驶指令包括第一驾驶指令与第二驾驶指令；
    所述根据所述预测行驶数据生成远程操控驾驶指令，包括：
    获取所述预测行驶数据对应的路况复杂度，根据所述网络配置规则以及所述路况复杂度确定所述无人驾驶交通工具的远程操控模式；
    若所述远程操控模式为线控模式，则根据所述预测行驶数据生成第一驾驶指令；所述第一驾驶指令用于为所述无人驾驶交通工具提供驾驶方向以及驾驶速度；
    若所述远程操控模式为路线引导模式，则根据所述预测行驶数据生成第二驾驶指令；所述第二驾驶指令用于为所述无人驾驶交通工具提供行驶路线信息，所述行驶路线信息用于指引所述无人驾驶交通工具分析得到驾驶方向以及驾驶速度。
  7. 根据权利要求6所述的方法，所述根据所述网络配置规则以及所述路况复杂度确定所述无人驾驶交通工具的远程操控模式，包括：
    获取所述网络配置规则中的约定远程操控模式；所述约定远程操控模式包括线控模式以及路线引导模式；
    若所述路况复杂度大于复杂度阈值，则将所述无人驾驶交通工具的远程操控模式确定为所述线控模式；
    若所述路况复杂度小于或等于所述复杂度阈值，则获取所述无人驾驶交通工具的自动驾驶功能等级，若所述自动驾驶功能等级不满足无人驾驶条件，则将所述无人驾驶交通工具的远程操控模式确定为所述线控模式；
    若所述自动驾驶功能等级满足所述无人驾驶条件，则将所述无人驾驶交通工具的远程操控模式确定为所述路线引导模式。
  8. 根据权利要求6所述的方法，所述方法还包括：
    在所述无人驾驶交通工具的远程操控模式为所述线控模式时，对所述无人驾驶交通工具的网络连接质量进行检测；
    若所述无人驾驶交通工具的网络连接质量对应的质量等级低于等级阈值，则生成驾驶停止指令，将所述驾驶停止指令发送至所述无人驾驶交通工具，以使所述无人驾驶交通工具通过所述驾驶停止指令，从驾驶状态切换为驾驶停止状态。
  9. 根据权利要求1所述的方法，所述获取针对目标物品的远程操控驾驶请求，包括：
    获取针对所述目标物品的运输业务请求，获取所述运输业务请求中所述目标物品的物品属性以及行驶地址信息；
    按照业务配置规则获取所述行驶地址信息对应的初始运输时间范围，根据所述行驶地址信息、所述初始运输时间范围以及所述物品属性生成针对所述目标物品的业务服务信息；
    生成包括所述业务服务信息的远程操控驾驶请求。
  10. 根据权利要求9所述的方法，所述物品属性包括物品体积；
    所述根据所述行驶地址信息、所述初始运输时间范围以及所述物品属性生成针对所述目标物品的业务服务信息，包括：
    根据所述物品体积所属的体积范围确定所述目标物品对应的运输等待时间范围；
    将所述初始运输时间范围与所述运输等待时间范围进行相加处理，得到所述目标物品的运输期望时间范围；
    根据所述行驶地址信息、所述物品属性以及所述运输期望时间范围，生成针对所述目标物品的业务服务信息。
  11. 根据权利要求10所述的方法，所述预测行驶数据包括预测行驶路线；
    所述根据所述业务服务信息确定与所述目标物品相关联的预测行驶数据，包括：
    获取所述业务服务信息中的行驶地址信息以及运输期望时间范围；
    根据所述行驶地址信息，确定所述目标物品的初始预测行驶路线；
    确定所述初始预测行驶路线对应的预测行驶时间范围，若所述预测行驶时间范围处于所述运输期望时间范围中，则将所述初始预测行驶路线确定为所述目标物品对应的预测行驶路线。
  12. 根据权利要求10所述的方法，所述预测驾驶行为数据包括预测行驶时间；
    所述根据所述业务服务信息确定与所述目标物品相关联的预测行驶数据，包括：
    获取所述业务服务信息中的运输期望时间范围；
    在约定行驶时间段内获取拥堵行驶时间段；所述拥堵行驶时间段是指行驶拥堵的发生概率大于概率阈值的时间段；
    将所述约定行驶时间段中的所述拥堵行驶时间段进行删除，得到候选行驶时间段；
    在所述候选行驶时间段中选择所述目标物品的预测行驶时间段，将所述预测行驶时间段组成的行驶时间确定为所述目标物品的预测行驶时间；所述预测行驶时间处于所述运输期望时间范围中。
  13. 根据权利要求10所述的方法，所述根据所述业务服务信息确定与所述目标物品相关联的预测行驶数据，包括：
    获取所述业务服务信息中的行驶地址信息以及运输期望时间范围；
    从所述网络配置规则中，获取与所述行驶地址信息以及所述运输期望时间范围相关联的网络覆盖范围以及网络连接质量；
    根据所述物品属性、所述行驶地址信息、所述运输期望时间范围、与所述行驶地址信息以及所述运输期望时间范围相关联的网络覆盖范围以及网络连接质量，对所述目标物品进行运输调度，得到预测行驶数据。
  14. 根据权利要求2所述的方法，所述方法还包括：
    若所述无人驾驶交通工具对所述目标物品运输完成，则获取与所述无人驾驶交通工具和所述目标物品相关联的数据统计报告；所述数据统计报告包括所述无人驾驶交通工具在运输过程中所统计到的网络覆盖范围以及网络连接质量；
    将所述数据统计报告发送至用户终端，以使所述用户终端通过所述在运输过程中所统计到的网络覆盖范围以及网络连接质量，生成驾驶质量评分。
  15. 根据权利要求2所述的方法，所述业务服务信息包括用于运输所述目标物品的第一消耗虚拟资产；
    所述方法还包括：
    根据所述网络配置规则中为所述服务质量参数约定的约定虚拟资产，将所述约定虚拟资产作为用于运输所述目标物品的第二消耗虚拟资产；
    根据所述第一消耗虚拟资产与所述第二消耗虚拟资产，确定用于运输所述目标物品的目标消耗虚拟资产。
  16. 一种业务数据处理装置，所述装置部署在计算机设备上，包括：
    请求获取模块，用于获取针对目标物品的远程操控驾驶请求；远程操控驾驶请求包括目标物品的业务服务信息；
    行驶数据确定模块，用于根据业务服务信息确定与目标物品相关联的预测行驶数据；
    指令生成模块，用于根据预测行驶数据生成远程操控驾驶指令；
    指令发送模块，用于将所述远程操控驾驶指令发送至无人驾驶交通工具，以使所述无人驾驶交通工具通过所述远程操控驾驶指令进行自动驾驶，所述无人驾驶交通工具是指用于运输所述目标物品的工具。
  17. 一种计算机设备，包括：处理器、存储器以及网络接口；
    所述处理器与所述存储器、所述网络接口相连，其中，所述网络接口用于提供网络通信功能，所述存储器用于存储程序代码，所述处理器用于调用所述程序代码，以执行权利要求1-15任一项所述的方法。
  18. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序包括程序指令，所述程序指令当被处理器执行时，执行权利要求1-15任一项所述的方法。
  19. 一种计算机程序产品，当所述计算机程序产品被执行时，用于实现如权利要求1至15任一项所述的方法。
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