CN114120290A - Self-learning vehicle processing method and device based on shared vehicle - Google Patents
Self-learning vehicle processing method and device based on shared vehicle Download PDFInfo
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Abstract
The embodiment of the specification provides a shared vehicle-based self-learning vehicle processing method and device, wherein the shared vehicle-based self-learning vehicle processing method comprises the following steps: calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach; performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach; if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier; and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a shared vehicle-based self-learning vehicle processing method and device.
Background
With the continuous development of internet technology, more and more industries adopt internet thinking to carry out technical improvement, the mode of obtaining the motor vehicle driving license through a self-learning direct-checking mode is popularized by more and more cities, and under the condition, a vehicle learning user can learn by himself, additionally install a self-learning vehicle according to the regulations, participate in driving skills through self-learning and take an examination by a uniform motor vehicle driver, so that the motor vehicle driving license is obtained.
Disclosure of Invention
One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing method, including: the IoT component that invokes the shared vehicle configuration collects the identity of the school bus, as well as the identity images of the school bus user and trainer. And detecting the self-learning vehicle relationship based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach. And if the shared vehicle passes the detection, determining the self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier. And issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing apparatus, including: an image capture module configured to invoke an IoT component of the shared vehicle configuration to capture an identification of the school bus, and identity images of a school bus user and a coach. The relationship detection module is configured to perform self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the learning vehicle user and the coach. And the task determination module is configured to determine a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identification if the detection is passed. The instruction issuing module is configured to issue a self-learning vehicle instruction to the driving auxiliary equipment configured by the shared vehicle based on the self-learning vehicle task so as to start the self-learning vehicle mode of the shared vehicle.
One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing apparatus, comprising: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: the IoT component that invokes the shared vehicle configuration collects the identity of the school bus, as well as the identity images of the school bus user and trainer. And detecting the self-learning vehicle relationship based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach. And if the shared vehicle passes the detection, determining the self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier. And issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed by a processor, implement the following: the IoT component that invokes the shared vehicle configuration collects the identity of the school bus, as well as the identity images of the school bus user and trainer. And detecting the self-learning vehicle relationship based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach. And if the shared vehicle passes the detection, determining the self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier. And issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
Drawings
In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise;
FIG. 1 is a flowchart illustrating a shared vehicle-based self-learning vehicle processing method according to one or more embodiments of the present disclosure;
FIG. 2 is a flowchart of a shared vehicle-based self-learning vehicle processing method applied to a self-learning direct-learning scenario according to one or more embodiments of the present disclosure;
FIG. 3 is a schematic diagram of a shared vehicle based self-learning vehicle processing device according to one or more embodiments of the present disclosure;
FIG. 4 is a schematic structural diagram of a shared vehicle-based self-learning vehicle processing device according to one or more embodiments of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
The embodiment of the self-learning vehicle processing method based on the shared vehicle provided by the specification comprises the following steps:
referring to fig. 1, which shows a processing flow chart of a shared vehicle-based self-learning vehicle processing method provided by the present embodiment, and referring to fig. 2, which shows a processing flow chart of a shared vehicle-based self-learning vehicle processing method applied to a self-learning direct-learning scenario provided by the present embodiment.
Referring to fig. 1, the self-learning vehicle processing method based on shared vehicles provided in this embodiment specifically includes steps S102 to S108.
Step S102, calling an IoT component of the shared vehicle configuration to collect the identity image of the school bus and the identity image of the school bus user and the coach.
In the process of acquiring the driving voucher by adopting the self-learning and direct-examination mode, the vehicle learning user can add the self-learning vehicle according to the requirement through self learning, and the vehicle learning user participates in the driving skill after obtaining the driving skill through self learning and obtains the driving voucher through unified driver examination.
In view of the above, the shared vehicle-based self-learning vehicle processing method provided in this embodiment is implemented by taking a shared vehicle as a self-learning vehicle, identifying and verifying the identities of a vehicle-learning user and a coach through collecting the identity of the self-learning vehicle and the identity images of the vehicle-learning user and the coach, and after the verification is passed, allowing a self-learning vehicle mode of the shared vehicle to be started, so as to provide convenience for the vehicle-learning process of the vehicle-learning user, save the time of the vehicle-learning user, improve the use experience of the vehicle-learning user, improve the utilization rate of vehicle resources, realize efficient reuse, reduce the vehicle threshold and cost for self-learning direct study, simplify the operation process, and ensure the reliability and safety of the vehicle through introducing an identity verification mechanism.
The vehicle learning users in the embodiment comprise users who execute vehicle learning tasks and apply driving vouchers in a self-learning and direct-learning mode.
In the traditional self-learning and direct-checking process, a self-learning vehicle is rented by a family, a coach or a self-service person, after the self-learning vehicle is added according to the regulation, the self-learning vehicle automatically applies for the admittance qualification of the self-learning and direct-checking vehicle, and then the self-learning vehicle participates in the self-learning vehicle after obtaining the driving skill through self-learning and passes the driver examination, so as to obtain the driving voucher.
The embodiment is different from the traditional self-learning driving test, the shared vehicle is provided by a shared vehicle provider, a vehicle management mechanism, a vehicle renting company or other mechanisms in a unified manner, the vehicle provider or the mechanism obtains the self-learning driving test admission qualification after applying for the qualification in a unified manner, the vehicle provider or the mechanism serves as the shared vehicle in a self-learning direct test mode, a channel for completing a self-learning vehicle task is provided for a vehicle learning user, the vehicle learning user is helped to obtain the application qualification of the self-learning driving test under the condition that the self-learning vehicle task is completed by a certain progress, so that the driver test is participated, and the driving certificate is obtained under the condition that the driving score is qualified.
Optionally, the vehicle learning user obtains the application qualification of participating in the self-learning driving examination by applying for the vehicle management mechanism after completing the self-learning vehicle task set by the vehicle management mechanism, and the vehicle learning user obtains the driving certificate issued by the vehicle management mechanism after passing through the self-learning driving examination.
For example, the preset condition or the vehicle-learning task set by the vehicle management mechanism is that "subject two learning time is accumulated for 18 hours", after the vehicle-learning user completes the subject two learning accumulated for 18 hours, the vehicle management mechanism can apply for participating in the self-learning driving test, and when the vehicle-learning user participates in the self-learning driving test and the score is qualified, the driving certificate issued by the vehicle management mechanism is obtained to improve the learning power of the vehicle-learning user, so that the vehicle-learning user is assisted in obtaining the driving certificate in time, and when the vehicle-learning task of the vehicle-learning user reaches a certain degree, the vehicle-learning user can be allocated with the application qualification of the self-learning driving test, the obtaining period of the driving certificate is shortened, and the use experience of the vehicle-learning user is improved.
After the shared vehicle obtains the self-learning driving test admission qualification, driving auxiliary equipment (such as an auxiliary braking device, an auxiliary rearview mirror and the like) can be installed on the shared vehicle, so that the safety of the vehicle learning is guaranteed, in the embodiment, the IoT component is configured on the driving auxiliary equipment, the integrated execution of the vehicle learning service is realized, and the more convenient and better self-learning service is provided for the vehicle learning user; optionally, the driving assistance device is configured after the shared vehicle obtains self-learning driving test admission eligibility.
The self-learning vehicle identification comprises an identification code, such as a two-dimensional code, specifically, an identification voucher to be displayed by a vehicle learning user in the process of making a shared vehicle appointment and completing the self-learning vehicle task through a shared vehicle, and optionally, the self-learning vehicle identification comprises a self-learning vehicle identification of the vehicle learning user and/or a self-learning vehicle identification of a trainer; the learning vehicle identification of the learning vehicle user and/or the learning vehicle identification of the coach are generated by encoding at least one item of data information:
the identification code of the self-learning vehicle identification, the user identity information of the user of the self-learning vehicle, the coach identity information of the coach, the driving voucher information of the coach and the issuing information of the vehicle management mechanism.
In an actual shared self-learning vehicle scene, a vehicle learning user can log in a self-learning vehicle service platform to visit self-learning vehicle service, in the visiting process, a self-learning vehicle identification can be applied, after a reservation application is submitted for the self-learning vehicle identification, a reservation order corresponding to the self-learning vehicle identification is generated, and the self-learning vehicle identification obtained through the application can be the self-learning vehicle identification sent to a client side of the vehicle learning user, the self-learning vehicle identification sent to a client side of a trainer, or the self-learning vehicle identification sent to the client side of the vehicle learning user and the self-learning vehicle identification sent to the client side of the trainer simultaneously.
Optionally, the shared vehicle-based self-learning vehicle processing method is applied to a vehicle terminal of the shared vehicle, and the vehicle terminal is provided with a client of a self-learning vehicle service platform; and the vehicle learning user applies and accesses the vehicle learning identification through the vehicle learning service provided by the vehicle learning service platform, and the reservation order is generated after the vehicle learning user accesses the vehicle learning service and submits a reservation application.
In specific implementation, in order to establish a good and safe student vehicle environment and ensure the compliance and reliability of a vehicle, after a student vehicle user and a coach arrive at a driving field and get on the vehicle, an IoT component configured by a shared vehicle is called to collect the self-student vehicle identification of the student vehicle user and/or the coach and the identity images (such as face images) of the student vehicle user and the coach so as to perform identity verification, so that the problem of the length of the student vehicle user during learning is effectively solved, and vehicle resources are reasonably distributed.
For example, the image recognition of the trainer is performed at the primary driving position, and the image recognition of the trainee user is performed at the secondary driving position.
In addition, except that the IoT component configured for the shared vehicle comprises the camera configured inside the shared vehicle, the IoT component configured for the shared vehicle can be further divided into a camera inside the vehicle and a camera outside the vehicle, the external camera can scan the identification of the learner-driven vehicle to unlock or start the vehicle, the identification serves as a basis for searching the shared vehicle to determine that the shared vehicle reserves for the learner-driven vehicle, and under the condition that the internal camera can be used as a master driving position for a trainer and the learner-driven vehicle user is in a secondary driving position, identity images of the learner-driven vehicle user and the trainer are collected to identify and verify the identity, so that the driving safety in the process of the learner-driven vehicle is guaranteed.
It should be noted that, in the present embodiment, the self-learning vehicle identifier is generated after a vehicle learning user makes a reservation request and performs reservation processing on the reservation request, specifically, the reservation processing process includes: after receiving an appointment request submitted by a user terminal of a vehicle learning user through accessing a self-learning vehicle service, returning a self-learning vehicle resource set to the user terminal of the vehicle learning user; according to a trainee selected by a trainee in the self-learning vehicle resource set, a driving field matched with the position of the trainee is screened from candidate driving fields contained in the self-learning vehicle resource set, a self-learning vehicle identifier corresponding to the reservation request is created, and the self-learning vehicle identifier is synchronized to the trainee and/or the trainee, so that the trainee and/or the trainee can use shared vehicles in the driving field obtained through matching to perform driving training based on the self-learning vehicle identifier.
And step S104, performing self-learning vehicle relation detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach.
In an actual shared self-learning vehicle scene, a shared vehicle is open for all vehicle learning users, and in view of this, in order to accurately judge that the vehicle learning users and coaches entering the shared vehicle are the vehicle learning users and coaches making reservations, ensure the vehicle utilization safety of the vehicle learning users and the coaches, and avoid the shared vehicle from carrying other personnel behaviors besides vehicle-mounted instructors, on the basis that the IoT component for calling the shared vehicle configuration collects the identification of the vehicle learning users and the identity images of the vehicle learning users and the coaches, the embodiment performs self-learning vehicle relationship detection based on the collected self-learning vehicle identification and the identity images of the vehicle learning users and the coaches.
In an optional implementation manner provided by the embodiment, during the self-learning vehicle relationship detection process based on the self-learning vehicle identification and the identity images of the learning vehicle user and the coach, the following operations are performed:
decoding the self-learning vehicle identification to obtain a user identity identification and a coach identity identification carried in the self-learning vehicle identification;
calling an identity recognition interface to recognize the identity images of the school bus user and the coach so as to obtain the identity of the school bus user and the identity of the coach;
and detecting whether the user identity identification and the coach identity identification are consistent with the identity identification of the school bus user and the identity identification of the coach, and if so, determining that the detection is passed.
The identity recognition interface is provided by the self-learning vehicle service platform.
Specifically, the collected self-learning vehicle identification can be decoded, the user identification and the coach identification obtained through decoding are obtained, the identification image of the vehicle learning user and the identity image of the coach are identified by calling the identification interface, the identification of the vehicle learning user and the identification of the coach are obtained, whether the user identification is consistent with the identification of the vehicle learning user or not is detected, whether the coach identification is consistent with the identification of the coach is detected or not is detected, if the user identification is consistent with the coach identification, the detection is determined to be passed, if the user identification is inconsistent with the coach identification, the detection is determined to be failed, and the voice component of the shared vehicle is called to perform voice reminding.
It should be noted that, in the process of calling the identity recognition interface to recognize the identity images of the car learning user and the coach, the same identity recognition interface can be adopted to recognize the identity images of the car learning user and the coach; the driving identity recognition interface can also be adopted to recognize the identity image of a coach, the copilot identity recognition interface can recognize the identity image of a vehicle learning user, the vehicle using requirements of the user can be met in a diversified identity recognition mode, the accuracy and the efficiency of image recognition are improved, and the one-to-one correspondence of shared vehicles, the vehicle learning user and the coach in the self-learning vehicle task time period is ensured.
And S106, if the detection is passed, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier.
As described above, the vehicle learning user can apply for the vehicle learning identifier during the process of accessing the vehicle learning service, and after submitting a reservation application for the vehicle learning identifier, a reservation order corresponding to the vehicle learning identifier of the vehicle learning user is generated. Optionally, the reservation order bound by the self-learning vehicle identifier is generated in the following manner:
according to the user information of the school bus user submitting the reservation request, matching a corresponding coach for the school bus user, and distributing a sharing vehicle in an available sharing vehicle set of a vehicle provider;
and creating a reservation order of the vehicle learning user according to the matched coach and the distributed shared vehicle, and binding the created reservation order with the corresponding self-learning vehicle identification.
The user information comprises geographical position information and the like of the residence of the vehicle learning user, and the available shared vehicle set comprises a set formed by available shared vehicles judged according to the driving time period; therefore, the coach can be matched according to the geographic position information or other user information, and the shared vehicle which is parked at the driving place with the driving place less than the preset threshold value from the geographic position of the residence of the student vehicle user is distributed in the available shared vehicle set of the vehicle provider.
In addition, the reservation order can also be created according to reservation self-learning vehicle resources which are selected by the vehicle learning user in a centralized manner and self-selected by the vehicle learning user in the self-learning vehicle resources returned by the vehicle learning user on the self-learning vehicle service platform, and reservation self-learning vehicle resources which are obtained by performing resource matching on the reservation self-learning vehicle resources selected by the vehicle learning user, wherein the reservation self-learning vehicle resources selected by the vehicle learning user can be reserved shared vehicles, reserved coaches, reserved driving areas and/or reserved driving subjects.
On the basis of self-learning vehicle identification and identity images of a vehicle learning user and a coach, on the basis of detecting the self-learning vehicle relationship, if the detection is passed, a self-learning vehicle task of the shared vehicle can be determined according to a reserved order bound by the self-learning vehicle identification, so that the condition that the acquired personnel information on the primary and secondary driving positions is consistent with the personnel information carried by the self-learning vehicle identification is met, and the effectiveness of the self-learning vehicle service is ensured, specifically, in an optional implementation manner provided by the embodiment, if the detection is passed, in the process of determining the self-learning vehicle task of the shared vehicle according to the reserved order bound by the self-learning vehicle identification, the following operations are executed:
allocating a driving area for the shared vehicle according to the driving subject information recorded in the reserved order and the available driving area of the driving site to which the shared vehicle belongs;
generating a self-learning vehicle task for the shared vehicle based on the driving subject information and the assigned driving zone.
For example, if the driving subject recorded in the reservation order of the trainee user u is "c 1", the available driving area of the driving site to which the shared vehicle belongs is "b 1 and b 2", the driving area corresponding to the driving subject c1 is "b 1", and the self-learning vehicle task of the shared vehicle is generated according to the driving subject information "c 1" and the allocated driving area "b 1".
It should be added that the available driving area of the driving field to which the shared vehicle belongs may include only one of "b 1" and "b 2", and may also include "b 1" and "b 2", so that, in the case of including "b 1" and "b 2", the trainee user can not only perform basic driving skill training at "b 1", but also perform road driving skill learning at the specified "b 2", so as to ensure the effectiveness of the trainee and improve the learning efficiency of the driving skill of the trainee user.
And S108, issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
During specific implementation, after the self-learning vehicle task of the shared vehicle is generated based on the driving subject information and the distributed driving area, a self-learning vehicle instruction can be issued to the driving auxiliary equipment configured for the shared vehicle according to the self-learning vehicle task to start a self-learning vehicle mode of the shared vehicle, enhance the operation management of the self-learning vehicle service, ensure that the vehicle learning user completes the self-learning vehicle task, and realize the remote management of the shared vehicle.
In an optional implementation manner provided by the embodiment, during the process of starting the self-learning vehicle mode of the shared vehicle, the following operations are executed: opening the driving authority of the shared vehicle to the vehicle learning user, and opening the auxiliary control authority of the shared vehicle for the driving auxiliary equipment; wherein the driving authority comprises a vehicle handling authority in a driving area of the self-learning vehicle task, the driving assistance device being handled by the coach.
Specifically, the vehicle control permission of the shared vehicle in the driving area is opened for the school bus users, and the vehicle control permission of the coach for driving the auxiliary equipment is opened, so that accidents caused by the fact that the school bus users do not react timely when meeting emergency situations are avoided, and the development of auxiliary work of the coach is promoted.
Further, on the basis of opening the driving authority of the shared vehicle to the trainee user and opening the auxiliary control authority of the shared vehicle for the driving auxiliary device, the trainee user may or may not travel in the driving area of the self-learning vehicle task. In order to restrict the driving behavior of the vehicle learning user, so that the vehicle learning user can travel in the driving area of the vehicle learning task as much as possible, improve the order and the orderliness of the vehicle learning service, and avoid potential safety hazards caused by traveling in other areas, the shared vehicle may be remotely detected after the shared vehicle starts traveling, and specifically, in an optional implementation manner provided by this embodiment, after the shared vehicle starts traveling, the following operations are performed:
if the fact that the running position of the shared vehicle exceeds the driving area of the self-learning vehicle task is detected, freezing the driving authority of the user of the learning vehicle and the auxiliary control authority of the driving auxiliary equipment;
and when receiving an authority unfreezing instruction of a vehicle provider of the shared vehicle or a site provider of a driving site to which a driving area belongs, unfreezing the frozen driving authority and the auxiliary control authority.
In practical application, if it is detected that the driving position of the shared vehicle exceeds the driving area of the self-learning vehicle task, the driving authority of the vehicle learning user is limited and the auxiliary control authority of the driving auxiliary device is limited, in this case, the vehicle learning user cannot control the shared vehicle, and the coach cannot control the driving auxiliary device, that is: the shared vehicle stops running and cannot be restarted until the frozen driving permission and the auxiliary control permission are defrosted under the condition that a permission unfreezing instruction of a vehicle provider of the shared vehicle or a site provider of a driving site to which a driving area belongs is received, a vehicle learning user can control the shared vehicle again after unfreezing processing, and a coach can control driving auxiliary equipment again to prevent vehicle accidents, reduce potential safety hazards in the running process of the vehicle, reduce the psychological pressure of the vehicle learning user and help the vehicle learning user to concentrate on the vehicle learning.
It is to be supplemented that, when it is detected that the driving position of the shared vehicle exceeds the driving area of the self-learning vehicle task, the driving speed exceeds a specified driving speed threshold, the driving time exceeds a specified driving time threshold, or a traffic violation occurs, an early warning prompt may be performed through a voice component configured in the shared vehicle, and when an early warning response is not detected, the driving authority of the trainee and the auxiliary control authority of the driving auxiliary device may be frozen to wait for a thawing process. Optionally, the driving area of the self-learning vehicle task is located in the driving field of the shared vehicle; the driving place of the shared vehicle is provided by a vehicle management agency which signs driving certificates, or is provided by a vehicle provider of the shared vehicle.
In addition, in order to improve the completion condition of the self-learning vehicle task by the vehicle learning user in the process of assisting the coach in guiding to drive the shared vehicle, in this embodiment, driving positioning data of the shared vehicle for the driving subject of the self-learning vehicle can be acquired, and the driving positioning data is acquired based on the identification component of the shared vehicle and the communication component of the driving area of the self-learning vehicle; and determining self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and the auxiliary driving data of the driving auxiliary equipment configured for the shared vehicle, and generating a driving prompt of the self-learning vehicle driving subject corresponding to the self-learning vehicle task based on the self-learning vehicle driving data.
The reservation order is generated after the learner-driven vehicle user accesses the self-learning vehicle service and submits the reservation application, on the basis, if the self-learning vehicle task of the learner-driven vehicle user is completed, a settlement request can be submitted for the reservation order, and correspondingly, the self-learning vehicle service platform executes the following operations after detecting the settlement request submitted by the learner-driven vehicle user for the reservation order:
calculating the coach cost and the vehicle cost of the reservation order according to the driving subject information recorded in the reservation order;
and paying fees to the coach and the vehicle provider sharing the vehicle according to the calculated coach fee and the calculated vehicle fee.
The following further describes the shared vehicle-based self-learning vehicle processing method provided in this embodiment by taking an application of the shared vehicle-based self-learning vehicle processing method provided in this embodiment in a self-learning direct-learning scenario as an example, and referring to fig. 2, the shared vehicle-based self-learning vehicle processing method applied in the self-learning direct-learning scenario specifically includes the following steps.
Step S202, an IoT component calling the shared vehicle configuration collects the identity of the school bus and the identity images of the school bus user and the coach.
And S204, decoding the self-learning vehicle identification to obtain the user identity identification and the coach identity identification carried in the learning vehicle identification.
And step S206, calling an identity recognition interface to recognize the identity images of the school bus user and the coach so as to obtain the identity of the school bus user and the identity of the coach.
Step S208, detecting whether the user identity identification and the coach identity identification are consistent with the identity identification of the vehicle learning user and the identity identification of the coach;
if yes, go to step S210 to step S222;
if not, no processing is carried out.
And step S210, allocating a driving area for the shared vehicle according to the driving subject information recorded in the reservation order bound by the self-learning vehicle identification and the available driving area of the driving site to which the shared vehicle belongs.
Step S212, a self-learning vehicle task of the shared vehicle is generated based on the driving subject information and the distributed driving area.
And step S214, issuing a remote self-learning vehicle instruction to the driving auxiliary equipment based on the self-learning vehicle task to release the driving authority of the shared vehicle to the vehicle learning user, and releasing the auxiliary control authority of the shared vehicle for the driving auxiliary equipment.
Wherein the driving assistance device is disposed in the shared vehicle.
In step S216, if the driving position of the shared vehicle is detected to exceed the driving area of the self-learning vehicle task, the driving authority of the vehicle learning user and the auxiliary control authority of the driving auxiliary equipment are frozen.
In step S218, when an authority unfreezing command is received from a vehicle provider sharing the vehicle or a site provider of a driving site to which the driving area belongs, the driving authority and the assist control authority that have been frozen are subjected to unfreezing processing.
And step S220, calculating the coach fee and the vehicle fee of the reservation order according to the driving subject information recorded in the reservation order.
And step S222, paying fees to the coach and the vehicle provider sharing the vehicle according to the calculated coach fee and the vehicle fee.
To sum up, in the self-learning vehicle processing method based on the shared vehicle provided in this embodiment, an IoT component configured for the shared vehicle is first called to collect a self-learning vehicle identifier and identity images of a vehicle learning user and a coach, then the self-learning vehicle identifier is decoded to obtain a user identity and a coach identity carried in the self-learning vehicle identifier, then an identity recognition interface is called to recognize the identity images of the vehicle learning user and the coach to obtain an identity of the vehicle learning user and an identity of the coach, and whether the user identity and the coach identity are consistent with the identity of the vehicle learning user and the identity of the coach is detected, if so, it is determined that the detection is passed;
secondly, allocating a driving area for the shared vehicle according to driving subject information recorded in a reserved order bound by the self-learning vehicle identifier and an available driving area of a driving site where the shared vehicle belongs, generating a self-learning vehicle task of the shared vehicle based on the driving subject information and the allocated driving area, and then issuing a self-learning vehicle instruction to driving auxiliary equipment configured for the shared vehicle based on the self-learning vehicle task so as to open driving permission of the shared vehicle to a vehicle learning user and open auxiliary control permission of the shared vehicle for the driving auxiliary equipment;
finally, if the driving position of the shared vehicle is detected to exceed the driving area of the self-learning task, freezing the driving authority of the vehicle learning user and the auxiliary control authority of the driving auxiliary equipment, carrying out unfreezing processing on the frozen driving authority and auxiliary control authority under the condition that an authority unfreezing instruction of a vehicle provider of the shared vehicle or a site provider of a driving site to which the driving area belongs is received, then calculating the coach cost and the vehicle cost of the reserved order according to the driving subject information recorded in the reserved order, and carrying out cost payment to a coach and the vehicle provider of the shared vehicle according to the obtained coach cost and vehicle cost;
the shared vehicle is used as a basis, the self-learning vehicle service is executed, the vehicle using cost in self-learning direct study is reduced, the vehicle resource recycling is realized, the driving safety in the self-learning vehicle process is ensured by introducing the identification image recognition and verification of the vehicle learning user and a coach, the integrated self-learning vehicle service is provided for the vehicle learning user, and the use experience of the vehicle learning user is improved.
The embodiment of the self-learning vehicle processing device based on the shared vehicle provided by the specification is as follows:
in the embodiment, a shared vehicle-based self-learning vehicle processing method is provided, and correspondingly, a shared vehicle-based self-learning vehicle processing device is also provided, which is described below with reference to the accompanying drawings.
Referring to fig. 3, a schematic diagram of a shared vehicle-based self-learning vehicle processing device provided by the embodiment is shown.
Since the device embodiments correspond to the method embodiments, the description is relatively simple, and the relevant portions may refer to the corresponding description of the method embodiments provided above. The device embodiments described below are merely illustrative.
The embodiment provides a self-learning vehicle processing device based on shared vehicles, which comprises:
an image capture module 302 configured to invoke an IoT component of the shared vehicle configuration to capture the identity of the school bus, and identity images of the school bus user and trainer;
a relationship detection module 304 configured to perform self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the learning vehicle user and the trainer;
the task determination module 306 is configured to determine a self-learning vehicle task of the shared vehicle according to the reserved order bound by the self-learning vehicle identifier if the detection is passed;
an instruction issuing module 308 configured to issue a self-learning vehicle instruction to the driving assistance device configured for the shared vehicle based on the self-learning vehicle task to initiate a self-learning vehicle mode of the shared vehicle.
The embodiment of the self-learning vehicle processing equipment based on the shared vehicle provided by the specification is as follows:
corresponding to the self-learning vehicle processing method based on shared vehicles described above, based on the same technical concept, one or more embodiments of the present specification further provide a self-learning vehicle processing device based on shared vehicles, which is used for executing the above-mentioned provided self-learning vehicle processing method based on shared vehicles, and fig. 4 is a schematic structural diagram of a self-learning vehicle processing device based on shared vehicles provided in one or more embodiments of the present specification.
The embodiment provides a self-learning vehicle processing device based on a shared vehicle, which comprises:
as shown in FIG. 4, the shared vehicle based self-learning vehicle processing device may vary significantly due to configuration or performance, and may include one or more processors 401 and memory 402, where one or more stored applications or data may be stored in the memory 402. Wherein memory 402 may be transient or persistent. The application stored in the memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a shared vehicle-based, self-learning vehicle processing device. Still further, the processor 401 may be configured to communicate with the memory 402 to execute a series of computer executable instructions in the memory 402 on a shared vehicle based, self-learning vehicle processing device. The shared vehicle based self-learning vehicle processing apparatus may also include one or more power sources 403, one or more wired or wireless network interfaces 404, one or more input/output interfaces 405, one or more keyboards 406, and the like.
In one particular embodiment, the shared vehicle-based, self-learning vehicle processing apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the shared vehicle-based, self-learning vehicle processing apparatus, and being configured for execution by the one or more processors the one or more programs including computer-executable instructions for:
calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach;
performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier;
and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
An embodiment of a storage medium provided in this specification is as follows:
on the basis of the same technical concept, one or more embodiments of the present specification further provide a storage medium corresponding to the self-learning vehicle processing method based on the shared vehicle described above.
The storage medium provided in this embodiment is used to store computer-executable instructions, and when the computer-executable instructions are executed by the processor, the following processes are implemented:
calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach;
performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier;
and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
It should be noted that the embodiment related to the storage medium in this specification and the embodiment related to the shared vehicle-based self-learning vehicle processing method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the foregoing corresponding method, and repeated details are not repeated.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In the 30 s of the 20 th century, improvements in a technology could clearly be distinguished between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.
One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.
Claims (15)
1. A self-learning vehicle processing method based on shared vehicles comprises the following steps:
calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach;
performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier;
and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
2. The shared vehicle-based self-learning vehicle processing method of claim 1, the IoT component configured to the driving assistance device, the driving assistance device configured after the shared vehicle obtains self-learning driving test admittance qualifications.
3. The shared vehicle based self-learning vehicle processing method of claim 1, said performing self-learning vehicle relationship detection based on said self-learning vehicle identification and said identity images of said learning vehicle user and said trainer, comprising:
decoding the self-learning vehicle identification to obtain a user identity identification and a coach identity identification carried in the self-learning vehicle identification;
calling an identity recognition interface to recognize the identity images of the school bus user and the coach so as to obtain the identity of the school bus user and the identity of the coach;
and detecting whether the user identity identification and the coach identity identification are consistent with the identity identification of the school bus user and the identity identification of the coach, and if so, determining that the detection is passed.
4. The self-learning vehicle processing method based on shared vehicles according to claim 1, wherein if the detection is passed, the self-learning vehicle task of the shared vehicle is determined according to the reservation order bound by the self-learning vehicle identification, and the method comprises the following steps:
allocating a driving area for the shared vehicle according to the driving subject information recorded in the reserved order and the available driving area of the driving site to which the shared vehicle belongs;
generating a self-learning vehicle task for the shared vehicle based on the driving subject information and the assigned driving zone.
5. The method for self-learning vehicle processing based on shared vehicles according to claim 1, wherein the self-learning vehicle identifies the bound reservation order by generating:
matching corresponding trainers for the vehicle learning users according to the user information of the vehicle learning users submitting the reservation requests, and distributing shared vehicles in an available shared vehicle set of a vehicle provider;
and creating a reservation order of the vehicle learning user according to the matched coach and the distributed shared vehicle, and binding the created reservation order with the corresponding self-learning vehicle identification.
6. The shared vehicle-based self-learning vehicle processing method of claim 1, the initiating the self-learning vehicle mode of the shared vehicle, comprising:
opening the driving authority of the shared vehicle to the vehicle learning user, and opening the auxiliary control authority of the shared vehicle for the driving auxiliary equipment;
wherein the driving authority comprises a vehicle handling authority in a driving area of the self-learning vehicle task, the driving assistance device being handled by the coach.
7. The shared vehicle-based self-learning vehicle processing method of claim 6, further comprising:
if the fact that the running position of the shared vehicle exceeds the driving area of the self-learning vehicle task is detected, freezing the driving authority of the user of the learning vehicle and the auxiliary control authority of the driving auxiliary equipment;
and when receiving an authority unfreezing instruction of a vehicle provider of the shared vehicle or a site provider of a driving site to which a driving area belongs, unfreezing the frozen driving authority and the auxiliary control authority.
8. The shared vehicle based self-learning vehicle processing method of claim 7, a driving area of the self-learning vehicle mission being within a driving yard of the shared vehicle; the driving place of the shared vehicle is provided by a vehicle management agency which signs driving certificates, or is provided by a vehicle provider of the shared vehicle.
9. The shared vehicle based self-learning vehicle processing method of claim 1, the self-learning vehicle identification comprising a self-learning vehicle identification of the learning vehicle user and/or a self-learning vehicle identification of the trainer;
the learning vehicle identification of the learning vehicle user and/or the learning vehicle identification of the coach are generated by encoding at least one item of data information:
the identification code of the self-learning vehicle identification, the user identity information of the user of the self-learning vehicle, the coach identity information of the coach, the driving voucher information of the coach and the issuing information of the vehicle management mechanism.
10. The self-learning vehicle processing method based on the shared vehicle as claimed in claim 1, wherein the self-learning vehicle processing method based on the shared vehicle is applied to a vehicle terminal of the shared vehicle, and the vehicle terminal is provided with a client of a self-learning vehicle service platform;
and the vehicle learning user applies and accesses the vehicle learning identification through the vehicle learning service provided by the vehicle learning service platform, and the reservation order is generated after the vehicle learning user accesses the vehicle learning service and submits a reservation application.
11. The shared vehicle based self-learning vehicle processing method of claim 10, the self-learning vehicle service platform performing the following operations after detecting a settlement request submitted by the trainee user for the reservation order:
calculating the coach cost and the vehicle cost of the reservation order according to the driving subject information recorded in the reservation order;
and paying fees to the coach and the vehicle provider sharing the vehicle according to the calculated coach fee and the calculated vehicle fee.
12. The shared vehicle-based self-learning vehicle processing method according to claim 1, wherein the vehicle learning user obtains an application qualification for applying for participation in self-learning driving test to the vehicle management organization when a task completion degree of a self-learning vehicle task of the vehicle learning user meets a preset condition, or the vehicle learning user obtains a driving certificate issued by the vehicle management organization after completing the self-learning vehicle task set by the vehicle management organization.
13. A shared vehicle based self-learning vehicle processing device comprising:
an image capture module configured to invoke an IoT component of the shared vehicle configuration to capture an identity of the school bus, and identity images of a school bus user and a coach;
the relationship detection module is configured to perform self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
the task determination module is configured to determine a self-learning vehicle task of the shared vehicle according to the reserved order bound by the self-learning vehicle identifier if the detection is passed;
the instruction issuing module is configured to issue a self-learning vehicle instruction to the driving auxiliary equipment configured by the shared vehicle based on the self-learning vehicle task so as to start the self-learning vehicle mode of the shared vehicle.
14. A shared vehicle based self-learning vehicle processing device comprising:
a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to:
calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach;
performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier;
and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
15. A storage medium storing computer-executable instructions that when executed by a processor implement the following:
calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach;
performing self-learning vehicle relationship detection based on the self-learning vehicle identification and the identity images of the vehicle learning user and the coach;
if the shared vehicle passes the detection, determining a self-learning vehicle task of the shared vehicle according to the reservation order bound by the self-learning vehicle identifier;
and issuing a self-learning vehicle instruction to driving auxiliary equipment configured on the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.
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