CN113919671A - Method, apparatus, medium, and electronic device for remote operator assignment of unmanned device - Google Patents

Method, apparatus, medium, and electronic device for remote operator assignment of unmanned device Download PDF

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CN113919671A
CN113919671A CN202111131821.5A CN202111131821A CN113919671A CN 113919671 A CN113919671 A CN 113919671A CN 202111131821 A CN202111131821 A CN 202111131821A CN 113919671 A CN113919671 A CN 113919671A
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target
operator
information
matching
determining
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王亚维
赵惠鹏
王乃峥
夏华夏
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Beijing Sankuai Online Technology Co Ltd
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Beijing Sankuai Online Technology Co Ltd
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    • G06Q10/063112—Skill-based matching of a person or a group to a task

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Abstract

The present disclosure relates to a remote operator allocation method, apparatus, medium, and electronic device for an unmanned aerial device. The method comprises the following steps: receiving abnormal information of the unmanned equipment; determining target unmanned equipment to be distributed according to the received abnormal information; according to the target abnormal information of the target unmanned equipment and the abnormal processing speciality information of each operator, determining an alternative operator with the matching degree of the abnormal processing speciality information and the target abnormal information higher than the preset degree from the operators in the idle state; and determining a target operator to perform exception handling on the target unmanned equipment from the candidate operators. Therefore, the most appropriate remote operator can be matched for the unmanned equipment, the operator can quickly assist the unmanned equipment to recover from the abnormality, and the utilization rate of the unmanned equipment is improved.

Description

Method, apparatus, medium, and electronic device for remote operator assignment of unmanned device
Technical Field
The present disclosure relates to the field of automatic driving, and in particular, to a method, an apparatus, a medium, and an electronic device for remote operator allocation of an unmanned device.
Background
Since some abnormal situations may occur during the operation of the unmanned device (e.g., unmanned vehicle), the unmanned device is assisted by relevant personnel through network intervention, which is generally called a security guard (or operator). For example, if an autonomous vehicle is abnormal during distribution, a remote operator is required to intervene to remotely operate the autonomous vehicle to solve the abnormal problem of the autonomous vehicle.
In general, due to the limitation of human resources and the like, the number of operators does not correspond to the number of unmanned devices one by one, and therefore, one operator is temporarily assigned to an unmanned device that is abnormal when the unmanned device is abnormal, so as to solve the problem of abnormality of the unmanned device. In the related art, the method of assigning personnel to a task typically relies on several single, specific factors. For example, in a case of a reserved riding service, the person assignment is performed according to the distance between the vehicle and the reserved passenger and the contribution value of the vehicle driver. For another example, in the case of a delivery service, the person assignment is performed according to the distance, the score of the delivery person, and the time. However, the allocation methods used in the related art are all specific allocation methods for specific scenarios, and are not applicable to the operator allocation scenario of the unmanned device, that is, there is no effective method for solving the problem of remote operator allocation of the unmanned device.
Disclosure of Invention
The purpose of the present disclosure is to provide a method, an apparatus, a medium, and an electronic device for remote operator allocation of an unmanned device, which can match a most suitable remote operator for the unmanned device, and assist the unmanned device to recover from an abnormality quickly by the operator, thereby improving the utilization rate of the unmanned device.
To achieve the above object, the present disclosure provides a remote operator allocation method of an unmanned aerial device, the method comprising:
receiving abnormal information of the unmanned equipment;
determining target unmanned equipment to be distributed according to the received abnormal information;
according to the target abnormal information of the target unmanned equipment and the abnormal processing speciality information of each operator, determining an alternative operator with the matching degree of the abnormal processing speciality information and the target abnormal information higher than the preset degree from the operators in the idle state;
and determining a target operator to perform exception handling on the target unmanned equipment from the candidate operators.
Optionally, the abnormality information includes first information indicating a time when the abnormality of the unmanned aerial vehicle occurs and second information indicating an urgency degree to which the abnormality of the unmanned aerial vehicle needs to be dealt with;
the determining the target unmanned device to be allocated according to the received abnormal information includes:
and determining the unmanned equipment corresponding to the abnormal information which is earliest in time and highest in urgency degree and indicated by the second information as the target unmanned equipment.
Optionally, the target exception information includes exception feature information corresponding to at least one preset dimension, and the exception handling expertise information includes expertise feature information corresponding to at least one preset dimension;
the determining, according to the target abnormality information of the target unmanned aerial vehicle and the respective abnormality processing expertise information of each operator, a candidate operator whose matching degree of the abnormality processing expertise information and the target abnormality information is higher than a preset degree from the operators in the idle state includes:
respectively taking each operator in an idle state as a target operator, and determining the feature matching quantity which can be successfully matched with the abnormal feature information in the special feature information of the target operator;
and determining at least one operator with the maximum feature matching number as the candidate operator according to the feature matching number corresponding to each operator.
Optionally, each preset dimension corresponds to a respective matching priority;
the determining the number of feature matches that can be successfully matched with the abnormal feature information in the expertise feature information of the target operator includes:
taking the sequence of the matching priorities from high to low as a matching sequence, matching the speciality feature information of the target operator corresponding to the preset dimension with the abnormal feature information of the preset dimension aiming at least one preset dimension, and determining the continuous times of successful matching of the target operator;
and determining the continuous times as the feature matching quantity.
Optionally, the step of taking the sequence of matching priorities from high to low as a matching sequence, matching, for at least one preset dimension, the expertise characteristic information of the target operator corresponding to the preset dimension with the anomaly characteristic information of the preset dimension, and determining the number of consecutive times of successful matching of the target operator includes:
selecting a preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as a target preset dimension for the matching;
judging whether the specialty characteristic information of the target operator corresponding to the target preset dimension is successfully matched with the abnormal characteristic information corresponding to the target preset dimension;
if the matching is determined to be successful, determining whether unmatched preset dimensionality exists or not;
if unmatched preset dimensions exist, the step of selecting the preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as the matched target preset dimension is executed again;
if the matching is determined to be failed, or if the unmatched preset dimensionality does not exist, stopping the matching, and determining the times of the successful matching of the target operator as the continuous times of the successful matching of the target operator.
Optionally, each operator corresponds to an operation level, wherein the operation level of the operator is positively correlated with the capability of the operator for handling the abnormality of the unmanned equipment;
the determining a target operator to perform exception handling on the target unmanned device from the candidate operators includes:
and determining the candidate operator with the highest operation grade as the target operator.
Optionally, the target exception information includes an exception level of the target unmanned aerial vehicle, and each of the operators has an operation level corresponding thereto, where the operation level of the operator is positively correlated with a capability of the operator to handle the exception of the unmanned aerial vehicle;
the determining a target operator to perform exception handling on the target unmanned device from the candidate operators includes:
and determining the candidate operator with the operation level matched with the abnormal level of the target unmanned equipment as the target operator according to the operation level corresponding to each operator.
According to a second aspect of the present disclosure there is provided a remote operator dispensing apparatus for an unmanned aerial device, the apparatus comprising:
the receiving module is used for receiving the abnormal information of the unmanned equipment;
the first determining module is used for determining target unmanned equipment to be distributed according to the received abnormal information;
a second determining module, configured to determine, according to the target abnormal information of the target unmanned aerial vehicle and the respective abnormal processing expertise information of each operator, a candidate operator whose matching degree between the abnormal processing expertise information and the target abnormal information is higher than a preset degree from among operators in an idle state;
and a third determining module, configured to determine, from the candidate operators, a target operator that is to perform exception handling on the target unmanned device.
According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect of the present disclosure.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of the first aspect of the disclosure.
According to the technical scheme, the abnormal information of the unmanned equipment is received, the target unmanned equipment to be distributed is determined according to the received abnormal information, then, according to the target abnormal information of the target unmanned equipment and the abnormal processing expertise information of each operator, the alternative operators with the matching degree of the abnormal processing expertise information and the target abnormal information higher than the preset degree are determined from the operators in the idle state, and the target operators to perform abnormal processing on the target unmanned equipment are determined from the alternative operators. Therefore, the target unmanned equipment which needs to be matched currently is determined from the plurality of abnormal unmanned equipment, the optimal operator which can solve the abnormality of the target unmanned equipment is matched for the target unmanned equipment according to the expertise of the operator in the aspect of abnormality processing, and the operator helps the target unmanned equipment to recover from the abnormality quickly. Therefore, when the unmanned equipment is abnormal, the most appropriate remote operator can be matched for the unmanned equipment, the operator can quickly assist the unmanned equipment to recover from the abnormality, and the utilization rate of the unmanned equipment is improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart of a method of remote operator assignment of an unmanned aerial device provided in accordance with one embodiment of the present disclosure;
FIG. 2 is an exemplary flow chart of steps for determining alternative operators in a method for remote operator assignment of an unmanned aerial vehicle provided in accordance with the present disclosure;
FIG. 3 is a block diagram of a remote operator distribution device of an unmanned aerial vehicle provided in accordance with one embodiment of the present disclosure;
FIG. 4 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment;
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flow chart of a method of remote operator assignment of an unmanned aerial device provided in accordance with one embodiment of the present disclosure. As shown in fig. 1, the method provided by the present disclosure may be applied to an unmanned equipment (e.g., unmanned vehicle, autonomous vehicle) scheduling system, i.e., a system for scheduling and managing unmanned equipment, which is used to assign a remote operator to the unmanned equipment with abnormality to solve the abnormal problem of the unmanned equipment. As shown in fig. 1, the method provided by the present disclosure may include the following steps 11 to 14:
in step 11, receiving abnormal information of the unmanned equipment;
in step 12, determining target unmanned equipment to be allocated according to the received abnormal information;
in step 13, according to the target abnormal information of the target unmanned aerial vehicle and the respective abnormal processing expertise information of each operator, determining an alternative operator with the matching degree of the abnormal processing expertise information and the target abnormal information higher than the preset degree from the operators in the idle state;
in step 14, a target operator who is to perform exception handling on the target unmanned aerial device is determined from the candidate operators.
When an abnormality occurs, the unmanned device usually generates abnormality information based on the current abnormality and transmits the abnormality information to the outside. Therefore, the abnormality information of the unmanned aerial vehicle includes both the relevant information capable of characterizing the abnormality occurred in the unmanned aerial vehicle and the identification information of the unmanned aerial vehicle, which may be, for example, an Identity Document (ID) of the unmanned aerial vehicle.
However, in the same period, the unmanned device in which the abnormality occurs may not be singular, that is, there may be a plurality of unmanned devices in which the abnormality occurs in the same period. However, the unmanned equipment scheduling system cannot allocate operators to all the unmanned equipments at the same time, and therefore, a mechanism is required to screen out the allocation tasks that should be processed currently. Therefore, step 12 may be performed to determine the drone to be assigned (i.e., the target drone) based on the received anomaly information.
After the target unmanned device which needs to be matched currently is determined, an operator can be selected for the target unmanned device to determine the target operator, so as to remotely solve the abnormal problem of the target unmanned device, namely, step 13 and step 14 are executed.
In the process of determining the target operator, a candidate operator whose matching degree of the abnormality processing expertise information and the target abnormality information is higher than a preset degree is determined from operators in an idle state according to the abnormality information (i.e., the target abnormality information) of the target unmanned device and the respective abnormality processing expertise information of each operator. The abnormality processing special information of the operator can represent which abnormality of the unmanned equipment the operator is good at processing, the matching degree of the abnormality processing special information and the target abnormality information is higher than the preset degree, and the abnormality which the operator is good at processing is matched with the abnormality which currently appears in the target unmanned equipment, so that the operator can better solve the abnormality of the target unmanned equipment. Based on this, candidate operators suitable for solving the abnormality of the target unmanned aerial device can be determined, and then, a target operator to perform abnormality processing on the target unmanned aerial device is further determined from the candidate operators.
According to the technical scheme, the abnormal information of the unmanned equipment is received, the target unmanned equipment to be distributed is determined according to the received abnormal information, then, according to the target abnormal information of the target unmanned equipment and the abnormal processing expertise information of each operator, the alternative operators with the matching degree of the abnormal processing expertise information and the target abnormal information higher than the preset degree are determined from the operators in the idle state, and the target operators to perform abnormal processing on the target unmanned equipment are determined from the alternative operators. Therefore, the target unmanned equipment which needs to be matched currently is determined from the plurality of abnormal unmanned equipment, the optimal operator which can solve the abnormality of the target unmanned equipment is matched for the target unmanned equipment according to the expertise of the operator in the aspect of abnormality processing, and the operator helps the target unmanned equipment to recover from the abnormality quickly. Therefore, when the unmanned equipment is abnormal, the most appropriate remote operator can be matched for the unmanned equipment, the operator can quickly assist the unmanned equipment to recover from the abnormality, and the utilization rate of the unmanned equipment is improved.
In order to make those skilled in the art understand the technical solutions provided by the embodiments of the present invention, the following detailed descriptions are provided for the corresponding steps in the above.
In step 12, the target unmanned device to be allocated is determined according to the received abnormal information, and explanation is performed.
In one possible embodiment, the abnormality information may include first information indicating a time when the abnormality of the unmanned aerial vehicle occurs and second information indicating an urgency degree to which the abnormality of the unmanned aerial vehicle needs to be dealt with. Accordingly, step 12 may include the steps of:
and determining the unmanned equipment corresponding to the abnormal information with the earliest time indicated by the first information and the highest emergency degree indicated by the second information as the target unmanned equipment.
That is, in determining the abnormality information that needs to be currently handled (i.e., determining the unmanned equipment to which the operator needs to be currently assigned), the determination may be made in conjunction with the time when the unmanned equipment has an abnormality and the urgency of the abnormal task. Wherein the second information may include two or more levels of urgency. Therefore, each emergency level may correspond to a queue of unmanned devices to which no operator is assigned, and the queues are processed in order of the highest degree of emergency represented by the emergency level, and when all the unmanned devices in the queue with the higher degree of emergency are assigned, the queue with the lower degree of emergency is processed, and for the same queue, the queues are processed in order of the earliest time to the latest time.
By the aid of the method, the sequence of allocating operators to the unmanned aerial vehicles is determined according to the abnormal occurrence time and the abnormal emergency degree of the unmanned aerial vehicles, the unmanned aerial vehicles with earlier abnormal occurrence time and higher abnormal emergency degree can be guaranteed to allocate operators more quickly, and accordingly the abnormality of the unmanned aerial vehicles can be processed preferentially.
Next, in step 13, according to the target abnormal information of the target unmanned aerial vehicle and the respective abnormal processing expertise information of each operator, a candidate operator whose matching degree between the abnormal processing expertise information and the target abnormal information is higher than a preset degree is determined from the operators in the idle state, and explanation is performed.
In one possible implementation, the target exception information includes exception feature information corresponding to at least one preset dimension, and the exception handling expertise information includes expertise feature information corresponding to at least one preset dimension. Wherein the preset dimension may include, but is not limited to, at least one of: location dimension, scene dimension, network signal dimension, and order type dimension. And, the characteristic information may be a tag, that is, the abnormal characteristic information may be an abnormal tag of the target abnormal apparatus, and the specialty characteristic information may be a tag of an abnormality which the operator is skilled in handling.
Accordingly, step 13 may include the following steps, as shown in fig. 2:
in step 21, each operator in an idle state is respectively used as a target operator, and the feature matching number which can be successfully matched with the abnormal feature information in the special feature information of the target operator is determined;
in step 22, at least one operator with the largest number of feature matches is determined as the candidate operator according to the number of feature matches corresponding to each operator.
In a possible embodiment, each preset dimension corresponds to a respective matching priority, and accordingly, the step 21 of determining the number of feature matches that can be successfully matched with the abnormal feature information in the expert feature information of the target operator may include the following steps:
taking the sequence of the matching priorities from high to low as a matching sequence, matching the speciality feature information of the target operator corresponding to the preset dimension with the abnormal feature information of the preset dimension aiming at least one preset dimension, and determining the continuous times of successful matching of the target operator;
the number of consecutive times is determined as the number of feature matches.
The sequence of the matching priorities from high to low is taken as a matching sequence, and aiming at least one preset dimension, the speciality feature information of the target operator corresponding to the preset dimension is matched with the abnormal feature information of the preset dimension, and the continuous times of successful matching of the target operator are determined, which can be carried out by referring to the following steps:
(1) selecting a preset dimension with the highest matching priority from preset dimensions which are not matched by a target operator as a target preset dimension for matching;
(2) judging whether the speciality feature information of the target operator corresponding to the target preset dimension is successfully matched with the abnormal feature information corresponding to the target preset dimension;
(3) if successful matching is determined through the step (2), whether unmatched preset dimensionality exists is determined;
(4) if unmatched preset dimensions exist through the step (3), returning to the step (1) of executing again to select the preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as the target preset dimension for matching at this time, and continuing the matching process with the new target preset dimension;
(5) and if the matching is determined to be failed through the step (2), or if the unmatched preset dimensionality does not exist through the step (3), stopping the matching, and determining the number of times that the target operator is successfully matched as the continuous number of times that the target operator is successfully matched.
In step 14, a target operator who is to perform exception handling on the target unmanned aerial device is determined from the candidate operators.
Each operator is corresponding to an operation level, wherein the operation level of the operator is positively correlated with the handling capacity of the operator for the unmanned equipment abnormity. That is, the higher the operation level of the operator, the higher the ability of the operator to handle the unmanned equipment anomaly, and the more difficult anomaly tasks can be handled.
In one possible embodiment, if only one candidate operator is determined, the candidate operator may be directly used as the target operator.
In another possible embodiment, step 14 may include the steps of:
and determining the candidate operator with the highest operation grade as the target operator.
Through the mode, the operator with the highest operation grade is selected from the candidate operators and used for processing the abnormity of the target unmanned equipment, the abnormity of the target unmanned equipment can be guaranteed to be processed by the operator with the strongest abnormity processing capability at the current time, and the abnormity processing efficiency of the unmanned equipment is improved.
In another possible approach, the target anomaly information may include an anomaly level of the target drone. Accordingly, step 14 may include the steps of:
and determining the candidate operator with the operation level matched with the abnormal level of the target unmanned equipment as the target operator according to the operation level corresponding to each operator.
That is, based on the abnormality level of the target unmanned aerial vehicle, an operator just capable of handling the abnormality of the target unmanned aerial vehicle is allocated to the target unmanned aerial vehicle, and operators of other operation levels are reserved for coping with tasks of other abnormality levels, so that the allocation reasonability of the whole allocation process is ensured, and the abnormality of each unmanned aerial vehicle can be solved by the most appropriate operator.
Fig. 3 is a block diagram of a remote operator distribution apparatus for an unmanned aerial device provided in accordance with an embodiment of the present disclosure, as shown in fig. 3, the apparatus 30 comprising:
a receiving module 31, configured to receive abnormal information of the unmanned device;
a first determining module 32, configured to determine, according to the received abnormal information, a target unmanned device to be allocated;
a second determining module 33, configured to determine, according to the target abnormal information of the target unmanned aerial vehicle and the respective abnormal processing expertise information of each operator, a candidate operator whose matching degree between the abnormal processing expertise information and the target abnormal information is higher than a preset degree from among operators in an idle state;
a third determining module 34, configured to determine, from the candidate operators, a target operator that is to perform exception handling on the target unmanned aerial device.
Optionally, the abnormality information includes first information indicating a time when the abnormality of the unmanned aerial vehicle occurs and second information indicating an urgency degree to which the abnormality of the unmanned aerial vehicle needs to be dealt with;
the first determining module 32 includes:
and the first determining submodule is used for determining the unmanned equipment corresponding to the abnormal information which is earliest in time and highest in emergency degree and indicated by the first information as the target unmanned equipment.
Optionally, the target exception information includes exception feature information corresponding to at least one preset dimension, and the exception handling expertise information includes expertise feature information corresponding to at least one preset dimension;
the second determining module 33 includes:
the second determining submodule is used for respectively taking each operator in an idle state as a target operator and determining the feature matching quantity which can be successfully matched with the abnormal feature information in the special feature information of the target operator;
and the third determining submodule is used for determining at least one operator with the maximum feature matching quantity as the candidate operator according to the feature matching quantity corresponding to each operator.
Optionally, each preset dimension corresponds to a respective matching priority;
the second determination submodule includes:
the matching sub-module is used for matching the speciality characteristic information of the target operator corresponding to the preset dimension with the abnormal characteristic information of the preset dimension by taking the sequence of the matching priority from high to low as the matching sequence and aiming at least one preset dimension, and determining the continuous times of successful matching of the target operator;
the second determining submodule is configured to determine the number of consecutive times as the number of feature matches.
Optionally, the matching sub-module includes:
the selection submodule is used for selecting a preset dimension with the highest matching priority from the preset dimensions which are not matched by the target operator as a target preset dimension for matching at this time;
the judgment sub-module is used for judging whether the specialty characteristic information of the target operator corresponding to the target preset dimension is successfully matched with the abnormal characteristic information corresponding to the target preset dimension;
the fourth determining submodule is used for determining whether unmatched preset dimensionality exists or not if successful matching is determined;
the repeated triggering submodule is used for triggering the selection submodule to select the preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as the target preset dimension of the current matching if the unmatched preset dimensions exist;
the matching submodule is used for stopping matching if the matching is determined to be failed or if the unmatched preset dimensionality does not exist, and determining the number of times that the target operator is successfully matched as the continuous number of times that the target operator is successfully matched.
Optionally, each operator corresponds to an operation level, wherein the operation level of the operator is positively correlated with the capability of the operator for handling the abnormality of the unmanned equipment;
the third determining module 34 includes:
and the fifth determination submodule is used for determining the candidate operator with the highest operation level as the target operator.
Optionally, the target exception information includes an exception level of the target unmanned aerial vehicle, and each of the operators has an operation level corresponding thereto, where the operation level of the operator is positively correlated with a capability of the operator to handle the exception of the unmanned aerial vehicle;
the third determining module 34 includes:
and a sixth determining submodule, configured to determine, as the target operator, an alternative operator whose operation level matches the abnormality level of the target unmanned aerial vehicle, according to the operation level corresponding to each operator.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 4 is a block diagram illustrating an electronic device 700 according to an example embodiment. As shown in fig. 4, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the above-mentioned remote operator allocation method for an unmanned aerial vehicle. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 705 may thus include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the remote operator distribution method of the above-described unmanned Device.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described method of remote operator allocation of an unmanned aerial device is also provided. For example, the computer readable storage medium may be the memory 702 described above including program instructions executable by the processor 701 of the electronic device 700 to perform the remote operator assignment method of the drone described above.
Fig. 5 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 5, an electronic device 1900 includes a processor 1922, which may be one or more in number, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processor 1922 may be configured to execute the computer program to perform the above-described remote operator assignment method for the unmanned device.
Additionally, electronic device 1900 may also include a power component 1926 and a communication component 1950, the power component 1926 may be configured to perform power management of the electronic device 1900, and the communication component 1950 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 1900. In addition, the electronic device 1900 may also include input/output (I/O) interfaces 1958. The electronic device 1900 may operate based on an operating system, such as Windows Server, stored in memory 1932TM，Mac OS XTM，UnixTM，LinuxTMAnd so on.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described method of remote operator allocation of an unmanned aerial device is also provided. For example, the computer readable storage medium may be the memory 1932 described above that includes program instructions executable by the processor 1922 of the electronic device 1900 to perform the remote operator assignment method of the drone described above.
In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described method of remote operator allocation of an unmanned aerial device when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims (10)

1. A method for remote operator assignment of an unmanned aerial device, the method comprising:
receiving abnormal information of the unmanned equipment;
determining target unmanned equipment to be distributed according to the received abnormal information;
according to the target abnormal information of the target unmanned equipment and the abnormal processing speciality information of each operator, determining an alternative operator with the matching degree of the abnormal processing speciality information and the target abnormal information higher than the preset degree from the operators in the idle state;
and determining a target operator to perform exception handling on the target unmanned equipment from the candidate operators.
2. The method according to claim 1, wherein the abnormality information includes first information indicating a time when an abnormality occurs in the unmanned aerial vehicle and second information indicating an urgency with which the abnormality of the unmanned aerial vehicle needs to be dealt with;
the determining the target unmanned device to be allocated according to the received abnormal information includes:
and determining the unmanned equipment corresponding to the abnormal information which is earliest in time and highest in urgency degree and indicated by the second information as the target unmanned equipment.
3. The method of claim 1, wherein the target exception information includes exception feature information corresponding to at least one preset dimension, and the exception handling expertise information includes expertise feature information corresponding to at least one of the preset dimensions;
the determining, according to the target abnormality information of the target unmanned aerial vehicle and the respective abnormality processing expertise information of each operator, a candidate operator whose matching degree of the abnormality processing expertise information and the target abnormality information is higher than a preset degree from the operators in the idle state includes:
respectively taking each operator in an idle state as a target operator, and determining the feature matching quantity which can be successfully matched with the abnormal feature information in the special feature information of the target operator;
and determining at least one operator with the maximum feature matching number as the candidate operator according to the feature matching number corresponding to each operator.
4. The method of claim 3, wherein each of the predetermined dimensions corresponds to a respective matching priority;
the determining the number of feature matches that can be successfully matched with the abnormal feature information in the expertise feature information of the target operator includes:
taking the sequence of the matching priorities from high to low as a matching sequence, matching the speciality feature information of the target operator corresponding to the preset dimension with the abnormal feature information of the preset dimension aiming at least one preset dimension, and determining the continuous times of successful matching of the target operator;
and determining the continuous times as the feature matching quantity.
5. The method according to claim 4, wherein the matching of the expertise characteristic information of the target operator corresponding to the preset dimension with the anomaly characteristic information of the preset dimension in the matching sequence with the matching priority from high to low is performed for at least one preset dimension, and the determination of the continuous times of successful matching of the target operator comprises:
selecting a preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as a target preset dimension for the matching;
judging whether the specialty characteristic information of the target operator corresponding to the target preset dimension is successfully matched with the abnormal characteristic information corresponding to the target preset dimension;
if the matching is determined to be successful, determining whether unmatched preset dimensionality exists or not;
if unmatched preset dimensions exist, the step of selecting the preset dimension with the highest matching priority from the unmatched preset dimensions of the target operator as the matched target preset dimension is executed again;
if the matching is determined to be failed, or if the unmatched preset dimensionality does not exist, stopping the matching, and determining the times of the successful matching of the target operator as the continuous times of the successful matching of the target operator.
6. The method according to claim 1, wherein each operator is respectively provided with an operation level, wherein the operation level of an operator is positively correlated with the handling capacity of the operator for the unmanned equipment abnormity;
the determining a target operator to perform exception handling on the target unmanned device from the candidate operators includes:
and determining the candidate operator with the highest operation grade as the target operator.
7. The method according to claim 1, wherein the target anomaly information includes an anomaly level of the target unmanned aerial vehicle, and each of the operators has an operation level corresponding thereto, wherein the operation level of an operator is positively correlated with the handling capability of the operator for the anomaly of the unmanned aerial vehicle;
the determining a target operator to perform exception handling on the target unmanned device from the candidate operators includes:
and determining the candidate operator with the operation level matched with the abnormal level of the target unmanned equipment as the target operator according to the operation level corresponding to each operator.
8. A remote operator dispensing apparatus for an unmanned aerial device, the apparatus comprising:
the receiving module is used for receiving the abnormal information of the unmanned equipment;
the first determining module is used for determining target unmanned equipment to be distributed according to the received abnormal information;
a second determining module, configured to determine, according to the target abnormal information of the target unmanned aerial vehicle and the respective abnormal processing expertise information of each operator, a candidate operator whose matching degree between the abnormal processing expertise information and the target abnormal information is higher than a preset degree from among operators in an idle state;
and a third determining module, configured to determine, from the candidate operators, a target operator that is to perform exception handling on the target unmanned device.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
CN202111131821.5A 2021-09-26 2021-09-26 Method, apparatus, medium, and electronic device for remote operator assignment of unmanned device Pending CN113919671A (en)

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CN202111131821.5A CN113919671A (en) 2021-09-26 2021-09-26 Method, apparatus, medium, and electronic device for remote operator assignment of unmanned device

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