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

Abstract

The invention provides an unmanned vehicle task processing method, an unmanned vehicle task processing device, electronic equipment and a computer readable storage medium, and belongs to the technical field of unmanned vehicles. The method comprises the following steps: acquiring a global task of an unmanned vehicle from a cloud; the global task at least comprises route area information of the unmanned vehicle; key name information and path feature information are extracted from the route area information; configuring attribute feature information of the route area information according to the key name information and the route feature information; and adding the key name information, the path feature information and the attribute feature information to a path information warehouse of the unmanned vehicle. According to the invention, the path information warehouse can be accurately and effectively generated, so that the path information warehouse can be used to control the driving of the unmanned vehicle.

Description

Background Art

[0002] With the rapid development of artificial intelligence, it has been widely used in many fields such as smart home and autonomous driving. In particular, in the field of autonomous driving, unmanned vehicles can perform various tasks efficiently and conveniently. However, in the existing technology, the driving data for controlling unmanned vehicles to perform autonomous driving is still not accurate and rich enough, and it is impossible to ensure the accurate driving of unmanned vehicles in practical applications.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] The present disclosure provides an unmanned vehicle task processing method, an unmanned vehicle task processing device, an electronic device and a computer-readable storage medium, thereby at least to a certain extent overcoming the problem that the prior art cannot more accurately control the movement of the unmanned vehicle.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a method for processing unmanned vehicle tasks is provided, including: obtaining a global task of the unmanned vehicle from the cloud; the global task includes at least route area information of the unmanned vehicle; extracting key name information and path feature information from the route area information; configuring attribute feature information of the route area information based on the key name information and the path feature information; and adding the key name information, path feature information and the attribute feature information to a path information warehouse of the unmanned vehicle.

[0007] According to one aspect of the present disclosure, there is provided an unmanned vehicle task processing device, comprising: a global task acquisition module, used to acquire the global task of the unmanned vehicle from the cloud; the global task includes at least the route area information of the unmanned vehicle; a feature information extraction module, used to extract key name information and path feature information from the route area information; an attribute information configuration module, used to configure the attribute feature information of the route area information according to the key name information and the path feature information; an information warehouse adding module, used to add the key name information, path feature information and the attribute feature information to the path information warehouse of the unmanned vehicle.

[0008] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned methods by executing the executable instructions.

[0009] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the above methods is implemented.

[0010] The exemplary embodiments of the present disclosure have the following beneficial effects:

[0011] Obtain the global task of the unmanned vehicle from the cloud; the global task includes at least the route area information of the unmanned vehicle; extract key name information and path feature information from the route area information; configure the attribute feature information of the route area information according to the key name information and the path feature information; add the key name information, the path feature information and the attribute feature information to the path information warehouse of the unmanned vehicle. On the one hand, this exemplary embodiment proposes a method for processing unmanned vehicle tasks, which can be considered from three dimensions of key name information, path feature information and attribute feature information, extract and determine these feature information from the global task of the unmanned vehicle and add them to the path information warehouse, so that the path information warehouse can store safe and drivable reference routes and area information, ensuring the effectiveness and accuracy of controlling the unmanned vehicle to travel based on the information in the path information warehouse. On the other hand, compared with the prior art, the path information warehouse generated by this exemplary embodiment is rich in data, with route and area information, which can reasonably consider the impact of area information when planning the route of the unmanned vehicle, and improve the effectiveness of unmanned vehicle control.

[0012] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0014] Figure 1 A flowchart schematically illustrates a method for processing an unmanned vehicle task in this exemplary embodiment;

[0015] Figure 2 A flowchart of information security verification of a global task in this exemplary embodiment is schematically shown;

[0016] Figure 3 A flowchart schematically illustrates another method for processing a task of an unmanned vehicle in this exemplary embodiment;

[0017] Figure 4A block diagram schematically shows a structure of an unmanned vehicle task processing device in this exemplary embodiment;

[0018] Figure 5 An electronic device for implementing the above method in this exemplary embodiment is schematically shown. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0020] An exemplary embodiment of the present disclosure first provides a method for processing an unmanned vehicle task.

[0021] The following is combined with Figure 1 This exemplary embodiment is further described as follows. Figure 1 As shown, the unmanned vehicle task processing method may include the following steps S110 to S140:

[0022] Step S110, obtaining the global task of the unmanned vehicle from the cloud; the global task at least includes the route area information of the unmanned vehicle.

[0023] Among them, the unmanned vehicle can be a vehicle that realizes automatic driving in a specific scenario, such as in the scenario of raw coal transportation in an open-pit mine, the unmanned vehicle can complete automatic driving according to a specific path in a specific scenario to perform a specific task. The global task can be task information used to control the driving of the unmanned vehicle, wherein at least the route area information of the unmanned vehicle can be included, and the path area information can include route information, which can be the main road information on which the unmanned vehicle can travel, such as the empty and full main road when performing the task, the main road for sending and receiving vehicles, etc., and can also include area information, which can be the information of the area associated with the unmanned vehicle when it moves in the scene, such as the loading area, unloading area, parking area, and information on the areas of each intersection, etc. The global task can also include the tasks to be performed or the working attributes of the unmanned vehicle according to actual needs, such as excavators, loading vehicles, and unloading vehicles. Each information can be embodied in the form of a file, for example, a global task can include multiple files, and the multiple files can be different main road files or area files, etc.

[0024] This exemplary embodiment can download the global tasks of the unmanned vehicle from the cloud, and after decompression, distribute the tasks to the local vehicle according to the path specified by the vehicle-side computing platform.

[0025] Step S120, extracting key name information and path feature information from the route area information.

[0026] Among them, the key name information may refer to keywords or key words in the name information, for example, the name is "path planning for controlling vehicle driving in area A", where area A represents the driving area of ​​the vehicle, then the keyword can be regarded as the key name information, and the extraction of the key name information can be determined according to preset rules, such as pre-configuring a key information list, such as a key information list including multiple driving areas, empty and heavy loads, driving directions, etc., and using the list to match in the global task, for example, matching the names of each path file in the global task in turn, and then determining the key name information, such as the specific driving area, empty and heavy load, driving direction, etc.

[0027] The path characteristic information may be information used to reflect the characteristics of the driving path, such as the longitude and latitude of the center line of the path, the longitude and latitude of the left and right boundaries of the path, the altitude, heading, slope, curvature, global planning speed, the width of the left and right boundaries of the path, the sequence number of the path point, etc. This exemplary embodiment may extract the path characteristic information from the route area information in a variety of ways, such as configuring a path characteristic lookup table to search for characteristic information appearing in the route area information in the table; or the route area information may be input into a pre-trained neural network model to output the path characteristic information; or the route area information may be processed by regular matching to determine the path characteristic information therefrom, and so on.

[0028] In an exemplary embodiment, the above step S120, extracting key name information and path feature information from route area information, may include:

[0029] Extract key name information from the file name of route area information;

[0030] Extract route feature information from the file content of route area information.

[0031] In this exemplary embodiment, the global task may include multiple files about route area information, and key name information may be extracted based on the file names of the multiple route area information files. For example, the file names of different files may be first extracted from the global task, and then the key name information may be extracted from the file names. Furthermore, path feature information may be extracted based on the file contents of the multiple route area information files. For example, the file contents may be input into a pre-trained model to extract path feature information therefrom.

[0032] Step S130, configuring attribute characteristic information of the route area information according to the key name information and the path characteristic information.

[0033] Taking into account that the global task includes a large amount of information, and this information may not be accurately identified, the subsequent unmanned vehicle cannot accurately and conveniently identify route area information such as roads, areas, path work content, etc. Therefore, this exemplary embodiment can configure corresponding attribute feature information for the route area information based on key name information and path feature information.

[0034] Specifically, this exemplary embodiment can add point attribute settings to each path file according to the path characteristics, and can configure attribute characteristic information for the route area information according to the information reflected in the key name information. The path characteristic information and the attribute characteristic information can have a mapping relationship, for example, for the area name, TA—transportation area, LA—loading area, UA—unloading area, PA—parking lot, MA—maintenance area; for the path name, HMR—heavy load main road, EMR—empty load main road, FAC—departure main road, SHC—receiver main road, FTBP—advance to the reversing point within the working area, BTWP—reverse to the work point within the working area, FTMP—advance within the working area and connect with the main road; for special identifiers, VR—virtual point area.

[0035] Step S140, adding the key name information, path feature information and attribute feature information to the path information warehouse of the unmanned vehicle.

[0036] Finally, the key name information, path feature information, and attribute feature information can be added to the path information warehouse of the unmanned vehicle, so that the unmanned vehicle or unmanned vehicle dispatching system can obtain the corresponding path information from it to control the unmanned vehicle to operate. When storing, this exemplary embodiment can add the file to the path information warehouse according to the preset storage path in a specific naming method. The path information in the path information warehouse can be used to control the unmanned vehicle to travel in the application scenario.

[0037] For example, key name information can be stored in the first column. When the key name information includes multiple columns, it can also be multiple columns. The path feature information can include multiple indicators such as longitude, latitude, altitude, curvature, etc., then the path feature information can be stored in the second column to the i-th column respectively, and the attribute feature information can be stored in the i+1th column or the i+1th column to the jth column, etc., specifically determined according to the attribute feature information. Different rows can constitute a path, for example, each row can correspond to a location or a point constituting a path. This exemplary embodiment can improve the path information warehouse of the unmanned vehicle based on the key name information, path feature information and attribute feature information, and improve the accuracy and richness of the unmanned vehicle path information stored therein.

[0038] Based on the above description, in this exemplary embodiment, the global task of the unmanned vehicle is obtained from the cloud; the global task includes at least the route area information of the unmanned vehicle; the key name information and the path feature information are extracted from the route area information; the attribute feature information of the route area information is configured according to the key name information and the path feature information; the key name information, the path feature information and the attribute feature information are added to the path information warehouse of the unmanned vehicle. On the one hand, this exemplary embodiment proposes a method for processing unmanned vehicle tasks, which can be considered from three dimensions of key name information, path feature information and attribute feature information, extract and determine these feature information from the global task of the unmanned vehicle and add them to the path information warehouse, so that the path information warehouse can store safe and drivable reference routes and area information, and ensure the effectiveness and accuracy of controlling the driving of the unmanned vehicle based on the information in the path information warehouse. On the other hand, compared with the prior art, the path information warehouse generated by this exemplary embodiment is rich in data, with route and area information, which can reasonably consider the impact of area information when planning the route of the unmanned vehicle, and improve the effectiveness of unmanned vehicle control.

[0039] In an exemplary embodiment, after obtaining the global task of the unmanned vehicle from the cloud, the unmanned vehicle task processing method may further include:

[0040] Extract multiple categories of task files from global tasks and perform information security verification on each task file;

[0041] When each task file passes the information security check, the key name information and path feature information are extracted from the route area information.

[0042] A global task includes multiple task files, and different task files may belong to the same category, such as route files, or may belong to different categories, such as route files and area files. This exemplary embodiment can extract task files of different categories from the global task and perform information security verification on the tasks respectively.

[0043] Figure 2 A flowchart of information security verification for a global task in this exemplary embodiment is shown, which may specifically include the following steps:

[0044] Step S202, obtaining the global task of the unmanned vehicle;

[0045] Afterwards, step S204 may be executed to extract the main path file, and step S206 may be executed to verify the main path file;

[0046] Step S208, extracting the files of loading position, unloading position, parking position, etc., and executing step S210, verifying the files of loading position, unloading position, parking position, etc.;

[0047] Step S212, extracting the operating area file, and executing step S214, verifying the operating area file;

[0048] Step S216, extracting the intersection area file, and executing step S218, verifying the intersection area file, etc.;

[0049] If the verification fails, step S220 is executed to report the file that failed the verification;

[0050] Step S222, determining whether multiple task files have all passed verification;

[0051] Step S224: if multiple task files have passed the verification, it is determined that the information security verification has passed, and status information indicating that the information security verification has passed is fed back to the cloud;

[0052] Step S226: If any task file fails the verification, the verification failure status information is fed back to the cloud.

[0053] After passing the information security check, the step of extracting key name information and path feature information from the route area information is performed.

[0054] In an exemplary embodiment, extracting task files corresponding to multiple attribute categories from a global task and performing information security verification on each task file may include:

[0055] Extracting a first type of task file from the global task, and determining whether the total amount of data corresponding to the first type of task file meets a first preset condition;

[0056] When the total amount of data corresponding to the first type of task file meets the first preset condition, it is determined that the first type of task file passes the information security verification.

[0057] Among them, the first type of task file refers to a file that requires data volume verification. In this exemplary embodiment, the first type of task files may include empty and fully loaded main roads, main roads for sending and receiving vehicles, entry and exit routes for each parking space, loading entry and exit lines, entry and exit routes for each unloading position, loading area files, unloading area files, parking lot area files, and intersection area files.

[0058] Determining whether the total amount of data corresponding to the first type of task file meets the first preset condition may be to determine whether the total number of available data rows corresponding to the first type of task file matches the specified number of available data rows corresponding to the first type of task file. For example, in the first type of task file, the first line of data includes multiple attributes, such as longitude, latitude, and altitude, the second line of data may include the specified number of available data rows, and the third to last lines of data are the actual number of available data rows. If the number of available data rows from the third to last lines of data is equal to the number of execution available data rows corresponding to the second line of data, then it indicates a match, i.e., it is determined that the first type of task file has passed the information security check.

[0059] This exemplary embodiment can compare the total number of available data rows with the number of available data rows specified in each file for the empty and fully loaded main roads, the main roads for sending and receiving vehicles, the entry and exit routes of each parking space, the loading entry and exit lines, the entry and exit routes of each unloading space, the loading area files, the unloading area files, the parking lot area files, and the intersection area files. All files must match, and a pass mark will be fed back if successful, otherwise a fail mark will be fed back, and the specific file name that failed the verification will be indicated.

[0060] In an exemplary embodiment, extracting task files corresponding to multiple attribute categories from a global task and performing information security verification on each task file may include:

[0061] Extract the second type of task files from the global task; the second type of task files include regional files;

[0062] The area corresponding to the second type of task file is validated, and when the area corresponding to the second type of task file meets the second preset condition, it is determined that the second type of task file passes the information security check.

[0063] Among them, the second type of task file is an area file, and the area file refers to the information in the file that reflects the information related to the area. In this exemplary embodiment, the second type of task file can include area files such as loading area, unloading area, parking lot, etc. Performing information security verification on the second type of task file is actually to verify the validity of the area included in it. The purpose of verifying the validity of the area is to determine whether the area is valid, for example, whether it is a closed area, or whether its area meets certain area conditions, etc.

[0064] In this exemplary embodiment, the second preset condition may be that the area corresponding to the second type of task file is a closed area, and the area of ​​the area exceeds the area threshold. Specifically, it can be determined whether it is a closed area based on whether the first and last lines of valid data in the area file are consistent or similar. If the line data are consistent or the difference is very small, it means that the end of the area is connected to form a closed area. If the line data is inconsistent or the difference is large, it means that the area does not constitute a closed area and is an invalid area. In addition, the area threshold can be customized according to actual needs or scenarios, and this disclosure does not specifically limit this.

[0065] Figure 3 A flowchart showing another unmanned vehicle task processing method in this exemplary embodiment is shown, which may specifically include the following steps:

[0066] Step S310, obtaining the global task of the unmanned vehicle;

[0067] Step S320, performing information security verification on the global task of the unmanned vehicle;

[0068] Step S330, extracting key name information from the file name of the route area information;

[0069] Step S340, extracting path feature information from the file content of the route area information;

[0070] Step S350, configuring attribute feature information for the waypoints in the route area information;

[0071] Step S360, adding the key name information, path feature information and attribute feature information to the path information warehouse of the unmanned vehicle.

[0072] In an exemplary embodiment, the unmanned vehicle task processing method may further include:

[0073] Extract the location data of the main driving path of the unmanned vehicle and the bounding box data of the intersection area from the route area information;

[0074] Calculate the intersection information of the main driving path and the intersection area based on the position data and the bounding box data;

[0075] Add the intersection information to the path information warehouse of the unmanned vehicle.

[0076] Among them, the route area information may include the location data of the driverless vehicle's driving path, such as the location data of each point on a main road when the driverless vehicle is driving on the main road. The route area information may also include the bounding box data of the intersection area. The bounding box data refers to the data used to constitute the intersection area. Generally speaking, an intersection can be regarded as a bounding box. For example, the intersection area of ​​a crossroads can be regarded as a rectangular bounding box, and the location data in the area is the bounding box data.

[0077] When the unmanned vehicle enters the intersection area from the main road and then leaves the intersection area, the intersection points of the entry bounding box and the exit bounding box will be generated. The location data of these intersection points is the intersection information. The intersection information can be the location information of the intersection point, such as coordinate information or identification information used to reflect the location point.

[0078] This exemplary embodiment can extract the location data of the main driving path from the route area information, such as longitude, latitude, heading and other information, and extract the bounding box data of the intersection area, such as the longitude, latitude and other information of the midpoint of the bounding box, and calculate the intersection of the main road and the intersection area by arrangement method, determine the intersection and intersection information, and add the calculated intersection information to the path information warehouse of the unmanned vehicle.

[0079] By adding the focus information to the path information warehouse of the unmanned vehicle, it can be ensured that the unmanned vehicle can accurately and effectively drive according to the reference path in the actual operation process based on the route information and area information in the path information warehouse, combined with the intersection information. For example, when driving to a certain intersection, it can choose to stop at the intersection position entering the intersection or a certain position before the intersection, etc., according to the current road status and whether there are obstacles. According to the intersection information, the driving status and movement position of the unmanned vehicle in the intersection area can be more accurately controlled.

[0080] In an exemplary embodiment, the unmanned vehicle task processing method may further include:

[0081] In response to the unmanned vehicle satisfying the route switching condition, the target path information is extracted from the path information warehouse, and the unmanned vehicle is controlled to move according to the target path information.

[0082] Among them, the route switching condition refers to the condition used to trigger the acquisition of the current path information, such as receiving a task execution instruction. Specifically, when the unmanned vehicle reaches a certain position or completes the previous task, it can go to work station No. 1 to the left and to work station No. 2 to the right. Then, when the unmanned vehicle receives an instruction or task to go to work station No. 1, it can extract the target path information about going to work station No. 1 from the path information warehouse, and use the target path information to control the movement of the unmanned vehicle in the environment.

[0083] Extracting target path information from the path information warehouse can perform path search based on task name, task keywords and other information to determine the target path information.

[0084] This exemplary embodiment can extract and publish the corresponding path file from the local path information warehouse when the tracking route switching conditions are met during the cyclic operation of the unmanned vehicle, ensure that the vehicle-side self-driving planning, perception, V2X (Vehicle to Everything, vehicle wireless communication technology) and other nodes are used for basic path data, ensure that the planning node realizes local path planning under the premise of the global reference line, ensure that the perception system can perform effective obstacle detection within the range of the vehicle's drivable route, ensure that the V2X node can send effective route data, and effectively communicate with the unmanned driving dispatching system, so as to ensure the safety, efficiency and smoothness of the unmanned transportation system.

[0085] This exemplary embodiment can be applied to the specific scenario of raw coal transportation in open-pit mining areas. The above method can ensure that the unmanned vehicle has a global reference line that can be safely followed; has the drivable area range required for local path planning; has the entry and exit point numbers of the intersection area required for interaction with scheduling and the names of their corresponding paths; has the required attributes of the area, route, load, etc. to control; has the ability to perceive the required driving route and area boundary range, etc., to ensure the accuracy and effectiveness of the unmanned vehicle's movement in the scene.

[0086] The exemplary embodiment of the present disclosure also provides a task processing device for an unmanned vehicle. Figure 4 The device 400 may include a global task acquisition module 410, which is used to obtain the global task of the unmanned vehicle from the cloud; the global task at least includes the route area information of the unmanned vehicle; a feature information extraction module 420, which is used to extract key name information and path feature information from the route area information; an attribute information configuration module 430, which is used to configure the attribute feature information of the route area information according to the key name information and the path feature information; an information warehouse adding module 440, which is used to add the key name information, the path feature information and the attribute feature information to the path information warehouse of the unmanned vehicle.

[0087] In an exemplary embodiment, after obtaining the global task of the unmanned vehicle from the cloud; the unmanned vehicle task processing device also includes: a file verification module, which is used to extract multiple categories of task files from the global task and perform information security verification on each task file; and when each task file passes the information security verification, extract key name information and path feature information from the route area information.

[0088] In an exemplary embodiment, a file verification module includes: a first condition judgment unit, used to extract a first type of task file from a global task, and judge whether the total amount of data corresponding to the first type of task file meets a first preset condition; when the total amount of data corresponding to the first type of task file meets the first preset condition, it is determined that the first type of task file passes the information security check.

[0089] In an exemplary embodiment, a file verification module includes: a second condition judgment unit, used to extract a second type of task file from a global task; the second type of task file includes a regional file; and performing validity verification on the region corresponding to the second type of task file, and when the region corresponding to the second type of task file meets a second preset condition, determining that the second type of task file passes the information security check.

[0090] In an exemplary embodiment, the feature information extraction module includes: a first information extraction unit for extracting key name information from the file name of the route area information; and a second information extraction unit for extracting path feature information from the file content of the route area information.

[0091] In an exemplary embodiment, the unmanned vehicle task processing device also includes: a position data extraction module, which is used to extract the position data of the main driving path of the unmanned vehicle and the bounding box data of the intersection area from the route area information; an intersection information calculation module, which is used to calculate the intersection information of the main driving path and the intersection area based on the position data and the bounding box data; an intersection information storage module, which is used to add the intersection information to the path information warehouse of the unmanned vehicle.

[0092] In an exemplary embodiment, the unmanned vehicle task processing device also includes: an unmanned vehicle control module, which is used to extract target path information from the path information warehouse in response to the unmanned vehicle meeting the route switching conditions, and control the unmanned vehicle to move according to the target path information.

[0093] The specific details of each module / unit in the above-mentioned device have been described in detail in the embodiments of the method part. The undisclosed details can be found in the embodiments of the method part, so they will not be repeated here.

[0094] Exemplary embodiments of the present disclosure also provide an electronic device capable of implementing the above method.

[0095] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0096] Refer to the following Figure 5 An electronic device 500 according to such an exemplary embodiment of the present disclosure is described below. Figure 5 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0097] like Figure 5As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and a display unit 540.

[0098] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 510 can execute Figure 1 Steps shown etc.

[0099] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0100] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0101] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0102] The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or may communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 550. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0103] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the exemplary embodiment of the present disclosure.

[0104] The exemplary embodiments of the present disclosure also provide a computer-readable storage medium on which a program product capable of implementing the above-mentioned method of the present specification is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above-mentioned "Exemplary Method" section of the present specification.

[0105] The exemplary embodiments of the present disclosure also provide a program product for implementing the above method, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0106] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0107] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0108] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0109] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0110] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0111] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to exemplary embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0112] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0113] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for processing unmanned vehicle tasks, characterized in that: include: Obtain the global mission of the unmanned vehicle from the cloud; The global task at least includes route area information of the unmanned vehicle; extracting key name information and path feature information from the route area information; configuring attribute characteristic information of the route area information according to the key name information and the path characteristic information; The key name information, path feature information and attribute feature information are added to the path information warehouse of the unmanned vehicle.

2. The method according to claim 1, characterized in that After obtaining the global mission of the unmanned vehicle from the cloud, the method further includes: Extracting multiple categories of task files from the global task, and performing information security verification on each of the task files; When each of the task files passes the information security check, key name information and path feature information are extracted from the route area information.

3. The method according to claim 2, characterized in that Extracting task files corresponding to multiple attribute categories from the global task, and performing information security verification on each of the task files, including: Extracting a first type of task file from the global task, and determining whether the total amount of data corresponding to the first type of task file meets a first preset condition; When the total amount of data corresponding to the first-type task file meets a first preset condition, it is determined that the first-type task file passes the information security check.

4. The method according to claim 2, characterized in that: The extracting task files corresponding to a plurality of attribute categories from the global task and performing information security verification on each of the task files includes: Extracting a second type of task file from the global task; the second type of task file includes a regional file; The validity of the area corresponding to the second-type task file is verified, and when the area corresponding to the second-type task file meets a second preset condition, it is determined that the second-type task file passes the information security check.

5. The method according to claim 1, characterized in that The extracting key name information and path feature information from the route area information includes: Extracting key name information from the file name of the route area information; Path feature information is extracted from the file content of the route area information.

6. The method according to claim 1, characterized in that The method further comprises: Extracting the location data of the main driving path of the unmanned vehicle and the bounding box data of the intersection area from the route area information; Calculate the intersection information of the main driving path and the intersection area according to the position data and the bounding box data; The intersection information is added to the path information warehouse of the unmanned vehicle.

7. The method according to claim 1, characterized in that The method further comprises: In response to the unmanned vehicle satisfying a route switching condition, the path information warehouse extracts target path information, and controls the unmanned vehicle to move according to the target path information.

8. An unmanned vehicle task processing device, characterized in that: include: The global task acquisition module is used to obtain the global tasks of the unmanned vehicle from the cloud; The global task at least includes route area information of the unmanned vehicle; A feature information extraction module, used to extract key name information and path feature information from the route area information; An attribute information configuration module, configured to configure attribute characteristic information of the route area information according to the key name information and the path characteristic information; An information warehouse adding module is used to add the key name information, path feature information and attribute feature information to the path information warehouse of the unmanned vehicle.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium comprising a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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