CN115827806A

CN115827806A - Map data job task processing method, device, medium and computer equipment

Info

Publication number: CN115827806A
Application number: CN202211436288.8A
Authority: CN
Inventors: 张东黎
Original assignee: Autonavi Software Co Ltd
Current assignee: Autonavi Software Co Ltd
Priority date: 2022-11-16
Filing date: 2022-11-16
Publication date: 2023-03-21

Abstract

The disclosure relates to a processing method, a processing device, a processing medium and computer equipment of a map data job task. In at least one embodiment of the disclosure, the fine-grained task is obtained by dividing on the basis of the coarse-grained task, and all target pavement frames in the coarse-grained task form a spatially continuous road surface, so all target pavement frames in the fine-grained task are also spatially continuous, and in addition, any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition track, so all target pavement frames in the fine-grained task are spatially continuous and are associated with the same acquisition track, so that the continuous change of the real world is reflected, the fine-grained task is used as a map data operation task, the data of the continuous change of the real world can be completely processed in the same operation process, and the quality of high-precision map data and the accuracy of data fusion are improved.

Description

Map data job task processing method, device, medium and computer equipment

Technical Field

The embodiment of the disclosure relates to the technical field of map data production, in particular to a method, a device, a medium and a computer device for processing a map data job task.

Background

With the development of technology, electronic maps are evolving from standard maps to high-precision maps. Because the main use scenes of the high-precision map are scenes such as intelligent driving, high-precision positioning and the like, particularly intelligent driving scenes, and the high-precision map is basic data supporting an intelligent driving function, in order to ensure the safe driving of a vehicle, the production of the high-precision map data needs to be performed by more operation times and operation flows so as to ensure the precision and the quality of the high-precision map data.

Data which can represent continuous change of the real world in collected data should be completely processed in the same operation process, otherwise, the generated high-precision map data may be degraded in quality (for example, errors such as inconsistency with the real world) and errors may occur when the data is fused with mother database data into a mother database, and the mother database is a shared resource database used for generating different high-precision map products (for example, high-precision map products cooperating with different vehicle enterprises).

Therefore, it is highly desirable to provide a processing scheme for a map data job task, so as to completely process real world continuously changing data in the same job process, and improve the quality of high-precision map data and the accuracy of data fusion.

Disclosure of Invention

At least one embodiment of the disclosure provides a processing method, device, medium and computer equipment for a map data job task.

In a first aspect, an embodiment of the present disclosure provides a method for processing a map data job task, where the method includes:

acquiring a plurality of acquisition tracks of acquisition equipment on a target road section, and acquiring a plurality of pavement frames corresponding to the target road section, wherein the pavement frames are used for expressing the geometric range of a road surface, and any one pavement frame has an adjacent pavement frame;

determining a target road surface frame which has intersection with any one of the acquisition tracks from the plurality of road surface frames to obtain a target road surface frame set;

performing primary task division on the target pavement frame set to obtain at least one coarse-grained task, wherein all target pavement frames in the coarse-grained task form a spatially continuous road surface;

and performing secondary task division on the coarse-grained task to obtain at least one fine-grained task, wherein any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition track.

In some embodiments, before obtaining the plurality of road frames corresponding to the target road segment, the method for processing the map data job task further includes:

determining a road surface frame corresponding to a road junction in a target road section based on a preset corresponding relation between the type of the road junction and the road surface frame;

and determining a road surface frame corresponding to the non-intersection road in the target road section based on the pre-configured non-intersection road surface frame.

In some embodiments, before performing the secondary task division on the coarse-grained task, the method for processing the map data job task further includes:

determining an observation frame on the target road section based on the farthest observable distance preset by the acquisition equipment; and correspondingly, performing secondary task division on the coarse-grained task based on the observation box.

In some embodiments, determining the observation frame on the target road segment based on the farthest observable distance preset by the acquisition device includes:

aiming at any two acquisition tracks, determining respective end points of the two acquisition tracks;

determining a road cross-sectional line corresponding to the farthest observable distance of the two acquisition tracks respectively expanded on the target road section as an expanded boundary by taking the respective end points of the two acquisition tracks as starting points;

and determining observation frames corresponding to the two acquisition tracks on the target road section, wherein the observation frames are geometric ranges formed by two side boundaries of the target road section and outward-expanded boundaries corresponding to the two acquisition tracks.

In some embodiments, performing secondary task partitioning on coarse-grained tasks based on observation boxes includes:

determining a target pavement frame with intersection with the observation frame in the coarse-grained task;

and if the observation frame does not cover the target pavement frame, respectively dividing the target pavement frame and the adjacent target pavement frame which has intersection with the observation frame into different fine-grained tasks.

aiming at any two adjacent target pavement frames in the coarse-grained task, respectively expanding the farthest observable distance towards two sides by taking the adjacent edges as the reference to obtain two expanded boundaries;

and determining a geometric range formed by the two outward-extended boundaries on the target road section as an observation frame.

and if an acquisition track exists, the acquisition track and each target pavement frame corresponding to the observation frame have intersection or the observation frame can cover the target pavement frame which has no intersection with the acquisition track, dividing each target pavement frame corresponding to the observation frame into the same fine-grained task.

In a second aspect, an embodiment of the present disclosure further provides an apparatus for processing a map data job task, where the apparatus includes:

the acquisition unit is used for acquiring a plurality of acquisition tracks of the acquisition equipment on a target road section and acquiring a plurality of road surface frames corresponding to the target road section, wherein the road surface frames are used for expressing the geometric range of a road surface, and any one road surface frame has an adjacent road surface frame;

the determining unit is used for determining a target road surface frame which has intersection with any one acquisition track from a plurality of road surface frames to obtain a target road surface frame set;

the first dividing unit is used for performing task division on the target pavement frame set for one time to obtain at least one coarse-grained task, and all target pavement frames in the coarse-grained task form a spatially continuous pavement;

and the second division unit is used for performing secondary task division on the coarse-grained task to obtain at least one fine-grained task, and two target pavement frames which are arbitrarily adjacent in the fine-grained task are associated with the same acquisition track.

In a third aspect, an embodiment of the present disclosure further provides a computer apparatus, which includes at least one computing device and at least one storage device storing instructions; the instructions, when executed by the at least one computing device, cause the at least one computing device to perform the steps of the method of processing a map data job task as described in any one of the embodiments of the first aspect.

In a fourth aspect, this disclosed embodiment further provides a computer-readable storage medium, where the computer-readable storage medium stores a program or instructions for causing a computer to execute the steps of the method for processing the map data job task according to any one of the embodiments of the first aspect.

In a fifth aspect, the present disclosure further provides a computer program product, where the computer program product includes a computer program, the computer program is stored in a computer-readable storage medium, and at least one processor of the computer reads from the storage medium and executes the computer program, so that the computer executes the steps of the method for processing the map data job task according to any one of the embodiments of the first aspect.

It can be seen that, in at least one embodiment of the present disclosure, the fine-grained task is obtained by dividing on the basis of the coarse-grained task, and all target pavement frames in the coarse-grained task form a spatially continuous road surface, so all target pavement frames in the fine-grained task are also spatially continuous, and in addition, since any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition trajectory, all target pavement frames in the fine-grained task are spatially continuous and associated with the same acquisition trajectory, reflecting continuous changes in the real world, and using the fine-grained task as a map data operation task, can completely process data continuously changing in the real world in the same operation process, thereby improving the quality of high-precision map data and the accuracy of data fusion.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art according to the drawings.

Fig. 1 is a flowchart illustrating a processing method for a map data job task according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram of a target road segment and a plurality of road surface frames corresponding to the target road segment according to an embodiment of the disclosure;

fig. 3 is a schematic diagram of a plurality of acquisition tracks of an acquisition device on a target road segment according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of determining an observation box according to an embodiment of the present disclosure;

FIG. 5 is a schematic view of the observation box determined on the basis of FIG. 3;

fig. 6 is a schematic flowchart of performing secondary task division on a coarse-grained task based on the observation box shown in fig. 5 according to the embodiment of the present disclosure;

FIG. 7 is a schematic flow chart illustrating another method for determining an observation box according to an embodiment of the present disclosure;

FIG. 8 is a schematic view of the observation box determined on the basis of FIG. 3;

fig. 9 is a schematic flowchart of performing secondary task division on a coarse-grained task based on the observation box shown in fig. 8 according to the embodiment of the present disclosure;

FIG. 10 is a block diagram of a processing device for a map data job task provided by an embodiment of the present disclosure;

fig. 11 is an exemplary block diagram of a computer device provided by an embodiment of the present disclosure.

Detailed Description

In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a more detailed description of the present disclosure is given below in conjunction with the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. The specific embodiments described herein are merely illustrative of the disclosure and do not delimit the disclosure. All other embodiments, which can be derived from the description of the embodiments of the disclosure by a person skilled in the art, are intended to be within the scope of the disclosure.

It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

Processing of high-precision map data includes, but is not limited to: the method comprises the steps of processing collected data, processing map data, processing real object data, processing virtual data and the like to generate high-precision map data, and fusing the generated high-precision map data with mother database data to enable the high-precision map data to enter a mother database. Therefore, the production of high-precision map data requires a large number of operations and a large number of work flows to realize the processing of the high-precision map data.

The processing of the high-precision map data adopts tasks as processing granularity no matter machine algorithm processing or manual processing, one task comprises a certain amount of collected data and/or intermediate data of the collected data after processing, and the operation sequence, priority, incidence relation between the tasks, whether the tasks can be operated or not and the like of each task can be managed by scheduling the tasks. A job process may include at least one task. That is, the task is a minimum processing unit in the high-precision map data processing process, and after the acquired data is acquired, the acquired data can be divided to obtain a plurality of tasks, wherein each task contains a certain amount of acquired data and/or intermediate data obtained by processing the acquired data.

In order to improve the quality of high-precision map data and the accuracy of data fusion, it is necessary to determine which tasks are included in a single operation process, so as to completely process data capable of representing real-world continuous changes, and therefore, embodiments of the present disclosure provide a method, an apparatus, a medium, or a computer device for processing a map data operation task, which defines a "road surface frame" for expressing a geometric range of a road surface, and any one of the road surface frames has an adjacent road surface frame, so that an overall geometric range of a target road section is expressed by obtaining a plurality of road surface frames corresponding to the target road section; after a plurality of road frames are obtained, a target road frame intersecting with any one of the collected tracks is screened out to obtain a target road frame set, and then a coarse-grained task is obtained by performing primary task division on the target road frame set, wherein the coarse-grained task is also a task and can be used as a minimum processing unit in a high-precision map data processing process, namely, collected data and/or intermediate data obtained after the collected data is processed in the coarse-grained task are used as a whole to be processed by a machine or a human.

Fig. 1 is a schematic flow diagram of a processing method of a map data job task according to an embodiment of the present disclosure, an execution main body of the processing method of the map data job task is an electronic device, and the electronic device includes, but is not limited to, a smart phone, a palmtop computer, a tablet computer, a wearable device with a display screen, a desktop computer, a notebook computer, an all-in-one machine, a smart home device, a server, and the like, where the server may be an independent server or a cluster of multiple servers, and may include a server built locally and a server built in a cloud.

As shown in fig. 1, the processing method of the map data job task may include, but is not limited to, steps 101 to 104:

in step 101, a plurality of acquisition tracks of the acquisition device on the target road section are obtained, and a plurality of road surface frames corresponding to the target road section are obtained, wherein the road surface frames are used for expressing the geometric range of the road surface, and any one of the road surface frames has an adjacent road surface frame.

It should be noted that, the acquisition of the multiple acquisition tracks of the acquisition device on the target road section and the acquisition of the multiple road frames corresponding to the target road section may be performed simultaneously or separately, and there is no sequential execution sequence between the two acquisition steps.

In the embodiment of the disclosure, the collection device may be a vehicle mounted with a laser radar and/or a camera, the laser radar may collect point cloud data of the vehicle surroundings, the camera may collect image data of the vehicle surroundings, and the point cloud data and the image data are collectively referred to as collection data of the collection device. The collection equipment is carrying out the data acquisition in-process, and positioner among the collection equipment can record the spatial position information of the orbit point of traveling of collection equipment, and spatial position information includes: longitude, latitude, and elevation information; and then can confirm the orbit of traveling of the gathering equipment based on the orbit point of traveling, the orbit of traveling can understand as the orbit of gathering of the gathering equipment.

In the embodiment of the present disclosure, the target road segment may be understood as a road segment to be subjected to map data processing, and by performing map data processing on the target road segment, the map element on the ground of the road segment, and the map element on the ground of the road segment may be finally visualized on the map. Map elements are elements used for constructing high-precision maps, and the types of map elements include, but are not limited to, ground elements such as direction arrows, lane lines, stop lines, and ground characters on the ground, and ground elements such as direction boards and speed measuring stands outside the ground.

In an embodiment of the present disclosure, acquiring a plurality of acquisition tracks of a device on a target road segment includes: the method comprises the steps of acquiring a plurality of acquisition tracks generated by a device in one acquisition operation process (called an acquisition project), and acquiring one or more acquisition tracks generated by the device in a plurality of acquisition operation processes (called an acquisition batch, wherein the acquisition batch is a set of a plurality of acquisition projects).

In the embodiment of the present disclosure, after the target road segment is determined, a plurality of road frames corresponding to the target road segment may be obtained based on the geometric range of the target road segment, where the road frames are used to express the geometric range of the road surface, and any one of the road frames has an adjacent road frame, so that the whole geometric range of the target road segment is expressed by obtaining the plurality of road frames corresponding to the target road segment. The road surface frame is a concept proposed in the embodiments of the present disclosure, and is a virtual parameter estimated based on a geometric range of a target link and a preset size of the road surface frame, and includes a position and a size of the road surface frame, and is used only for assisting a map data task, instead of a feature element existing in the real world.

For example, fig. 2 is a schematic diagram of a target road segment and a plurality of road frames corresponding to the target road segment according to an embodiment of the disclosure, in fig. 2, the target road segment is entirely represented as an intersection formed by two roads, that is, the target road segment includes an intersection and a road extending from each intersection, therefore, in fig. 2, the plurality of road frames corresponding to the target road segment are a road frame 1 corresponding to the intersection (i.e., shown by a shaded portion in fig. 2), and

non-intersection road frames

2, 3, 4, and 5, it is seen that, any road frame has adjacent road frames, for example, the road frame 1 is adjacent to the road frames 2, 3, and 5, respectively, and the road frame 4 is adjacent to the road frame 3.

In some embodiments, before obtaining the plurality of road surface frames corresponding to the target road segment, it is required to determine the road surface frame corresponding to the target road segment, including: determining a road surface frame corresponding to a road junction in a target road section based on a preset corresponding relation between the type of the road junction and the road surface frame; and determining a road surface frame corresponding to the non-intersection road in the target road section based on the pre-configured non-intersection road surface frame.

Wherein, the corresponding relation of crossing type and road surface frame includes: the type of the crossroad corresponds to a crossroad frame, the type of the T-shaped crossroad corresponds to a T-shaped road frame, and the type of the irregularly-shaped crossroad corresponds to an irregularly-shaped road frame. For example, in fig. 2, the intersection in the target link is an intersection, and therefore, based on the correspondence relationship between the intersection type and the road surface frame, it can be determined that the road surface frame 1 corresponding to the intersection in the target link is a cross-shaped road surface frame. The pre-configured non-intersection road surface frames may be square frames, for example, the non-intersection road surface frames 2, 3, 4 and 5 in fig. 2 are all square frames. It should be noted that the size of the road surface frame may be manually configured based on actual requirements, and the embodiment does not limit the specific size of the road surface frame.

In step 102, a target road surface frame intersecting with any one of the collected tracks is determined from the plurality of road surface frames, and a target road surface frame set is obtained.

In the embodiment of the disclosure, a spatial superposition relationship between the acquisition track and the road surface frame can be determined based on spatial position information of track points in the acquisition track and position information of the road surface frame, so as to obtain a target road surface frame having an intersection with any acquisition track.

On the basis of fig. 2, fig. 3 is a schematic diagram of a plurality of acquisition tracks of the acquisition device on the target road segment according to the embodiment of the present disclosure. As shown in fig. 3, the plurality of acquisition tracks of the acquisition device on the target road segment include a track a, a track B, and a track C, and the track a, the track B, and the track C may be tracks generated by the acquisition device in one acquisition operation process, or may also be tracks generated by the acquisition device in multiple acquisition operation processes.

In fig. 3, the track a relates to the road surface frame 2 and the road surface frame 1, that is, the road surface frame 2 and the road surface frame 1 intersect with the track a, and therefore, the road surface frame 2 and the road surface frame 1 are both the target road surface frame. The track B relates to the road surface frame 1 and the road surface frame 5, that is, the intersection exists between the road surface frame 1 and the road surface frame 5 and the track B, and therefore, the road surface frame 5 is also the target road surface frame. The track C relates to the road surface frame 3 and the road surface frame 4, that is, the intersection exists between the road surface frame 3 and the road surface frame 4 and the track C, and therefore, the road surface frame 3 and the road surface frame 4 are also the target road surface frames. To sum up, the target road surface frame set includes:

pavement boxes

1, 2, 3, 4, and 5.

In step 103, a task division is performed on the target road frame set once to obtain at least one coarse-grained task, and all target road frames in the coarse-grained task form a spatially continuous road surface.

In the embodiment of the disclosure, the task is a minimum processing unit in the high-precision map data processing process, after acquiring the collected data, the collected data can be divided to obtain a plurality of tasks, each task contains a certain amount of collected data and/or intermediate data of the collected data after processing, and the task order, priority, incidence relation between tasks, whether the tasks can be operated and the like of each task can be managed by scheduling the tasks. A job process may include at least one task. The coarse-grained task is also a task and can be used as a minimum processing unit in the high-precision map data processing process, that is, collected data included in each road frame in the coarse-grained task and/or intermediate data obtained after the collected data is processed are used as a whole to be processed by a machine or manually.

In the embodiment of the disclosure, after the target road surface frame set is obtained, the target road surface frame set is subjected to task division once to obtain at least one coarse-grained task, and each coarse-grained task corresponds to at least one target road surface frame.

For example, based on the acquisition tracks A, B and C shown in fig. 3, obtaining the target road surface frame set includes: the method comprises the following steps that a target pavement frame set is subjected to primary task division by

pavement frames

1, 2, 3, 4 and 5, specifically, all target pavement frames (the pavement frames 1, 2, 3, 4 and 5) in the target pavement frame set form a spatially continuous pavement, so that a coarse-grained task is obtained after the target pavement frame set is subjected to primary task division, and the target pavement frames corresponding to the coarse-grained task are the pavement frames 1, 2, 3, 4 and 5.

In step 104, performing secondary task division on the coarse-grained task to obtain at least one fine-grained task, wherein any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition track.

For example, in fig. 3, the target pavement frames corresponding to the coarse-grained tasks are

pavement frames

1, 2, 3, 4, and 5, and the coarse-grained tasks are subjected to secondary task division to obtain two fine-grained tasks, where the target pavement frame corresponding to one fine-grained task is pavement frames 2, 1, and 5, and the target pavement frame corresponding to the other fine-grained task is pavement frames 3 and 4.

In the embodiment of the disclosure, the fine-grained task is obtained by dividing on the basis of the coarse-grained task, and all the target pavement frames in the coarse-grained task form a spatially continuous road surface, so all the target pavement frames in the fine-grained task are also spatially continuous, and in addition, because any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition track, all the target pavement frames in the fine-grained task are spatially continuous and associated with the same acquisition track, so that the continuous change of the real world is reflected. Furthermore, the region formed by all the target road frames in the fine-grained task can be recorded as a scheduling region, the scheduling region reflects the continuous change of the real world, and the method is suitable for being used as a relatively complete processing range of map data processing.

Therefore, the fine-grained tasks are used as map data operation tasks, each map data operation process takes the fine-grained tasks as operation units, one map data operation process can process one or more fine-grained tasks, and the fine-grained tasks correspond to at least one target pavement frame, so that the fine-grained tasks are operated, the acquired data in each target pavement frame corresponding to the fine-grained tasks are processed, real-world continuously-changed data can be completely processed in the same operation process, the quality of high-precision map data and the accuracy of data fusion are improved, continuous changes can be guaranteed to a great extent to be reflected in a high-precision data mother base as a complete whole, and data compiling and releasing are completely carried out.

In some embodiments, before performing secondary task division on the coarse-grained task, an observation frame may be determined on the target road segment based on a farthest observable distance preset by the acquisition device; and then performing secondary task division on the coarse-grained task based on the observation frame. The farthest observable distance is determined by the performance of the acquisition equipment, and the unit is meter. Therefore, in the embodiment of the present disclosure, fine-grained tasks are comprehensively divided by combining the spatial position, the adjacency relation, the acquisition trajectory, and the real-world observation capability of the acquisition device of the real-world frame, so as to obtain a scheduling area that embodies the continuous change of the real world.

On the basis of the foregoing embodiment, fig. 4 is a schematic flowchart of determining an observation box according to an embodiment of the present disclosure, which includes, but is not limited to, steps 401 to 403:

in step 401, for any two acquisition trajectories, respective end points of the two acquisition trajectories are determined.

For example, in fig. 3, for the track a and the track C, it may be determined that the position of the arrow of the track a is the end point of the track a, and the position of the arrow of the track C is the end point of the track C.

In step 402, with the respective end points of the two acquisition tracks as starting points, the road transversal lines corresponding to the farthest observable distances of the two acquisition tracks respectively extending outward are determined as outward extending boundaries on the target road segment.

For example, fig. 5 is a schematic view of the observation box determined on the basis of fig. 3, in fig. 5, the end point of the track a extends to the right of the horizontal direction in fig. 5 by the farthest observable distance to obtain a corresponding road transversal line as an extended boundary, i.e., the right boundary of the dark box in fig. 5; and (3) extending the terminal point of the track C to the left in the horizontal direction in fig. 5 by the farthest observable distance to obtain a corresponding road transversal line as an extended boundary, namely the left boundary of the dark square in fig. 5.

In step 403, an observation frame corresponding to the two acquisition tracks is determined on the target road section, where the observation frame is a geometric range formed by two side boundaries of the target road section and outward-extended boundaries corresponding to the two acquisition tracks.

In the embodiment of the disclosure, the observation frame embodies the observation capability of the acquisition device, and for one acquisition operation, the acquisition device can still observe the environmental information within a certain distance range at the end point of the acquisition track, that is, although a certain road frame does not intersect with the acquisition track, the certain road frame may still be within the observation range of the acquisition device, and therefore, the certain road frame should be associated with the acquisition track, and the other road frames associated with the acquisition track together as a whole reflect the continuous change range of the real world. Therefore, the embodiment of the present disclosure provides an observation frame concept, so as to implement secondary division of a coarse-grained task based on an observation frame to obtain a fine-grained task, and since all target pavement frames in the coarse-grained task form a spatially continuous road surface, all target pavement frames in the fine-grained task are also spatially continuous, and in addition, since any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition trajectory, all target pavement frames in the fine-grained task are spatially continuous and associated with the same acquisition trajectory, which reflects continuous change of the real world, and the fine-grained task is used as a map data operation task, so that data of the continuous change of the real world can be completely processed in the same operation process, and the quality of high-precision map data and the accuracy of data fusion are improved.

For example, in fig. 5, based on the expanded boundary corresponding to the track a and the expanded boundary corresponding to the track C, the observation frames corresponding to the track a and the track C are determined on the target road segment as shown by the dark frame in fig. 5.

On the basis of the above embodiment, the secondary task division is performed on the coarse-grained task based on the observation box shown in fig. 5, and includes steps 601 to 603 shown in fig. 6:

in step 601, a target road box in the coarse-grained task, which intersects with the observation box, is determined.

For example, in fig. 5, the target road surface frames corresponding to the coarse-grained task are road surface frames 1, 2, 3, 4, and 5, and therefore, the target road surface frames intersecting with the observation frame in the coarse-grained task may be determined as road surface frame 1 and road surface frame 3.

In step 602, it is determined whether the observation box covers the target road surface box with intersection with the observation box.

In fig. 5, the observation box covers only a part of the road surface frame 3, and therefore the observation box does not cover the road surface frame 3. Similarly, the observation box covers only a portion of the pavement frame 1, and thus the observation box does not cover the pavement frame 1.

In step 603, if the observation frame does not cover the target pavement frame with the intersection with the observation frame, dividing the target pavement frame and the adjacent target pavement frame with the intersection with the observation frame into different fine-grained tasks respectively; otherwise, dividing the target pavement frame and the adjacent target pavement frame which has intersection with the observation frame into the same fine-grained task.

In fig. 5, because the observation frame does not cover the pavement frame 3, the pavement frame 3 and the adjacent pavement frame 1 which is intersected with the observation frame are respectively divided into different fine-grained tasks, and the division relationship of other pavement frames is not changed, so as to obtain two fine-grained tasks, wherein the target pavement frames corresponding to one fine-grained task are the pavement frames 2, 1 and 5, and the target pavement frames corresponding to the other fine-grained task are the pavement frames 3 and 4.

In fig. 5, if the observation frame covers all of the road surface frames 3, it is described that the acquisition device can observe all of the road surface areas corresponding to the road surface frames 3 during data acquisition along the trajectory a, and therefore, it is considered that the road surface frames 3 are associated with the trajectory a, and the acquisition device can complete data acquisition on the areas corresponding to the road surface frames 3 during the acquisition operation corresponding to the trajectory a.

On the basis of the above embodiment, fig. 7 is another schematic flow chart of determining an observation box according to the embodiment of the present disclosure, which includes, but is not limited to, step 701 and step 702:

in step 701, for any two adjacent target pavement frames in the coarse-grained task, the farthest observable distances are respectively extended to both sides by using the adjacent edges as a reference, so as to obtain two extended boundaries.

Fig. 8 is a schematic view of the observation frame determined on the basis of fig. 3, in fig. 8, the target pavement frames corresponding to the coarse-grained task are

pavement frames

1, 2, 3, 4, and 5, and for the adjacent pavement frame 2 and the adjacent pavement frame 1, the two sides are respectively extended outward by the farthest observable distance with the adjacent edge as a reference, so as to obtain two extended boundaries, such as the left and right boundaries of the leftmost dark frame in fig. 8; similarly, for the adjacent road surface frame 1 and the adjacent road surface frame 3, the adjacent edges are used as references to respectively extend the farthest observable distances to the two sides to obtain two extended boundaries, such as the left and right boundaries of the deep color frame on the most lateral side in fig. 8; similarly, for the adjacent road surface frame 1 and the adjacent road surface frame 5, the adjacent edges are used as references to respectively extend the farthest observable distances to the two sides to obtain two extended boundaries, such as the upper and lower boundaries of the lowest dark color frame in fig. 8.

In step 702, a geometric range formed by the two outward extending boundaries on the target road segment is determined as an observation frame.

In fig. 8, the observation frame formed on the target link based on the two extended boundaries obtained by the adjacent edges between the adjacent road surface frames 2 and 1 is shown as the leftmost dark frame in fig. 8; an observation frame formed on the target road section based on two outward-expanded boundaries obtained by the adjacent edges between the adjacent road surface frames 1 and 3 is shown as a dark frame on the rightmost side in fig. 8; an observation frame formed on the target link based on two flaring boundaries obtained from the adjacent edges between the adjacent road surface frames 1 and 5 is shown as a lowermost Fang Shense frame in fig. 8.

On the basis of the above embodiment, the secondary task division is performed on the coarse-grained task based on the observation box shown in fig. 8, and includes steps 901 and 902 shown in fig. 9:

in step 901, it is determined whether there is an acquisition track in the observation frame, and whether there is an intersection between the acquisition track and each target pavement frame corresponding to the observation frame.

For example, in fig. 8, a track a exists in the leftmost observation frame, and the track a intersects with the road surface frame 2 and the road surface frame 1 corresponding to the leftmost observation frame; the rightmost observation frame does not have an acquisition track which can intersect with the pavement frame 1 and the pavement frame 3 corresponding to the rightmost observation frame; a track B exists in the bottommost observation frame, and the track B and the pavement frame 1 and the pavement frame 5 corresponding to the bottommost observation frame are intersected.

In step 902, if there is an acquisition track, the acquisition track intersects with each target pavement frame corresponding to the observation frame, or the observation frame can cover a target pavement frame that does not intersect with the acquisition track, then the target pavement frames corresponding to the observation frames are divided into the same fine-grained task; otherwise, dividing each target pavement frame corresponding to the observation frame into different fine-grained tasks.

As can be seen, in fig. 8, since the trajectory a exists in the leftmost observation frame, and the intersection exists between the trajectory a and the road surface frame 2 and the road surface frame 1 corresponding to the leftmost observation frame, the road surface frame 2 and the road surface frame 1 should be divided into the same fine-grained task; because the trace B exists in the bottommost observation frame and the intersection exists between the trace B and the pavement frame 1 and the pavement frame 5 corresponding to the bottommost observation frame, the pavement frame 1 and the pavement frame 5 should be divided into the same fine-grained task, and in conclusion, the pavement frames 2, 1 and 5 are divided into the same fine-grained task.

In fig. 8, since there is no intersection between the right-most observation frame and the road frame 1 and the road frame 3 corresponding to the right-most observation frame, the road frame 1 and the road frame 3 should be divided into different fine-grained tasks, and the dividing relationship between other road frames is not changed, so that the road frame 3 and the road frame 4 are divided into the same fine-grained task.

Therefore, through the secondary task division of the coarse-grained task, two fine-grained tasks can be obtained, wherein the target pavement frames corresponding to one fine-grained task X are the pavement frames 2, 1 and 5, and the target pavement frames corresponding to the other fine-grained task Y are the pavement frames 3 and 4.

It can be seen that, since all the target road frames (road frames 1 to 5) in the coarse-grained task form a road surface with continuous space, all the target road frames (road frames 1 to 5) in the fine-grained task X and the fine-grained task Y are also continuous in space, for example, the road frames 2, 1 and 5 corresponding to the fine-grained task X are continuous in space, and the road frames 3 and 4 corresponding to the fine-grained task Y are continuous in space, and in addition, since any two adjacent target road frames in the fine-grained task are associated with the same acquisition trajectory, for example, the road frames 2 and 1 in the fine-grained task X are adjacent and associated with the acquisition trajectory a, the road frames 1 and 5 in the fine-grained task X are adjacent and associated with the acquisition trajectory B, and the road frames 3 and 4 in the fine-grained task Y are adjacent and associated with the acquisition trajectory C, all the target road frames in the fine-grained task X and the fine-grained task Y are continuous in space and associated with the same acquisition trajectory, which reflect the continuous change of the real world, and use the fine-grained task X and the fine-grained task Y as a map data processing task, and can process the high-granularity data of the real world processing and the high-granularity data of the fine-granularity data.

Fig. 10 is a schematic diagram of a processing apparatus for a map data job task, where the processing apparatus for a map data job task may be applied to an electronic device, and the electronic device includes, but is not limited to, a smart phone, a palm computer, a tablet computer, a wearable device with a display screen, a desktop, a notebook computer, an all-in-one machine, a smart home device, a server, and the like, where the server may be an independent server or a cluster of multiple servers, and may include a server built locally and a server built in a cloud. The processing device for a map data job task according to the embodiment of the present disclosure may execute the processing flow provided by each embodiment of the processing method for a map data job task, and as shown in fig. 10, the processing device for a map data job task includes: an acquisition unit 1001, a determination unit 1002, a first division unit 1003, and a second division unit 1004.

The acquisition unit 1001 is configured to acquire multiple acquisition tracks of the acquisition device on a target road segment, and acquire multiple road surface frames corresponding to the target road segment, where the road surface frames are used to express a geometric range of a road surface, and any one of the road surface frames has an adjacent road surface frame;

the determining unit 1002 is configured to determine a target road surface frame, which intersects with any one of the acquired tracks, from the multiple road surface frames to obtain a target road surface frame set;

the first dividing unit 1003 is configured to perform task division on the target road surface frame set for one time to obtain at least one coarse-grained task, where all target road surface frames in the coarse-grained task form a spatially continuous road surface;

the second dividing unit 1004 is configured to perform secondary task division on the coarse-grained task to obtain at least one fine-grained task, where any two adjacent target road frames in the fine-grained task are associated with the same acquisition track.

In some embodiments, the processing device of the map data job task further comprises: a road frame determining unit, configured to determine, before the obtaining unit 1001 obtains the plurality of road frames corresponding to the target road segment, a road frame corresponding to a road junction in the target road segment based on a pre-configured correspondence between the type of the road junction and the road frame; and determining a road surface frame corresponding to the non-intersection road in the target road section based on the pre-configured non-intersection road surface frame.

In some embodiments, the processing device of the map data job task further comprises: the observation frame determining unit is used for determining an observation frame on the target road section based on the farthest observable distance preset by the acquisition equipment before the second dividing unit 1004 performs secondary task division on the coarse-grained task; accordingly, the second dividing unit 1004 performs secondary task division on the coarse-grained task based on the observation box.

In some embodiments, the observation box determining unit is specifically configured to:

and determining an observation frame corresponding to the two acquisition tracks on the target road section, wherein the observation frame is a geometric range formed by two side boundaries of the target road section and outward-expanded boundaries corresponding to the two acquisition tracks.

In some embodiments, the second dividing unit 1004 is specifically configured to:

and if one acquisition track exists, the acquisition track and each target pavement frame corresponding to the observation frame have intersection or the observation frame can cover the target pavement frame which has no intersection with the acquisition track, dividing each target pavement frame corresponding to the observation frame into the same fine-grained task.

The details of the embodiments of the map data job task processing apparatus disclosed above may refer to the details of the embodiments of the map data job task processing method described above, and are not repeated for avoiding repetition.

Fig. 11 is an exemplary block diagram of a computer device provided by an embodiment of the disclosure. As shown in fig. 11, the computer apparatus includes: at least one computing device 1101, at least one storage device 1102 storing instructions. It will be appreciated that the storage 1102 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.

In some embodiments, storage 1102 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof: an operating system and an application program.

The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic tasks and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application tasks. A program for implementing the processing method for map data job tasks provided by the embodiments of the present disclosure may be included in an application program.

In the embodiment of the present disclosure, at least one computing device 1101 is configured to execute the steps of each embodiment of the processing method for the map data job task provided by the embodiment of the present disclosure by calling a program or an instruction stored in at least one storage device 1102, specifically, a program or an instruction stored in an application program.

The processing method of the map data job task provided by the embodiment of the present disclosure may be applied to the computing device 1101, or implemented by the computing device 1101. The computing device 1101 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in software in the computing device 1101. The computing device 1101 may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The steps of the processing method for map data job tasks provided by the embodiment of the present disclosure can be directly embodied as the execution of a hardware decoding processor, or the execution of the hardware decoding processor and a software module in the decoding processor is combined. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a storage device 1102, and a computing device 1101 reads information in the storage device 1102 and completes the steps of the method in combination with the hardware thereof.

The embodiments of the present disclosure further provide a computer-readable storage medium, where the computer-readable storage medium stores a program or an instruction, where the program or the instruction causes a computer to execute steps of various embodiments of a processing method for a map data job task, and details are not repeated here in order to avoid repeated descriptions. The computer readable storage medium may be a non-transitory computer readable storage medium, among others.

The embodiments of the present disclosure further provide a computer program product, where the computer program product includes a computer program, the computer program is stored in a non-transitory computer-readable storage medium, and at least one processor of the computer reads and executes the computer program from the storage medium, so that the computer executes the steps of the embodiments of the processing method of the map data job task, and in order to avoid repeated descriptions, the steps are not repeated here.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.

Those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments.

Those skilled in the art will appreciate that the descriptions of the various embodiments have different emphasis, and reference may be made to the related descriptions of other embodiments for those parts of one embodiment that are not described in detail.

Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method of processing a map data job task, the method comprising:

acquiring a plurality of acquisition tracks of acquisition equipment on a target road section, and acquiring a plurality of road frames corresponding to the target road section, wherein the road frames are used for expressing the geometric range of a road surface, and any one of the road frames has an adjacent road frame;

determining a target pavement frame which has intersection with any one of the acquisition tracks from the multiple pavement frames to obtain a target pavement frame set;

2. The method of claim 1, wherein before the obtaining the plurality of road surface frames corresponding to the target road segment, the method further comprises:

determining a road surface frame corresponding to the intersection in the target road section based on a preset corresponding relation between the intersection type and the road surface frame;

3. The method of claim 1, wherein prior to the performing the secondary task partitioning on the coarse-grained task, the method further comprises:

determining an observation frame on the target road section based on the farthest observable distance preset by the acquisition equipment; correspondingly, the coarse-grained tasks are subjected to secondary task division based on the observation frame.

4. The method of claim 3, wherein the determining an observation box on the target road segment based on the farthest observable distance preset by the acquisition device comprises:

determining that the road cross-sectional lines corresponding to the farthest observable distances of the two acquisition tracks are respectively extended outwards as outward extending boundaries on the target road section by taking the respective end points of the two acquisition tracks as starting points;

5. The method of claim 4, wherein the secondary task partitioning of the coarse-grained task based on the observation box comprises:

determining a target pavement frame which has intersection with the observation frame in the coarse-grained task;

and if the target pavement frame is not covered by the observation frame, respectively dividing the target pavement frame and the adjacent target pavement frame which has intersection with the observation frame into different fine-grained tasks.

6. The method of claim 3, wherein the determining an observation box on the target road segment based on the farthest observable distance preset by the acquisition device comprises:

respectively expanding the farthest observable distance outwards to two sides by taking the adjacent edges as the reference aiming at any two adjacent target pavement frames in the coarse-grained task to obtain two expanded boundaries;

and determining a geometric range formed by the two outward-extending boundaries on the target road section as an observation frame.

7. The method of claim 6, wherein the secondary task partitioning of the coarse-grained task based on the observation box comprises:

8. A device for processing a map data job task, the device comprising:

the acquisition unit is used for acquiring a plurality of acquisition tracks of acquisition equipment on a target road section and acquiring a plurality of road frames corresponding to the target road section, wherein the road frames are used for expressing the geometric range of a road surface, and any one of the road frames has an adjacent road frame;

the determining unit is used for determining a target road surface frame which has intersection with any one of the acquisition tracks from the plurality of road surface frames to obtain a target road surface frame set;

the first dividing unit is used for performing task division on the target pavement frame set for one time to obtain at least one coarse-grained task, and all target pavement frames in the coarse-grained task form a spatially continuous road surface;

and the second division unit is used for performing secondary task division on the coarse-grained task to obtain at least one fine-grained task, and any two adjacent target pavement frames in the fine-grained task are associated with the same acquisition track.

9. A computer-readable storage medium, wherein the computer-readable storage medium stores a program or instructions for causing a computer to execute the steps of the processing method for a map data job task according to any one of claims 1 to 7.

10. A computer apparatus comprising at least one computing device and at least one storage device storing instructions; the instructions, when executed by the at least one computing device, cause the at least one computing device to perform the steps of the method of processing a map data job task as claimed in any one of claims 1 to 7.