Crowdsourcing map data processing method and device, equipment and medium

Abstract

The invention discloses a crowdsourcing map data processing method and device, equipment and a medium. Comprising the following steps: acquiring crowdsourcing map data corresponding to different to-be-processed areas in a target area; expanding the region boundary of the to-be-processed region to obtain a target task region of the corresponding to-be-processed region; generating high-precision map data of the target task area according to the crowdsourcing map data corresponding to the target task area; and according to the high-precision map data of different target task areas, edge matching processing is not needed, and a complete high-precision map of the target area is generated. According to the technical scheme provided by the embodiment of the invention, the complete high-precision map of the target area can be generated without performing edge matching processing operation on the edge data, and the convenience and the generation efficiency of the high-precision map making process are improved under the condition of considering the definition and the accuracy of the high-precision map.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geographic information technology, and in particular to a method, device, equipment and medium for processing crowdsourced map data. Background Art

[0002] With the development of geographic information technology, high-precision maps have played an important role in practical applications in the field of driving. Different from traditional navigation maps, high-precision maps add more lane information, traffic sign information, etc., and the accuracy is also required to reach the centimeter level. These complex and numerous traffic information in high-precision maps can be obtained by processing the data collected by crowdsourcing to improve data collection efficiency.

[0003] When processing crowdsourced data with existing technologies, it is necessary to partition the crowdsourced data into tiles, and after processing the data of different partitions separately, the data results are connected at the disconnected boundaries to ensure the continuity of the data in the final high-precision map.

[0004] However, edge processing increases the complexity and time of high-precision map data processing to a certain extent, reduces data processing efficiency and also reduces the data quality at the edge. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for processing crowdsourced map data, so as to improve the convenience and generation efficiency of high-precision maps without the need for edge processing.

[0006] In a first aspect, an embodiment of the present invention provides a crowdsourced map data processing method, comprising:

[0007] Obtain crowdsourced map data corresponding to different areas to be processed in the target area;

[0008] Expanding the area boundary of the area to be processed to obtain the target task area of ​​the corresponding area to be processed;

[0009] Generate high-precision map data of the target mission area based on the crowdsourced map data corresponding to the target mission area;

[0010] Based on the high-precision map data of different target mission areas, a complete high-precision map of the target area is generated without edge processing.

[0011] In a second aspect, an embodiment of the present invention further provides a crowdsourced map data processing device, comprising:

[0012] A crowdsourced map data acquisition module, used to acquire crowdsourced map data corresponding to different areas to be processed in the target area;

[0013] The region boundary expansion module is used to expand the region boundary of the region to be processed to obtain the target task region of the corresponding region to be processed;

[0014] A high-precision map data generation module is used to generate high-precision map data of the target task area according to the high-precision map data corresponding to the target task area;

[0015] The complete high-precision map acquisition module is used to generate a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, including:

[0017] at least one processor; and

[0018] a memory communicatively connected to at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute any crowdsourced map data processing method provided in the embodiments of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, enables the computer processor to execute any one of the crowdsourcing map data processing methods provided by the embodiments of the present invention.

[0021] The embodiment of the present invention improves the generation efficiency of high-precision map data corresponding to the area to be processed by expanding the area boundary of the area to be processed and generating corresponding high-precision map data based on the expanded target task area. At the same time, since the target task area covers the boundary extension area outside the area to be processed, the loss of key information in the area to be processed can be avoided, and the boundary extension area is also covered by other areas to be processed. Therefore, when generating a complete high-precision map corresponding to the target area, there is no need to perform cumbersome edge connection processing, and a continuous and clear complete high-precision map can be generated. In addition, since the amount of data calculation for the area expansion operation and the high-precision map data generation process of the boundary extension area is much smaller than the amount of data calculation for the edge connection processing, the crowdsourcing map data processing method involved in the present invention is more convenient and has a lower amount of data calculation.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flowchart of a crowdsourced map data processing method provided in Embodiment 1 of the present invention;

[0025] Figure 2A A flowchart of another crowdsourced map data processing method provided in Embodiment 2 of the present invention;

[0026] Figure 2B A schematic diagram of a target area provided in Embodiment 2 of the present invention;

[0027] Figure 2C A schematic diagram of target area expansion provided in Embodiment 2 of the present invention;

[0028] Figure 2D A schematic diagram of a boundary extension result provided in Embodiment 2 of the present invention;

[0029] Figure 2E A comparison chart of crowdsourced map data before and after processing provided by the second embodiment of the present invention;

[0030] Figure 3 A schematic diagram of the structure of a crowd-sourced map data processing device provided in Embodiment 3 of the present invention;

[0031] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 This is a flowchart of a crowdsourced map data processing method provided in the first embodiment of the present invention. This embodiment is applicable to the case where high-precision maps are generated without edge processing operations. The method can be executed by a crowdsourced map data processing device, which can be implemented in the form of hardware and / or software. The crowdsourced map data processing device can be configured in an electronic device, which can be a terminal device or a server, etc., and the embodiment of the present invention does not limit this.

[0036] like Figure 1 As shown, a crowdsourcing map data processing method provided by an embodiment of the present invention specifically includes the following steps:

[0037] S110, obtaining crowdsourced map data corresponding to different areas to be processed in the target area.

[0038] In this embodiment, the target area can be specifically understood as the area where the crowdsourced map data obtained at a certain moment is used for high-precision map production. Among them, at least one to-be-processed area can be divided in the target area, and different to-be-processed areas can be treated as a whole and can be merged into complete high-precision map data after independent production.

[0039] Crowdsourced map data can be specifically understood as reference data for generating high-precision maps obtained through crowdsourcing. Crowdsourced map data can include semantic data and trajectory data. Trajectory data is used to characterize the driving trajectory of the mobile device used for crowdsourcing collection; semantic data is used to characterize lane-level feature data such as lane markings and lane signs carried on the road.

[0040] S120, expanding the region boundary of the area to be processed to obtain a target task region of the corresponding area to be processed.

[0041] The extension can be specifically understood as an operation of extending the processing in a certain direction; the target task area is the area obtained after the to-be-processed area is extended.

[0042] Exemplarily, when the boundary of the area to be processed is extended, the preset extension length can be extended according to the preset extension direction to obtain the boundary extension area of ​​the area to be processed; the area to be processed and the corresponding boundary extension area are combined to obtain the target task area corresponding to the area to be processed. The preset extension length can be set by the technician according to the needs or experience value, or determined through a large number of experiments. The preset extension lengths corresponding to different directions can be the same or different, and the present invention does not make any limitation to this.

[0043] Optionally, the preset lengths corresponding to the extension directions of different areas may be determined according to the complexity of intersections appearing in that direction.

[0044] In a specific implementation, the region boundary of the region to be processed may be uniformly expanded along the region extension direction to obtain a target task region of the corresponding region to be processed.

[0045] The so-called uniform expansion means that the corresponding preset extension lengths in the extension directions of different regions are the same.

[0046] By uniformly expanding the regional boundary of the area to be processed along the direction of the regional extension, while ensuring that more crowdsourced data information around the area to be processed can be covered and the information integrity of the area to be processed is ensured, the standardization and consistency of the regional expansion can be improved, which is convenient for batch processing of data and helps to improve the efficiency of regional expansion.

[0047] In a specific implementation, the area boundary of the area to be processed is uniformly expanded along the area extension direction to obtain the target task area of ​​the corresponding area to be processed. The area boundary of the area to be processed can be expanded along the area extension direction by a preset extension length to obtain the target task area of ​​the corresponding area to be processed. The preset extension length can be determined based on the intersection area length of the composite intersection. The composite intersection is a complex element composed of multiple road connection points (nodes) within a complex intersection. When abstracted, multiple nodes are formed due to the dual-line expression of some roads. These related nodes are integrated into a complex composite intersection for conveniently expressing the information of the intersection.

[0048] Among them, the intersection area length is used to characterize the intersection size of the composite intersection. Optionally, the intersection area length can be a statistical value (such as a maximum value) of the intersection length corresponding to the intersection in different directions. Or optionally, the intersection area length can also be the intersection length of the composite intersection in the direction of the area extension. In a specific example, the preset extension length can be 200 meters.

[0049] By uniformly expanding the regional boundary of the area to be processed along the direction of the area extension with a preset extension length, it is possible to uniformly expand the area to be processed in different directions with a uniform length while ensuring that the boundary expansion area can cover complex intersections and avoid the loss of key information in the boundary expansion area, thereby ensuring the information integrity of the area to be processed and helping to subsequently improve the data quality of the high-precision maps of the connected parts of different areas to be processed.

[0050] S130. Generate high-precision map data of the target task area based on the crowdsourced map data corresponding to the target task area.

[0051] Among them, the high-precision map data of the target task area carries lane-level data in the target task area, which may include at least one of lane markings, curbs, lane topology structures, and intersections.

[0052] Specifically, the crowdsourced map data corresponding to the target task area is used as an independent task processing unit to replace the crowdsourced map data of the area to be processed to generate high-precision map data, so that the generated high-precision map data can carry the full amount of information in the area to be processed, avoiding the omission of key information on the edge of the area to be processed.

[0053] Specifically, for any target task area, the crowdsourced map data corresponding to the target task area can be input into the preset mapping algorithm to obtain the high-precision map data of the target task area. Among them, the preset mapping algorithm is a pre-set algorithm for generating high-precision map data, which can be implemented by at least one of the prior arts, and the present invention does not impose any restrictions on this. Since the target task area is used as an independent task processing unit to generate high-precision maps, different target task areas can use a serial or parallel method to generate corresponding high-precision map data. In order to improve the generation efficiency of high-precision map data for different target task areas, and then improve the processing efficiency of crowdsourced map data, in a specific implementation method, a parallel processing method of multiple target task areas can be adopted to realize the automatic generation of high-precision maps of the corresponding target task areas.

[0054] S140. Generate a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing.

[0055] Specifically, since the high-precision map data of different target task areas all cover the high-precision map data of the corresponding area to be processed, a complete high-precision map of the target area can be generated by splicing the high-precision map data of different target task areas according to the area location. In the process of generating a complete high-precision map, since the high-precision map data of the edge part of the area to be processed is covered in different target task areas, there is no need to perform edge processing to ensure the integrity and clarity of the corresponding area data.

[0056] In order to avoid duplication of high-precision map data at the edge of the area to be processed, which would result in a waste of storage resources, in an optional embodiment, the high-precision map data corresponding to the boundary extension area can be removed from the high-precision map data of the target task area to obtain the high-precision map data of the corresponding area to be processed; based on the high-precision map data of different areas to be processed, a complete high-precision map of the target area is generated.

[0057] Specifically, for any target task area, the high-precision map data of the boundary extension area outside the corresponding to-be-processed area in the target task area is eliminated, and only the high-precision map data of the corresponding to-be-processed area is retained, thereby avoiding the partial duplication of the high-precision map data of this part of the boundary extension area with the high-precision map data of other to-be-processed areas, reducing the usage of storage resources. Accordingly, the high-precision map data of different to-be-processed areas are spliced ​​according to the regional distribution of the to-be-processed areas in the target area, and a complete high-precision map of the target area can be generated.

[0058] It can be understood that since any area to be processed carries complete high-precision map data due to the boundary extension operation, this part of the high-precision map data also carries complete high-precision map data in the adjacent area to be processed, thereby ensuring that the adjacent area to be processed associated with the boundary extension area can be well spliced ​​without the need for edge processing, thereby improving the efficiency of high-precision map generation while taking into account the accuracy and clarity of the complete high-precision map generation results.

[0059] The embodiment of the present invention improves the efficiency of generating high-precision map data corresponding to the area to be processed by expanding the area boundary of the area to be processed and generating corresponding high-precision map data based on the expanded target task area. At the same time, since the target task area covers the boundary extension area outside the area to be processed, the loss of key information in the area to be processed can be avoided, and the boundary extension area is also covered by other areas to be processed. Therefore, when generating a complete high-precision map corresponding to the target area, there is no need to perform cumbersome edge connection processing, and a continuous and clear complete high-precision map can be generated. In addition, since the amount of data calculation for the area expansion operation and the high-precision map data generation process of the boundary extension area is much smaller than the amount of data calculation for the edge connection processing, the crowdsourcing map data processing method involved in the present invention is more convenient and has a lower amount of data calculation.

[0060] Embodiment 2

[0061] Figure 2A This is a flowchart of another crowdsourced map data processing method provided in the second embodiment of the present invention. The technical solution of the embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions.

[0062] Furthermore, "obtaining crowdsourced map data corresponding to different to-be-processed areas in the target area" is further refined into "obtaining crowdsourced map data corresponding to different crowdsourced partitions in the target area; generating an area to be processed including at least two adjacent crowdsourced partitions according to the position distribution between different crowdsourced partitions; determining crowdsourced map data corresponding to different to-be-processed areas" to realize automatic generation of the to-be-processed area. It should be noted that for the parts not described in this embodiment, reference can be made to the relevant descriptions of other embodiments, and no further description is given here.

[0063] like Figure 2A As shown, another crowdsourcing map data processing method provided by an embodiment of the present invention includes:

[0064] S210: Obtain crowdsourcing map data corresponding to different crowdsourcing partitions in the target area.

[0065] The crowdsourcing partition can be understood as the data collection area divided when crowdsourcing map data is collected; different crowdsourcing partitions are independent of each other and do not overlap. The number of crowdsourcing partitions in the target area is at least one, usually multiple; the number and division method of crowdsourcing partitions in the target area can be set by technicians according to actual needs or experience, and the present invention does not impose any limitation on this.

[0066] For an example, see Figure 2B The target area schematic diagram shown in FIG. 1 includes 7 crowdsourcing partitions in the target area, wherein a minimum blank square is a unit square corresponding to a crowdsourcing partition.

[0067] S220: Generate a to-be-processed area including at least two adjacent crowdsourcing partitions according to the location distribution between different crowdsourcing partitions.

[0068] Among them, the location distribution is used to characterize the relative position relationship between different crowdsourcing partitions in the target area.

[0069] It can be understood that constructing a processing area including at least two adjacent crowdsourcing partitions can facilitate the independent generation of high-precision maps in different subsequent processing areas, thereby avoiding the loss of key information and low data processing efficiency caused by the dispersion of crowdsourcing partitions.

[0070] Exemplarily, based on a preset partition quantity threshold and the adjacency relationship between different crowdsourcing partitions, and in a preset area extension direction, each crowdsourcing partition may be merged to obtain a to-be-processed area including at least two adjacent crowdsourcing partitions.

[0071] Among them, different crowdsourcing partitions with an adjacent relationship indicate that the corresponding crowdsourcing partitions are adjacent to the region and there is a regional connection. Among them, the preset partition quantity threshold and the preset region extension direction can be set by technicians according to needs or experience, or determined through a large number of experiments, and the present invention does not impose any limitation on this.

[0072] Specifically, according to the preset area extension direction, crowdsourced partitions with a preset partition quantity threshold having an adjacency relationship may be merged to obtain a corresponding area to be processed.

[0073] Continuing with the previous example, see Figure 2C , you can Figure 2B The target area shown in the figure takes the crowdsourcing partition corresponding to the cell square in the lower left corner as the starting point, and expands the area in a manner of first rightward and then upward, to obtain two different to-be-processed areas (corresponding to the to-be-processed area in the shaded part and the to-be-processed area in the blank part of the figure) containing at most 10 crowdsourcing partitions. The preset partition number threshold is 10; the preset area extension direction is to take the crowdsourcing partition corresponding to the unmerged cell square in the lower left corner as the starting point, and extend the area in a manner of first rightward and then upward.

[0074] S230: Determine crowdsourced map data corresponding to different areas to be processed.

[0075] S240, the region boundary of the area to be processed is expanded to obtain a target task region of the corresponding area to be processed.

[0076] Continuing with the previous example, see Figure 2D , respectively Figure 2CThe to-be-processed areas divided in the figure are expanded to obtain the target task areas of each to-be-processed area. The to-be-processed area is the oblique line shaded area, and the other parts are the boundary extension areas expanded from the corresponding to-be-processed areas.

[0077] S250. Generate high-precision map data of the target task area based on the crowdsourced map data corresponding to the target task area.

[0078] S260. Generate a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing.

[0079] Figure 2E This is a comparison chart of crowdsourced map data before and after processing. The processed crowdsourced map data is high-precision map data, and the vertical lines correspond to the connection positions of different crowdsourced partitions.

[0080] In an optional embodiment, in order to facilitate the storage of a complete high-precision map, the high-precision map data of each crowdsourcing partition can be determined based on the location distribution of different crowdsourcing partitions in the area to be processed and the high-precision map data of the area to be processed.

[0081] Specifically, for any area to be processed, the high-precision map data of the area to be processed is divided according to the location distribution of the crowdsourcing partitions of the area to be processed, so as to obtain high-precision map data corresponding to different crowdsourcing partitions; the high-precision map data corresponding to each crowdsourcing partition is stored, and when it is necessary to display high-precision maps of different areas to be displayed, the high-precision map data of the covered crowdsourcing partitions can be read and rendered as needed.

[0082] The embodiment of the present invention obtains crowdsourcing map data corresponding to different crowdsourcing partitions in the target area, and generates a to-be-processed area including at least two adjacent crowdsourcing partitions according to the position distribution between different crowdsourcing partitions, thereby realizing automatic division of the to-be-processed area without the need for human intervention, thereby improving the division efficiency of the to-be-processed area and further contributing to improving the processing efficiency of crowdsourcing map data.

[0083] Embodiment 3

[0084] Figure 3 This is a schematic diagram of the structure of a crowdsourced map data processing device provided in the third embodiment of the present invention, the crowdsourced map data processing device includes: a crowdsourced map data acquisition module 310, a regional boundary expansion module 320, a high-precision map data generation module 330 and a complete high-precision map acquisition module 340. Among them,

[0085] The crowdsourced map data acquisition module 310 is used to acquire crowdsourced map data corresponding to different to-be-processed areas in the target area;

[0086] The region boundary expansion module 320 is used to expand the region boundary of the region to be processed to obtain the target task region of the corresponding region to be processed;

[0087] A high-precision map data generation module 330 is used to generate high-precision map data of the target task area according to the high-precision map data corresponding to the target task area;

[0088] The complete high-precision map obtaining module 340 is used to obtain a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing.

[0089] The embodiment of the present invention improves the efficiency of generating high-precision map data corresponding to the area to be processed by expanding the area boundary of the area to be processed and generating corresponding high-precision map data based on the expanded target task area. At the same time, since the target task area covers the boundary extension area outside the area to be processed, the loss of key information in the area to be processed can be avoided, and the boundary extension area is also covered by other areas to be processed. Therefore, when generating a complete high-precision map corresponding to the target area, there is no need to perform cumbersome edge connection processing, and a continuous and clear complete high-precision map can be generated. In addition, since the amount of data calculation for the area expansion operation and the high-precision map data generation process of the boundary extension area is much smaller than the amount of data calculation for the edge connection processing, the crowdsourcing map data processing method involved in the present invention is more convenient and has a lower amount of data calculation.

[0090] Optionally, the region boundary expansion module 320 includes:

[0091] The region determination unit is used to uniformly expand the region boundary of the region to be processed along the region extension direction to obtain the target task region of the corresponding region to be processed.

[0092] Optionally, the region determination unit comprises:

[0093] The task area determination subunit is used to extend the area boundary of the area to be processed by a preset extension along the area extension direction to obtain a target task area of ​​the corresponding area to be processed.

[0094] Optionally, the complete high-precision map acquisition module 340 includes:

[0095] A high-precision map data acquisition unit is used to remove the high-precision map data corresponding to the boundary extension area from the high-precision map data of the target task area, and obtain the high-precision map data of the corresponding area to be processed;

[0096] The target map acquisition unit is used to generate a complete high-precision map of the target area based on the high-precision map data of different areas to be processed.

[0097] Optionally, the crowdsourced map data acquisition module 310 includes:

[0098] A map data acquisition unit, used to acquire crowdsourced map data corresponding to different crowdsourced partitions in the target area;

[0099] A to-be-processed area generating unit, configured to generate, according to the position distribution between different crowdsourcing partitions, an to-be-processed area including at least two adjacent crowdsourcing partitions;

[0100] The map data corresponding unit is used to determine the crowdsourced map data corresponding to different areas to be processed.

[0101] Optionally, the unit for generating the area to be processed includes:

[0102] The region merging subunit is used to merge the crowdsourcing partitions according to a preset partition quantity threshold and the adjacency relationship between different crowdsourcing partitions and in a preset region extension direction to obtain a to-be-processed region including at least two adjacent crowdsourcing partitions.

[0103] Optionally, the unit for generating the area to be processed further includes:

[0104] The map data determination subunit is used to determine the high-precision map data of each crowdsourcing partition based on the location distribution of different crowdsourcing partitions in the area to be processed and the high-precision map data of the area to be processed.

[0105] A crowdsourced map data processing device provided in an embodiment of the present invention can execute the crowdsourced map data processing method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to executing each crowdsourced map data processing method.

[0106] Embodiment 4

[0107] Figure 4 A schematic diagram of the structure of an electronic device for implementing a crowdsourced map data processing method provided for Embodiment 4 of the present invention. The electronic device 400 for implementing a crowdsourced map data processing method may be an electronic device intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0108] like Figure 4As shown, an electronic device 400 implementing a crowdsourced map data processing method includes at least one processor 410, and a memory connected to the at least one processor 410, such as a read-only memory (ROM) 420, a random access memory (RAM) 430, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 410 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 420 or the computer program loaded from the storage unit 480 to the random access memory (RAM) 430. In the (RAM) 430, various programs and data required for the operation of the electronic device 400 implementing a crowdsourced map data processing method can also be stored. The processor 410, the (ROM) 420, and the (RAM) 430 are connected to each other through a bus 440. An input / output (I / O) interface 450 is also connected to the bus 440.

[0109] Multiple components in the electronic device 400 implementing a crowdsourced map data processing method are connected to the I / O interface 450, including: an input unit 460, such as a keyboard, a mouse, etc.; an output unit 470, such as various types of displays, speakers, etc.; a storage unit 480, such as a disk, an optical disk, etc.; and a communication unit 490, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 490 implements the electronic device 400 implementing a crowdsourced map data processing method, and exchanges information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0110] The processor 410 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 410 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 410 performs the various methods and processes described above, such as a crowdsourcing map data processing method.

[0111] In some embodiments, a crowdsourced map data processing method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 480. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device 400 that implements a crowdsourced map data processing method via (ROM) 420 and / or a communication unit 490. When the computer program is loaded into (RAM) 430 and executed by the processor 410, one or more steps of a crowdsourced map data processing method described above may be performed. Alternatively, in other embodiments, the processor 410 may be configured to perform a crowdsourced map data processing method in any other appropriate manner (e.g., by means of firmware).

[0112] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0114] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer 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 foregoing.

[0115] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0118] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A crowdsourced map data processing method, characterized in that: include: Obtain crowdsourced map data corresponding to different areas to be processed in the target area; Expanding the area boundary of the area to be processed to obtain a target task area of ​​the corresponding area to be processed; Generate high-precision map data of the target task area based on the crowdsourced map data corresponding to the target task area; Based on the high-precision map data of different target mission areas, a complete high-precision map of the target area is generated without edge processing.

2. The method according to claim 1, characterized in that: The step of expanding the area boundary of the area to be processed to obtain a target task area of ​​the corresponding area to be processed includes: The region boundary of the area to be processed is uniformly expanded along the region extension direction to obtain the target task region of the corresponding area to be processed.

3. The method according to claim 2, characterized in that The region boundary of the region to be processed is uniformly expanded along the region extension direction to obtain the target task region of the corresponding region to be processed, including: Extending the region boundary of the region to be processed by a preset extension along the region extension direction to obtain a target task region of the corresponding region to be processed; Wherein, the preset outer extension length is determined based on the length of the intersection area of ​​the composite intersection.

4. The method according to claim 1, characterized in that The method of generating a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing includes: From the high-precision map data of the target task area, remove the high-precision map data corresponding to the boundary extension area to obtain the high-precision map data of the corresponding area to be processed; Based on the high-precision map data of different areas to be processed, a complete high-precision map of the target area is generated.

5. The method according to claim 1, characterized in that: The obtaining of crowdsourced map data corresponding to different to-be-processed areas in the target area includes: Obtain crowdsourcing map data corresponding to different crowdsourcing partitions in the target area; According to the location distribution between different crowdsourcing partitions, a to-be-processed area including at least two adjacent crowdsourcing partitions is generated; Determine the crowdsourced map data corresponding to different areas to be processed.

6. The method according to claim 5, characterized in that The step of generating a to-be-processed area including at least two adjacent crowdsourcing partitions according to the position distribution between different crowdsourcing partitions includes: According to a preset partition quantity threshold and the adjacency relationship between different crowdsourcing partitions, and in accordance with a preset area extension direction, each crowdsourcing partition is region-merged to obtain a to-be-processed area including at least two adjacent crowdsourcing partitions.

7. The method according to claim 5, characterized in that The method further comprises: Based on the location distribution of different crowdsourcing partitions in the area to be processed, the high-precision map data of each crowdsourcing partition is determined according to the high-precision map data of the area to be processed.

8. A crowd-sourced map data processing device, characterized in that: include: A crowdsourced map data acquisition module, used to acquire crowdsourced map data corresponding to different areas to be processed in the target area; An area boundary expansion module is used to expand the area boundary of the area to be processed to obtain a target task area of ​​the corresponding area to be processed; A high-precision map data generation module, used to generate high-precision map data of the target task area according to the high-precision map data corresponding to the target task area; The complete high-precision map acquisition module is used to generate a complete high-precision map of the target area based on the high-precision map data of different target mission areas without edge processing.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the crowdsourcing map data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the crowdsourcing map data processing method as described in any one of claims 1 to 7 is implemented.

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