Network path planning methods, devices, electronic equipment and storage media

Abstract

This invention provides a network path planning method, apparatus, electronic device, and storage medium. The network path planning method includes: dividing an initial map into multiple grids according to a set scale; obtaining a first-level grid map based on the multiple grids; setting the grid state according to the current grid capacity; obtaining the connectivity relationships between grids in the first-level grid map; aggregating a set number of grids in the first-level grid map based on the connectivity relationships to obtain a second-level grid map; obtaining the connectivity relationships between the aggregated grids in the second-level grid map; obtaining an initial path in the second-level grid map; mapping the nodes of the initial path to the corresponding paths in the first-level grid map; and drawing the corresponding paths onto the initial map based on the grid state and connectivity relationships of the first-level grid map to obtain the planned path. This achieves the goal of improving computational efficiency.

Description

Technical Field

[0001] This invention belongs to the field of airspace management technology, and in particular relates to a network path planning method, device, electronic device and storage medium. Background Technology

[0002] While China's decision to open up low-altitude airspace brings opportunities, it also faces many pressing problems and needs. Traditional airspace management is not suitable for low-altitude airspace management in terms of spatial organization, multi-aircraft calculation, dynamic display, and user operation.

[0003] The current airspace division is based on safety considerations, requiring controllers to manage zones. Considering various practical constraints, machine learning algorithms are used to optimize sectors. However, current airspace management still suffers from shortcomings in terms of uniformity, efficiency, and autonomy in addressing my country's demands for low-altitude airspace opening and maximizing airspace utilization: low-altitude airspace division lacks uniformity and completeness, and underlying surface information is insufficient; the computational methods for low-altitude airspace path planning and traffic management are complex and can easily affect system efficiency.

[0004] Traditional grid path planning algorithms are computationally inefficient, and their speedup varies depending on map characteristics and start / end point locations. This is because the time consumption of grid path planning algorithms is mainly consumed in traversing the grid. When the number of traversed grids accounts for a high percentage of the total number of grids, the computational efficiency is reduced. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a network path planning method, apparatus, electronic device and storage medium, which at least partially solves the problem of low computational efficiency in the prior art.

[0006] In a first aspect, embodiments of this disclosure provide a network path planning method, including:

[0007] The initial map is divided into multiple grids according to a set scale. A first-level grid map is obtained based on the multiple grids. The grid state is set according to the current grid capacity, and the connectivity between grids in the first-level grid map is obtained.

[0008] Based on the connectivity between grids, a set number of grids in the first-level grid map are aggregated to obtain the second-level grid map, and the connectivity of the aggregated grids in the second-level grid map is obtained;

[0009] An initial path is obtained by using a path planning algorithm in the second-level grid map. The nodes of the initial path are mapped to the corresponding paths in the first-level grid map according to the correspondence relationship. The correspondence relationship is the correspondence relationship between the second-level grid map and the first-level grid map.

[0010] Based on the grid status and connectivity between grids in the first-level grid map, the corresponding paths are drawn onto the initial map to obtain the planned paths.

[0011] Optionally, setting the grid state based on the current grid capacity includes:

[0012] Determine if the current grid capacity exceeds the threshold;

[0013] If the threshold is exceeded, it is set as an obstacle grid; if the threshold is not exceeded, it is set as a non-obstacle grid.

[0014] Optionally, obtaining the connectivity between grids in the first-level grid map includes:

[0015] Obtain the edge connectivity and corner connectivity of the grid, and then determine the connectivity relationships between the grids based on the edge connectivity and corner connectivity.

[0016] Optionally, the connectivity of the aggregated grids in the second-level grid map includes:

[0017] Obtain the edge connectivity and corner connectivity of the aggregated mesh, and then determine the connectivity relationships between the aggregated meshes based on their edge connectivity and corner connectivity.

[0018] Optionally, the obstacle mesh includes a static obstacle mesh and a dynamic obstacle mesh;

[0019] The static obstacle grid setting includes dividing the obstacle object into a grid according to its geometric shape using the minimum hierarchical subdivision scale.

[0020] The static obstacle mesh is aggregated step by step according to the subdivision scale level to obtain the aggregated mesh of the corresponding level at the set scale;

[0021] The clustered grid state is set based on the relationship between the current capacity of the clustered grid and the threshold.

[0022] Optionally, the dynamic obstruction mesh setting includes:

[0023] Set the dynamic object as a point mass, and set a buffer around the point mass based on the volume and velocity of the dynamic object;

[0024] The state of the occupied grid is set based on the motion direction and velocity of the mass and buffer.

[0025] Optionally, uniform coding can be used to encode the properties of static obstructing objects and dynamic objects.

[0026] Secondly, embodiments of this disclosure also provide a network path planning apparatus, comprising:

[0027] The grid division module is used to divide the initial map into multiple grids according to a set scale, obtain a first-level grid map based on the multiple grids, set the grid state according to the current grid capacity, and obtain the connectivity between grids in the first-level grid map.

[0028] The aggregation module is used to aggregate a set number of grids in the first-level grid map based on the connectivity between grids to obtain the second-level grid map, and to obtain the connectivity of the aggregated grids in the second-level grid map;

[0029] The path calculation module is used to obtain an initial path using a path planning algorithm in the second-level grid map, and to map the nodes of the initial path to the corresponding path in the first-level grid map according to the correspondence relationship, wherein the correspondence relationship is the correspondence relationship between the second-level grid map and the first-level grid map.

[0030] The path planning module is used to draw the corresponding path onto the initial map based on the grid status and connectivity between grids in the first-level grid map, thus obtaining the planned path.

[0031] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0032] At least one processor; and,

[0033] A memory communicatively connected to the at least one processor; wherein,

[0034] The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform any of the network path planning methods described in the first aspect.

[0035] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions for causing a computer to perform any of the network path planning methods described in the first aspect.

[0036] The present invention provides a network path planning method, apparatus, electronic device, and storage medium. The network path planning method aggregates high-level grid information onto low-level grids and executes a path planning algorithm on the low-level grids. By aggregating high-level grids, the total number of grids is significantly reduced, thereby reducing the number of traversals and improving computational efficiency.

[0037] By dividing the obstacle mesh into static obstacle mesh and dynamic obstacle mesh, the static obstacle mesh is treated as a fixed parameter in the path planning algorithm, and only the variable factors of the dynamic obstacle mesh need to be considered, thereby reducing the computational parameters and further improving computational efficiency. Attached Figure Description

[0038] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0039] Figure 1a and Figure 1b This is a schematic diagram of the GeoSOT mesh provided in an embodiment of the present disclosure;

[0040] Figure 2a and Figure 2b This is a schematic diagram of the three-dimensional meshing of GeoSOT provided in the embodiments of this disclosure;

[0041] Figure 3 A flowchart of a network path planning method provided in this embodiment of the disclosure;

[0042] Figure 4 A schematic diagram illustrating the abstract representation of a buffer provided in this disclosure embodiment;

[0043] Figure 5 A schematic block diagram of a network path planning device provided in this disclosure embodiment;

[0044] Figure 6 This is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0045] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0046] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0047] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0048] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0049] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0050] GeoSOT is a global subdivision grid with one-dimensional integer on two to n-th power, a latitude and longitude subdivision grid system. The GeoSOT grid utilizes three degrees of latitude and longitude expansion in Earth's space to extend geographic space to 512°×512°, expanding 1° to 64′ and 1′ to 64″, achieving integer quadtree partitioning of geographic space in degrees, minutes, and seconds. The GeoSOT Level 0 grid is defined as a 512°×512° square centered at the intersection of the equator and the Prime Meridian. The GeoSOT Level 1 grid is based on the Level 0 grid, divided into four equal parts, each Level 1 grid measuring 256°×256°. The GeoSOT Level 2 grid is also based on the Level 1 grid, divided into four equal parts, each Level 2 grid measuring 128°×128°. GeoSOT Level 3 and lower levels of partitioning follow the quadtree partitioning principle, and so on. Figure 1a and Figure 1bAs shown. GeoSOT's meshing and coding have advantages such as global uniqueness, multi-scale, and flexible expansion. The mesh coding, controlled by the number of bits, is sufficient to accurately express spatial information of any location and size, and has the characteristics of small deformation, multi-scale, simple meshing, and easy computer processing.

[0051] GeoSOT-3D's height dimension partitioning is independent of the two-dimensional partitioning. A height dimension is introduced into the basic two-dimensional mesh partitioning, mapping its information to 512°, corresponding to the altitude range from 50,000 kilometers above the Earth's surface to the Earth's core. Simultaneously, the two-dimensional quadtree mesh is expanded into a three-dimensional octree mesh, which is the three-dimensional geospatial partitioning mesh GeoSOT-3D. There is a natural mapping relationship between the two-dimensional and three-dimensional meshes: when the height dimension (geodetic height) of the GeoSOT-3D geospatial partitioning mesh is 0, the three-dimensional mesh framework becomes a two-dimensional mesh framework on the Earth's sphere. The three-dimensional partitioning process is essentially an octree division of this partitioned space. This octree division method assigns each GeoSOT-3D partitioning voxel a corresponding spatial location and spatial range. The structure of these voxels is as follows: Figure 2a and Figure 2b As shown, the resulting set of partitioned voxels can seamlessly and without overlap cover the entire Earth's space. GeoSOT-3D partitioning encoding identifies spatial voxels using an encoding method, with a one-to-one correspondence between the partition code and the voxel; each partition code represents a voxel and also corresponds to a spatial location. GeoSOT-3D partitioning encoding has three basic forms: binary 3D encoding, binary 1D encoding, and octal 1D encoding. Binary 3D encoding is the foundation for other types of encoding, while octal 1D encoding is mainly used for octree retrieval. Binary 1D and binary 3D encodings are primarily used for storage and computation within computers.

[0052] In this embodiment, the low-altitude airspace is pre-divided into several scales, and the size of the airspace is adjusted in real time according to task or computational requirements. The selection of different scales mainly considers three factors: first, a certain difference is needed between scales; second, different scales should be able to achieve an inclusive relationship; and third, the selected scale range is conducive to rapid computation. Based on these three considerations, five scales—1 meter, 8 meters, 64 meters, 512 meters, and 4096 meters—were selected. Table 1 shows the grid size and number of grids corresponding to different scales. The division dimensions shown in Table 1 are only for illustrative purposes and are not intended to limit the scope of this division method.

[0053] Table 1. Grid size and number corresponding to different scales

[0054]

[0055] In a specific application scenario, the network path planning method of this embodiment is implemented under the following constraints: First, the grid capacity has a threshold, and if the threshold is exceeded, no more path points can be added to the grid; Second, flight tasks have priorities, and tasks with the same priority are sorted according to their start time; Third, if an obstacle is encountered during flight, the flight path can be changed or the flight can wait depending on the aircraft type and the flight task.

[0056] In this embodiment, the first level is a high-level mesh, and the second level is a low-level mesh. That is, the first level has higher precision. As shown in Table 1, if the mesh size is 8×8×8, it is the second level, and 1×1×1 is the first level.

[0057] For ease of understanding, such as Figure 3 As shown, this embodiment discloses a network path planning method, including:

[0058] Step S301: Divide the initial map into multiple grids according to a set scale, obtain a first-level grid map based on the multiple grids, set the grid state according to the current grid capacity, and obtain the connectivity between grids in the first-level grid map;

[0059] Optionally, setting the grid state based on the current grid capacity includes:

[0060] Determine if the current grid capacity exceeds the threshold;

[0061] If the threshold is exceeded, it is set as an obstacle grid; if the threshold is not exceeded, it is set as a non-obstacle grid.

[0062] Optionally, obtaining the connectivity between grids in the first-level grid map includes:

[0063] Obtain the edge connectivity and corner connectivity of the grid, and then determine the connectivity relationships between the grids based on the edge connectivity and corner connectivity.

[0064] Optionally, the obstacle mesh includes a static obstacle mesh and a dynamic obstacle mesh;

[0065] The static obstacle grid setting includes dividing the obstacle object into a grid according to its geometric shape using the minimum hierarchical subdivision scale.

[0066] The static obstacle mesh is aggregated step by step according to the subdivision scale level to obtain the aggregated mesh of the corresponding level at the set scale;

[0067] The clustered grid state is set based on the relationship between the current capacity of the clustered grid and the threshold.

[0068] Optionally, the dynamic obstruction mesh setting includes:

[0069] Set the dynamic object as a point mass, and set a buffer around the point mass based on the volume and velocity of the dynamic object;

[0070] The state of the occupied grid is set based on the motion direction and velocity of the mass and buffer.

[0071] Optionally, uniform coding can be used to encode the properties of static obstructing objects and dynamic objects.

[0072] Step S302: Aggregate a set number of grids in the first-level grid map based on the connectivity between grids to obtain the second-level grid map, and obtain the connectivity of the aggregated grids in the second-level grid map;

[0073] Optionally, the connectivity of the aggregated grids in the second-level grid map includes:

[0074] Obtain the edge connectivity and corner connectivity of the aggregated mesh, and then determine the connectivity relationships between the aggregated meshes based on their edge connectivity and corner connectivity.

[0075] The number can be set to the octree partitioning in GeoSOT-3D, that is, aggregating 8 first-level meshes into one second-level mesh.

[0076] Step S303: Use a path planning algorithm in the second-level grid map to obtain an initial path, and map the nodes of the initial path to the first-level grid map according to the correspondence relationship to obtain the corresponding path. The correspondence relationship is the correspondence relationship between the second-level grid map and the first-level grid map.

[0077] Planning algorithms include A* path planning algorithm or genetic algorithm, etc.

[0078] Step S304: Based on the grid status and connectivity between grids in the first-level grid map, draw the corresponding path onto the initial map to obtain the planned path.

[0079] Optionally, setting the grid state based on the current grid capacity includes:

[0080] Determine if the current grid capacity exceeds the threshold;

[0081] If the threshold is exceeded, it is set as an obstacle grid; if the threshold is not exceeded, it is set as a non-obstacle grid.

[0082] Optionally, obtaining the connectivity between grids in the first-level grid map includes:

[0083] Obtain the edge connectivity and corner connectivity of the grid, and then determine the connectivity relationships between the grids based on the edge connectivity and corner connectivity.

[0084] Optionally, the connectivity of the aggregated grids in the second-level grid map includes:

[0085] Obtain the edge connectivity and corner connectivity of the aggregated mesh, and then determine the connectivity relationships between the aggregated meshes based on their edge connectivity and corner connectivity.

[0086] Given the dynamic nature of aircraft activities in low-altitude airspace and the presence of obstructions such as mountains and buildings, a three-dimensional dynamic display with time-lapse capabilities is required. This embodiment uses a situation map to display environmental data of low-altitude airspace and the flight status of various aircraft in a multi-dimensional, dynamic, and multi-scale manner. Based on the time-lapse of the situation map and aircraft parameter calculations, it enables various tasks such as mission planning, flight route navigation, and low-altitude airspace control.

[0087] The low-altitude airspace situation map is a dynamic map that reflects the environmental status and flight status of low-altitude airspace. In this embodiment, the low-altitude airspace is first divided into multiple scales in the form of a three-dimensional grid. Then, the data changes of the situation are recorded and calculated in real time over time, and the overall shape and usage of the low-altitude airspace are presented in the form of a visualization of the situation map.

[0088] In the time dimension, some objects are immobile, while others are moving at high speeds. They can be distinguished by their positions in the situation map and grouped into different situation maps for management. Therefore, this embodiment divides the low-altitude airspace situation into two types: static situation maps and dynamic situation maps. The static situation map mainly records static obstacle environment data, that is, it displays the position information of obstacles that are stationary from the perspective of the aircraft in a dynamic 3D environment. Path planning and mission planning need to consider the environmental grid information of the static situation map to prevent collisions between the aircraft and obstacles. Environmental data is imported from DEM data and recorded in the database according to multi-scale grid positions. In the static situation map, obstacles are marked with color using grids, warning that aircraft should not enter those grids. The dynamic situation map mainly records the dynamic information of aircraft in the low-altitude airspace grid. If an aircraft exists in a grid, the grid is marked as occupied, and the occupied grid is marked with color in the dynamic situation map. The union of static and dynamic situation maps is used to represent the situation in low-altitude airspace. Therefore, the mathematical description of the low-altitude airspace grid situation map model is obtained: In the low-altitude airspace AL, let GS be the static situation map grid and GD be the dynamic situation map grid. The two together represent the low-altitude airspace, that is, AL = GS∪GD.

[0089] The representations of static obstacles and airspace regions are as follows:

[0090] The low-altitude airspace is defined as 0 to 1000 meters. Within this range, besides the airspace usable by aircraft, there is a significant amount of space occupied by impassable obstacles. These are categorized as natural obstacles and man-made obstacles. Natural obstacles include mountains, hills, and trees, while man-made obstacles include buildings, bridges, and elevated roads. Both types occupy a certain amount of three-dimensional geographical space and do not change position over a short period; therefore, they are called static obstacles. The most basic requirement for aircraft during flight is to avoid collisions with static obstacles. The representation of static obstacles involves the following steps:

[0091] Step 1: First, the concrete obstacle object is meshed according to its geometric shape. The meshing scale is the smallest level. If a certain object area is less than the smallest level of 3D mesh block, but occupies part of the mesh block, it is marked as occupied. In the static obstacle storage mesh cell in the database, the field of whether it is fully occupied is marked as FALSE.

[0092] Step 2: Perform high-level mesh aggregation. If the current level is not the highest level, then take the current higher level as the level to be aggregated.

[0093] Step 3: Repeat step 2 until the current level equals the highest level. This indicates that the obstacle subdivision results have been aggregated into the highest level grid. In this case, the obstacles are represented by a warning-colored 3D grid in the static situation diagram.

[0094] The representation of static spatial regions is divided into two cases based on the shape of the region: irregular static spatial regions and regular static spatial regions.

[0095] The dynamic obstacle construction representation is as follows:

[0096] In dynamic low-altitude airspace situation maps, aircraft are one of the most important components. When representing the location of an aircraft, the focus is on its point of origin, and its geometric and velocity properties can be ignored. However, when calculating whether two aircraft will collide, physical properties such as aircraft length and altitude need to be considered. To address the representation of aircraft in dynamic low-altitude airspace, this embodiment represents dynamic obstacles as point masses and buffer zones.

[0097] Mass Point: The fuselage of an aircraft may occupy multiple grids in the smallest scale grid. Recording the location of the aircraft is the most basic operation for route planning and information exchange. If the location of an aircraft is represented by a set of multiple grid codes, it will cause the complexity of data storage and information exchange. Therefore, in this embodiment, the aircraft Ac is abstracted as a mass point Point_Ac. The location of the mass point is the physical center of gravity of the aircraft. The aircraft Ac is represented as the associated set of geometrically occupied grids and physical attribute sets. The location of the aircraft is the location of the mass point Point_Ac. The location of the aircraft is represented as the code Code_gd of the grid in which the mass point is located.

[0098] Buffer Zone: Describing an aircraft as a point mass facilitates position recording and data storage. However, in some cases, ignoring the fuselage can pose safety hazards, hence the concept of a buffer zone. A buffer zone refers to the dynamic spatial range surrounding an aircraft during its movement. Other aircraft cannot enter this area, otherwise a collision hazard will occur. The mathematical description of a buffer zone is:

[0099] Buffer x =LengthX Ac +F(V_Ac);

[0100] Buffer y =LengthY Ac +F(V_Ac);

[0101] Buffer z =LengthZ Ac +F(V_Ac);

[0102] Among them, Buffer x Buffer y Buffer z These are the component lengths of the buffer in three directions, LengthX. Ac LengthY represents the fuselage length. Ac LengthZ is the width of the fuselage. Ac Let F(V_Ac) be the aircraft altitude, and F(V_Ac) be a function of the aircraft velocity V_Ac. The settings for the buffer function need to comprehensively consider flight safety and maximizing airspace occupancy, and can be dynamically adjusted based on mission requirements and specific airspace information. For example... Figure 4As shown. The buffer zone can be configured with different security levels. For example, the area within the dashed line represents the fuselage and has the highest security level. In path planning, other aircraft are strictly prohibited from entering this area to ensure safety. The area within the dotted line represents the buffer zone calculated based on speed and has the next highest security level. In path planning, this area can be defined as either inaccessible or partially accessible based on the specific state of the actual scenario.

[0103] After the flight airspace is divided into grids, each grid (gd) is assigned a grid code. Each grid represents a specific airspace, thus representing the aircraft U0, U1, U2, etc., within that grid at a given time. The state of the aircraft within the grid can be abstracted through simple determination or calculation. Dynamic grid attributes include at least: grid occupancy, current grid capacity, and grid occupancy uniformity.

[0104] The grid coding of low-altitude airspace is based on GeoSOT-3D partitioning technology, and consists of a two-dimensional grid coding part of the Beidou navigation code and a corresponding height-dimensional grid coding part.

[0105] Static obstacle data is the most basic representation of static entities, that is, the attributes of static objects. For mountains, it can include mountain name, highest point altitude, area, vegetation type, vegetation height, whether it crosses provinces, whether it crosses countries, and other attribute information. For buildings, it can include building name, type, number of floors, height, area, building material, whether it has WIFI signal, and other attribute information. The location information of static objects is not involved here. Static object data expresses the attributes that the static object itself has. Some of these attributes can be used in application methods to determine whether certain operations can be performed on the object, or to set thresholds for the object to participate in calculations, etc.

[0106] The static obstacle mesh representation is shown in Table 2.

[0107] Table 2. Data Structure for Static Obstacle Mesh Representation

[0108]

[0109] The static spatial domain representation is shown in Table 3.

[0110] Table 3. Data Structure for Static Irregular Spatial Grid Subdivision

[0111]

[0112] The data structure for static rule-based spatial grid partitioning is shown in Table 4.

[0113] Table 4. Data Structure for Static Regular Spatial Grid Subdivision Representation

[0114]

[0115] The dynamic object mesh position representation is shown in Table 5.

[0116] Table 5. Dynamic Object Grid Location Data Structure

[0117]

[0118] The path planning grid representation is shown in Table 6.

[0119] Table 6. Path Planning Grid Representation Data Structure

[0120]

[0121] The mesh representation of the flight airspace is shown in Table 7.

[0122] Table 7. Data Structure for Flight Airspace Grid Subdivision Representation

[0123]

[0124] The dynamic spatial domain representation is shown in Table 8.

[0125] Table 8. Data Structure for Dynamic Irregular Spatial Mesh Representation

[0126]

[0127] The data structure for dynamic rule-based spatial grid partitioning is shown in Table 9.

[0128] Table 9. Data Structure for Dynamic Regular Spatial Mesh Representation

[0129]

[0130] like Figure 5 As shown, this embodiment also discloses a network path planning device, including:

[0131] The grid division module is used to divide the initial map into multiple grids according to a set scale, obtain a first-level grid map based on the multiple grids, set the grid state according to the current grid capacity, and obtain the connectivity between grids in the first-level grid map.

[0132] The aggregation module is used to aggregate a set number of grids in the first-level grid map based on the connectivity between grids to obtain the second-level grid map, and to obtain the connectivity of the aggregated grids in the second-level grid map;

[0133] The path calculation module is used to obtain an initial path using a path planning algorithm in the second-level grid map, and to map the nodes of the initial path to the corresponding path in the first-level grid map according to the correspondence relationship, wherein the correspondence relationship is the correspondence relationship between the second-level grid map and the first-level grid map.

[0134] The path planning module is used to draw the corresponding path onto the initial map based on the grid status and connectivity between grids in the first-level grid map, thus obtaining the planned path.

[0135] The electronic device according to embodiments of this disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0136] The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory, causing the electronic device to perform all or part of the steps of the network path planning methods of the foregoing embodiments of this disclosure.

[0137] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0138] like Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the electronic device in the embodiment of the present disclosure. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0139] like Figure 6As shown, an electronic device may include a processing unit (such as a central processing unit, graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from a storage device into random access memory (RAM). The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0140] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow electronic devices to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0141] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, all or part of the steps of the network path planning method of embodiments of this disclosure are performed.

[0142] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0143] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the network path planning methods described in the foregoing embodiments of the present disclosure are performed.

[0144] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0145] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0146] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0147] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0148] Additionally, as used herein, the “or” used in a list of items beginning with “at least one” indicates a separate list, such that a list of, for example, “at least one of A, B, or C” means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word “exemplary” does not imply that the described example is preferred or better than other examples.

[0149] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0150] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0151] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0152] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A network path planning method characterized by, comprise: grid the initial map is divided according to the set scale to obtain a plurality of grids, based on the plurality of grids to obtain the first level grid map, the grid state is set according to the current capacity of the grid, comprising: determine whether the current capacity of the grid exceeds the threshold value; If it exceeds the threshold value, it is set as an obstacle grid, and if it does not exceed the threshold value, it is set as a non-obstacle grid;The obstacle grid includes static obstacle grid and dynamic obstacle grid; The dynamic obstacle grid setting comprises: Set the dynamic object as a particle, and set a buffer zone around the particle based on the volume and running speed of the dynamic object; The occupied grid state is set based on the motion direction and motion speed of the particle and the buffer zone; Get the connectivity between the grids in the first level grid map; Based on the connectivity between the grids, a certain number of grids in the first level grid map are aggregated to obtain a second level grid map, and the connectivity of the aggregated grids in the second level grid map is obtained; An initial path is obtained in the second level grid map using a path planning algorithm, and the nodes of the initial path are corresponded to the first level grid map to obtain a corresponding path according to the corresponding relationship, and the corresponding relationship is the corresponding relationship between the second level grid map and the first level grid map; Based on the grid state and the connectivity between the grids of the first level grid map, the corresponding path is drawn into the initial map to obtain a planning path.

2. The network path planning method of claim 1, wherein, The connectivity between the grids in the first level grid map comprises: Get the edge connectivity and corner connectivity of the grid, and get the connectivity between the grids based on the edge connectivity and corner connectivity of the grid.

3. The network path planning method of claim 1, wherein, The connectivity of the aggregated grid in the second level grid map comprises: Get the edge connectivity and corner connectivity of the aggregated grid, and get the connectivity between the aggregated grids based on the edge connectivity and corner connectivity of the aggregated grid.

4. The network path planning method of claim 1, wherein The static obstacle grid setting comprises: Grid the initial map is divided according to the set scale to obtain a plurality of grids, based on the plurality of grids to obtain the first level grid map, the grid state is set according to the current capacity of the grid, obtaining the connectivity between the grids in the first level grid map; Based on the connectivity between the grids, a certain number of grids in the first level grid map are aggregated to obtain a second level grid map, and the connectivity of the aggregated grids in the second level grid map is obtained; 5. The network path planning method of claim 1, wherein, An initial path is obtained in the second level grid map using a path planning algorithm, and the nodes of the initial path are corresponded to the first level grid map to obtain a corresponding path according to the corresponding relationship, and the corresponding relationship is the corresponding relationship between the second level grid map and the first level grid map; 6. A network path planning apparatus characterized by comprising: Based on the grid state and the connectivity between the grids of the first level grid map, the corresponding path is drawn into the initial map to obtain a planning path. The connectivity between the grids in the first level grid map comprises: Get the edge connectivity and corner connectivity of the grid, and get the connectivity between the grids based on the edge connectivity and corner connectivity of the grid. The connectivity of the aggregated grid in the second level grid map comprises: Get the edge connectivity and corner connectivity of the aggregated grid, and get the connectivity between the aggregated grids based on the edge connectivity and corner connectivity of the aggregated grid.

4. The network path planning method of claim 1, wherein The static obstacle grid setting comprises: Grid the initial map is divided according to the set scale to obtain a plurality of grids, based on the plurality of grids to obtain the first level grid map, the grid state is set according to the current capacity of the grid, obtaining the connectivity between the grids in the first level grid map; Based on the connectivity between the grids, a certain number of grids in the first level grid map are aggregated to obtain a second level grid map, and the connectivity of the aggregated grids in the second level grid map is obtained; An initial path is obtained in the second level grid map using a path planning algorithm, and the nodes of the initial path are corresponded to the first level grid map to obtain a corresponding path according to the corresponding relationship, and the corresponding relationship is the corresponding relationship between the second level grid map and the first level grid map; Based on the grid state and the connectivity between the grids of the first level grid map, the corresponding path is drawn into the initial map to obtain a planning path. The connectivity between the grids in the first level grid map comprises: Get the edge connectivity and corner connectivity of the grid, and get the connectivity between the grids based on the edge connectivity and corner connectivity of the grid. The connectivity of the aggregated grid in the second level grid map comprises: Get the edge connectivity and corner connectivity of the aggregated grid, and get the connectivity between the aggregated grids based on the edge connectivity and corner connectivity of the aggregated grid.

4. The network path planning method of claim 1, wherein The static obstacle grid setting comprises: Grid the initial map is divided according to the set scale to obtain a plurality of grids, based on the plurality of grids to obtain the first level grid map, the grid state is set according to the current capacity of the grid, obtaining the connectivity between the grids in the first level grid map; Based on the connectivity between the grids, a certain number of grids in the first level grid map are aggregated to obtain a second level grid map, and the connectivity of the aggregated grids in the second level grid map is obtained; An initial path is obtained in the second level grid map using a path planning algorithm, and the nodes of the initial path are corresponded to the first level grid map to obtain a corresponding path according to the corresponding relationship, and the corresponding relationship is the corresponding relationship between the second level grid map and the first level grid map; Based on the grid state and the connectivity between the grids of the first level grid map, the corresponding path is drawn into the initial map to obtain a planning path. The connectivity between the grids in the first level grid map comprises: Get the edge connectivity and corner connectivity of the grid, and get the connectivity between the grids based on the edge connectivity and corner connectivity of the grid. The connectivity of the aggregated grid in the second level grid map comprises: Get the edge connectivity and corner connectivity of the aggregated grid, and get the connectivity between the aggregated grids based on the edge connectivity and corner connectivity of the aggregated grid. A path calculation module is configured to obtain an initial path by using a path planning algorithm in the second-level grid map, and to obtain a corresponding path by mapping nodes of the initial path to the first-level grid map according to a corresponding relationship between the second-level grid map and the first-level grid map; A path planning module is configured to draw the corresponding path into the initial map based on the grid state of the first-level grid map and the connectivity relationship between grids, and to obtain a planning path.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the network path planning method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to perform the network path planning method of any one of claims 1-5.

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