Path planning method, device, equipment and medium

Abstract

The invention provides a path planning method, apparatus and device, and a medium. The method comprises the steps of predicting a target congestion degree of each to-be-selected area in a preset area; wherein the to-be-selected area is an area from a starting position of a target unmanned vehicle to a final position acquired from an operation system, and the target congestion degree represents the congestion degree when the target unmanned vehicle passes through the to-be-selected area; and in response to a congestion degree threshold setting operation, determining an initial path block from the to-be-selected blocks according to the initial position and the congestion degree threshold so as to form a target path for the target unmanned vehicle to travel from the initial position to the final position. According to the method, the acceptable congestion degree can be adjusted by field personnel, and man-machine integrated global optimal path planning is realized.

Description

Technical Field

[0001] This application relates to the field of path planning technology, and more specifically, to a path planning method, apparatus, device, and medium. Background Technology

[0002] Path planning is the process of finding the optimal or feasible path from a starting point to a destination for intelligent devices, such as robots and vehicles, within a given environment. Current path planning technologies do not consider the shortest path planning results based on dynamic changes in traffic flow, or they do consider path planning results that avoid congestion based on current traffic conditions. This approach only considers extreme cases of congestion and lack of congestion, while in some special scenarios, a certain level of congestion is acceptable. For example, in port operations, existing path planning methods are not suitable for such scenarios. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a path planning method, apparatus, device and medium to overcome the problems in the prior art.

[0004] In a first aspect, embodiments of this application provide a path planning method, the path planning method comprising: Predict the target congestion level of each candidate block in a preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block; In response to the congestion threshold setting operation, an initial path block is determined from the candidate blocks based on the starting position and the congestion threshold to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0005] In some technical solutions of this application, the above method obtains the candidate block in the following manner: In response to the grid area attribute setting operation, the acquired preset overall map is divided into a two-dimensional grid map according to the set grid area attribute. By adding a time dimension to the two-dimensional raster map, a three-dimensional raster map is obtained. Each three-dimensional grid in the three-dimensional grid map is used as the candidate block.

[0006] In some technical solutions of this application, the above-mentioned determination of an initial path block from the candidate blocks based on the starting position and the congestion threshold to form a target path for the target unmanned vehicle from the starting position to the ending position includes: Starting from the aforementioned starting position, adjacent candidate blocks whose target congestion level is less than or equal to the congestion level threshold are sequentially selected as initial candidate blocks; If multiple initial selection blocks exist simultaneously, the initial selection block that takes the shortest time to reach the destination is selected as the initial path block.

[0007] In some technical solutions of this application, the aforementioned prediction of the target congestion level of each candidate block in the preset area includes: Construct an influence reduction function based on preset influence parameters; or, in response to the setting operation of influence parameters, construct an influence reduction function based on the set influence parameters. Based on the influence reduction function, calculate the first influence of the first obstacle on the target block and the second influence of the second obstacles contained in other blocks on the target block; wherein, the first obstacle is the obstacle contained in the target block when the target unmanned vehicle passes through the target block, and the other blocks are blocks in the preset area other than the target block; The target congestion level of the target block is calculated based on the first influence level and the second influence level.

[0008] In some technical solutions of this application, the above method also includes: The target path and the target congestion level of each of the candidate blocks are sent to the terminal device; The terminal device displays the target path and the target congestion level corresponding to each of the candidate blocks.

[0009] In some technical solutions of this application, the above method also includes: In response to the adjustment operation for the target path, the initial path block contained in the target path is modified to obtain a modified first path block, and a new target path is constructed based on the first path block, so as to form a target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0010] In some technical solutions of this application, the above method also includes: If the initial path block cannot form a complete path from the starting position to the ending position, wait for the target time and then filter the second path block again. The target path is constructed based on the second path block, so as to form the target unmanned vehicle traveling from the starting position to the ending position. Or, in response to a third path block specification operation, determine the third path block; The target path is constructed based on the initial path block and the third path block, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0011] Secondly, embodiments of this application provide a path planning device, the device comprising: The prediction module is used to predict the target congestion level of each candidate block in a preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block; The planning module is used to respond to the congestion threshold setting operation, and determine the initial path block from the candidate blocks according to the starting position and the congestion threshold, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0012] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the path planning method described above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the path planning method described above.

[0014] The technical solutions provided by the embodiments of this application may include the following beneficial effects: This application provides a path planning method, apparatus, device, and medium. The method includes: predicting the target congestion level of each candidate block in a preset area; wherein the candidate block is an area between the starting position of a target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level characterizes the congestion level when the target unmanned vehicle passes through the candidate block; in response to a congestion level threshold setting operation, determining an initial path block from the candidate blocks according to the starting position and the congestion level threshold to form a target path for the target unmanned vehicle to travel from the starting position to the ending position. This method allows on-site personnel to adjust the acceptable congestion level, achieving globally optimal path planning through human-machine collaboration.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart illustrating a path planning method provided in an embodiment of this application is shown. Figure 2 A schematic diagram of a candidate grid provided in an embodiment of this application is shown; Figure 3 This illustration shows a schematic diagram of a three-dimensional raster map provided in an embodiment of this application; Figure 4a This illustration shows a schematic diagram of the movement of a first type of obstacle provided in an embodiment of this application; Figure 4b This illustration shows a schematic diagram of the movement of a second type of obstacle provided in an embodiment of this application; Figure 4c This invention provides a schematic diagram illustrating the movement of a third type of obstacle according to an embodiment of this application. Figure 5 This paper illustrates a schematic diagram of an influence reduction function provided in an embodiment of this application. Figure 6 This illustration shows a diagram illustrating a level of congestion provided in an embodiment of this application; Figure 7 A schematic diagram of a path planning device provided in an embodiment of this application is shown; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0019] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0021] During port operations, unmanned vehicles carrying containers travel within the port area. Conventional path planning algorithms will provide the shortest path without considering dynamic changes in traffic flow; or they will provide a path planning result that avoids congestion while considering the current traffic situation. When considering optimal global operational efficiency, these two extreme path planning algorithms (either ignoring current congestion or neglecting it) present challenges: 1. Only considers the current situation; 2. No intermediate options are provided, and on-site dispatchers cannot intervene in route planning based on actual observed and predicted traffic conditions and operational efficiency.

[0022] Based on this, embodiments of this application provide a path planning method, apparatus, device, and medium, enabling on-site personnel to adjust the acceptable congestion level (considering current and future congestion) and achieve globally optimal path planning through human-machine collaboration. The following is a description through embodiments.

[0023] Figure 1 The diagram illustrates a path planning method provided in an embodiment of this application, wherein the method includes steps S101-S102; specifically: S101. Predict the target congestion level of each candidate block in the preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block. S102. In response to the congestion threshold setting operation, an initial path block is determined from the candidate blocks according to the starting position and the congestion threshold to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0024] The following describes some embodiments of this application in detail. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0025] This application discloses a path planning method applied to a path planning system, which includes an operating system, a target unmanned vehicle (UAV), a server, and terminal equipment. The operating system is for transporting goods, and transport personnel can input the required goods, the starting and ending points of the transport into the operating system. The target UAV is a transport device with road recognition capabilities; after receiving a recognition command from the server, it identifies the direction to proceed based on the command. The server is a specific IT device that provides computing power and runs software applications in a network environment. The terminal equipment is a device with display, data input, and data transmission functions. Specifically, on-site personnel send adjustment data to the server through the terminal equipment. The server performs path planning based on the adjustment data and sends the planned target path to the target UAV, which then transports the goods according to the target path.

[0026] The path planning method in this application operates within a preset area. The server needs to be able to obtain the location information of objects already existing in the preset area (including target unmanned vehicles and obstacles) or the location information of objects about to enter the preset area (including target unmanned vehicles and obstacles). For example, in a port area, the specific location information of containers, target unmanned vehicles, etc., needs to be determined.

[0027] After determining the preset area, the server needs to identify the candidate blocks within that area. These candidate blocks are portions of the preset area and can be obtained through equal or non-equal division. Preferably, they are obtained through equal division. Specifically, on-site personnel input the raster area attributes via a terminal device and then send these attributes to the server. The server divides the overall map of the preset area into a two-dimensional raster map based on the raster area attributes. The two-dimensional raster map includes pairs of two-dimensional graticles with corresponding areas, such as... Figure 2 As shown. After obtaining the two-dimensional grid map, in order to facilitate the prediction of the movement position of the target unmanned vehicle or obstacles, this embodiment of the application adds a time dimension to the two-dimensional grid map to obtain a three-dimensional grid map, which is stored in memory as follows. Figure 3 As shown. After obtaining the 3D raster map, each 3D raster included in the 3D raster map is used as the candidate block.

[0028] In practical implementation, the grid area attribute can be represented by specific length and width. Smaller grid sizes offer higher precision but result in larger data volumes; for example, a 0.1m x 0.1m square can be used as the grid unit. Larger grid sizes produce coarser data but result in smaller data volumes; for example, a 1m x 1m square can be used as the grid unit. Based on a two-dimensional grid map, a time dimension is added to represent the potential movement of obstacles; the Z-axis represents time. For example... Figures 4a-4cThe red block represents the obstacle itself, and the green right arrow indicates that the obstacle is moving to the right, assuming a movement speed of 2 blocks per second. Figure 4a This indicates that Z=0 represents the current occupancy status of the block to be selected by obstacles. Figure 4b This indicates that Z=1 represents the occupancy status of the candidate block by obstacles after 1 second. Figure 4c This indicates that Z=2 represents the occupancy status of the block to be selected by obstacles after 2 seconds.

[0029] After obtaining the candidate blocks, the path planning method in this embodiment is transformed into the process of selecting path blocks from the candidate blocks. When selecting path blocks, since the target autonomous vehicle needs a certain amount of time to travel, the target congestion level for each candidate block needs to be predicted. Here, the target congestion level is the congestion level when the target autonomous vehicle reaches the candidate block. In other words, this embodiment needs to predict the candidate congestion level for each candidate block at multiple time points, and the candidate congestion level when the target autonomous vehicle reaches the candidate block is taken as the target congestion level.

[0030] For example, the preset area contains 10x10 candidate blocks. The target unmanned vehicle starts at (1,1) and needs to travel to the end point (10,10). Its path planning process is to determine the next path block to travel from (1,1) and (1,2), (2,1) and (2,2), and then determine the next path block to travel from the candidate blocks adjacent to the path block, and so on until it reaches (10,10).

[0031] In predicting the congestion levels of candidate blocks at multiple times, this application requires first determining the block where each obstacle is located at each time. Here, "obstacle" is relative; for a target autonomous vehicle, it refers to objects that affect its movement. Examples include trees, rocks, or other obstacles. In other words, this application needs to predict the location of each obstacle at each time based on its movement attributes (if it is a fixed object, its movement speed can be considered to be 0).

[0032] After determining the location of each obstacle at any given time, when determining the candidate congestion level of the target block, two aspects need to be considered: the first influence of the target block itself containing the first obstacle at that time and the second influence of other blocks containing the second obstacle at that time. Based on the first influence and the second influence, the target congestion level of the target block is calculated.

[0033] In determining the impact level, this embodiment of the application is still based on the settings of on-site personnel. Specifically, on-site personnel can input impact parameters through a terminal device, which then sends the impact parameters to the server. The server constructs an impact level reduction function based on the received impact parameters. The impact level reduction function guarantees the relationship between the distance between the obstacle and the target block and the impact level.

[0034] For example, the influence parameters here include α and β, where α represents the rate of decline and β represents the range of influence. The constructed influence decline function is then:

[0035] Preferably, α=1.3, β=3, and its function graph is as follows: Figure 5 As shown. At a distance of 0m, the influence is 50; at a distance of 1m, the influence is 14; at a distance of 2m, the influence is 4; at a distance of 3m, the influence is 1; and at a distance of 4m, the influence is 0. This not only reduces grid memory usage, but also, assuming a maximum of 5 vehicles can overlap at the intersection, the maximum influence after overlap is 5 * 50 = 250, which is within the byte representation range. Visualizing congestion prediction. The grayscale value range is [0~255], therefore the maximum influence is within the grayscale range, requiring no conversion; the influence can be directly displayed as the grayscale of a color (or as the saturation of a color).

[0036] After determining the target congestion level for each candidate block, this embodiment of the application can filter based on a congestion level threshold. To facilitate adjustments by on-site personnel, the congestion level threshold in this embodiment is set by the on-site personnel themselves. For example, on-site personnel input the congestion level threshold through a terminal device, which then sends the threshold to the server. The server compares the target congestion level with the congestion level threshold and selects candidate blocks whose target congestion level is less than or equal to the threshold. In actual operation, there may be multiple candidate blocks with congestion levels less than or equal to the threshold. To ensure driving efficiency, this embodiment of the application refers to candidate blocks with congestion levels less than or equal to the threshold as initial selection blocks, and then, based on the time taken, selects the initial selection block with the shortest time as the initial path block.

[0037] For example, the congestion threshold here is a decimal between 0 and 1. When the value is 0, the standard is extremely averse to congestion; when the value is 1, the standard is completely accepting of congestion; when it is a decimal between 0 and 1, it represents the intermediate value between complete acceptance and extreme aversion. For example, 0.2: a path that is not very willing to accept congestion, 0.3: another path that does not want to accept congestion, but the degree of acceptance is >0.2.

[0038] In an optional implementation, to facilitate on-site personnel's intuitive understanding of the congestion level, this embodiment of the application, after obtaining the target path, sends the target path and the target congestion level of each of the candidate blocks to a terminal device; the terminal device then displays the target path and the target congestion level corresponding to each of the candidate blocks. Figure 6 As shown, this represents the congestion impact of an obstacle on surrounding grid cells in a 3D grid map over the next 3 seconds. If the future movement trajectories of multiple obstacles overlap, the grid value is the sum of the congestion impact values ​​of all obstacles. During the maintenance of the 3D obstacle grid map, the maximum value N in the grid is recorded, and N is maintained as the maximum value in the grid as the map is maintained.

[0039] Furthermore, after on-site personnel observe the congestion levels corresponding to each target path and candidate block, if they need to modify the target path for other reasons, they can input the initial path block contained in the target path to be modified through a terminal device to obtain the modified first path block. Then, a new target path is constructed based on the first path block, thus forming the target path for the autonomous vehicle to travel from the starting position to the destination position.

[0040] In practice, on-site personnel can replace the initial path block with the first path block by inputting the coordinates of the first path block and the initial path block, and then use the touch replacement control.

[0041] In an optional implementation, when selecting initial path blocks, some candidate blocks may have a target congestion level greater than a congestion threshold, resulting in the selected initial path blocks failing to form a complete path from the starting position to the destination. In such cases, this application provides different processing methods: The first method involves waiting for obstacles to move, then selecting a suitable path (i.e., waiting for a target duration), and then re-selecting the candidate blocks to obtain a second path block. This second path block forms the target path, enabling the target unmanned vehicle to travel from the starting position to the destination. Here, the target duration is a time set by on-site personnel. The second method involves on-site personnel instructing a third path block, and based on the initial path block and the third path block, forming the target path, enabling the target unmanned vehicle to travel from the starting position to the destination. This process ensures that the target unmanned vehicle can successfully reach the destination and complete the transportation operation.

[0042] Figure 7This invention provides a schematic diagram of the structure of a path planning device according to an embodiment of the present application. The device includes: The prediction module is used to predict the target congestion level of each candidate block in a preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block; The planning module is used to respond to the congestion threshold setting operation, and determine the initial path block from the candidate blocks according to the starting position and the congestion threshold, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0043] The candidate blocks are obtained in the following way: In response to the grid area attribute setting operation, the acquired preset overall map is divided into a two-dimensional grid map according to the set grid area attribute. By adding a time dimension to the two-dimensional raster map, a three-dimensional raster map is obtained. Each three-dimensional grid in the three-dimensional grid map is used as the candidate block.

[0044] Based on the starting position and the congestion threshold, an initial path block is determined from the candidate blocks to form the target path for the target autonomous vehicle from the starting position to the ending position, including: Starting from the aforementioned starting position, adjacent candidate blocks whose target congestion level is less than or equal to the congestion level threshold are sequentially selected as initial candidate blocks; If multiple initial selection blocks exist simultaneously, the initial selection block that takes the shortest time to reach the destination is selected as the initial path block.

[0045] The prediction of the target congestion level of each candidate block in the preset area includes: Construct an influence reduction function based on preset influence parameters; or, in response to the setting operation of influence parameters, construct an influence reduction function based on the set influence parameters. Based on the influence reduction function, calculate the first influence of the first obstacle on the target block and the second influence of the second obstacles contained in other blocks on the target block; wherein, the first obstacle is the obstacle contained in the target block when the target unmanned vehicle passes through the target block, and the other blocks are blocks in the preset area other than the target block; The target congestion level of the target block is calculated based on the first influence level and the second influence level.

[0046] The sending module is used to send the target path and the target congestion level of each of the candidate blocks to the terminal device; The terminal device displays the target path and the target congestion level corresponding to each of the candidate blocks.

[0047] The modification module is configured to, in response to an adjustment operation on the target path, modify the initial path block contained in the target path to obtain a modified first path block, and construct a new target path based on the first path block, so as to form a target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0048] The planning module is used to filter the second path block again after waiting for the target time if the initial path block cannot form a complete path from the starting position to the ending position. The target path is constructed based on the second path block, so as to form the target unmanned vehicle traveling from the starting position to the ending position. Or, in response to a third path block specification operation, determine the third path block; The target path is constructed based on the initial path block and the third path block, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

[0049] like Figure 8 As shown, this application provides an electronic device for executing the path planning method of this application. The device includes a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the path planning method described above.

[0050] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor runs a computer program stored in the memory, it can execute the aforementioned path planning method.

[0051] Corresponding to the path planning method in this application, this application embodiment also provides a computer-readable storage medium storing a computer program, which is executed by a processor to perform the steps of the path planning method described above.

[0052] Specifically, the storage medium can be a general-purpose storage medium, such as a removable disk or hard disk, and when the computer program on the storage medium is run, it can execute the path planning method described above.

[0053] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0054] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0055] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0056] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the path planning method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0058] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A path planning method, characterized in that, The path planning method, applied to a server, includes: Predict the target congestion level of each candidate block in a preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block; In response to the congestion threshold setting operation, an initial path block is determined from the candidate blocks based on the starting position and the congestion threshold to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

2. The path planning method according to claim 1, characterized in that, The method obtains the candidate block in the following manner: In response to the grid area attribute setting operation, the acquired preset overall map is divided into a two-dimensional grid map according to the set grid area attribute. By adding a time dimension to the two-dimensional raster map, a three-dimensional raster map is obtained. Each three-dimensional grid in the three-dimensional grid map is used as the candidate block.

3. The path planning method according to claim 2, characterized in that, The step of determining an initial path block from the candidate blocks based on the starting position and the congestion threshold to form a target path for the target unmanned vehicle from the starting position to the ending position includes: Starting from the aforementioned starting position, adjacent candidate blocks whose target congestion level is less than or equal to the congestion level threshold are sequentially selected as initial candidate blocks; If multiple initial selection blocks exist simultaneously, the initial selection block that takes the shortest time to reach the destination is selected as the initial path block.

4. The path planning method according to claim 1, characterized in that, The prediction of the target congestion level of each candidate block in the preset area includes: Construct an influence reduction function based on preset influence parameters; or, in response to the setting operation of influence parameters, construct an influence reduction function based on the set influence parameters. Based on the influence reduction function, calculate the first influence of the first obstacle on the target block and the second influence of the second obstacles contained in other blocks on the target block; wherein, the first obstacle is the obstacle contained in the target block when the target unmanned vehicle passes through the target block, and the other blocks are blocks in the preset area other than the target block; The target congestion level of the target block is calculated based on the first influence level and the second influence level.

5. The path planning method according to claim 1, characterized in that, The method further includes: The target path and the target congestion level of each candidate block are sent to the terminal device; The terminal device displays the target path and the target congestion level corresponding to each of the candidate blocks.

6. The path planning method according to claim 5, characterized in that, The method further includes: In response to the adjustment operation for the target path, the initial path block contained in the target path is modified to obtain a modified first path block, and a new target path is constructed based on the first path block, so as to form a target path for the target unmanned vehicle to travel from the starting position to the ending position.

7. The path planning method according to claim 1, characterized in that, The method further includes: If the initial path block cannot form a complete path from the starting position to the ending position, wait for the target time and then filter the second path block again. The target path is constructed based on the second path block, so as to form the target unmanned vehicle traveling from the starting position to the ending position. Or, in response to a third path block specification operation, determine the third path block; The target path is constructed based on the initial path block and the third path block, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

8. A path planning device, characterized in that, The device includes: The prediction module is used to predict the target congestion level of each candidate block in a preset area; wherein, the candidate block is the area between the starting position of the target unmanned vehicle and the ending position obtained from the operation system, and the target congestion level represents the congestion level when the target unmanned vehicle passes through the candidate block; The planning module is used to respond to the congestion threshold setting operation, and determine the initial path block from the candidate blocks according to the starting position and the congestion threshold, so as to form the target path for the target unmanned vehicle to travel from the starting position to the ending position.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the path planning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the path planning method as described in any one of claims 1 to 7.

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