Unmanned vehicle scheduling method, device and equipment and storage medium

Abstract

The invention discloses an unmanned vehicle scheduling method, device and equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a map of a specified area; performing gridding processing on the map of the specified area by using preset polygon information to obtain a plurality of polygon meshes; in response to the condition that the polygonal grid comprises a logistics warehouse and a to-be-dispatched unmanned vehicle, obtaining basic information of the logistics warehouse and basic information of the unmanned vehicle; based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid; and scheduling the unmanned vehicles in each polygonal grid based on the matching relationship between the unmanned vehicles in each polygonal grid and the logistics warehouse. According to the invention, the unmanned vehicle scheduling performance can be optimized.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to technical fields such as unmanned vehicle technology, and in particular to a method, device, equipment and storage medium for dispatching an unmanned vehicle. Background Art

[0002] Usually, for an unmanned logistics vehicle dispatching platform, it is necessary to dispatch idle unmanned vehicles at each station to logistics warehouses with transportation needs in real time and efficiently, while meeting the dispatching needs and shortening the dispatching distance of each vehicle as much as possible.

[0003] At present, the relevant technical solutions include administrative district slicing scheduling algorithms, which specifically divide the country into multiple isolated scheduling areas according to administrative districts, and then match the logistics vehicles to be scheduled and the logistics warehouses that need to be scheduled in pairs in each scheduling area. Summary of the invention

[0004] The present application provides a method, device, equipment and storage medium for dispatching unmanned vehicles, which can solve the problem of poor reliability of dispatching unmanned logistics vehicles. The technical solution is as follows:

[0005] In a first aspect, a method for dispatching an unmanned vehicle is provided, the method comprising:

[0006] Get a map of the specified area;

[0007] Using the preset polygon information, gridding the map of the designated area to obtain a plurality of polygonal grids;

[0008] In response to the polygonal mesh including a logistics warehouse and an unmanned vehicle to be dispatched, basic information of the logistics warehouse and basic information of the unmanned vehicle are acquired;

[0009] Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid;

[0010] Based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, the unmanned vehicle in each polygonal grid is dispatched.

[0011] In a possible implementation, the matching process is performed on the unmanned vehicle and the logistics warehouse in each polygonal mesh based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to obtain the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh, including:

[0012] Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to be dispatched, a preset matching algorithm is used to match the vehicle information and the logistics warehouse information in each polygonal grid;

[0013] Based on the result of the matching process, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid is obtained.

[0014] In a possible implementation, the basic information of the unmanned vehicle includes the location information of the unmanned vehicle, and the basic information of the logistics warehouse includes the location information of the logistics warehouse and the demand information of the logistics warehouse. Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, a preset matching algorithm is used to match the vehicle information and the logistics warehouse information in each polygonal grid, including:

[0015] Based on the unmanned vehicle location information, the logistics warehouse location information and the logistics warehouse demand information, a preset matching algorithm is used to match the unmanned vehicle and the logistics warehouse in each polygonal grid.

[0016] In a possible implementation, based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, including:

[0017] Using multiple threads, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal mesh in parallel based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle; the number of the threads is determined according to the number of the polygonal meshes;

[0018] Based on the matching processing results of each thread, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh is obtained in parallel.

[0019] In a possible implementation, the scheduling process of the unmanned vehicle in each polygonal mesh based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh includes:

[0020] By utilizing the plurality of threads, the unmanned vehicles in each polygonal grid are dispatched in parallel based on the matching relationship between the unmanned vehicles in each polygonal grid and the logistics warehouse.

[0021] In a possible implementation, the preset polygon information includes the shape of the polygon and the side length of the polygon, and the gridding process is performed on the map of the designated area using the preset polygon information to obtain a plurality of polygon grids, including:

[0022] Gridding the map of the designated area using the shape of the polygon and the side length of the polygon;

[0023] Based on the result of the meshing process, a plurality of polygonal meshes are obtained.

[0024] In a second aspect, a device for dispatching an unmanned vehicle is provided, the device comprising:

[0025] A first acquisition unit, used to acquire a map of a specified area;

[0026] A first obtaining unit is used to perform gridding processing on the map of the designated area by using preset polygon information to obtain a plurality of polygonal grids;

[0027] A second acquisition unit, configured to acquire basic information of the logistics warehouse and basic information of the unmanned vehicle in response to the polygonal mesh including the logistics warehouse and the unmanned vehicle to be dispatched;

[0028] A second obtaining unit is used to perform matching processing on the unmanned vehicle and the logistics warehouse in each polygonal grid based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, so as to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid;

[0029] The first scheduling unit is used to schedule the unmanned vehicle in each polygonal grid based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid.

[0030] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0031] In a fourth aspect, an electronic device is provided, including:

[0032] at least one processor; and

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

[0034] The memory stores instructions that can be executed 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 method of any possible implementation manner and the aspects described above.

[0035] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0036] The beneficial effects of the technical solution provided by this application include at least:

[0037] It can be seen from the above technical solution that the embodiment of the present application can obtain a map of a specified area, and use preset polygon information to grid the map of the specified area to obtain multiple polygonal grids. In response to the polygonal grid including a logistics warehouse and an unmanned vehicle to be dispatched, the basic information of the logistics warehouse and the basic information of the unmanned vehicle are obtained. Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, the unmanned vehicle and the logistics warehouse in each polygonal grid are matched to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid. Based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, the unmanned vehicle in each polygonal grid is dispatched. Since the unmanned vehicle scheduling in the polygonal grid area can be performed separately based on each polygonal grid, it is possible to achieve the effectiveness of the unmanned vehicle scheduling while avoiding the waste of computing power of data processing caused by cross-city demand and load imbalance, thereby improving the performance and efficiency of data processing, thereby effectively optimizing the reliability of the unmanned vehicle scheduling.

[0038] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a flowchart of a method for dispatching an unmanned vehicle provided by an embodiment of the present application;

[0041] Figure 2 is a schematic diagram of a polygonal grid in a method for dispatching an unmanned vehicle provided by an embodiment of the present application;

[0042] Figure 3 is a structural block diagram of an unmanned vehicle dispatching device provided in yet another embodiment of the present application;

[0043] Figure 4 It is a block diagram of an electronic device used to implement the method for dispatching an unmanned vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0045] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0046] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0047] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0048] At present, for medium-scale unmanned logistics vehicle dispatching platforms, the administrative district slicing dispatching algorithm is generally used. This algorithm often divides the country into multiple isolated dispatching areas according to administrative districts, and then matches the unmanned logistics vehicles to be dispatched and the warehouses that need to be dispatched in pairs in each dispatching area.

[0049] In actual business operation scenarios, unlike the dispatching logic of online car-hailing or food delivery, the dispatching of unmanned logistics vehicles has the significant characteristic of uneven geographical distribution, and most of the demand for the use of unmanned logistics vehicles is concentrated in a few large logistics outlets. Therefore, the traditional administrative district slicing algorithm has the following defects: the administrative district is large, and the distances between objects to be matched in the same administrative district are different. Some of the distances are large, which will occupy a large amount of computing resources and lead to waste of processing performance; it is difficult to handle cross-city needs. If there is a cross-city dispatching demand, it is necessary to manually divide the custom dispatching area, which has a certain maintenance cost; there is a load imbalance problem. Different administrative districts have different dispatching pressures due to their large differences in business volume and administrative district size. For distributed dispatching, there is a problem of machine load imbalance.

[0050] Therefore, there is an urgent need for a method for dispatching unmanned vehicles that can solve the problems of performance waste, difficulty in handling cross-city demand and load imbalance in traditional dispatching algorithms without affecting the dispatching effect, thereby optimizing the dispatching of unmanned vehicles.

[0051] Please refer to Figure 1 , which shows a flow chart of a method for dispatching an unmanned vehicle provided by an embodiment of the present application. The method for dispatching an unmanned vehicle may specifically include:

[0052] Step 101: Get a map of a specified area.

[0053] Step 102: Gridding the map of the designated area using preset polygon information to obtain a plurality of polygon grids.

[0054] Step 103: In response to the polygonal mesh including a logistics warehouse and an unmanned vehicle to be dispatched, basic information of the logistics warehouse and basic information of the unmanned vehicle are obtained.

[0055] Step 104: Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, the unmanned vehicle and the logistics warehouse in each polygonal grid are matched to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid.

[0056] Step 105: Based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, the unmanned vehicle in each polygonal grid is dispatched.

[0057] It should be noted that the word "specified" in the designated area involved here does not have any special meaning, and is only used to specify the current operation object. Therefore, the designated area is an ordinary area, and this embodiment does not specifically limit this.

[0058] It should be noted that the designated area may be a relatively large geographical area to be gridded, for example, a geographical area at the city level or the province level.

[0059] It should be noted that the unmanned vehicle may include but is not limited to an unmanned logistics vehicle, an unmanned delivery vehicle, etc. There may be multiple unmanned vehicles in the polygonal grid, and there may also be multiple logistics warehouses.

[0060] It should be noted that part or all of the execution subject of steps 101 to 105 may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in an unmanned vehicle dispatching platform on the network side, etc. This embodiment does not specifically limit this.

[0061] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0062] In this way, by obtaining a map of a specified area, the map of the specified area can be gridded using preset polygon information to obtain multiple polygonal grids. In response to the polygonal grid including a logistics warehouse and an unmanned vehicle to be dispatched, basic information of the logistics warehouse and basic information of the unmanned vehicle are obtained. Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid. Based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, the unmanned vehicle in each polygonal grid is dispatched. Since the unmanned vehicle scheduling in the polygonal grid area can be performed separately based on each polygonal grid, the effectiveness of the unmanned vehicle scheduling can be ensured. Because the distance difference between all the unmanned vehicles in a single grid and the logistics warehouse is not large, geographic location filtering is achieved, avoiding the waste of computing power for data processing due to cross-city demand and load imbalance, saving server computing resources, and improving the performance and efficiency of scheduling processing, thereby effectively optimizing the reliability of unmanned vehicle scheduling.

[0063] Optionally, in a possible implementation of the present embodiment, in step 104, based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to be dispatched, a preset matching algorithm can be used to match the vehicle information and the logistics warehouse information in each polygonal grid, and then based on the result of the matching process, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid can be obtained.

[0064] In this implementation, the basic information of the unmanned vehicle may include but is not limited to the location information of the unmanned vehicle. The basic information of the logistics warehouse information may include but is not limited to the location information of the logistics warehouse and the demand information of the logistics warehouse. Here, the demand information of the logistics warehouse may include the number of unmanned vehicles required, etc.

[0065] In a specific implementation process of this implementation method, based on the unmanned vehicle position information, the logistics warehouse position information and the logistics warehouse demand information, a preset matching algorithm is used to match the unmanned vehicle and the logistics warehouse in each polygonal grid.

[0066] In this implementation, the preset matching algorithm may include but is not limited to a greedy algorithm, a KM algorithm, a Hungarian algorithm, and the like.

[0067] One scenario of the specific implementation process is that based on the unmanned vehicle location information, the logistics warehouse location information and the logistics warehouse demand information, a greedy algorithm is used to match the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse.

[0068] For example, the greedy algorithm can be used to prioritize the combination of the closest unmanned vehicle and logistics warehouse, mark them as assigned, and then continue to match the remaining unmanned vehicle and logistics warehouse combinations through the greedy algorithm. Using the greedy algorithm, a local optimal solution can be matched, and then the corresponding matching relationship between the unmanned vehicle and the logistics warehouse can be obtained.

[0069] In this implementation, the matching relationship between the unmanned vehicle and the logistics warehouse can be the optimal combination of unmanned vehicles and logistics warehouses that meet the matching conditions. Here, a logistics warehouse can have a matching relationship with multiple unmanned vehicles. It is understandable that multiple unmanned vehicles can be dispatched to a logistics warehouse to meet the number of unmanned vehicles required for the delivery orders of the logistics warehouse.

[0070] In this way, by utilizing the matching algorithm, the matching relationship between the unmanned vehicle and the logistics warehouse can be determined, and the unmanned vehicle can be effectively allocated to the corresponding logistics warehouse, thereby further improving the reliability of scheduling.

[0071] Optionally, in a possible implementation of the present embodiment, in step 104, further, multiple threads can be utilized to perform matching processing on the unmanned vehicle and the logistics warehouse in each polygonal grid in parallel based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, and then based on the result of the matching processing of each thread, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid can be obtained in parallel.

[0072] In this implementation, the number of threads may be determined according to the number of polygon meshes.

[0073] In a specific implementation process of this implementation method, after obtaining a map with polygonal grids, first, all polygonal grids can be scanned at preset intervals to determine the polygonal grids to be allocated. The polygonal grids to be allocated can be grids including logistics warehouses and unmanned vehicles to be scheduled. Secondly, for each polygonal grid to be allocated, a corresponding thread is started for scheduling. Thirdly, based on each thread, the unmanned vehicles to be scheduled and the logistics warehouses are combined in pairs through a greedy algorithm, and the optimal combination of unmanned vehicles and logistics warehouses is calculated to determine the matching relationship between the unmanned vehicles and the logistics warehouses.

[0074] In this specific implementation process, the preset interval may be determined according to actual business requirements. For example, the preset interval may be 30 seconds.

[0075] It is understandable that due to the particularity of the scheduling between the unmanned vehicle and the logistics warehouse, most of the polygonal meshes in the map of the specified area may not have a scheduling task. Here, not having a scheduling task may mean that the polygonal mesh does not include at least one of the unmanned vehicle and the logistics warehouse. Therefore, the unmanned vehicle scheduling process can be performed only for the polygonal meshes that include the unmanned vehicle and the logistics warehouse, which can reduce the amount of data to be processed and optimize the performance of data processing.

[0076] In another specific implementation process of this implementation method, in step 105, further, multiple threads can be used to schedule the unmanned vehicles in each polygonal grid in parallel based on the matching relationship between the unmanned vehicles and the logistics warehouses in each polygonal grid.

[0077] In this implementation, there may be multiple unmanned vehicles and multiple logistics warehouses in each polygonal grid.

[0078] Here, for any polygonal mesh, the thread corresponding to the polygonal mesh can be used to execute the matching relationship between the unmanned vehicle and the logistics warehouse in the polygonal mesh, and dispatch each unmanned vehicle in the polygonal mesh to the logistics warehouse that matches the unmanned vehicle.

[0079] In this way, a thread can be started through each polygonal grid with a scheduling task to perform data processing for unmanned vehicle scheduling within the polygonal grid, which can realize distributed scheduling and facilitate rapid capacity expansion, thereby improving the performance of unmanned vehicle scheduling.

[0080] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the aforementioned implementation to implement the method for dispatching an unmanned vehicle in this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0081] Optionally, in a possible implementation of the present embodiment, the preset polygon information includes the shape of the polygon and the side length of the polygon. In step 102, the shape of the polygon and the side length of the polygon can be used to grid the map of the specified area, and then multiple polygon meshes can be obtained based on the result of the gridding processing.

[0082] In this implementation, the shape of the polygon may include but is not limited to regular polygons such as regular triangles, squares, regular pentagons, regular hexagons, etc. The side length of the polygon may be determined according to actual logistics business requirements.

[0083] Preferably, the polygon may be in the shape of a regular hexagon. The side length of the regular hexagon may be 3 kilometers. Here, the regular hexagon makes the distance between the points in each grid and the center point of the grid as consistent as possible, and different grids can be seamlessly connected, thereby ensuring the reliability of subsequent unmanned vehicle dispatching.

[0084] In another specific implementation process of this implementation method, the map of the specified area is gridded using regular hexagons and the side lengths of regular hexagons to obtain a map of the specified area gridded with regular hexagons, and then the map of the specified area gridded with regular hexagons can be stored in a server.

[0085] For example, Figure 2 is a schematic diagram of a polygonal grid in a method for dispatching an unmanned vehicle provided by an embodiment of the present application, such as Figure 2 As shown, the regular hexagon represents a polygonal grid, the green circle can represent the unmanned vehicle to be dispatched, that is, the idle unmanned logistics vehicle that can be dispatched, and the yellow circle can represent the logistics warehouse.

[0086] In this way, the map of the specified area can be gridded to obtain a gridded map of the specified area, so that the unmanned vehicle scheduling can be performed based on each grid in the future. Distributed deployment and rapid capacity expansion can be supported. Since the computing pressure of a single grid is very small, a small number of machines can be deployed to perform scheduling tasks when there are fewer scheduling businesses. As the business volume increases, the number of grids that need to be calculated increases, and the capacity can be rapidly expanded directly based on the gridded map of the specified area to meet business growth.

[0087] Moreover, the designated area can also be determined by more business scenarios. There is no administrative district barrier, and cross-city matching scheduling can be supported.

[0088] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the aforementioned implementation to implement the method for dispatching an unmanned vehicle in this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0089] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] Figure 3 A structural block diagram of an unmanned vehicle dispatching device provided by an embodiment of the present application is shown. Figure 3 As shown. The unmanned vehicle scheduling device 300 of this embodiment may include a first acquisition unit 301, a first acquisition unit 302, a second acquisition unit 303, a second acquisition unit 304 and a first scheduling unit 305. Among them, the first acquisition unit 301 is used to acquire a map of a specified area; the first acquisition unit 302 is used to grid the map of the specified area using preset polygon information to obtain multiple polygonal grids; the second acquisition unit 303 is used to obtain the basic information of the logistics warehouse and the basic information of the unmanned vehicle in response to the polygonal grid including a logistics warehouse and an unmanned vehicle to be scheduled; the second acquisition unit 304 is used to match the unmanned vehicle and the logistics warehouse in each polygonal grid based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to obtain the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid; the first scheduling unit 305 is used to schedule the unmanned vehicle in each polygonal grid based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid.

[0092] It should be noted that part or all of the unmanned vehicle dispatching device of this embodiment may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in an unmanned vehicle dispatching platform on the network side, etc. This embodiment does not specifically limit this.

[0093] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0094] Optionally, in a possible implementation of this embodiment, the second acquisition unit 304 can be used to match the vehicle information and logistics warehouse information in each polygonal mesh based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to be scheduled, using a preset matching algorithm; and based on the result of the matching processing, obtain the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh.

[0095] Optionally, in a possible implementation of this embodiment, the basic information of the unmanned vehicle includes the unmanned vehicle location information, and the basic information of the logistics warehouse information includes the logistics warehouse location information and the logistics warehouse demand information. The second acquisition unit 304 can be used to match the unmanned vehicle and the logistics warehouse in each polygonal grid based on the unmanned vehicle location information, the logistics warehouse location information and the logistics warehouse demand information, using a preset matching algorithm.

[0096] Optionally, in a possible implementation of this embodiment, the second obtaining unit 304 can be used to utilize multiple threads to perform matching processing on the unmanned vehicle and the logistics warehouse in each polygonal mesh in parallel based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle; the number of the threads is determined according to the number of the polygonal meshes; based on the result of the matching processing of each thread, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh is obtained in parallel.

[0097] Optionally, in a possible implementation of this embodiment, the first scheduling unit 305 can be used to utilize multiple threads to schedule the unmanned vehicles in each polygonal grid in parallel based on the matching relationship between the unmanned vehicles and logistics warehouses in each polygonal grid.

[0098] Optionally, in a possible implementation of this embodiment, the preset polygon information includes the shape of the polygon and the side length of the polygon, and the first obtaining unit 302 can be used to grid the map of the specified area using the shape of the polygon and the side length of the polygon; based on the result of the gridding processing, multiple polygon meshes are obtained.

[0099] In this embodiment, a map of a specified area can be obtained by a first acquisition unit, and then the first acquisition unit can use preset polygon information to grid the map of the specified area to obtain multiple polygonal grids. The second acquisition unit can respond to the fact that the polygonal grid includes a logistics warehouse and an unmanned vehicle to be dispatched, and obtain basic information of the logistics warehouse and basic information of the unmanned vehicle. The second acquisition unit can match the unmanned vehicle and the logistics warehouse in each polygonal grid based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, so that the first scheduling unit can schedule the unmanned vehicle in each polygonal grid based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid. Since the unmanned vehicle scheduling in the polygonal grid area can be performed separately based on each polygonal grid, it can be achieved while ensuring the effectiveness of the unmanned vehicle scheduling. Because the distance difference between all the unmanned vehicles in a single grid and the logistics warehouse is not large, geographic location filtering is achieved, avoiding the waste of computing power for data processing caused by cross-city demand and load imbalance, saving server computing resources, and improving the performance and efficiency of scheduling processing, thereby effectively optimizing the reliability of unmanned vehicle scheduling.

[0100] In the technical solution of this application, the user personal information involved, such as the collection, storage, use, processing, transmission, provision and disclosure of user images and attribute data, etc., complies with the provisions of relevant laws and regulations and does not violate public order and good morals.

[0101] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0102] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0103] like Figure 4As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0104] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the method of unmanned vehicle scheduling. For example, in some embodiments, the method of unmanned vehicle scheduling may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the method of unmanned vehicle scheduling described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method of unmanned vehicle scheduling in any other appropriate manner (e.g., by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0111] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0113] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for dispatching an unmanned vehicle, characterized in that: The method comprises: Get a map of the specified area; Using the preset polygon information, gridding the map of the designated area to obtain a plurality of polygonal grids; In response to the polygonal mesh including a logistics warehouse and an unmanned vehicle to be dispatched, basic information of the logistics warehouse and basic information of the unmanned vehicle are acquired; Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid; Based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, the unmanned vehicle in each polygonal grid is dispatched.

2. The method according to claim 1, characterized in that The matching process of the unmanned vehicle and the logistics warehouse in each polygonal grid based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to obtain the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid includes: Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle to be dispatched, a preset matching algorithm is used to match the vehicle information and the logistics warehouse information in each polygonal grid; Based on the result of the matching process, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid is obtained.

3. The method according to claim 2, characterized in that The basic information of the unmanned vehicle includes the location information of the unmanned vehicle, and the basic information of the logistics warehouse includes the location information of the logistics warehouse and the demand information of the logistics warehouse. Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, the vehicle information and the logistics warehouse information in each polygonal grid are matched by using a preset matching algorithm, including: Based on the unmanned vehicle location information, the logistics warehouse location information and the logistics warehouse demand information, a preset matching algorithm is used to match the unmanned vehicle and the logistics warehouse in each polygonal grid.

4. The method according to any one of claims 1 to 3, characterized in that Based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal grid to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid, including: Using multiple threads, matching processing is performed on the unmanned vehicle and the logistics warehouse in each polygonal mesh in parallel based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle; the number of the threads is determined according to the number of the polygonal meshes; Based on the matching processing results of each thread, the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal mesh is obtained in parallel.

5. The method according to claim 4, characterized in that The method of scheduling the unmanned vehicle in each polygonal grid based on the matching relationship between the unmanned vehicle and the logistics warehouse includes: By utilizing the plurality of threads, the unmanned vehicles in each polygonal grid are dispatched in parallel based on the matching relationship between the unmanned vehicles in each polygonal grid and the logistics warehouse.

6. The method according to claim 1, characterized in that The preset polygon information includes the shape of the polygon and the side length of the polygon. The gridding process is performed on the map of the designated area using the preset polygon information to obtain a plurality of polygon grids, including: Gridding the map of the designated area using the shape of the polygon and the side length of the polygon; Based on the result of the meshing process, a plurality of polygonal meshes are obtained.

7. A device for dispatching unmanned vehicles, characterized in that: The device comprises: A first acquisition unit, used to acquire a map of a specified area; A first obtaining unit is used to perform gridding processing on the map of the designated area by using preset polygon information to obtain a plurality of polygonal grids; A second acquisition unit, configured to acquire basic information of the logistics warehouse and basic information of the unmanned vehicle in response to the polygonal mesh including the logistics warehouse and the unmanned vehicle to be dispatched; A second obtaining unit is used to perform matching processing on the unmanned vehicle and the logistics warehouse in each polygonal grid based on the basic information of the logistics warehouse and the basic information of the unmanned vehicle, so as to obtain a matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid; The first scheduling unit is used to schedule the unmanned vehicle in each polygonal grid based on the matching relationship between the unmanned vehicle and the logistics warehouse in each polygonal grid.

8. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; 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 method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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