A smart logistics park management method, device, medium and product

Abstract

This application relates to a smart logistics park management method, equipment, medium, and product, applied in the field of route planning. The method includes: acquiring the current and destination locations of logistics vehicles; planning a route for the logistics vehicles with the shortest travel time based on the destination location, the current location of the logistics vehicles, and a map of the logistics park, and sending the route to the corresponding client device of the logistics vehicles; acquiring images of road intersections where the logistics vehicles are located while they are traveling along the route; determining the position of the road intersection image within the travel route; and determining the travel direction of the logistics vehicles at the road intersections based on the location and the travel route, and providing reminders based on the travel direction. This application helps assist drivers in making correct driving decisions at intersections, improving driving safety and efficiency, reducing traffic congestion, and achieving efficient management of logistics vehicles.

Description

Technical Field

[0001] This application relates to the field of route planning technology, and in particular to a smart logistics park management method, equipment, medium and product. Background Technology

[0002] Smart logistics parks are products of intelligent transformation and upgrading of traditional logistics parks using advanced technologies such as the Internet of Things, big data, and artificial intelligence. They aim to achieve efficient and intelligent management of logistics operations, improve logistics efficiency, reduce logistics costs, and promote industrial upgrading. As an important trend in the development of the logistics industry and a key means to promote its transformation and upgrading, smart logistics parks, through information integration, intelligent operation, resource sharing, and full-process visibility, achieve collaborative sharing and optimized allocation of all resources in the logistics chain, as well as visualization and controllability of the entire logistics process.

[0003] After general route planning for logistics vehicles, although the drivers and passengers can follow the planned route, in more complex or sophisticated environments, the positioning of the client equipment may have location errors. This can cause the drivers and passengers to not know their exact location when they reach a road fork, thus leading to incorrect driving decisions and low driving efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a smart logistics park management method, equipment, medium, and product that can help drivers make correct driving decisions at intersections, improve driving safety and efficiency, reduce traffic congestion, and achieve efficient management of logistics vehicles.

[0005] Firstly, a smart logistics park management method is provided, including:

[0006] Obtain the current location and destination location of the logistics vehicle;

[0007] Based on the destination location, the current location of the logistics vehicle, and the map of the logistics park, the route of the logistics vehicle is planned with the goal of minimizing the travel time, and then sent to the client device corresponding to the logistics vehicle.

[0008] During the process of the logistics vehicle traveling along the route, images of the road intersections where the logistics vehicle is located are acquired;

[0009] The location of the image of the road intersection where the logistics vehicle is located is determined in the driving route; and based on the location and the driving route, the driving direction of the logistics vehicle at the road intersection is determined, and a reminder is given based on the driving direction.

[0010] The above technical solution plans the route of logistics vehicles based on the destination location, current location, and park map, aiming to minimize travel time and improve transportation efficiency. During the journey, the system continuously tracks the vehicle's position using images of road intersections to pinpoint its exact location on the route and accurately determine its direction of travel at intersections, providing directional guidance. This assists drivers in making correct driving decisions at intersections, improving driving safety and efficiency, reducing traffic congestion, and achieving efficient management of logistics vehicles.

[0011] In one possible implementation, acquiring an image of the road intersection where the logistics vehicle exists includes:

[0012] Acquire images of each road intersection;

[0013] Determine vehicle information in the image of the target road intersection;

[0014] When the vehicle information is the vehicle information corresponding to the logistics vehicle, the image of the target road intersection is determined to be the road intersection image where the logistics vehicle exists, and the image of the target road intersection is the image of any road intersection.

[0015] By using the above technical solution, after obtaining images of multiple road intersections, the images are analyzed to determine whether they are images of road intersections for logistics vehicles, thus accurately obtaining images of road intersections for logistics vehicles.

[0016] In one possible implementation, determining the vehicle information in the image of the target road intersection includes:

[0017] Identify license plates in the image of the target road intersection;

[0018] If the license plate information is complete, the vehicle information can be determined based on the license plate.

[0019] If the license plate information is incomplete, determine the historical driving route that exists at the target road intersection within the historical planning period;

[0020] Identify vehicle images corresponding to historical driving routes;

[0021] Match the vehicle area in the image of the target road intersection with the vehicle image corresponding to the historical driving route to obtain a successfully matched target vehicle image.

[0022] Vehicle information is determined based on the target vehicle image.

[0023] Using the above technical solution, if a complete license plate is identified in the image of the target road intersection, vehicle information can be directly obtained based on the identified license plate. Otherwise, vehicle information can be tracked by analyzing historical information. Specifically, this involves identifying vehicle images corresponding to historical driving routes that exist at the target road intersection within the historical planning time period, and matching the vehicle images with the vehicles in the images of the target road intersection to locate the successfully matched target vehicle image from the vehicle images. Vehicle information can then be determined through the target vehicle image. Even in the case of incomplete license plates, vehicles can be found and vehicle information can be obtained through historical data analysis.

[0024] One possible implementation also includes:

[0025] If an abnormal traffic event occurs on the route while the logistics vehicle is traveling along the route, the route will be replanned and sent to the corresponding client device of the logistics vehicle.

[0026] Through the above technical solutions, the traffic situation in the logistics park can be monitored in real time. When a traffic incident is detected on the route of a logistics vehicle, a new route can be planned in time, so that the logistics vehicle can reach its destination quickly and accurately.

[0027] In one possible implementation, the step of planning the logistics vehicle's route based on the destination location, the current location of the logistics vehicle, and a map of the logistics park, with the goal of minimizing travel time, includes:

[0028] Based on the park map, the current location of the logistics vehicles, and the location of the destination warehouse, multiple initial driving routes are planned;

[0029] Acquire fixed foreground data, moving foreground data, and size information of logistics vehicles in the logistics park. The fixed foreground data includes the width data of obstacles on the park roads.

[0030] Based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data, the driving path that allows the logistics vehicle to pass normally and has the shortest travel time is determined from the multiple initial driving paths.

[0031] By comprehensively considering the size information of logistics vehicles, the road width affected by obstacles on the park roads, and the motion prospect data on the road, the best path that can ensure the normal passage of logistics vehicles and the shortest travel time is selected from multiple initial driving paths, so that the final driving path has better reliability.

[0032] In one possible implementation, based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data, a travel path that allows the logistics vehicle to pass normally and has the shortest travel time is determined from the plurality of initial travel paths, including:

[0033] Based on the size information of the logistics vehicle and the width data of obstacles on the park roads, a first driving path is determined from the multiple initial driving paths, wherein the drivable size information of each road in the first driving path is greater than the size information of the logistics vehicle.

[0034] When the number of the first driving paths is greater than 1, a driving path is determined from the first driving paths based on the motion foreground data with the goal of minimizing the driving time.

[0035] The above technical solution, based on the size information of logistics vehicles, selects the first driving path from multiple initial driving paths that will not obstruct the passage of logistics vehicles. Then, when the number of the first driving paths is greater than 1, the driving time in the first driving path is analyzed using motion foreground data, and the first driving path with the shortest driving time is selected as the final driving path. This helps to improve logistics efficiency, reduce transportation time and costs, and at the same time ensure the safe driving of logistics vehicles within the park.

[0036] In one possible implementation, the fixed foreground data further includes obstacle types, which indicate whether the obstacle will cause damage to the vehicle;

[0037] The step of determining a first driving path from the plurality of initial driving paths based on the size information of the logistics vehicle and the width data of obstacles on the park roads includes:

[0038] Based on the obstacle type and the width data of the obstacles on the park roads, determine the actual width data of the obstacles that affect driving.

[0039] Based on the size information of the logistics vehicle and the actual width data, a first driving path is determined from the plurality of driving paths.

[0040] The above technical solution can effectively assess the actual impact of obstacle type and obstacle width, thereby improving the accuracy of determining the first driving path.

[0041] Secondly, a smart logistics park management device is provided, which includes:

[0042] One or more processors;

[0043] Memory;

[0044] One or more applications, wherein the applications are stored in memory and configured to be executed by one or more processors, the applications being configured to: perform operations corresponding to the methods shown in any possible implementation of the first aspect.

[0045] Thirdly, a computer-readable storage medium is provided, the storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the method as shown in any possible implementation of the first aspect.

[0046] Fourthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method as shown in any possible implementation of the first aspect.

[0047] In summary, this application includes at least one of the following beneficial technical effects:

[0048] 1. Based on the destination location, current location, and park map, the system plans the route for logistics vehicles with the shortest possible travel time, which helps reduce travel time and improve transportation efficiency. During the journey, the system continuously tracks the location of logistics vehicles using images of road intersections to determine their specific position on the route and accurately determine their direction of travel at intersections, providing directional guidance. This helps drivers make correct driving decisions at intersections, improving driving safety and efficiency, reducing traffic congestion, and achieving efficient management of logistics vehicles. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a smart logistics park management method provided in an embodiment of this application;

[0050] Figure 2 A schematic flowchart illustrating a process for acquiring an image of a road intersection where logistics vehicles are present, provided as an embodiment of this application;

[0051] Figure 3 A schematic diagram of a road obstacle provided in an embodiment of this application;

[0052] Figure 4 This is a structural schematic diagram of a smart logistics park management device provided in an embodiment of this application. Detailed Implementation

[0053] The following is in conjunction with the appendix Figure 1 To be continued Figure 4 This application will be described in further detail.

[0054] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0055] 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. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0057] This application provides an example scenario for smart logistics park management, specifically including: smart logistics park management equipment, client equipment corresponding to logistics vehicles, camera devices at various road intersections in the logistics park, and loudspeakers at various road intersections.

[0058] When a client device sends a route planning request to the smart logistics park management device, the device obtains the current and destination locations of the logistics vehicles to plan their routes. The smart logistics park management device can acquire images from cameras in real time. When a vehicle appears in the image, it can determine the vehicle's location and, based on the vehicle's route, determine its direction of travel at road intersections. This direction can then be sent to the client device as a reminder, or announced via loudspeakers at road intersections, enabling the vehicle to quickly reach its destination.

[0059] This application's solution is applicable to route planning for vehicles traveling on the ground in logistics parks, and also applicable to route planning for handling machinery vehicles in large warehouses of logistics parks; this application does not impose any further limitations.

[0060] This application provides a smart logistics park management method, such as... Figure 1As shown, the method provided in this embodiment can be executed by a smart logistics park management device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This embodiment does not impose any limitations on this connection. The method includes:

[0061] Step S101: Obtain the current location and destination location of the logistics vehicle;

[0062] The number of logistics vehicles is one or more.

[0063] Step S102: Based on the destination location, the current location of the logistics vehicle, and the park map of the logistics park, plan the route of the logistics vehicle with the goal of minimizing the travel time, and send it to the client device corresponding to the logistics vehicle.

[0064] Step S103: During the process of the logistics vehicle traveling along the route, acquire images of the road intersections where the logistics vehicle is located;

[0065] Cameras at road intersections capture images in real time. When an image includes a vehicle, it is sent to the smart logistics park management equipment. The image of a road intersection containing logistics vehicles can be identified from multiple images of road intersections with vehicles present, based on vehicle license plate information, driver and passenger information, etc.

[0066] Step S104: Determine the location of the image of the road intersection where the logistics vehicle is located in the driving route; and based on the location and driving route, determine the driving direction of the logistics vehicle at the road intersection, and issue a reminder based on the driving direction.

[0067] The system identifies signs in images of road intersections where logistics vehicles are present. Based on the signs and sign-road intersection information, it determines the corresponding road intersection and then locates that intersection along the driving route. The sign-road intersection information is a mapping relationship between multiple signs and multiple road intersections. The system then determines the driving direction at that intersection to guide the drivers and passengers of the logistics vehicles, enabling them to quickly reach their destination.

[0068] In this embodiment, the route of the logistics vehicle is planned with the shortest travel distance based on the destination location, current location, and park map. This helps to reduce the travel time of the logistics vehicle and improve transportation efficiency. During the journey, the location of the logistics vehicle is continuously tracked through images of road intersections to determine its specific position on the route and accurately determine its direction of travel at intersections, providing directional reminders. This helps the driver make correct driving decisions at intersections, improves driving safety and efficiency, reduces traffic congestion, and achieves efficient management of logistics vehicles.

[0069] One possible implementation of the embodiments of this application is referred to, see below. Figure 2 , Figure 2 This application provides a flowchart illustrating the process of acquiring an image of a road intersection where logistics vehicles are present. Specifically, step S103, acquiring the image of a road intersection where logistics vehicles are present, includes: S1031-S1033, wherein:

[0070] S1031. Obtain images of each road intersection;

[0071] The images of each road intersection show vehicles present.

[0072] S1032. Determine vehicle information in the image of the target road intersection;

[0073] In one feasible manner, S1032 determines vehicle information in the image of the target road intersection, including: SC1-SC6 (not shown in the attached figures), wherein:

[0074] SC1. Identify license plates in images of target road intersections;

[0075] SC2. If the license plate information is complete, the vehicle information is determined based on the license plate.

[0076] If the license plate information is complete, the license plate will be used as vehicle information.

[0077] SC3. If the license plate information is incomplete, determine the historical driving route that exists at the target road intersection within the historical planning period;

[0078] In the case of incomplete license plate information, obtain the initial historical driving routes within the historical planning time period of a preset time length before the current time, and filter out the historical driving routes that have the target road intersection from the initial historical driving routes.

[0079] SC4. Determine the vehicle images corresponding to the historical driving routes;

[0080] When a vehicle enters the logistics park, the gate captures an image of the vehicle, and various cameras within the park also capture images. These images, along with the capture time, are recorded as vehicle events until the vehicle leaves the logistics park. Vehicle events encompass the entire process of the vehicle within the logistics park. By analyzing the stored vehicle events, vehicle images corresponding to historical travel routes are obtained. Each vehicle image includes at least one of a basic vehicle image and images of the driver and passengers.

[0081] SC5. Match the vehicle regions in the target road intersection image with the vehicle images corresponding to the historical driving routes to obtain the successfully matched target vehicle images.

[0082] SC6. Determine vehicle information based on the target vehicle image.

[0083] Since the target vehicle image is complete, the license plate or the identified driver and passengers in the target vehicle image can be used as vehicle information.

[0084] It can be seen that if a complete license plate is identified in the image of the target road intersection, the vehicle information can be obtained directly based on the identified license plate. Otherwise, the vehicle information is tracked by analyzing historical information. Specifically, the vehicle images corresponding to the historical driving routes that exist at the target road intersection within the historical planning time period are identified, and the vehicle images are matched with the vehicles in the images of the target road intersection to locate the successfully matched target vehicle image from the vehicle images. The vehicle information is determined by the target vehicle image. Even in the case of an incomplete license plate, the vehicle can be found and the vehicle information can be obtained through historical data analysis.

[0085] S1033. When the vehicle information is the vehicle information corresponding to the logistics vehicle, the image of the target road intersection is determined to be the image of the road intersection where the logistics vehicle exists, and the image of the target road intersection is the image of any road intersection.

[0086] As can be seen, after obtaining images of multiple road intersections, the images can be analyzed to determine whether they are images of road intersections for logistics vehicles, thus accurately obtaining images of road intersections for logistics vehicles.

[0087] One possible implementation of this application embodiment includes:

[0088] If an abnormal traffic event occurs on the route while the logistics vehicle is traveling along the route, the route will be replanned and sent to the corresponding client device of the logistics vehicle.

[0089] Specifically, the intelligent logistics park management equipment can continuously monitor the driving environment and route conditions of logistics vehicles. When abnormal traffic events are detected on the driving route, which may prevent logistics vehicles from reaching their destination in time, the equipment will promptly replan the driving route for the logistics vehicles.

[0090] Abnormal traffic events include, but are not limited to: traffic accidents, road closures, and severe congestion.

[0091] It is important to note that when replanning routes, roads involved in abnormal traffic events will be filtered out from the park map before the driving routes are replanned.

[0092] As can be seen, in this embodiment of the application, the traffic situation in the logistics park is monitored in real time. When a traffic incident is detected on the route of the logistics vehicle, a new route can be planned in time, so that the logistics vehicle can reach the destination quickly and accurately.

[0093] One possible implementation of this application embodiment, S102, plans the logistics vehicle's route based on the destination location, the current location of the logistics vehicle, and the logistics park map, with the goal of minimizing travel time, including: S1021-S1023 (not shown in the accompanying drawings), wherein:

[0094] S1021. Based on the park map, the current location of the logistics vehicles, and the location of the destination warehouse, plan multiple initial driving routes;

[0095] S1022. Obtain fixed foreground data, moving foreground data and size information of logistics vehicles in the logistics park. The fixed foreground data includes the width data of obstacles on the park roads.

[0096] Specifically, fixed data such as the overall layout, road network, and building locations of the logistics park are obtained through official maps or GIS data. Then, fixed initial foreground data is obtained by capturing images from cameras within the park. The difference between the fixed data and the initial foreground data is then used as the park's foreground data. Next, based on the location of the road network and the foreground data, obstacles on the park's roads are identified, and the width of these obstacles is obtained from the foreground data, serving as fixed foreground data.

[0097] Images collected by the park's cameras are analyzed to obtain information about moving foregrounds, such as the movement trajectory, speed, and direction of vehicles or people.

[0098] S1023. Based on the size information of the logistics vehicle, the width data of obstacles on the park road, and the motion prospect data, determine the driving path that allows the logistics vehicle to pass normally and has the shortest driving time from multiple initial driving paths.

[0099] Taking into account the size information of logistics vehicles, the road width affected by obstacles on the park roads, and the motion prospect data on the road, the best path that can ensure the normal passage of logistics vehicles and has the shortest travel time is selected from multiple initial driving paths, so that the final driving path has better reliability.

[0100] One possible implementation of this application embodiment is as follows: S1023, determining the driving path that allows the logistics vehicle to pass normally and has the shortest travel time from multiple initial driving paths based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data, including:

[0101] SA1. Based on the size information of the logistics vehicle and the width data of obstacles on the park roads, determine the first driving path from multiple initial driving paths. The drivable size information of each road in the first driving path is larger than the size information of the logistics vehicle.

[0102] Specifically, for each initial driving path, several road information of the initial driving path are obtained. The road information includes at least the road width and road location. Based on the road location and the location of obstacles on the park roads, it is determined whether there are obstacles on the initial driving path. If there are obstacles, the passable width of the road is determined based on the road width and obstacle width data. If all passable widths are greater than or equal to the width in the size information of the logistics vehicle, the initial driving path is determined to be the first driving path.

[0103] The process of determining the width data of the obstacle includes: determining the shortest distance between the obstacle and the first road side, and the shortest distance between the obstacle and the second road side; selecting the shortest distance between the shortest distance between the obstacle and the first road side and the shortest distance between the obstacle and the second road side; and using the sum of the shortest distance and the actual width of the obstacle as the width data of the obstacle.

[0104] Please refer to Figure 3 , Figure 3 This is a schematic diagram of a road obstacle provided in an embodiment of this application. The arrow points to the driving direction. Both circles and rectangles are obstacles. The length L1 of the line segment with point A as the endpoint is the width data of the circular obstacle, and the length L2 of the line segment with point B as the endpoint is the width data of the rectangular obstacle.

[0105] Furthermore, the fixed foreground data also includes obstacle types, which indicate whether the obstacle will cause damage to the vehicle;

[0106] Specifically, obstacles that will not cause damage include trees, flowers, piles of fallen leaves, and snowdrifts, while obstacles that will cause damage include parked vehicles and large rocks.

[0107] SA1. Based on the size information of the logistics vehicle and the width data of obstacles on the park roads, determine the first driving path from multiple initial driving paths, including: determining the actual width data of the obstacle caused by the obstacle to the vehicle based on the obstacle type and the width data of the obstacle on the park roads; and determining the first driving path from multiple driving paths based on the size information of the logistics vehicle and the actual width data.

[0108] The system has a pre-defined correspondence between obstacle types and adjustment values. Based on the obstacle type and the correspondence, the adjustment value is determined. Then, based on the value and the obstacle width data, the actual width data is determined before planning, which better reflects the actual situation. Furthermore, the obstacle types in this application are divided into damaged and non-damaged. Non-damaged obstacles all correspond to the same adjustment value, or non-damaged obstacles are further divided into multiple subcategories, each corresponding to a different adjustment value. This application embodiment does not impose any limitations; users can set these according to their actual needs. In this application embodiment, the actual impact of obstacle type and obstacle width can be effectively evaluated, improving the accuracy of the first driving path determination.

[0109] It should be noted that if no primary driving route exists, a prompt message will be issued to facilitate on-site road management by park administrators.

[0110] If a first driving path exists and its quantity is 1, then the first driving path is directly determined as the driving path.

[0111] SA2. When the number of first driving paths is greater than 1, determine the driving path from the first driving paths based on the motion foreground data with the goal of minimizing the driving time.

[0112] Specifically, the motion foreground data consists of the motion data of moving targets on each road, including the speed of pedestrians, the speed and direction of vehicles, etc. A three-dimensional model of the road motion in the park is constructed, and the travel time of each first travel path is simulated sequentially based on the motion foreground data. The first travel path with the shortest travel time is selected as the final travel path.

[0113] As can be seen, in this embodiment of the application, based on the size information of the logistics vehicle, the first driving path that will not obstruct the passage of the logistics vehicle is selected from multiple initial driving paths. Then, the driving time in the first driving path is analyzed using motion foreground data, and the first driving path with the shortest driving time is selected as the final driving path. This helps to improve logistics efficiency, reduce transportation time and costs, and at the same time ensure the safe driving of logistics vehicles in the park.

[0114] Furthermore, once the logistics vehicle arrives at its destination, a parking image of the vehicle is acquired, along with target parking location information. If a parking space exists, the target parking location information is the target corner point information; otherwise, it is a preset virtual target corner point information. The parking image of the logistics vehicle is analyzed to determine whether the vehicle is within the parking space corresponding to the target corner point information. If so, normal parking is confirmed; otherwise, adjustment information is generated to instruct the driver and passengers to adjust the vehicle to park within the parking space.

[0115] This application provides a smart logistics park management device that corresponds to the above-mentioned method.

[0116] A smart logistics park management device includes:

[0117] The first acquisition module is used to acquire the current location and destination location of the logistics vehicle;

[0118] The planning module is used to plan the route of the logistics vehicle with the goal of minimizing the travel time based on the destination location, the current location of the logistics vehicle, and the map of the logistics park, and then send the route to the corresponding client device of the logistics vehicle.

[0119] The second acquisition module is used to acquire images of road intersections where logistics vehicles are present while the logistics vehicles are traveling along the driving route.

[0120] The alert module is used to determine the location of the image of the road intersection where the logistics vehicle is located in the driving route; and based on the location and driving route, to determine the driving direction of the logistics vehicle at the road intersection, and to issue an alert based on the driving direction.

[0121] In one possible implementation, when the second acquisition module acquires an image of a road intersection where logistics vehicles exist, it is used for:

[0122] Acquire images of each road intersection;

[0123] Determine vehicle information in the image of the target road intersection;

[0124] When the vehicle information is the vehicle information corresponding to the logistics vehicle, the image of the target road intersection is determined to be the image of the road intersection where the logistics vehicle exists, and the image of the target road intersection is the image of any road intersection.

[0125] In one possible implementation, when determining vehicle information in the image of the target road intersection, the second acquisition module is used to:

[0126] Identify license plates in images of target road intersections;

[0127] If the license plate information is complete, the vehicle information can be determined based on the license plate.

[0128] If the license plate information is incomplete, determine the historical driving route that exists at the target road intersection within the historical planning period;

[0129] Identify vehicle images corresponding to historical driving routes;

[0130] Match the vehicle area in the image of the target road intersection with the vehicle images corresponding to the historical driving routes to obtain the target vehicle images that have been successfully matched.

[0131] Vehicle information is determined based on the target vehicle image.

[0132] In one possible implementation, the device further includes:

[0133] The rerouting module is used to reroute a logistics vehicle if an abnormal traffic event occurs on the route while the vehicle is traveling along the route, and then send the rerouting information to the corresponding client device of the logistics vehicle.

[0134] In one possible implementation, the planning module, when planning the route of the logistics vehicle based on the destination location, the current location of the logistics vehicle, and the map of the logistics park, with the goal of minimizing travel time, is used to:

[0135] Based on the park map, the current location of the logistics vehicles, and the location of the destination warehouse, multiple initial driving routes are planned;

[0136] Acquire fixed foreground data, moving foreground data, and size information of logistics vehicles in the logistics park. The fixed foreground data includes the width data of obstacles on the park's roads.

[0137] Based on the size information of the logistics vehicles, the width data of obstacles on the park roads, and the motion prospect data, the driving path that allows the logistics vehicles to pass normally and has the shortest travel time is determined from multiple initial driving paths.

[0138] In one possible implementation, the planning module determines the shortest travel path from multiple initial travel paths, based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data. This path is used for:

[0139] Based on the size information of the logistics vehicle and the width data of obstacles on the park roads, a first driving path is determined from multiple initial driving paths. The drivable size information of each road in the first driving path is larger than the size information of the logistics vehicle.

[0140] When the number of first driving paths is greater than 1, a driving path is determined from the first driving paths based on the motion foreground data with the goal of minimizing the driving time.

[0141] In one possible implementation, the fixed foreground data also includes obstacle types, which indicate whether the obstacle will cause damage to the vehicle.

[0142] The planning module determines the first driving path from multiple initial driving paths based on the size information of the logistics vehicle and the width data of obstacles on the park roads. This path is used for:

[0143] Based on the type of obstacle and the width data of obstacles on the park roads, determine the actual width data of the obstacle that affects driving.

[0144] Based on the size information and actual width data of the logistics vehicle, the first driving path is determined from multiple driving paths.

[0145] This application provides a smart logistics park management device, such as... Figure 4 As shown, Figure 4 The smart logistics park management device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the smart logistics park management device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this smart logistics park management device 300 does not constitute a limitation on the embodiments of this application.

[0146] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0147] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0148] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0149] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0150] Among them, the management equipment for smart logistics parks includes, but is not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (such as vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The smart logistics park management equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0151] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0152] This application provides a computer program product, including a computer program that, when executed by a processor, implements the corresponding content in the aforementioned method embodiments.

[0153] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0154] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A smart logistics park management method, characterized in that, include: Obtain the current location and destination location of the logistics vehicle; Based on the destination location, the current location of the logistics vehicle, and the map of the logistics park, the route of the logistics vehicle is planned with the goal of minimizing the travel time, and then sent to the client device corresponding to the logistics vehicle. During the process of the logistics vehicle traveling along the route, images of the road intersections where the logistics vehicle is located are acquired; The location of the image of the road intersection where the logistics vehicle is located is determined in the driving route; and based on the location and the driving route, the driving direction of the logistics vehicle at the road intersection is determined, and a reminder is given based on the driving direction; Acquiring images of road intersections where the logistics vehicles are located includes: Acquire images of each road intersection; Determine vehicle information in the image of the target road intersection; When the vehicle information is the vehicle information corresponding to the logistics vehicle, the image of the target road intersection is determined to be the road intersection image where the logistics vehicle exists, and the image of the target road intersection is the image of any road intersection.

2. The method according to claim 1, characterized in that, The determination of vehicle information in the image of the target road intersection includes: Identify license plates in the image of the target road intersection; If the license plate information is complete, the vehicle information can be determined based on the license plate. If the license plate information is incomplete, determine the historical driving route that exists at the target road intersection within the historical planning period; Identify vehicle images corresponding to historical driving routes; Match the vehicle area in the image of the target road intersection with the vehicle image corresponding to the historical driving route to obtain a successfully matched target vehicle image. Vehicle information is determined based on the target vehicle image.

3. The method according to claim 1, characterized in that, Also includes: If an abnormal traffic event occurs on the route while the logistics vehicle is traveling along the route, the route will be replanned and sent to the corresponding client device of the logistics vehicle.

4. The method according to any one of claims 1 to 3, characterized in that, The process of planning the logistics vehicle's route based on the destination location, the current location of the logistics vehicle, and a map of the logistics park, with the goal of minimizing travel time, includes: Based on the park map, the current location of the logistics vehicles, and the location of the destination warehouse, multiple initial driving routes are planned; Acquire fixed foreground data, moving foreground data, and size information of logistics vehicles in the logistics park. The fixed foreground data includes the width data of obstacles on the park roads. Based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data, the driving path that allows the logistics vehicle to pass normally and has the shortest travel time is determined from the multiple initial driving paths.

5. The method according to claim 4, characterized in that, Based on the size information of the logistics vehicle, the width data of obstacles on the park roads, and the motion foreground data, a travel path with the shortest travel time and enabling the logistics vehicle to pass normally is determined from the multiple initial travel paths, including: Based on the size information of the logistics vehicle and the width data of obstacles on the park roads, a first driving path is determined from the multiple initial driving paths, wherein the drivable size information of each road in the first driving path is greater than the size information of the logistics vehicle. When the number of the first driving paths is greater than 1, a driving path is determined from the first driving paths based on the motion foreground data with the goal of minimizing the driving time.

6. The method according to claim 5, characterized in that, The fixed foreground data also includes obstacle types, which indicate whether the obstacle will cause damage to the vehicle; The step of determining a first driving path from the plurality of initial driving paths based on the size information of the logistics vehicle and the width data of obstacles on the park roads includes: Based on the obstacle type and the width data of the obstacles on the park roads, determine the actual width data of the obstacles that affect driving. Based on the size information of the logistics vehicle and the actual width data, a first driving path is determined from the plurality of initial driving paths.

7. A smart logistics park management device, characterized in that, It includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the steps of the method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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