Intelligent park navigation method and device based on digital twinning, equipment and storage medium

Abstract

The application relates to the field of warehouse logistics, and discloses a smart park navigation method, device and equipment based on digital twinning and a storage medium. The method comprises the following steps: generating a logistics park simulation map containing landmarks according to modeling data; generating a transportation navigation route on the logistics park simulation map based on the landmarks; acquiring logistics vehicle physical data and logistics vehicle real-time data, generating a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously displaying the digital twin in the simulation map; and when the logistics vehicle reaches a designated area of a logistics park entrance, completing navigation according to the transportation navigation route and the digital twin corresponding to the logistics vehicle. Through the generation of the digital twin of the logistics vehicle on the simulation map, the simulation and visualization of the logistics park can be realized, the logistics vehicles in the logistics park can be accurately managed and guided, and the operation efficiency and safety of the logistics park are improved.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics, and in particular to a smart park navigation method, device, equipment and storage medium based on digital twins. Background Technology

[0002] Logistics services are playing an increasingly important role in our social and economic lives. Every day, a large volume of goods needs to be transported from one place to another. Current vehicle management methods in logistics parks have significant problems, leading to increased logistics costs and the accumulation of unnecessary expenses. Due to the lack of effective management strategies, many vehicles within logistics parks cannot achieve intelligent navigation.

[0003] Drivers rely on GPS-based navigation systems within the park to plan routes and reach their destinations, but this method is not always reliable. The accuracy of GPS navigation within the logistics park is not high, making it difficult to find suitable routes, resulting in low operational efficiency and safety within the logistics park. Summary of the Invention

[0004] The main objective of this invention is to solve the problems of low operational efficiency and low safety in logistics parks in the prior art.

[0005] The first aspect of this invention provides a smart park navigation method based on digital twins, the smart park navigation method based on digital twins comprising:

[0006] Based on the modeling data, a simulation map of the logistics park containing landmarks is generated; based on the landmarks, a transportation navigation route is generated on the simulation map of the logistics park; physical data and real-time data of the logistics vehicles are acquired, and a digital twin corresponding to the logistics vehicle is generated in the simulation map and displayed synchronously in the simulation map; when the logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed according to the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0007] Optionally, in a second implementation of the first aspect of the present invention, generating a transportation navigation route on the logistics park simulation map based on the landmarks includes: selecting geographical features or equipment as landmarks within the logistics park simulation map, wherein the landmarks are anchor points of the transportation navigation route; generating a road grid of the logistics park based on the landmarks; and generating a transportation navigation route corresponding to the logistics vehicle on the logistics park simulation map based on the target platform and the road grid.

[0008] Optionally, in a third implementation of the first aspect of the present invention, generating a road grid for the logistics park based on the landmark includes: generating a basic road grid based on the location of the landmark and the geographical information of the logistics park, wherein each grid cell can be regarded as a possible vehicle location; determining the coordinates of each grid cell based on the landmark to obtain the grid cell coordinates; and generating a road grid for the logistics park based on the grid cell coordinates and the landmark location.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, generating the transportation navigation route corresponding to the logistics vehicle based on the target platform and the road grid includes: determining the target platform that the logistics vehicle needs to reach based on the logistics task and requirements; using a path planning algorithm to find the optimal path from the vehicle's current position to the target platform on the road grid; generating the transportation navigation route of the logistics vehicle on a logistics park simulation map based on the result of the path planning algorithm; and generating a visualized transportation navigation route on the logistics park simulation map.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of using a path planning algorithm to find the optimal path from the vehicle's current position to the target platform on a road grid includes: an initialization step: defining information for each node on the map, including the node's coordinates, whether it is passable, its distance from the starting point (G value), its estimated distance to the target point (H value), and its F value, wherein the F value is the sum of the distances G and H; during initialization, the G value of all nodes is set to infinity, and the H value is set according to their estimated distances to the target point; a step of setting the starting and ending points: marking the vehicle's current position on the map as the starting point and the target platform as the ending point; path Planning steps: Starting from the starting point, calculate the G value from the starting point to its neighboring nodes and update the F value of the nodes. Add the starting point to the visited list and the neighboring nodes to the unvisited list. Select the node with the smallest F value from the unvisited list as the new current node and add it to the visited list. Repeated search steps: Repeat the path planning steps, continuously updating the G and F values ​​of the nodes around the current node until the target node is added to the visited list or the unvisited list is empty. Path generation steps: Starting from the target node, backtrack along the parent node of each node, which is the neighboring node that minimizes its G value, until returning to the starting point to obtain the optimal path.

[0011] Optionally, in the sixth implementation of the first aspect of the present invention, the acquisition of the H value in the path planning step includes the following steps: marking all landmarks on the simulation map and assigning a weight to each landmark, the weight representing the importance of the landmark, i.e., whether the landmark is frequently used by logistics vehicles; determining the nearest landmark and determining the weight of the nearest landmark; the H value calculation step, wherein the H value calculation formula is: H = (α * estimated distance to the target point + β * estimated distance to the nearest landmark) * (1 - weight of the nearest landmark), where α and β are the weight parameters of the distance to the target point and the distance to the nearest landmark, respectively.

[0012] Optionally, in the seventh implementation of the first aspect of the present invention, the step of acquiring physical data and real-time data of the logistics vehicles, generating a digital twin corresponding to the logistics vehicles in the simulation map, and synchronously displaying the digital twins in the simulation map includes: acquiring physical data of the logistics vehicles, the physical data including the vehicle's size, weight, load capacity, and maximum speed; acquiring real-time data of the logistics vehicles, the real-time data including position, speed, direction, and load; creating a corresponding digital twin, the digital twin being a digital representation of the vehicle, containing all the vehicle's physical data and real-time data; and synchronously displaying the digital twin of each logistics vehicle in a corresponding graphic on the simulation map, while highlighting a landmark that the logistics vehicle is approaching.

[0013] A second aspect of the present invention provides a smart park navigation device based on digital twins, comprising: a simulation map generation unit for generating a logistics park simulation map containing landmarks based on modeling data; a transportation navigation route generation unit for generating a transportation navigation route on the logistics park simulation map based on the landmarks; a digital twin display unit for acquiring physical data and real-time data of logistics vehicles, generating a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously displaying the digital twin in the simulation map; and a navigation unit for completing navigation based on the transportation navigation route and the digital twin corresponding to the logistics vehicle when the logistics vehicle arrives at a designated area at the entrance of the logistics park.

[0014] A third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor invokes the instructions in the memory to cause the electronic device to execute the above-described digital twin-based smart park navigation method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned digital twin-based smart park navigation method.

[0016] The technical solution of the present invention has the following beneficial effects:

[0017] 1. Real-time Logistics Navigation and Optimization: By processing real-time data and transportation navigation routes of logistics vehicles, this solution enables real-time navigation of logistics vehicles and optimizes navigation routes based on landmarks and other factors. This helps improve logistics efficiency, reduce congestion, and lower operating costs.

[0018] 2. Logistics Park Simulation and Visualization: By generating digital twins of logistics vehicles on a simulation map, this solution enables the simulation and visualization of logistics parks. This helps users better understand the park's operations, support decision-making, and identify potential problems and areas for improvement.

[0019] 3. Landmark Use and Management: By marking landmarks on the simulation map and using them during route planning and navigation, this solution enables the effective use and management of landmarks. This helps improve navigation accuracy and efficiency, enhancing the user experience. Attached Figure Description

[0020] Figure 1 A first flowchart of a smart park navigation method based on digital twins provided in an embodiment of the present invention;

[0021] Figure 2 A second flowchart of a smart park navigation method based on digital twins provided in an embodiment of the present invention;

[0022] Figure 3 A third flowchart of a smart park navigation method based on digital twins provided in an embodiment of the present invention;

[0023] Figure 4 A fourth flowchart of a smart park navigation method based on digital twins provided in an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of a smart park navigation device based on digital twin provided in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Detailed Implementation

[0026] This invention provides a smart park navigation method, apparatus, device, and storage medium based on digital twins. The method includes: generating a simulated map of the logistics park containing landmarks based on modeling data; generating a transportation navigation route on the simulated map based on the landmarks; acquiring physical data and real-time data of logistics vehicles, generating a digital twin corresponding to each logistics vehicle on the simulated map, and synchronously displaying the digital twin on the simulated map; and completing navigation based on the transportation navigation route and the corresponding digital twin when a logistics vehicle arrives at a designated area at the entrance of the logistics park. This invention, by generating digital twins of logistics vehicles on a simulated map, enables the simulation and visualization of the logistics park, achieving effective and precise management and guidance of vehicles within the logistics park, thereby improving the operational efficiency and safety of the logistics park.

[0027] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the smart park navigation method based on digital twins in this invention includes:

[0029] 101. Based on the modeling data, generate a simulation map of the logistics park containing landmarks;

[0030] This step involves using GIS (Geographic Information System) or similar map modeling tools to generate a simulation map of the logistics park based on the input modeling data (such as building locations, road layouts, traffic rules, etc.). This map should include all the important landmarks of the logistics park, such as warehouses, loading and unloading areas, parking lots, office buildings, etc.

[0031] 102. Based on the landmarks, generate a transportation navigation route on the simulation map of the logistics park;

[0032] In this step, the system will use a route planning algorithm to generate transport navigation routes based on landmarks and other input parameters (such as road conditions, traffic rules, etc.). These routes should instruct logistics vehicles how to get from the park entrance to the designated loading / unloading area or warehouse.

[0033] 103. The step of acquiring physical data and real-time data of logistics vehicles, generating a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously displaying the digital twin in the simulation map;

[0034] In this step, the system needs to acquire physical data (such as size, weight, horsepower, etc.) and real-time data (such as position, speed, direction, etc.) from sensors on the logistics vehicle. Then, the system will generate a digital twin of the logistics vehicle on the simulation map—a digitized virtual entity that reflects the actual status of the logistics vehicle. The system will update the position of this digital twin in real time to reflect the real-time location of the logistics vehicle within the logistics park.

[0035] 104. When a logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0036] In this step, when the logistics vehicle arrives at the designated area at the entrance of the logistics park, the system will activate its navigation function. Based on the previously generated transport navigation route and the digital twin of the logistics vehicle, the system will provide the driver with navigation instructions on how to reach the designated location. This includes turn prompts, estimated arrival time, and other information. Throughout the process, the system will update the position of the digital twin in real time to ensure navigation accuracy.

[0037] In this embodiment, by generating a simulation map and transportation navigation routes for the logistics park, the transportation paths of logistics vehicles can be planned and managed more accurately, improving the management efficiency of the logistics park. By generating landmark-based transportation navigation routes, transportation paths can be planned more precisely, avoiding unnecessary routes and thus reducing unnecessary transportation costs. By using digital twins and landmark-based navigation, the status of logistics vehicles can be reflected in real time, improving the accuracy and efficiency of navigation.

[0038] Please see Figure 2 The second embodiment of the smart park navigation method based on digital twins in this invention includes:

[0039] 201. Based on the modeling data, generate a simulation map of the logistics park containing landmarks;

[0040] 202. Based on the landmarks, generate a transportation navigation route on the simulation map of the logistics park;

[0041] 203. Obtain physical data of the logistics vehicle, including the vehicle's dimensions, weight, load capacity, and maximum speed;

[0042] Physical data describes the basic characteristics of logistics vehicles, including their dimensions, weight, payload capacity, and maximum speed. This data is typically available from the vehicle manufacturer or operator. For example, the manufacturer can provide the vehicle's dimensions and weight, while the operator can provide its payload capacity and maximum speed.

[0043] 204. Obtain real-time data of logistics vehicles, including location, speed, direction, and load;

[0044] Real-time data describes the current status of logistics vehicles, including location, speed, direction, and load. This data is typically obtained from sensors on the vehicle or from Global Positioning System (GPS) devices. For example, a GPS device can provide the vehicle's location, speed, and direction, while the vehicle's load is obtained through other sensors, such as weight sensors.

[0045] 205. Create a corresponding digital twin, which is a digital representation of the vehicle and contains all physical and real-time data of the vehicle;

[0046] A digital twin is a digital representation of a vehicle, containing all its physical and real-time data. Creating a digital twin typically involves data integration and model building. First, the system needs to integrate the acquired physical and real-time data. Then, the system needs to use this data to build a digital model that reflects the vehicle's state. For example, this model might use physical data to describe the vehicle's basic characteristics and real-time data to describe its current state.

[0047] 206. On the simulation map, a digital twin of each logistics vehicle is displayed synchronously with a corresponding graphic, while highlighting a landmark that the logistics vehicle is approaching.

[0048] In this step, the system needs to synchronously display a digital twin of each logistics vehicle on the simulation map as a corresponding graphic. This graphic should reflect the vehicle's basic characteristics (such as size and shape) and current status (such as location and orientation). Simultaneously, the system should highlight a landmark that the logistics vehicle is approaching to help drivers and managers understand the vehicle's location and surroundings.

[0049] 207. When a logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0050] Through digital twins and simulation maps, managers can track the location, speed, direction, and load of logistics vehicles in real time, enabling timely decision-making and management.

[0051] Acquiring both physical and real-time data of logistics vehicles is crucial for creating digital twins. Physical data helps the system understand the vehicle's fundamental characteristics, allowing for more accurate simulation of its behavior. Real-time data helps the system understand the vehicle's current state, reflecting its real-time status more accurately. Simultaneously, this data also assists the system in more efficient route planning and navigation, improving the operational efficiency and safety of logistics parks.

[0052] In complex logistics parks, landmarks help drivers better understand their location and the directions they should take. Highlighting nearby landmarks provides clear visual cues, helping drivers accurately follow navigation instructions. For managers, knowing which vehicles are approaching which landmarks helps optimize resource allocation. For example, knowing that a vehicle is approaching the loading / unloading area allows for advance planning of personnel and equipment for unloading operations. Therefore, highlighting nearby landmarks not only improves the navigation experience and accuracy but also enhances safety and operational efficiency.

[0053] Please see Figure 3 The third embodiment of the smart park navigation method based on digital twins in this invention includes:

[0054] 301. Based on the modeling data, generate a simulation map of the logistics park containing landmarks;

[0055] 302. Select geographical features or equipment as landmarks within the logistics park simulation map, whereby the landmarks serve as anchor points for the transportation navigation route;

[0056] Within the logistics park simulation map, select geographical features or equipment as landmarks. These landmarks can be buildings (such as warehouses or office buildings), equipment (such as cranes or conveyor belts), or other prominent geographical features (such as intersections or parking lots). The selection of landmarks should be based on their location and importance within the logistics park, and whether they can serve as navigation anchors.

[0057] 303. Generate a road grid for the logistics park based on the aforementioned landmarks;

[0058] Generate a road grid for the logistics park based on the selected landmarks. This grid should include all roads accessible to logistics vehicles and should use landmarks as key connection points. Methods for generating the road grid can include manual drawing using GIS technology or the use of automated map generation algorithms.

[0059] 304. Based on the target platform and road grid, generate the transportation navigation route corresponding to the logistics vehicle on the simulation map of the logistics park;

[0060] Based on the target platform and road grid, transportation navigation routes for logistics vehicles are generated on the simulation map of the logistics park. This process typically includes the following steps:

[0061] Determine the starting point and the destination: The starting point is usually the current location of the logistics vehicle, and the destination is the target loading platform.

[0062] Identify feasible routes: Using a road grid and landmarks, identify all feasible routes from the origin to the destination. These routes should avoid areas where driving is prohibited, such as sidewalks or buildings.

[0063] Selecting the optimal path: Based on certain optimization criteria (such as shortest distance, least time, least energy consumption, etc.), select an optimal path as the transportation navigation route.

[0064] 305. Acquire physical data and real-time data of logistics vehicles, generate a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously display the real-time position of the digital twin in the simulation map.

[0065] 306. When a logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0066] In this implementation, landmarks are prominent features within the logistics park, easily identifiable and memorable. Using landmarks as anchor points for navigation routes makes navigation instructions easier to understand and improves accuracy. During route planning, landmarks can serve as key decision points, helping to determine the optimal route from one place to another.

[0067] Please see Figure 4 The fourth embodiment of the smart park navigation method based on digital twins in this invention includes:

[0068] 401. Based on the modeling data, generate a simulation map of the logistics park containing landmarks;

[0069] 402. Select geographical features or equipment as landmarks within the logistics park simulation map, whereby the landmarks serve as anchor points for the transportation navigation route;

[0070] 403. Based on the location of the landmarks and the geographical information of the logistics park, generate a basic road grid, where each grid cell can be considered a possible vehicle location; determine the coordinates of each grid cell based on the landmarks to obtain the grid cell coordinates; generate the road grid of the logistics park based on the grid cell coordinates and the location of the landmarks.

[0071] Identifying Landmarks: First, it is necessary to identify landmarks within the logistics park. These can be buildings, equipment, or other prominent geographical features. These landmarks will be used as reference points in the following steps.

[0072] Defining Grid Cells: Next, we need to define the grid cells. Each grid cell can be considered a possible vehicle location. The size of the grid cell should be determined based on the size of the logistics park and the size of the vehicle. For example, if the logistics park is large or the vehicle is small, the grid cell size can be relatively large. Conversely, if the logistics park is small or the vehicle is large, the grid cell size should be relatively small.

[0073] Determine grid cell coordinates: Assign coordinates to each grid cell based on the location of the landmark. These coordinates should reflect the grid cell's position relative to the landmark. For example, if a grid cell is directly above a landmark, its coordinates might be (0,1). The method for determining grid cell coordinates can be chosen based on the specific circumstances and needs of the logistics park; common methods include Cartesian coordinate systems and polar coordinate systems.

[0074] Generating the Road Grid: Finally, based on the grid cell coordinates and landmark locations, a road grid for the logistics park is generated. This step requires determining which grid cells are navigable (i.e., roads) and which are not (e.g., buildings or other obstacles). This allows for the generation of a basic road grid based on landmarks and the logistics park's geographic information, providing a foundation for subsequent navigation and route planning.

[0075] 404. Based on the logistics tasks and requirements, determine the target platform that the logistics vehicles need to reach; use a path planning algorithm to find the optimal path from the current position of the vehicle to the target platform on the road grid; based on the results of the path planning algorithm, generate the transportation navigation route of the logistics vehicles on the logistics park simulation map; generate a visualized transportation navigation route on the logistics park simulation map.

[0076] Identify the target platform: Based on the logistics task and requirements, determine the target platform that the logistics vehicles need to reach. This may involve parsing detailed information about the transportation task, such as the type, quantity, and destination of the goods. The target platform should be located within the logistics park and be able to accommodate the scheduled logistics vehicles.

[0077] Obtain the vehicle's current location: This involves retrieving the current location of the logistics vehicle. This can be achieved using GPS or other positioning systems. The current location should be converted into coordinates on the previously generated road grid.

[0078] Running the path planning algorithm: On the road grid, the path planning algorithm is used to find the optimal path from the vehicle's current position to the target platform. Specifically, it includes the following steps:

[0079] Initialization steps:

[0080] Define the information of each node on the map, including the node's coordinates, whether it is passable, its distance from the starting point (G value), its estimated distance to the target point (H value), and its F value, where the F value is the sum of the distances G and H values. During initialization, the G value of all nodes is set to infinity, and the H value is set according to their estimated distances to the target point.

[0081] Obtaining the H value involves the following steps:

[0082] All landmarks are marked on the simulation map, and a weight is assigned to each landmark, which represents the importance of the landmark, i.e., whether the landmark is frequently used by logistics vehicles;

[0083] Determine the nearest landmark and its weight;

[0084] The steps for calculating the H value, and the formula for calculating the H value are as follows:

[0085] H = (α * estimated distance to target point + β * estimated distance to nearest landmark) * (1 - weight of nearest landmark), where α and β are the weight parameters for the distance to the target point and the distance to the nearest landmark, respectively.

[0086] Steps to set the start and end points:

[0087] Mark the vehicle's current location on the map as the starting point and the target platform as the destination;

[0088] Path planning steps:

[0089] Starting from the starting point, calculate the G value from the starting point to its neighboring nodes, update the F value of the nodes, put the starting point into the visited list, put the neighboring nodes into the unvisited list, select the node with the smallest F value from the unvisited list as the new current node, and put it into the visited list.

[0090] Repeat the search steps:

[0091] Repeat the path planning steps, continuously updating the G and F values ​​of the nodes surrounding the current node, until the target node is added to the visited list, or the unvisited list is empty;

[0092] Steps to generate the path:

[0093] Starting from the target node, backtrack along the parent node of each node, which is the neighboring node that minimizes its G value, until returning to the starting point to obtain the optimal path.

[0094] Generate transportation navigation routes: Based on the results of the path planning algorithm, generate transportation navigation routes for logistics vehicles on the simulation map of the logistics park. This route should clearly show the starting point (current vehicle location), the destination (target platform), and the landmarks passed through.

[0095] Generate visualized transportation navigation routes: Visualize transportation navigation routes on a simulation map of the logistics park. This can be achieved by drawing a line from the starting point to the destination; the line's color and width can be adjusted as needed. Additionally, key navigation points, such as turning points and intersections, can be marked on the route.

[0096] 405. Acquire physical data and real-time data of logistics vehicles, generate a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously display the real-time position of the digital twin in the simulation map.

[0097] 406. When a logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0098] In this embodiment, the weight of landmarks plays a crucial role in route planning. A high-weight landmark indicates that it is frequently used by logistics vehicles and therefore may be prioritized in route planning. Conversely, a low-weight landmark may indicate that it is less frequently used or may be a difficult-to-access area. By assigning weights to landmarks, route planning can be optimized, making the generated routes more consistent with actual driving conditions.

[0099] In this embodiment, if a landmark is frequently used by logistics vehicles, it may be necessary to optimize the resources surrounding that landmark. For example, it might be necessary to add more parking spaces near the landmark or increase the road capacity near it. By assigning weights to landmarks, logistics park managers can understand which areas have the greatest resource demand and thus make optimizations.

[0100] Please see Figure 5 The embodiments of the smart park navigation device based on digital twins in this invention include:

[0101] 501. Simulation map generation unit, used to generate a simulation map of the logistics park containing landmarks based on the modeling data;

[0102] 502. A transportation navigation route generation unit, used to generate a transportation navigation route on the simulation map of the logistics park based on the landmarks;

[0103] 503. A digital twin display unit, used to acquire physical data and real-time data of logistics vehicles, generate a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously display the real-time position of the digital twin in the simulation map.

[0104] 504. Navigation unit, used to complete navigation when a logistics vehicle arrives at a designated area at the entrance of a logistics park, based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

[0105] above Figure 5 The apparatus in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. The electronic device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0106] Figure 6 This is a schematic diagram of the structure of an electronic device 600 provided in an embodiment of the present invention. The electronic device 600 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) for storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the electronic device 600.

[0107] Electronic device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 6 The illustrated electronic device structure does not constitute a limitation on electronic devices and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0108] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a smart park navigation method based on digital twins.

[0109] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart park navigation method based on digital twinning, characterized in that, The smart park navigation method based on digital twins includes: Based on the modeling data, a simulation map of the logistics park containing landmarks is generated; Based on the aforementioned landmarks, a transportation navigation route is generated on the simulation map of the logistics park, including: Within the logistics park simulation map, select geographical features or equipment as landmarks, which serve as anchor points for transportation navigation routes; generate a road grid for the logistics park based on these landmarks; Based on the logistics tasks and requirements, determine the target platform that the logistics vehicles need to reach; use a path planning algorithm to find the optimal path from the current position of the vehicle to the target platform on the road grid; based on the results of the path planning algorithm, generate the transportation navigation route of the logistics vehicles on the logistics park simulation map; generate a visualized transportation navigation route on the logistics park simulation map. Acquire physical data and real-time data of logistics vehicles, generate a digital twin corresponding to the logistics vehicle in the simulation map, and display the digital twin synchronously in the simulation map; When a logistics vehicle arrives at the designated area at the entrance of the logistics park, navigation is completed based on the transportation navigation route and the digital twin corresponding to the logistics vehicle. The method of using a path planning algorithm to find the optimal path from the vehicle's current position to the target platform on the road grid includes: Initialization steps: Define the information of each node on the map, including the node's coordinates, whether it is passable, its distance from the starting point (G value), its estimated distance to the target point (H value), and its F value, where the F value is the sum of the distances G and H values. During initialization, the G value of all nodes is set to infinity, and the H value is set according to their estimated distances to the target point. Steps to set the start and end points: Mark the vehicle's current location on the map as the starting point and the target platform as the destination; Path planning steps: Starting from the starting point, calculate the G value from the starting point to its neighboring nodes, update the F value of the nodes, put the starting point into the visited list, put the neighboring nodes into the unvisited list, select the node with the smallest F value from the unvisited list as the new current node, and put it into the visited list. Repeat the search steps: Repeat the path planning steps, continuously updating the G and F values ​​of the nodes surrounding the current node, until the target node is added to the visited list, or the unvisited list is empty; Steps to generate the path: Starting from the target node, backtrack along the parent node of each node, where the parent node is the neighboring node that minimizes its G value, until returning to the starting point to obtain the optimal path; Obtaining the H value in the path planning step includes the following steps: All landmarks are marked on the simulation map, and a weight is assigned to each landmark, which represents the importance of the landmark, i.e., whether the landmark is frequently used by logistics vehicles; Determine the nearest landmark and its weight; The steps for calculating the H value, and the formula for calculating the H value are as follows: H = (α * estimated distance to target point + β * estimated distance to nearest landmark) * (1 - weight of nearest landmark), where α and β are the weight parameters for the distance to the target point and the distance to the nearest landmark, respectively.

2. The digital-twin-based smart park navigation method of claim 1, wherein, The step of generating a road grid for the logistics park based on the landmarks includes: Based on the location of landmarks and the geographical information of the logistics park, a basic road grid is generated, with each grid cell considered as a possible vehicle location; Based on the landmarks, determine the coordinates of each grid cell to obtain the grid cell coordinates; A road grid for the logistics park is generated based on grid cell coordinates and landmark locations.

3. The digital-twin-based smart park navigation method of claim 1, wherein, The process of acquiring physical data and real-time data of logistics vehicles, generating a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously displaying the digital twin in the simulation map includes: Acquire physical data of logistics vehicles, including vehicle dimensions, weight, load capacity, and maximum speed; Acquire real-time data of logistics vehicles, including location, speed, direction, and load; Create a corresponding digital twin, which is a digital representation of the vehicle and contains all the vehicle's physical and real-time data; On the simulation map, a digital twin of each logistics vehicle is displayed synchronously with a corresponding graphic, while highlighting a landmark that the logistics vehicle is approaching.

4. A smart park navigation device based on digital twins, based on the method of claim 1, characterized in that, include: The simulation map generation unit is used to generate a simulation map of the logistics park containing landmarks based on the modeling data. A transportation navigation route generation unit is used to generate a transportation navigation route on the simulation map of the logistics park based on the landmarks. The digital twin display unit is used to acquire physical data and real-time data of logistics vehicles, generate a digital twin corresponding to the logistics vehicle in the simulation map, and synchronously display the digital twin in the simulation map. The navigation unit is used to complete navigation when a logistics vehicle arrives at a designated area at the entrance of the logistics park, based on the transportation navigation route and the digital twin corresponding to the logistics vehicle.

5. An electronic device comprising a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the digital twin-based smart park navigation method as described in any one of claims 1-3.

6. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the smart park navigation method based on digital twins as described in any one of claims 1-3.

Images