Unmanned delivery vehicle path optimization system and method based on real-time road condition prediction

Abstract

The invention discloses an unmanned delivery vehicle path optimization system and method based on real-time road condition prediction, and the system comprises a road condition prediction module which is used for collecting and analyzing road condition information in real time; the path planning module is used for dynamically generating an optimal distribution path according to the road condition prediction data; the energy consumption optimization module is used for calculating energy consumption of different paths and selecting the path with the lowest energy consumption; the safety monitoring module is used for monitoring the running state of the unmanned distribution vehicle in real time; the distribution task management module is used for managing distribution tasks; the communication module is used for carrying out data interaction with the cloud server and other unmanned distribution vehicles; the user feedback module is used for feeding back the distribution state and the predicted arrival time to the user; according to the invention, the distribution path can be dynamically adjusted through real-time road condition prediction, and the distribution time is reduced; the path with the lowest energy consumption is selected through the energy consumption optimization module, so that the operation cost is reduced; the vehicle state is monitored in real time through the safety monitoring module, and the risks of collision and route deviation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent logistics, and particularly to an unmanned delivery vehicle path optimization system and method based on real-time road condition prediction. Background Art

[0002] With the rapid development of e-commerce, unmanned delivery vehicles are increasingly widely used in the logistics field. However, the existing unmanned delivery vehicle systems have the following problems: 1. Low path planning efficiency: The existing systems usually adopt static path planning algorithms and cannot respond to road condition changes (such as traffic congestion, construction, etc.) in real time, resulting in low delivery efficiency.

[0003] 2. Insufficient energy consumption optimization: The energy consumption of unmanned delivery vehicles is closely related to the path selection, and the existing systems fail to fully consider the problem of energy consumption optimization.

[0004] 3. Poor safety: In complex road conditions, unmanned delivery vehicles are prone to collisions or route deviations, affecting the safety and reliability of delivery.

[0005] Therefore, there is an urgent need for an unmanned delivery vehicle path planning system that can combine real-time road condition prediction and energy consumption optimization. Summary of the Invention

[0006] The purpose of the present invention is to provide an unmanned delivery vehicle path optimization system and method based on real-time road condition prediction for the above problems in the prior art, and thus solve all or one of the above problems existing in the prior art.

[0007] To solve the above technical problems, the specific technical solutions of the present invention are as follows: On the one hand, the present invention provides an unmanned delivery vehicle path optimization system based on real-time road condition prediction, including: A road condition prediction module for collecting and analyzing road condition information in real time; A path planning module for dynamically generating an optimal delivery path according to the road condition prediction data; An energy consumption optimization module for calculating the energy consumption of different paths and selecting the path with the lowest energy consumption; A safety monitoring module for monitoring the running state of the unmanned delivery vehicle in real time; A delivery task management module for managing delivery tasks; A communication module for data interaction with the cloud server and other unmanned delivery vehicles; A user feedback module for feeding back the delivery status and estimated arrival time to the user.

[0008] Further, the road condition information collected by the road condition prediction module includes traffic flow, weather conditions, and construction information.

[0009] Furthermore, the path planning module is also used to generate an optimal delivery path by using a dynamic path planning algorithm.

[0010] Furthermore, the energy consumption optimization module selects the optimal path by calculating the path distance and vehicle energy consumption.

[0011] Furthermore, the safety monitoring module detects the vehicle's surrounding environment in real time through sensors.

[0012] Furthermore, the delivery task management module supports task priority sorting and task status tracking.

[0013] Furthermore, the communication module supports 5G and Wi-Fi communication protocols.

[0014] Furthermore, the user feedback module sends delivery information to users via text messages or applications.

[0015] Furthermore, the path planning module is also used to support multi-vehicle collaborative path planning.

[0016] On the other hand, the present invention also provides a method for optimizing the path of an unmanned delivery vehicle based on real-time road condition prediction, including the following steps: Collect and analyze road condition information in real time; Dynamically generate an optimal delivery path according to the road condition prediction data; Calculate the energy consumption of different paths and select the path with the lowest energy consumption; Monitor the running state of the unmanned delivery vehicle in real time; Manage delivery tasks; Perform data interaction with the cloud server and other unmanned delivery vehicles; Provide feedback to users on the delivery status and estimated arrival time.

[0017] The beneficial effects of the technical solution of the present invention are as follows: 1. The unmanned delivery vehicle path optimization system based on real-time road condition prediction according to the present invention can dynamically adjust the delivery path through real-time road condition prediction, reducing the delivery time; select the path with the lowest energy consumption through the energy consumption optimization module, reducing the operating cost; monitor the vehicle state in real time through the safety monitoring module, reducing the risk of collision and deviation from the route; provide real-time delivery information through the user feedback module, improving user satisfaction.

[0018] 2. The method for optimizing the path of an unmanned delivery vehicle based on real-time road condition prediction according to the present invention can orderly call the system modules, thereby realizing the system logic of the unmanned delivery vehicle path optimization system based on real-time road condition prediction according to the present invention. Description of the Drawings

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic diagram of the architecture of the unmanned delivery vehicle path optimization system based on real-time road condition prediction described in Embodiment 1 of the present invention; Figure 2 It is a schematic flowchart of the method for optimizing the path of an unmanned delivery vehicle based on real-time road condition prediction described in Embodiment 2 of the present invention. Specific Embodiments

[0021] The following will elaborate on the preferred embodiments of the present invention in conjunction with the drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0022] In the description of the present invention, it should be noted that the embodiments described in the present invention are some embodiments of the present invention, rather than all embodiments; based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0023] The terms "first", "second", etc. in the description and claims of this article and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or equipment. Embodiment 1

[0024] This embodiment provides an unmanned delivery vehicle path optimization system based on real-time road condition prediction, as Figure 1 shown, including: Road condition prediction module: used to collect and analyze road condition information in real time (such as traffic flow, weather conditions, construction information).

[0025] Path planning module: Dynamically generate the optimal delivery path according to the data of the road condition prediction module.

[0026] Energy consumption optimization module: used to calculate the energy consumption of different paths and select the path with the lowest energy consumption.

[0027] Safety monitoring module: used to monitor the running status of the unmanned delivery vehicle in real time to ensure delivery safety.

[0028] Delivery task management module: used to manage delivery tasks, including task assignment, priority sorting, and task status tracking.

[0029] Communication module: used to interact with the cloud server and other unmanned delivery vehicles for data.

[0030] User feedback module: used to feedback the delivery status and estimated arrival time to the user.

[0031] Specifically, in one implementation, the working logic of each of the above modules is as follows: (1) The road condition prediction module collects road condition information in real time through sensors and cloud data; for example, the system detects that a certain section of the road is congested due to construction and predicts that this section of the road will be impassable within the next 30 minutes.

[0032] (2) The path planning module generates multiple alternative paths based on the road condition prediction data and calculates the estimated delivery time and energy consumption of each path; for example, the system generates Path A (short distance but congested) and Path B (long distance but unobstructed), and selects Path B as the optimal path.

[0033] (3) The energy consumption optimization module calculates the energy consumption of each path; for example, the energy consumption of Path A is 100 units, and the energy consumption of Path B is 80 units, and the system selects the path with lower energy consumption, Path B.

[0034] (4) The safety monitoring module monitors the running status of the unmanned delivery vehicle in real time; for example, the system detects an obstacle in front of the vehicle and immediately adjusts the driving route to avoid collision.

[0035] (5) The delivery task management module assigns delivery tasks according to task priorities; for example, the system gives priority to delivering urgent orders to ensure their timely delivery.

[0036] (6) The user feedback module sends the delivery status and estimated arrival time to the user; for example, the system prompts "Your order is expected to be delivered within 30 minutes".

[0037] Specifically, in one implementation, the system architecture of the present invention includes the following components: (i) Hardware layer: includes an unmanned delivery vehicle, sensors (such as cameras, radars), communication devices, and batteries.

[0038] (ii)Communication layer: Used for data interaction with cloud servers and other unmanned delivery vehicles, supporting communication protocols such as 5G and Wi-Fi.

[0039] (iii)Application layer: Includes road condition prediction module, path planning module, energy consumption optimization module, safety monitoring module, delivery task management module, and user feedback module.

[0040] Specifically, in one implementation, taking urban logistics distribution as an example, the application effects of this system are as follows: (1)After receiving a delivery task, the road condition prediction module collects road condition information in real time.

[0041] (2)The path planning module generates multiple alternative paths and selects the optimal path (such as path B).

[0042] (3)The energy consumption optimization module calculates the energy consumption of path B and confirms that it is the path with the lowest energy consumption.

[0043] (4)The safety monitoring module monitors the vehicle operation status in real time to ensure delivery safety.

[0044] (5)The user feedback module sends the delivery status and estimated arrival time to the user.

[0045] It should be noted that the above examples are only for explaining the present invention and should not limit the protection scope of the present invention. Embodiment 2

[0046] This embodiment provides a method for optimizing the path of an unmanned delivery vehicle based on real-time road condition prediction, based on the same inventive concept as the unmanned delivery vehicle path optimization system based on real-time road condition prediction described in Embodiment 1, as Figure 2 shown, including the following steps: S100. Collect and analyze road condition information in real time; S200. Dynamically generate the optimal delivery path according to the road condition prediction data; S300. Calculate the energy consumption of different paths and select the path with the lowest energy consumption; S400. Monitor the operation status of the unmanned delivery vehicle in real time; S500. Manage delivery tasks; S600. Perform data interaction with cloud servers and other unmanned delivery vehicles; S700. Feedback the delivery status and estimated arrival time to the user.

[0047] It should be understood that in various embodiments herein, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments herein.

[0048] It should also be understood that in the embodiments herein, the term "and / or" is merely a correlative relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, in this text, the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0049] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this text.

[0050] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific logical processes of the methods described above can refer to the corresponding working processes of the systems, devices, and units in the foregoing method embodiments, and will not be elaborated herein.

[0051] In several embodiments provided in this text, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be in electrical, mechanical, or other forms of connection.

[0052] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments herein.

[0053] In addition, the functional units in each embodiment herein can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0054] If the integrated unit is implemented in the form of 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 this article, 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this article. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0055] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A route optimization system for unmanned delivery vehicles based on real-time road condition prediction, characterized in that: include: Traffic condition prediction module, used to collect and analyze traffic information in real time; The route planning module is used to dynamically generate the optimal delivery route based on traffic prediction data; Energy consumption optimization module, used to calculate the energy consumption of different paths and select the path with the lowest energy consumption; Safety monitoring module, used to monitor the operating status of unmanned delivery vehicles in real time; Delivery task management module, used to manage delivery tasks; Communication module, used for data exchange with cloud servers and other unmanned delivery vehicles; The user feedback module is used to provide users with feedback on delivery status and estimated arrival time.

2. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The road condition information collected by the road condition prediction module includes traffic flow, weather conditions and construction information.

3. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The path planning module is also used to generate an optimal delivery path using a dynamic path planning algorithm.

4. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The energy consumption optimization module selects the optimal path by calculating the path distance and the vehicle energy consumption.

5. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The safety monitoring module detects the vehicle's surrounding environment in real time through sensors.

6. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The delivery task management module supports task priority sorting and task status tracking.

7. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The communication module supports 5G and Wi-Fi communication protocols.

8. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The user feedback module sends the delivery information to the user via SMS or application.

9. The unmanned delivery vehicle route optimization system based on real-time road condition prediction according to claim 1 is characterized by: The path planning module is also used to support multi-vehicle collaborative path planning.

10. A method for optimizing the route of an unmanned delivery vehicle based on real-time road condition prediction, characterized in that: The following steps are involved: Collect and analyze traffic information in real time; Dynamically generate the optimal delivery route based on traffic forecast data; Calculate the energy consumption of different paths and select the path with the lowest energy consumption; Real-time monitoring of the operating status of unmanned delivery vehicles; Manage delivery tasks; Interact with cloud servers and other unmanned delivery vehicles for data; Provide users with feedback on delivery status and estimated arrival time.

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