Transportation route optimization method and system based on Internet of Things

Abstract

The invention discloses a transportation route optimization method and system based on the Internet of Things, and relates to the technical field of transportation route optimization, by integrating multi-source environment information and combining dynamic traffic data and transportation vehicle position data, transportation route planning is more intelligent and flexible, and when traffic jam, sudden accidents or road closing occur, the transportation route can be optimized more intelligently and flexibly. The system can detect the traffic condition of a transportation path in real time and recalculate and optimize the path through cloud computing when needed so as to ensure that the transportation task is completed on time, and the method not only can automatically adjust the transportation path, but also can continuously monitor the transportation process according to the real-time feedback data so as to ensure that the distribution is smoothly carried out, and meanwhile, the transportation efficiency is improved. A self-adaptive compensation algorithm is adopted to correct path deviation, so that the transportation route can be quickly adjusted, the transportation efficiency and customer requirements are ensured to be met in time, the transportation flexibility, timeliness and safety are greatly improved, and the development of intelligent transportation and logistics management is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation route optimization, and in particular to a transportation route optimization method and system based on the Internet of Things. Background Art

[0002] Through Internet of Things technology, the logistics system can obtain real-time road conditions, weather changes, traffic facility conditions and other data, and combine historical data, real-time positioning information of transport vehicles, cargo distribution needs and other factors to use algorithms to optimize transportation routes. This method can not only ensure the optimization of transportation time and cost, but also improve the safety and reliability of the transportation process.

[0003] The existing technology has the following defects: 1. The existing optimization system is unable to flexibly adjust transportation routes during actual transportation when dynamic factors such as sudden traffic incidents, weather changes, or road closures occur. This leads to low transportation efficiency and may even cause delays or unsafe transportation conditions. 2. The optimization system fails to fully utilize multi-source environmental information (such as historical data, weather forecasts, real-time traffic conditions, etc.), but relies on a single data source for route selection, which makes it less adaptable to the environment. In addition, due to the lack of real-time monitoring and feedback adjustment of the transportation process, it is often unable to respond to real-time data changes in a timely manner, nor can it dynamically optimize according to the actual transportation status, thus affecting the flexibility and timeliness of transportation. It is also difficult to intelligently correct route deviations. Once deviations occur, the adjustment speed is slow, affecting delivery time and customer satisfaction.

[0004] Based on this, the present invention proposes a transportation route optimization method and system based on the Internet of Things, which can quickly adjust the transportation route, ensure transportation efficiency and timely satisfaction of customer needs, greatly improve the flexibility, timeliness and safety of transportation, and promote the development of intelligent transportation and logistics management. Summary of the Invention

[0005] The purpose of the present invention is to provide a transportation route optimization method and system based on the Internet of Things to address the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a transportation route optimization method based on the Internet of Things, the optimization method comprising the following steps: S1: Generate preliminary transportation route planning based on multi-source environmental information; S2: Based on the real-time location data of the transport vehicle and dynamic traffic data, the transport route’s access range is automatically detected. If the access range is within the preset interval threshold, the current transport route is maintained. S3: If the access range on the path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transportation vehicle conditions; S4: Direct the transport vehicle to move according to the current transport route or the best alternative route, monitor the transport vehicle information in real time and adjust the transport vehicle's driving direction; S5: During the transportation process, real-time feedback data from transport vehicles is collected and the actual arrival location of the distribution node is continuously monitored; S6: If there is a deviation between the actual arrival location and the expected target location, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted; S7: After completing the delivery task, the entire process data is comprehensively traced and analyzed through the data cloud platform to generate a delivery report.

[0007] In a preferred embodiment, during the transportation process, real-time feedback data from the transport vehicle is collected, and the actual arrival position of the distribution node is continuously monitored, including the following steps: The feedback data of the transport vehicle includes the current position of the transport vehicle, the current speed, the load of the transport vehicle and the fuel consumption; The feedback data is aggregated and input into the dynamic adjustment model, which then calculates and outputs the time adjustment required for the transport vehicle. Continuously monitor the deviation between the actual arrival location and the expected target location of the distribution node. If there is a deviation between the actual arrival location and the expected target location, analyze the cause of the deviation and adjust the transportation route in a timely manner.

[0008] In a preferred embodiment, this data is collected in real time by onboard sensors and GPS systems and transmitted to a cloud computing center for processing via an IoT platform. The system continuously updates this information and compares and analyzes it with the planned route and arrival time to determine the current performance of the transportation process. Specifically, the feedback data is aggregated and input into a dynamic adjustment model, which calculates and outputs the time adjustment required for the transportation vehicle, expressed as: ,in, An adjustment for the time required to transport the vehicle, Indicates the load of the transport vehicle. is the road condition type function, which is used to adjust the impact of different road conditions on transportation efficiency. is the current speed of the transport vehicle, Indicates fuel consumption data, The maximum fuel consumption standard for transport vehicles, It is a time delay caused by unexpected situations.

[0009] In a preferred embodiment, directing the transport vehicle to travel according to the current transport route or the best alternative route, monitoring the transport vehicle information in real time, and adjusting the travel direction of the transport vehicle include the following steps: Track the location, speed and driving status of each transport vehicle in real time through GPS and on-board sensors. and the current speed of the transport vehicle Respectively represent the transport vehicles at time The position and instantaneous velocity of According to the current position, speed and predetermined target position of the transport vehicle, the current driving status and arrival time of the transport vehicle are calculated, and the driving direction and speed of the transport vehicle are adjusted by optimizing the objective function.

[0010] In a preferred embodiment, if the travel range on a path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transport vehicle conditions, including the following steps: If the travel range on the path is not within the interval threshold, the optimization mechanism is activated, the comprehensive optimization values ​​of all alternative paths are recalculated, and the transportation path with the largest comprehensive optimization value is selected as the best alternative route.

[0011] In a preferred embodiment, the optimization objective function is expressed as: ,in, Indicates the arrival time, is the remaining distance from the current position of the transport vehicle to the target position, is the current speed of the transport vehicle, Indicates the time delay caused by unexpected conditions.

[0012] In a preferred embodiment, based on the real-time position data of the transport vehicle and dynamic traffic data, automatically detecting the traffic range of the transport route includes the following steps: Based on the real-time location data and dynamic traffic data of transport vehicles, the traffic status of path p within the time window is evaluated through the traffic range evaluation function; If an abnormal situation is detected on the path, it will automatically detect whether the travel range is within the interval threshold. If it is within the interval threshold, the current path will be maintained and transportation will continue.

[0013] In a preferred embodiment, the traffic status of path p within the time window is evaluated by a traffic range evaluation function, which is expressed as follows: , where T(p) is the travel range of path p, S(p) is the current available section length of path p, D(p) is the total section length of path p, and F(p) is the impact factor of path p.

[0014] In a preferred embodiment, generating a preliminary transport route plan based on multi-source environmental information includes the following steps: Collect and integrate historical data, weather forecasts, and real-time traffic information; Historical data includes traffic flow, road conditions, and average speed of transport vehicles; Weather forecasts include providing predictive information on future weather conditions, including precipitation, wind speed, and temperature factors; Real-time traffic information reflects changes in road congestion, sudden traffic accidents, and road closures due to construction; The efficiency is calculated for each transport path, and by evaluating multiple paths, the transport path with the highest efficiency is selected as the initial transport path.

[0015] The present application also provides a transportation route optimization system based on the Internet of Things, including a traffic analysis module, a path optimization module, and an adaptive correction module; Traffic analysis module: Generates preliminary transport route planning based on multi-source environmental information. Based on the real-time location data of transport vehicles and dynamic traffic data, it automatically detects the traffic range of the transport route. If the traffic range is within the preset interval threshold, the current transport route is maintained. Path Optimization Module: If the travel range on a path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transport vehicle conditions. Transport vehicles are directed to travel according to the current transport route or the best alternative route, and transport vehicle information is monitored in real time and the direction of travel of transport vehicles is adjusted; Adaptive correction module: During the transportation process, feedback data from the transport vehicles is collected in real time, and the actual arrival position of the distribution node is continuously monitored. If there is a deviation between the actual arrival position and the expected target position, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention makes transportation route planning more intelligent and flexible by integrating multi-source environmental information (including historical data, weather forecasts and real-time road conditions) and combining dynamic traffic data and transportation vehicle location data. When traffic congestion, sudden accidents or road closures occur, the system can detect the traffic conditions of the transportation route in real time and recalculate the optimized route through cloud computing when necessary, thereby ensuring that the transportation task is completed on time. This method can not only automatically adjust the transportation route, but also continuously monitor the transportation process based on real-time feedback data (such as transportation vehicle location, speed, load, fuel consumption, etc.) to ensure smooth distribution. At the same time, an adaptive compensation algorithm is used to correct path deviations, which can quickly adjust the transportation route to ensure transportation efficiency and timely satisfaction of customer needs. The application of this system has greatly improved the flexibility, timeliness and safety of transportation, and promoted the development of intelligent transportation and logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 Flowchart of the optimization method of the present invention.

[0019] Figure 2 This is an architectural diagram of the optimization system of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Example 1: Please refer to Figure 1 As shown, the present embodiment provides a method for optimizing transportation routes based on the Internet of Things, and the optimization method includes the following steps: S1: Generate preliminary transportation route planning based on multi-source environmental information, including historical data, weather forecasts, and real-time traffic information; S2: Automatically detect the transport route's access range based on the transport vehicle's real-time location data and dynamic traffic data (such as sudden traffic accidents, road congestion, temporary road closures, etc.). If the access range is within the preset interval threshold, the current transport route is maintained, achieving transport flexibility and timeliness. S3: If the access range on the path is not within the interval threshold, all possible optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transportation vehicle conditions; S4: Direct the transport vehicle based on the current transport route or the best alternative route, monitor the transport vehicle's position, speed, and arrival time in real time, and adjust the transport vehicle's driving direction in a timely manner to ensure that the delivery task is completed within the scheduled time window; S5: During the transportation process, real-time feedback data from transport vehicles (such as location, speed, load, fuel consumption, etc.) is collected and the actual arrival location of the distribution node is continuously monitored to ensure the smooth progress of the transportation process; S6: If there is a deviation between the actual arrival location and the expected target location, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted; S7: After completing the delivery task, the entire process data is comprehensively traced and analyzed through the data cloud platform to generate a delivery report, thereby providing more accurate and efficient decision-making support for future transportation route optimization.

[0022] This application makes transportation route planning more intelligent and flexible by integrating multi-source environmental information (including historical data, weather forecasts and real-time road conditions) and combining dynamic traffic data and transportation vehicle location data. When traffic congestion, sudden accidents or road closures occur, the system can detect the traffic conditions of the transportation route in real time and recalculate the optimized route through cloud computing when necessary to ensure that the transportation task is completed on time. This method can not only automatically adjust the transportation route, but also continuously monitor the transportation process based on real-time feedback data (such as transportation vehicle location, speed, load, fuel consumption, etc.) to ensure smooth delivery. At the same time, the adaptive compensation algorithm is used to correct path deviations, which can quickly adjust the transportation route to ensure transportation efficiency and timely satisfaction of customer needs. The application of this system has greatly improved the flexibility, timeliness and safety of transportation, and promoted the development of intelligent transportation and logistics management.

[0023] S1: Generate preliminary transportation route planning based on multi-source environmental information, which includes historical data, weather forecasts and real-time traffic information.

[0024] Generating preliminary transport route plans based on multi-source environmental information involves a multi-dimensional, multi-layered data fusion process. First, by collecting and integrating historical data, weather forecasts, and real-time traffic information, the system provides comprehensive decision support for transport route selection. Historical data refers to long-term accumulation of traffic flow, road conditions, and the average speed of transport vehicles. This data helps assess common transport bottlenecks and peak traffic periods. Weather forecasts provide information on future weather conditions, including precipitation, wind speed, and temperature, which often directly impact road conditions and transport safety. Real-time traffic information, derived from traffic monitoring systems, sensors, and GPS data, provides real-time insights into changes such as road congestion, sudden traffic accidents, and road closures due to construction.

[0025] When generating a transport route plan, the system first integrates data from different sources using a weighted algorithm and evaluates each possible transport route based on factors such as traffic flow and weather conditions. A capacity model is set for a particular route, where the overall efficiency of the route can be expressed using the following formula: , where E(p) is the efficiency of path p, V(p) represents the traffic flow on path p, C(p) is the capacity of path p (i.e., the road's traffic capacity), and W(p) is the weather impact weight on path p. Specifically, V(p) is estimated using historical data and real-time traffic data, C(p) depends on infrastructure information such as the road design and number of lanes, and W(p) is dynamically adjusted based on weather forecast data. For example, when there is heavy precipitation, the road friction coefficient decreases, which affects driving speed.

[0026] The value of W(p) typically ranges from 0 to 1, and the specific value varies depending on the actual weather conditions: W(p) = 1: indicates that the weather conditions have no impact on the path, typically used in sunny or ideal weather conditions. W(p) < 1: indicates that the weather conditions have some impact on the path's capacity, typically in inclement weather such as rain, snow, or fog. The specific value will be adjusted according to the severity of the weather.

[0027] For example, light precipitation might set W(p) to 0.9, indicating a slight decrease in the efficiency of the path. Heavy snow or rain might set W(p) to 0.5 or lower, indicating a significant decrease in the path's capacity.

[0028] The value of W(p) is usually determined by weather forecast data. The specific logic is as follows: the system determines the weather conditions along the route based on real-time weather forecasts (such as precipitation, wind speed, temperature, visibility, etc.). After analyzing the weather data, it is mapped to an influencing factor, for example: Sunny day: W(p)=1; Light rain or frost: W(p)=0.9; Heavy rain or snow: W(p)=0.6; This weight is also adjusted based on the type of route. For example, inclement weather may have a greater impact on mountainous roads or low-lying areas, and the value of W(p) will decrease accordingly. Therefore, the value of W(p) is dynamically adjusted based on real-time weather data and road characteristics to more accurately reflect the impact of weather on the transport route's capacity.

[0029] Integrating multiple sources of information (traffic flow, road capacity, and weather conditions) into route selection decisions provides a quantitative basis for transportation route planning. By evaluating multiple routes, the system can initially determine the optimal transportation route, ensuring both timely delivery and road safety.

[0030] During route planning, real-time monitoring of weather and traffic conditions is also required to ensure timely and adaptable route selection. For example, in the event of traffic congestion or an accident, real-time traffic information is fed back to the cloud platform via sensors and road monitoring systems, influencing the current route planning assessment. At this point, the system dynamically updates the feasibility and optimal options based on changes in various parameters, ensuring that transportation routes can adapt to the ever-changing external environment during actual operation.

[0031] S2: Based on the real-time location data of transport vehicles and dynamic traffic data (such as sudden traffic accidents, road congestion, temporary road closures, etc.), the transport route's access range is automatically detected. If the access range is within the preset interval threshold, the current transport route is maintained, achieving transport flexibility and timeliness.

[0032] Based on the real-time location data and dynamic traffic data of the transport vehicles, the system can automatically detect the accessibility of the current transport route and make necessary adjustments to ensure the timeliness and flexibility of the transport task. The core of this step is to use the real-time location of the transport vehicles and dynamic traffic data obtained from channels such as traffic management systems, sensors, and traffic monitoring cameras, such as information on sudden traffic accidents, road congestion, and temporary road closures, to evaluate and monitor the accessibility of the transport route in real time. Once an abnormality on the path is detected, the system will automatically detect whether the accessibility is within the preset safety range. If the accessibility meets the preset threshold requirements, the system will maintain the current path and continue transportation; if the accessibility deviates from the threshold, the system will start the path optimization algorithm to recalculate and select the best alternative path.

[0033] Specifically, the real-time location data of the transport vehicle comes from the GPS system or on-vehicle sensors, which can provide key information such as the current latitude and longitude, speed, and driving direction of the transport vehicle. The dynamic traffic data includes traffic accident reports, road section flow monitoring, road construction, traffic signal control, etc. These data are uploaded to the cloud server through the Internet of Things platform and analyzed in real time to determine the feasibility of the current route.

[0034] To quantify the通行范围 of the route, a通行范围 evaluation function is defined , which is used to evaluate the通行状态 of route p within a specific time window. This function can be calculated according to the following formula: , where T(p) is the通行范围 of route p, S(p) is the length of the currently available road sections of route p, D(p) is the total length of route p, and F(p) is the influence factor of factors such as traffic accidents, congestion, and road closures on route p (0 , and the larger the value, the more severely the route is affected by traffic interference. Specifically, S(p) and D(p) represent the lengths of the unobstructed road sections and the total road sections on the route respectively, while F(p) is the interference factor dynamically calculated based on real-time traffic information. For example, when a traffic accident occurs on a certain road section, the value of F(p) will increase, indicating that the traffic capacity of this road section is greatly affected, thus affecting the通行范围 of the entire route. By dynamically evaluating the actual通行状态 of the route, it helps the system determine whether the current route meets the safety and timeliness requirements. If is less than the preset threshold (i.e., the通行范围 does not meet the standard), the system will consider that this route cannot be used continuously and start the route optimization algorithm to select an alternative route; if is within the preset interval, the system will maintain the current route to ensure smooth and efficient transportation.

[0035] The value range of F(p) is between 0 and 1: F(p) = 0: It means that the route is not affected at all and the traffic is smooth.

[0036] F(p) = 1: It means that the route is completely affected by factors such as traffic accidents, congestion, or road closures and cannot be通行.

[0037] 0 < F(p) < 1: It means that the route is partially affected, and the traffic speed may decrease due to mild congestion or temporary traffic accidents.

[0038] The value of F(p) is determined by analyzing real-time traffic data, mainly based on the following factors: It should be noted that there are some terms like "通行范围" and "通行状态" in the original text that seem to be specific to a certain context and may need to be accurately defined according to the actual situation. Here, they are directly translated as they are in the original for the purpose of following the translation rules.If a traffic accident occurs on path p, F(p) increases. The severity and location of the accident (for example, whether it blocks multiple lanes) directly influence the value of F(p). A minor accident might increase F(p) to 0.2-0.4, while a major accident might increase F(p) to 0.8-1.0.

[0039] Traffic flow and speed data along the route will reflect congestion. Traffic monitoring systems or GPS data can provide real-time traffic flow data. Congestion will increase the value of F(p). For mild congestion, F(p) may be 0.3-0.5, while severe congestion may reach 0.7-0.9.

[0040] If a section of road is inaccessible due to reasons such as road closures or construction, the value of F(p) will be close to 1. A temporary road closure may cause F(p) to be close to 1.0, while a short period of construction may cause F(p) to be between 0.6 and 0.8.

[0041] The system reflects dynamic traffic conditions in real time and automatically adjusts based on real-time data to avoid delays or unsafe transport tasks caused by unforeseen traffic problems. Through these measures, the system not only improves transportation flexibility but also enhances the intelligence of route selection.

[0042] The specific value of F(p) is usually calculated from multiple data sources such as traffic management systems, real-time road condition monitoring, and sensor data. It is a dynamic value obtained by evaluating multiple factors such as real-time traffic flow, road conditions, and weather. For example: Under normal circumstances, F(p) may be close to 0, indicating no traffic incidents. If a serious traffic accident or road closure occurs, F(p) may reach 1, indicating that the path is completely impassable. In mild traffic congestion, the value of F(p) may be between 0.4 and 0.6.

[0043] S3: If the travel range on the path is not within the interval threshold, all possible optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transportation vehicle conditions.

[0044] If the route's traffic range falls outside the preset threshold, the system uses cloud computing technology to recalculate all possible optimized routes in real time and select the best alternative route based on dynamically changing traffic flows and the real-time status of transport vehicles. This process involves a complex route optimization algorithm that comprehensively considers factors such as traffic flow, road conditions, and the location and status of transport vehicles. This ensures that the system can quickly respond to traffic anomalies and provide the best alternative route for the transport task, thereby maximizing transportation efficiency and timeliness.

[0045] First, the system assesses the accessibility of the current transport route based on real-time traffic conditions (such as accidents, congestion, and road closures), as well as the real-time location and speed of transport vehicles. When the accessibility of the current route, T(p), falls below a set safety threshold, the system initiates an optimization mechanism to recalculate all possible alternative routes. During this calculation, the system comprehensively considers various factors, including the capacity of each alternative route, road traffic volume, and weather conditions, to determine the optimal route.

[0046] The path optimization process can be expressed by the following optimization objective function: ,in, is the comprehensive optimization value of path p, represents the length of the i-th segment on path p, is the traffic flow of the i-th road section, is the traffic flow volatility of the i-th road segment (for example, traffic flow fluctuation caused by traffic accidents or construction), is the traffic capacity of the i-th road section, and The weighting factors representing the impact of traffic flow and traffic capacity on route optimization are used to comprehensively evaluate each alternative route and select the most suitable alternative route for the current transportation task through a weighted assessment of traffic flow and road conditions.

[0047] (traffic weight factor) and The value range of (capacity weight factor) is usually between 0 and 1. and The larger the value, the greater the impact of the factor on path optimization; otherwise, the smaller the impact of the factor. .

[0048] Traffic Weight Factor: This factor represents the impact of traffic flow on route optimization. High traffic levels increase congestion, impacting route feasibility and timeliness. This weight factor is adjusted based on actual traffic conditions. For example, when traffic is high, the value may be higher (e.g., 0.7 to 0.9), while when traffic is low, the value may be lower (e.g., 0.1 to 0.3). This weight factor may also be affected by time of day (e.g., peak or off-peak hours). The specific value will be determined based on actual road conditions and traffic monitoring data.

[0049] Capacity Weight Factor: This factor represents the impact of road capacity (e.g., width, number of lanes, road quality, etc.) on route optimization. Roads with higher capacity can support more transport vehicles and generally have a positive impact on route optimization. The value is adjusted based on the road's capacity. For wide highways, the value may be higher (e.g., 0.8 to 1), while for narrow single-lane or poor-quality roads, the value may be lower (e.g., 0.3 to 0.5). This value may also be related to the design standards and infrastructure conditions of the road segment (e.g., number of lanes, road surface condition, etc.).

[0050] In actual calculations, the system processes data from multiple alternative routes in parallel through a cloud computing platform, optimizing and comparing them using fast algorithms (such as Dijkstra and A* algorithms). The advantage of cloud computing lies in its ability to process massive amounts of data, respond to route changes in real time, and rapidly calculate the optimal route. During the optimization process, the system considers not only the time cost (such as transportation time) and space cost (such as road length) of the route, but also factors such as fuel consumption, load capacity, and vehicle type. This ensures that the selected route not only meets timeliness requirements but also minimizes vehicle load and fuel consumption.

[0051] Furthermore, the optimization algorithm must receive and process real-time feedback from transport vehicle sensors and traffic monitoring systems, monitoring any changes in the transport process. If traffic conditions or vehicle conditions along the route change, the system will quickly update the optimization plan and recalculate the route to ensure optimal transport at all times.

[0052] This cloud-based route optimization method allows the system to efficiently respond to various traffic changes and intelligently adjust transportation routes, significantly improving the flexibility, timeliness, and safety of the transportation process. Furthermore, because the optimization process is based on real-time data and intelligent algorithms, it can dynamically adapt to changes in the traffic environment, avoiding the drawbacks of traditional route planning methods that make routes unresponsive to unexpected situations.

[0053] S4: Direct the transport vehicle according to the current transport route or the best alternative route, monitor the position, speed and arrival time of the transport vehicle in real time, and adjust the driving direction of the transport vehicle in time to ensure that the delivery task is completed within the scheduled time window.

[0054] Based on the current delivery route or the best alternative, delivery vehicles are directed in real time, their location, speed, and arrival time are monitored, and their directions are dynamically adjusted based on real-time data to ensure delivery is completed within the scheduled time window. This process requires not only accurate real-time monitoring of delivery vehicles but also the ability to flexibly adjust driving strategies based on various external conditions (such as traffic conditions and weather changes) to ensure efficient and safe delivery.

[0055] First, the system uses GPS and on-board sensors to track the location, speed, and driving status of each transport vehicle in real time. and the current speed of the transport vehicle Is the core data, which represents the transport vehicle at time The location and instantaneous speed of the transport vehicle are recorded. These data are uploaded to the cloud through the Internet of Things platform, and the cloud computing platform processes them and directs the driving of the transport vehicle according to the predetermined route or optimized path. At this time, the system will use data such as traffic flow, road conditions, and weather conditions to adjust the driving strategy of the transport vehicle in real time to avoid the impact of sudden traffic conditions on the delivery task. For each transport vehicle, the system will calculate the current driving status and arrival time of the transport vehicle based on its current position, speed, and predetermined target location. Set an optimization objective function to adjust the driving direction and speed of the transport vehicle so that it can reach the target location in the shortest time. This optimization objective function can be expressed as: ,in, Indicates the arrival time, is the remaining distance from the current position of the transport vehicle to the target position, is the current speed of the transport vehicle, Represents the time delay caused by unexpected events. This formula predicts the arrival time of a transport vehicle by combining its current speed and remaining distance. It also takes into account the impact of external delay factors and adjusts the speed and direction of the transport vehicle to minimize any potential delays.

[0056] When detecting increased traffic, road congestion, or other unexpected situations, the system automatically adjusts the transport vehicle's route and speed. For example, if a section of road experiences congestion or an accident, the system will recalculate the optimal route for the transport vehicle based on traffic flow data and guide the transport vehicle's route adjustment. If the transport vehicle is about to deviate from the scheduled time window, the system will adjust the transport vehicle's speed (by optimizing the speed control strategy) or recommend rerouting to ensure that the transport vehicle reaches its destination within the scheduled time.

[0057] The system also performs comprehensive optimization based on the dynamic status of the transport vehicle (such as fuel consumption and load), and the actual requirements of the transport mission. For example, if a transport vehicle is low on fuel, the system will notify the nearest gas station and optimize the refueling plan to avoid running out of fuel en route. Load monitoring also ensures that the transport vehicle is not overloaded, thereby preventing any impact on transport efficiency and safety. Real-time monitoring and dynamic strategy adjustments ensure that transport vehicles follow the optimal route in the shortest possible time. This process not only improves the flexibility and timeliness of transport missions, but also significantly reduces the negative impact of traffic delays on the delivery process, ensuring that deliveries are completed efficiently within the scheduled time window.

[0058] S5: During the transportation process, feedback data from the transport vehicles (such as location, speed, load, fuel consumption, etc.) is collected in real time, and the actual arrival location of the distribution node is continuously monitored to ensure the smooth progress of the transportation process.

[0059] By collecting real-time feedback from transport vehicles (such as location, speed, load, fuel consumption, etc.) and continuously monitoring the actual arrival locations of distribution nodes, we ensure the smooth progress of the transportation process. This process is based on comprehensive monitoring of the real-time status of transport vehicles, ensuring that transport vehicles can maintain efficient operation throughout the transportation process. At the same time, by tracking the arrival of distribution nodes in real time, we can identify potential problems in advance and make adjustments to ensure the smooth completion of transportation tasks.

[0060] First, the feedback data from the transport vehicle includes a variety of important information, such as the current location of the transport vehicle , current speed , transport vehicle load , fuel consumption These data are collected in real time by onboard sensors and GPS systems, and transmitted to the cloud computing center for processing via the Internet of Things platform. The system continuously updates this information and compares and analyzes it with the planned route and arrival time to determine the execution status of the current transportation process. Specifically, the location of the transport vehicle and speed These are core data that help the system evaluate the driving status and progress of transport vehicles. Reflects the current load condition of the transport vehicle, fuel consumption This directly affects fuel management during transportation. During transportation, the feedback data of these parameters will be aggregated and input into the following dynamic adjustment model: ,in, An adjustment for the time required to transport the vehicle, Indicates the load of the transport vehicle. is the road condition type function, which is used to adjust the impact of different road conditions on transportation efficiency. is the current speed of the transport vehicle, Indicates fuel consumption data, The maximum fuel consumption standard for transport vehicles, Delays are caused by unexpected circumstances, such as traffic accidents and road congestion. This formula comprehensively considers various factors, assesses the current transport vehicle's progress, and calculates the time required for adjustment when delays occur, thereby ensuring accurate management of the transport progress.

[0061] The system also continuously monitors deviations between the actual arrival location of delivery nodes and their expected destinations. If there is a significant deviation between the actual arrival location and the expected destination, the system analyzes the cause and promptly adjusts the delivery route. For example, if a traffic accident or road congestion during delivery delays the delivery, the system recalculates and selects the optimal alternative route based on real-time traffic data and historical route information to ensure the delivery vehicle arrives at the scheduled destination on time. To ensure smoother delivery, the system also monitors the fuel status of delivery vehicles. When the fuel level falls below a set threshold, the system proactively alerts the driver or dispatch center and recommends the nearest refueling station. Furthermore, load monitoring ensures that delivery vehicles are not overloaded, preventing accidents or damage to the vehicle due to overloading, which could compromise transportation safety and efficiency. Through real-time data collection and feedback, all factors in the delivery process are monitored and dynamically adjusted. This process not only improves transportation safety and stability, but also ensures that delivery tasks are completed on time and efficiently, thereby enhancing the reliability and intelligence of the overall transportation system.

[0062] S6: If there is a deviation between the actual arrival location and the expected target location, the deviation will be intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path will be adjusted in real time and the delivery time window will be dynamically adjusted.

[0063] If there's a discrepancy between the actual arrival location and the expected destination, the system intelligently corrects the discrepancy based on an adaptive compensation algorithm and historical route data, adjusting the transport route in real time and dynamically updating the delivery time window. This process, combining historical data analysis with real-time data feedback, ensures that if deviations occur during transport, the system can quickly respond and make the necessary corrections, ensuring smooth transport missions and accurate delivery times.

[0064] First, the system calculates the deviation between the current actual arrival position and the expected target position by monitoring the current position and speed of the transport vehicle in real time. Set a deviation evaluation function , which is used to quantify the deviation between the actual arrival position and the target position. This function can be expressed as: ,in, is the deviation between the actual arrival position and the expected target position, is the current position of the transport vehicle, is the expected target position, is the current speed of the transport vehicle, The function of this formula is to quantify the transportation error of the transport vehicle by calculating the spatial and temporal deviation between the actual position and the target position, thereby providing a basis for path correction. Once the deviation exceeds the preset tolerance range, the system will start the adaptive compensation algorithm to make corrections. The adaptive compensation algorithm analyzes historical path data and real-time traffic information, combined with current transportation conditions (such as road conditions, weather, traffic flow, etc.), to predict the best possible correction path. The algorithm will consider the transportation paths under similar conditions in history, and use historical path data to calculate the best possible correction path. To adjust the current path. The choice of corrected path can be calculated using the following formula: ,in, is the optimal path after adjustment, is the set of all alternative paths, For path The traffic capacity, For path time consumption, This is the weighting factor for different factors when adjusting the route. This formula selects the most appropriate alternative route by weightedly evaluating the route's capacity, time cost, and current deviation, ensuring that deviations during transportation are effectively corrected.

[0065] The value range is usually between 0 and 1. The larger the value is, the more important the factor is in path adjustment, and vice versa. The sum of these coefficients is usually 1 to ensure that the relative weights of different factors in adjusting the path are balanced.

[0066] (Time Adjustment Weight Factor): This factor is used to indicate the amount of time adjustment ( Importance in route adjustment. When the route adjustment is mainly subject to time constraints (for example, the delivery task needs to be completed within a specific time window), The value of will be larger. According to the timeliness requirements of the actual transportation task, dynamically set If timeliness is a priority, The value of will be larger (for example, 0.5 to 0.7), and if the timeliness requirement is lower, it may be smaller (for example, 0.2 to 0.4).

[0067] (Capacity Weight Factor): This factor represents the impact of road capacity (C(p)) on route adjustment. If the route has a higher capacity and can support more traffic, its weight should be greater during adjustment. By evaluating the capacity of the route, if the road is wider and has more lanes, Will be larger (such as 0.3 to 0.5), if the road is narrow or traffic jams often occur, The value of will be small (such as 0.1 to 0.2).

[0068] (Traffic Range Weight Factor): This factor is used to measure the impact of the route's travel range (T(p)) on route adjustment. If the route has a large travel range (e.g., clear roads, good weather), its weight factor should be larger, determined by real-time monitoring of traffic conditions and weather forecasts. If the weather and traffic conditions are good, The value of is large (for example, 0.4 to 0.6). If there are unfavorable factors (such as congestion, bad weather), The value of is small (such as 0.1 to 0.3).

[0069] The corrected route will be fed back to the transport vehicle, and the vehicle's driving direction, speed, and new route plan will be dynamically updated. At the same time, to ensure that the delivery task is completed on time, the system will also dynamically adjust the delivery time window. The adjustment of the delivery time window can be calculated using the following formula: ,in, is the adjusted time window, For the original delivery time window, For transportation delay time, is the adjustment factor based on the current road conditions and transportation status (for example, when traffic is congested, This formula dynamically adjusts the delivery time window by compensating for transportation delays, ensuring that even in the event of a delay, the delivery task can be completed within the adjusted time.

[0070] The value of γ usually ranges from 0 to 1: γ=0: indicates that the current road conditions and transportation status have no impact on the transportation time window, and the transportation proceeds as planned.

[0071] γ>0: Indicates that the current road conditions or transportation status have an impact on the transportation time window. A larger γ value indicates a larger adjustment, which usually occurs in situations such as traffic congestion or bad weather.

[0072] γ = 0.2: This may indicate a minor traffic delay or a small number of transportation status issues.

[0073] γ = 1: This may indicate severe traffic congestion or other emergencies, such as road closures.

[0074] The value of γ is usually dynamically calculated or adjusted by the following factors: Traffic congestion: Real-time traffic flow and road conditions (such as accidents, construction, and road closures) determine the value of γ. When traffic is heavy or accidents are frequent, γ increases. For example, during rush hour, γ might be 0.5 to 0.7, indicating that a significant adjustment of the time window is required. When traffic is smooth, γ is close to 0.

[0075] Weather factors: Severe weather (such as heavy rain, snow, and haze) can reduce road capacity, increasing the γ value. For example, during heavy rain, γ might be 0.3, indicating that the time window needs to be extended. On rainy days or with light haze, γ can be set to 0.1 or less.

[0076] Transport status: The transport vehicle's status (such as speed, fuel consumption, and load) also affects the adjustment factor. When the transport vehicle is in good condition, the value of γ is small. However, when the transport vehicle encounters problems (such as insufficient fuel or overloading), the value of γ may increase. For example, if the transport vehicle is low on fuel, the value of γ may increase by 0.1 to 0.2, indicating that the transport time window needs to be adjusted.

[0077] Ultimately, the system continuously monitors and makes corrections to minimize deviations throughout the transportation process, promptly adjusting routes and delivery times to address unexpected situations or environmental changes. These intelligent corrections not only improve the adaptability and flexibility of transportation routes, but also ensure that deliveries are completed within the expected time window, significantly enhancing the reliability and efficiency of the transportation system.

[0078] S7: After completing the delivery task, the entire process data is comprehensively traced and analyzed through the data cloud platform to generate a delivery report, thereby providing more accurate and efficient decision-making support for future transportation route optimization.

[0079] After a delivery is completed, the system uses the data cloud platform to comprehensively review and analyze the entire process data, generating a detailed delivery report. This provides accurate and efficient decision support for future transportation route optimization. This process involves multiple steps, including data collection, processing, analysis, feedback, and generation of optimization recommendations, ultimately providing a strong basis for decision-making during the transportation process. The following are the specific steps and processing logic.

[0080] After the delivery task is completed, the system first collects various data from the transportation process. This data includes but is not limited to: Transport route data: the real-time position, speed, places passed, and deviation between the planned route and the actual route of the transport vehicle.

[0081] Traffic condition data: information on road traffic, traffic accidents, road closures, etc.

[0082] Environmental data: weather conditions, such as rainfall, wind speed, temperature, etc.

[0083] Transport vehicle status data: fuel consumption, load, speed, etc.

[0084] This data is transmitted in real time via the IoT platform to the cloud platform for storage and management. The stored data is cleaned and deduplicated to ensure data quality for subsequent analysis.

[0085] After data collection, the system performs data cleaning and preprocessing, which mainly includes the following aspects: For example, if data at the same time is uploaded repeatedly during multiple collection processes, the system will automatically deduplicate it to ensure the uniqueness of each data item. If some data is missing due to equipment failure or communication interruption, the system will use interpolation methods to fill in the gaps. For example, time series interpolation can be used to estimate missing speed or position data. By performing fluctuation analysis on the data during the transportation process, outliers (such as abnormal increases in fuel consumption, abnormal changes in speed, etc.) are identified and these illogical data are eliminated. Anomaly detection methods may include statistical standard deviation methods or training machine learning models to identify unreasonable patterns. The cleaned data will be further processed for subsequent analysis.

[0086] By analyzing data from the entire journey, the system tracks the actual performance of the transport route. This process first calculates deviations from the planned route and analyzes possible causes. For example, the system compares the actual arrival time with the planned arrival time, calculates the time delay, and checks whether it is caused by factors such as traffic accidents, weather changes, or temporary road closures.

[0087] On this basis, the system uses historical data and real-time data to perform route optimization analysis. The algorithms used here include: Dijkstra algorithm: Calculates the shortest path from a starting point to a destination based on static traffic data. The algorithm traverses all possible paths and selects the shortest path as the final recommended path.

[0088] A* algorithm: Considers heuristic functions, such as the straight-line distance from the current location to the destination, to select the optimal path during route planning. A* not only finds the shortest path but also effectively handles dynamic traffic conditions.

[0089] After the path backtracking and optimization analysis is completed, the system will conduct a comprehensive evaluation of the delivery task performance and generate a detailed delivery report. The report mainly includes the following contents: This includes comparisons of transportation time, fuel consumption, and customer satisfaction. For example, the system will list the difference between the actual arrival time and the scheduled arrival time, as well as the length of delays caused by traffic congestion or weather. Costs throughout the transportation process, such as fuel consumption, transportation fees, and vehicle maintenance costs, are analyzed. The system also calculates per-kilometer transportation costs to provide a basis for future cost optimization. If any safety incidents occur during transportation (such as traffic accidents or road congestion), the system will record and analyze them, providing recommendations for safety improvements.

[0090] The ultimate goal of the report is to provide decision support for future transportation route optimization. Based on the data analysis results, the system will provide the following optimization suggestions: Based on historical data and retrospective analysis, the system recommends optimal future routes. For example, if a route frequently experiences traffic congestion during a specific time period, the system may recommend avoiding that route during that time and using a smoother route instead. If the system detects that certain traffic events (such as accidents or road closures) frequently affect certain routes, future route planning will automatically adjust to prioritize routes with more stable traffic conditions. If fuel consumption is excessively high during a delivery mission or if transport vehicles are unevenly loaded, the system will provide adjustment suggestions to help dispatchers better allocate transportation resources and reduce unnecessary waste of resources.

[0091] After each delivery mission is completed, the system automatically incorporates new data and analysis results into the database for training and updating the route optimization model. Over time, the system's optimization capabilities will continue to improve, enabling it to continuously optimize route selection, improve transportation efficiency, and reduce transportation costs based on a combination of real-time and historical data. Furthermore, through continuous monitoring and feedback mechanisms, the system can respond in real time to various changes in the delivery process, such as sudden weather changes and traffic accidents, ensuring that transportation strategies can be quickly adjusted to maximize the guarantee of timely completion of missions. Through comprehensive retrospective analysis and intelligent decision-making support, the system not only provides accurate route optimization recommendations but also helps transportation companies improve efficiency, reduce costs, increase customer satisfaction, and enhance the reliability of future decisions. The comprehensive retrospective analysis and analysis of the data cloud platform provide strong support for the continuous optimization of transportation routes.

[0092] See also Figure 2 As shown, the transportation route optimization system based on the Internet of Things described in this embodiment includes a traffic analysis module, a path optimization module and an adaptive correction module; The Traffic Analysis Module generates preliminary transport route plans based on multi-source environmental information, including historical data, weather forecasts, and real-time traffic information. It automatically detects the traffic range of the transport route based on the real-time location data of transport vehicles and dynamic traffic data (such as sudden traffic accidents, road congestion, and temporary road closures). If the traffic range is within the preset interval threshold, the currently planned transport route is maintained to achieve transport flexibility and timeliness. The traffic range detection results are sent to the route optimization module. Path Optimization Module: If the travel range on the path is not within the interval threshold, all possible optimized paths are recalculated in real time through cloud computing. The best alternative route is reselected based on traffic flow changes and the status of the transport vehicle. The transport vehicle is directed according to the current transport route or the best alternative route. The position, speed and arrival time of the transport vehicle are monitored in real time, and the driving direction of the transport vehicle is adjusted in a timely manner to ensure that the delivery task is completed within the scheduled time window. The adjusted transport path is sent to the adaptive correction module. Adaptive correction module: During the transportation process, feedback data of the transport vehicle (such as location, speed, load, fuel consumption, etc.) is collected in real time, and the actual arrival position of the distribution node is continuously monitored to ensure the smooth progress of the transportation process. If there is a deviation between the actual arrival position and the expected target position, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted.

[0093] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0094] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A transportation route optimization method based on the Internet of Things, characterized by: The optimization method comprises the following steps: S1: Generate preliminary transportation route planning based on multi-source environmental information; S2: Based on the real-time location data of the transport vehicle and dynamic traffic data, the transport route’s access range is automatically detected. If the access range is within the preset interval threshold, the current transport route is maintained. S3: If the access range on the path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transportation vehicle conditions; S4: Direct the transport vehicle to move according to the current transport route or the best alternative route, monitor the transport vehicle information in real time and adjust the transport vehicle's driving direction; S5: During the transportation process, real-time feedback data from transport vehicles is collected and the actual arrival location of the distribution node is continuously monitored; S6: If there is a deviation between the actual arrival location and the expected target location, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted; S7: After completing the delivery task, the entire process data is comprehensively traced and analyzed through the data cloud platform to generate a delivery report.

2. The method for optimizing transportation routes based on the Internet of Things according to claim 1, wherein: During the transportation process, real-time feedback data from transport vehicles is collected and the actual arrival location of the distribution node is continuously monitored, including the following steps: The feedback data of the transport vehicle includes the current position of the transport vehicle, the current speed, the load of the transport vehicle and the fuel consumption; The feedback data is aggregated and input into the dynamic adjustment model, which then calculates and outputs the time adjustment required for the transport vehicle. Continuously monitor the deviation between the actual arrival location and the expected target location of the distribution node. If there is a deviation between the actual arrival location and the expected target location, analyze the cause of the deviation and adjust the transportation route in a timely manner.

3. The method for optimizing transportation routes based on the Internet of Things according to claim 2, wherein: The feedback data is aggregated and input into the dynamic adjustment model, which then calculates and outputs the time adjustment required for the transport vehicle, expressed as: ,in, An adjustment for the time required to transport the vehicle, Indicates the load of the transport vehicle. is the road condition type function, which is used to adjust the impact of different road conditions on transportation efficiency. is the current speed of the transport vehicle, Indicates fuel consumption data, The maximum fuel consumption standard for transport vehicles, It is a time delay caused by unexpected situations.

4. The method for optimizing transportation routes based on the Internet of Things according to claim 3, wherein: Directing the transport vehicle to travel according to the current transport route or the best alternative route, monitoring the transport vehicle information in real time and adjusting the transport vehicle's travel direction, including the following steps: Track the location, speed and driving status of each transport vehicle in real time through GPS and on-board sensors. and the current speed of the transport vehicle Respectively represent the transport vehicles at time The position and instantaneous velocity of According to the current position, speed and predetermined target position of the transport vehicle, the current driving status and arrival time of the transport vehicle are calculated, and the driving direction and speed of the transport vehicle are adjusted by optimizing the objective function.

5. The method for optimizing transportation routes based on the Internet of Things according to claim 4, characterized in that: If the access range on the path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transportation vehicle conditions, including the following steps: If the travel range on the path is not within the interval threshold, the optimization mechanism is activated, the comprehensive optimization values ​​of all alternative paths are recalculated, and the transportation path with the largest comprehensive optimization value is selected as the best alternative route.

6. The method for optimizing transportation routes based on the Internet of Things according to claim 5, characterized in that: The optimization objective function is expressed as: ,in, Indicates the arrival time, is the remaining distance from the current position of the transport vehicle to the target position, is the current speed of the transport vehicle, Indicates the time delay caused by unexpected conditions.

7. The method for optimizing transportation routes based on the Internet of Things according to claim 6, characterized in that: Based on the real-time location data of transport vehicles and dynamic traffic data, the traffic range of the transport route is automatically detected, including the following steps: Based on the real-time location data and dynamic traffic data of transport vehicles, the traffic status of path p within the time window is evaluated through the traffic range evaluation function; If an abnormal situation is detected on the path, it will automatically detect whether the travel range is within the interval threshold. If it is within the interval threshold, the current path will be maintained and transportation will continue.

8. The method for optimizing transportation routes based on the Internet of Things according to claim 7, wherein: The traffic status of path p within the time window is evaluated by the traffic range evaluation function, and the expression is: , where T(p) is the travel range of path p, S(p) is the current available section length of path p, D(p) is the total section length of path p, and F(p) is the impact factor of path p.

9. The method for optimizing transportation routes based on the Internet of Things according to claim 8, characterized in that: Generate preliminary transportation route planning based on multi-source environmental information, including the following steps: Collect and integrate historical data, weather forecasts, and real-time traffic information; Historical data includes traffic flow, road conditions, and average speed of transport vehicles; Weather forecasts include providing predictive information on future weather conditions, including precipitation, wind speed, and temperature factors; Real-time traffic information reflects changes in road congestion, sudden traffic accidents, and road closures due to construction; The efficiency is calculated for each transport path, and by evaluating multiple paths, the transport path with the highest efficiency is selected as the initial transport path.

10. A transportation route optimization system based on the Internet of Things, used to implement the optimization method according to any one of claims 1 to 9, characterized in that: Including traffic analysis module, path optimization module and adaptive correction module; Traffic analysis module: Generates preliminary transport route planning based on multi-source environmental information. Based on the real-time location data of transport vehicles and dynamic traffic data, it automatically detects the traffic range of the transport route. If the traffic range is within the preset interval threshold, the current transport route is maintained. Path Optimization Module: If the travel range on a path is not within the interval threshold, all optimized paths are recalculated in real time through cloud computing, and the best alternative route is reselected based on traffic flow changes and transport vehicle conditions. Transport vehicles are directed to travel according to the current transport route or the best alternative route, and transport vehicle information is monitored in real time and the direction of travel of transport vehicles is adjusted; Adaptive correction module: During the transportation process, feedback data from the transport vehicles is collected in real time, and the actual arrival position of the distribution node is continuously monitored. If there is a deviation between the actual arrival position and the expected target position, the deviation is intelligently corrected based on the adaptive compensation algorithm and historical path data, and the transportation path is adjusted in real time and the delivery time window is dynamically adjusted.

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