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CN-121998214-A - Logistics distribution path intelligent management method and system based on Internet of things

CN121998214ACN 121998214 ACN121998214 ACN 121998214ACN-121998214-A

Abstract

The invention relates to the technical field of logistics management, and discloses an intelligent logistics distribution path management method and system based on the Internet of things, wherein the method firstly performs static path planning, then in the distribution process, and acquiring vehicle, goods, traffic and order data in real time through an internet of things perception layer, and dynamically judging whether to trigger path re-planning after cleaning, fusion and standardization processing of edge computing nodes. The triggering conditions include abrupt change of road section state, insertion of new orders and abnormal delivery. Once triggered, the system takes the current position of the vehicle as a new starting point, the rest nodes as a new task set, and the total optimal path is quickly regenerated. Aiming at large-scale distribution, a three-level radiation area layering progressive planning mechanism is introduced, the three-level radiation area layering progressive planning mechanism is independently planned layer by layer, and the actual end point relay is used for realizing self-adaption, efficient optimization and reliable execution of a path in a complex environment.

Inventors

  • LV HAO
  • ZHANG YUQIANG

Assignees

  • 广东工业大学
  • 广州哆啦科技有限公司

Dates

Publication Date
20260508
Application Date
20251226

Claims (9)

  1. 1. The intelligent logistics distribution path management method based on the Internet of things is characterized by comprising the following steps of: step S1, road information and a distribution task are obtained, and static planning of a distribution path is performed; Step S2, in the path execution process, acquiring multidimensional real-time dynamic data in the logistics distribution process through various sensors and data acquisition equipment deployed in an Internet of things sensing layer; S3, transmitting the collected multidimensional real-time dynamic data to an edge computing node, and obtaining high-quality standardized data through data cleaning, data fusion and format standardization processing; And S4, analyzing the standardized data, judging whether a dynamic path re-planning condition is triggered, and if the triggering condition is met, carrying out path re-planning.
  2. 2. The intelligent logistics distribution path management method based on the internet of things according to claim 1, wherein the specific working process of the step S1 comprises: acquiring a complete distribution task data set, wherein the distribution task data set comprises a distribution center node and all client nodes, and the distribution center node is the starting point of a distribution path; acquiring road network connectivity data, wherein the road network connectivity data comprises connection relations between all client nodes and a central node; based on the road network connectivity data, establishing an adjacency relation matrix among nodes, and determining physical communication paths existing between all client nodes and a central node; An optimal delivery path is generated based on the physical communication paths existing between all client nodes and the central node.
  3. 3. The intelligent logistics distribution path management method based on the internet of things according to claim 2, wherein the working process of generating all global distribution paths comprises the following steps: defining customer node complete set to be served by taking the distribution center node as a circle center Wherein each node Having defined cargo demand I belongs to n; receiving a path planning instruction, wherein the instruction designates an actual starting point node of the current distribution , wherein, , Is a distribution center node; If it is The starting point node Marked as serviced state, if The initial cargo capacity is full, and all distribution center nodes are in a to-be-serviced state; Based on the current actual starting point node S and the rest of the node set to be served The following operations are performed, wherein, : Generating one-time traversals starting from S The distribution path of all nodes in the network is selected to be an optimal or near-optimal distribution path as an execution path The execution path The method is characterized in that feasible roads exist between any adjacent nodes in the path, and the total travel distance or total time of the path is the minimum value in all feasible sequences; Selecting a minimum total travel distance or a minimum total travel time as a final execution path according to a preset optimization objective The vehicle follows the generated path And sequentially driving and serving the nodes.
  4. 4. The intelligent logistics distribution path management method based on the internet of things according to claim 3, wherein the working process of the step S4 comprises: judging whether a path dynamic re-planning condition is triggered or not, wherein the path dynamic re-planning condition comprises the following steps: condition 1, abrupt change of road network state, resulting in a path The next road section can not pass or the passing time is far beyond the estimated time; condition 2, receiving a new instant delivery order, wherein the new order node is positioned in the delivery area; Condition 3, vehicle anomaly or cargo anomaly; if any triggering condition is met, immediately defining the current position of the vehicle as a new actual starting point node S, and defining the nodes which are not yet served as a new residual node set Generating a new residual node set from the new actual starting point node S and traversing the new residual node set once According to the current optimization target, screening an optimal or near-optimal distribution path as a new final execution path 。
  5. 5. The intelligent logistics distribution path management method based on the internet of things according to claim 1, wherein the working process of the step S2 comprises: The system comprises a vehicle, a GPS positioning module, an inertial navigation module, a weight sensor, a fault diagnosis sensor, a real-time traffic data acquisition terminal, an urban traffic management platform interface, a temperature sensor, a humidity sensor and a vibration sensor, wherein the GPS positioning module, the inertial navigation module, the weight sensor and the fault diagnosis sensor are arranged on the vehicle, the real-time traffic data is acquired, the cargo state data is acquired through the temperature sensor, the humidity sensor and the vibration sensor which are arranged in cargo packages, the demand data of the delivery points are input in advance through a dispatching center, the real-time feedback of the delivery points is acquired, the demand data comprise the position of the delivery points, the cargo receiving requirement and the contact information, and the wireless communication module of an Internet of things sensing layer transmits the acquired data to an edge calculation node in real time.
  6. 6. The intelligent logistics distribution path management method based on the internet of things according to claim 1, wherein the working process of the step S3 comprises: In the data cleaning stage, abnormal data exceeding a reasonable range is removed by adopting a Z-score algorithm based on a statistic abnormal value detection algorithm, the missing data is supplemented by adopting a linear interpolation method, the integrity of the data is ensured, in the data fusion stage, the same type of data from different sensors is subjected to weighted fusion, the weight is determined according to the accuracy of the sensors, the accuracy of the data is improved, in the format standardization stage, the acquired data in different formats are converted into a uniform CSV format, and the processing of a subsequent dynamic path optimization model is facilitated.
  7. 7. The intelligent logistics distribution path management method based on the internet of things according to claim 4, wherein the road network state judging process in the condition 1 is as follows: acquiring real-time traffic data of a next road segment, and carrying out weighted summation on all the real-time traffic data to obtain a road state comprehensive coefficient, wherein if any real-time traffic data is larger than a corresponding threshold set by a system or the road state comprehensive coefficient is larger than the corresponding threshold set by the system, the fact that the next road segment cannot pass or the passing time is far beyond the estimated time is indicated; the abnormal judgment process of the vehicle in the condition 3 is as follows: Acquiring real-time vehicle state data, and carrying out weighted summation on all the real-time vehicle state data to obtain a vehicle state comprehensive coefficient, wherein if any real-time vehicle state data is larger than a corresponding threshold set by a system or the vehicle state comprehensive coefficient is larger than the corresponding threshold set by the system, vehicle abnormality is indicated; the cargo abnormality judging process in the condition 3 is as follows: And acquiring real-time cargo state data, and carrying out weighted summation on all the real-time cargo state data to obtain cargo state comprehensive coefficients, wherein if any one of the real-time cargo state data is larger than a corresponding threshold set by the system or the cargo state comprehensive coefficients are larger than the corresponding threshold set by the system, cargo abnormality is indicated.
  8. 8. The intelligent logistics distribution path management method based on the internet of things of claim 1, further comprising: Step S6, if the logistics distribution range is large, taking the distribution center node O as the center of a circle, and according to the progressive distance threshold value 、 、 Wherein < < Sequentially defining a first-stage radiation area from a logistics distribution range Second-stage radiation area And third-stage radiation region ; All customer nodes to be served are gathered Respectively map attribution to the geographic coordinates according to the geographic coordinates 、 Or (b) And for each client node Marking the hierarchical mark of the area to which the hierarchical mark belongs, and independently planning a path for each hierarchical mark; The system does not preset and fix the end point of each level, after the vehicle distributes the last node of the previous level, the system judges that the distribution of the level is completed, and the vehicle uses the last node of the current level as a new distribution center node to generate a global distribution path of the next level.
  9. 9. The intelligent logistics distribution path management system based on the Internet of things is characterized by being used for implementing the intelligent logistics distribution path management method based on the Internet of things according to any one of claims 1-8.

Description

Logistics distribution path intelligent management method and system based on Internet of things Technical Field The invention relates to the technical field of logistics management, in particular to an intelligent logistics distribution path management method and system based on the Internet of things. Background With the rapid development of the electronic commerce industry, the logistics distribution industry has come to have unprecedented development opportunities, but also has the problems of low distribution efficiency, high cost, unreasonable path planning, difficult guarantee of cargo distribution quality and the like. Most of the existing logistics distribution path planning methods are static planning, namely path planning is carried out according to pre-acquired road information and distribution tasks, dynamic interference such as real-time traffic jam, sudden accidents, weather changes and the like in actual distribution cannot be effectively solved, tasks such as temporarily adding orders and changing customer orders change, and frequent failure and delay increase of the planning are caused. Therefore, the invention provides an intelligent logistics distribution path management method and system based on the Internet of things. Disclosure of Invention The invention aims to provide an intelligent logistics distribution path management method and system based on the Internet of things, and the technical problems are solved. A logistics distribution path intelligent management method based on the Internet of things comprises the following steps: step S1, road information and a distribution task are obtained, and static planning of a distribution path is performed; Step S2, in the path execution process, acquiring multidimensional real-time dynamic data in the logistics distribution process through various sensors and data acquisition equipment deployed in an Internet of things sensing layer; S3, transmitting the collected multidimensional real-time dynamic data to an edge computing node, and obtaining high-quality standardized data through data cleaning, data fusion and format standardization processing; And S4, analyzing the standardized data, judging whether a dynamic path re-planning condition is triggered, and if the triggering condition is met, carrying out path re-planning. As a further description of the technical solution of the present invention, the specific working process of step S1 includes: acquiring a complete distribution task data set, wherein the distribution task data set comprises a distribution center node and all client nodes, and the distribution center node is the starting point of a distribution path; acquiring road network connectivity data, wherein the road network connectivity data comprises connection relations between all client nodes and a central node; based on the road network connectivity data, establishing an adjacency relation matrix among nodes, and determining physical communication paths existing between all client nodes and a central node; An optimal delivery path is generated based on the physical communication paths existing between all client nodes and the central node. As a further description of the technical solution of the present invention, the working process of generating all global distribution paths includes: defining customer node complete set to be served by taking the distribution center node as a circle center Wherein each nodeHaving defined cargo demandI belongs to n; receiving a path planning instruction, wherein the instruction designates an actual starting point node of the current distribution , wherein,,Is a distribution center node; If it is The starting point nodeMarked as serviced state, ifThe initial cargo capacity is full, and all distribution center nodes are in a to-be-serviced state; Based on the current actual starting point node S and the rest of the node set to be served The following operations are performed, wherein,: Generating one-time traversals starting from SThe distribution path of all nodes in the network is selected to be an optimal or near-optimal distribution path as an execution pathThe execution pathThe method is characterized in that feasible roads exist between any adjacent nodes in the path, and the total travel distance or total time of the path is the minimum value in all feasible sequences; Selecting a minimum total travel distance or a minimum total travel time as a final execution path according to a preset optimization objective The vehicle follows the generated pathAnd sequentially driving and serving the nodes. As a further description of the technical solution of the present invention, the working process of step S4 includes: judging whether a path dynamic re-planning condition is triggered or not, wherein the path dynamic re-planning condition comprises the following steps: condition 1, abrupt change of road network state, resulting in a path The next road section can not pass or the passing time is