International logistics task state management method and device based on edge AI technology, equipment and medium

Abstract

The invention relates to an international logistics task state management method, device and equipment based on an edge AI technology and a medium, and belongs to the field of logistics management. The method comprises the steps that a delivery end uploads international logistics information and creates an international logistics task state; the delivery end delivers goods and synchronizes the international logistics information and the international logistics task state to the scheduling end through the improved edge AI technology, and the scheduling end updates the international logistics task state; adjusting the queue length and the incoming flow rate in the process of synchronizing information to a scheduling end through a flow controller to realize flow control; and the scheduling end synchronizes the international logistics information and the updated international logistics task state to the receiving end. According to the invention, effective transmission management of information and state in the international material conveying process is realized.

Description

Technical Field

[0001] The present invention belongs to the field of logistics management technology, and specifically relates to an international logistics task status management method, device, equipment and medium based on edge AI technology. Background Art

[0002] Transportation is an important part of the international logistics industry and the central link of various business activities in the international logistics process, playing a vital role. With the continuous development of modern logistics, the current international logistics and transportation industry has great development potential, but in fact there are still problems such as low level of information management; at the same time, due to the rapid expansion of international logistics companies, the international logistics and transportation business is increasing day by day, and the original transportation system cannot support the expansion of business and cannot uniformly manage the status of international logistics tasks.

[0003] In recent years, AI technology has achieved rapid development, but the cloud computing model requires data to be uploaded to the cloud, which may pose potential privacy issues, especially in the international logistics industry, which has high privacy requirements. At the same time, the high latency of cloud computing transmission will also limit the performance of AI applications. Therefore, edge AI technology has gradually become a trend. The importance of edge computing in meeting real-time, privacy protection, and resource management continues to increase, especially in processing large-scale AI data streams, but it also has the disadvantages of delay and packet loss. Excessive delay leads to a time lag from the source to the target, and packet loss may lead to incomplete information, further affecting real-time and integrity. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present invention provides an international logistics task status management method, device, equipment and medium based on edge AI technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A method for managing the status of international logistics tasks based on edge AI technology, the implementation of which includes the following steps: The shipping end uploads international logistics information and creates international logistics task status; The shipping end ships the goods and synchronizes the international logistics information and the international logistics task status to the scheduling end through improved edge AI technology, and the scheduling end updates the international logistics task status; The flow controller adjusts the queue length and incoming flow rate during the information synchronization process to the dispatch end to achieve flow control; The dispatching end synchronizes the international logistics information and the updated international logistics task status to the receiving end.

[0006] Preferably, the international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information; the international logistics task status includes task pending confirmation status, task confirmation status, task vehicle dispatched status, task bound to power of attorney status, task power of attorney full receipt status, task rejection status, task modification status, and task invalidation status.

[0007] Preferably, the improved edge AI technology includes a data plane layer, a control layer, and a knowledge layer.

[0008] Preferably, the data plane layer is composed of edge nodes, and the hosts communicate with each other through MPI. The control layer includes a node monitoring module and a decision-making issuing module, and the knowledge layer includes a decision-making core module.

[0009] Preferably, the decision core module is implemented by an intelligent routing algorithm, which includes: using a random initialization method to set weight parameters, the weight parameters include a first weight parameter, a second weight parameter, and a third weight parameter, the value range of the first weight parameter and the second weight parameter is a closed interval [-1,0], and the value range of the third weight parameter is a closed interval [0,1], increasing the number of algorithm training times and calculating the algorithm state every i seconds, the algorithm state includes link delay, packet loss rate, and link utilization, when the algorithm state is not a terminated state, determining the routing action and calculating the incentive value according to the algorithm state, obtaining the algorithm state at i+1 seconds, and storing the algorithm state, the routing action, and the incentive value in an experience pool; when the link delay and link capacity meet the restriction conditions, updating the intelligent routing algorithm.

[0010] Preferably, the intelligent routing algorithm specifically includes: The network topology is represented by an undirected graph G(V, E, C), where V is a data network node, E is a node connection path between the data network nodes, and C is the link capacity on the node connection path. The node traffic is obtained, and the node traffic includes the node sending traffic and the node receiving traffic. The node time is obtained, and the node time includes the node sending time and the node receiving time. The host data packet processing time is obtained; The link delay, the packet loss rate, and the link utilization are calculated based on the node traffic, the node time, and the host data packet processing time. The calculation formula for the link delay is: , where delay is the link delay, N is the number of data network nodes, Tr j is the node receiving time of the jth data network node, Ts j is the node sending time of the jth data network node, Tc jis the host data packet processing time of the jth data network node; the calculation formula of the packet loss rate is: , where loss is the packet loss rate, Dr j The node receiving traffic for the jth data network node, Ds j The node sends traffic to the jth data network node; the calculation formula of the link utilization is: , where utilization is the link utilization, C j is the link capacity of the jth data network node; The calculation formula of the excitation value is: , where R is the excitation value, α is the first weight parameter, β is the second weight parameter, and γ is the third weight parameter; The constraints are , where TD is the maximum tolerable delay threshold of the link and TC is the maximum capacity of the link.

[0011] Preferably, the flow controller generates a reference trajectory based on a given reference input, uses a reference trajectory method to compare the actual system response with the reference trajectory, and adjusts the reference input to approach or achieve the desired system behavior; performs rolling optimization on the reference trajectory, calculates the optimization results, uses the optimization results to adjust the queue length and the incoming flow rate, and uses the least squares method to adjust the system parameters, predicts the outgoing flow rate of the adjusted system, compares the predicted results with the actual output, and calculates the prediction error; passes the prediction error to feedback correction, and the feedback correction performs the flow adjustment based on the cost function.

[0012] An international logistics task status management device based on edge AI technology, used to execute the international logistics task status management method described above, including a task creation module, a task synchronization module, and a flow control module; The task creation module is used for the shipping end to upload international logistics information and create international logistics task status. The international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information. The international logistics task status includes task pending confirmation status, task confirmation status, task dispatched vehicle status, task bound power of attorney status, task power of attorney full receipt status, task rejection status, task modification status, and task invalidation status. The task synchronization module is used for the shipping end to ship and synchronize the international logistics information and the international logistics task status to the dispatching end through improved edge AI technology, and the dispatching end updates the international logistics task status; synchronize the international logistics information and the updated international logistics task status to the receiving end; The flow control module is used to adjust the queue length and the incoming flow rate during the process of information synchronization to the scheduling end through the flow controller to achieve flow control.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the international logistics task status management method described above when executing the program.

[0014] A storage medium containing computer executable instructions, which are used to execute the international logistics task status management method described above when executed by a computer processor.

[0015] The beneficial effects of the present invention are: (1) Use edge AI technology to manage the status of international logistics tasks and process data locally instead of uploading it to the cloud, reducing the risk of potential data leakage. Edge computing can significantly reduce network latency and provide faster response speeds. (2) Improved edge AI technology through the data plane layer, control layer, and knowledge layer, which is superior to traditional edge algorithms in terms of packet loss rate and link latency; (3) Rapidly identify network congestion, transmission bottlenecks, and equipment failures through the real-time perception capabilities of the control layer, so that adjustment and optimization measures can be taken in a timely manner; (4) Through the knowledge layer, the experience pool can be replayed to continuously learn from historical experience, optimize flow table rules, improve data flow routing, improve network operation efficiency, achieve intelligent adaptation to complex network environments, ensure load balancing of edge nodes, and make important contributions to the flexibility and performance optimization of the overall architecture; (5) Through MPI, different nodes in edge AI technology can work together to cope with complex network tasks, adapt to traffic changes more quickly, adjust routes, and optimize data transmission paths. At the same time, MPI's communication mechanism helps avoid single points of failure and improve the robustness and reliability of the system; (6) The traffic controller dynamically adjusts the queue length and incoming flow rate to ensure stable operation of the system in high-concurrency scenarios and to guarantee network performance and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the steps of an international logistics task status management method based on edge AI technology of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] The working principle and use process of the present invention: See also Figure 1 , an international logistics task status management method based on edge AI technology, comprising: S1: The shipping end uploads international logistics information and creates international logistics task status, wherein the international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information. The international logistics task status includes task pending confirmation status, task confirmation status, task dispatched status, task bound to power of attorney status, task power of attorney fully received status, task rejection status, task modification status, and task invalidation status. The task pending confirmation status means that the logistics is waiting for confirmation by the carrier, the task confirmation status means that the carrier confirms acceptance of the task, the task dispatched status means that the logistics task has been dispatched, the task bound to power of attorney status means that the logistics personnel accept the logistics task and create the corresponding power of attorney, the task power of attorney fully received status means that the consignee signs for all power of attorney, the task rejection status means that the logistics transportation request is rejected and the goods are returned to the shipper, the task modification status means that the international logistics information is incorrect and is being corrected, and the task invalidation status means that the logistics task is invalidated and all operations are stopped; S2: The shipping end ships the goods and synchronizes the international logistics information and the international logistics task status to the scheduling end through improved edge AI technology, and the scheduling end updates the international logistics task status; S3: The flow controller adjusts the queue length and incoming flow rate during the information synchronization to the scheduling end to achieve flow control; S4: Synchronize the international logistics information and the updated international logistics task status to the receiving end.

[0020] In this embodiment, the improved edge AI technology includes a data plane layer, a control layer, and a knowledge layer. It is superior to traditional edge algorithms in terms of packet loss rate and link delay. Through the real-time perception capability of the control layer, it can quickly identify network congestion, transmission bottlenecks, and equipment failures, so as to take timely adjustment and optimization measures; through the knowledge layer, the experience pool mechanism can be replayed to continuously learn historical experience, optimize flow table rules, improve data flow routing, improve network operation efficiency, achieve intelligent adaptation to complex network environments, ensure load balancing of edge nodes, and make important contributions to the flexibility and performance optimization of the overall architecture.

[0021] In this embodiment, the data plane layer is composed of edge nodes, and the hosts communicate with each other through MPI. The control layer includes a node monitoring module and a decision-making module. The knowledge layer includes a decision-making core module. Through MPI, different nodes in edge AI technology can work together to deal with complex network tasks, adapt to traffic changes more quickly, adjust routes, and optimize data transmission paths. At the same time, the communication mechanism of MPI helps to avoid single point failures and improve the robustness and reliability of the system.

[0022] The MPI includes an SDN controller and an SDN-MPI daemon; the SDN controller is deployed on the management node to intelligently control the entire cluster system; the SDN-MPI daemon runs in the background of all computing nodes and is responsible for the initialization and information communication phases of the MPI; The node monitoring module realizes real-time perception of network conditions by continuously monitoring network resources and intensive AI data streams transmitted by terminal devices, identifies problems such as network congestion, transmission bottlenecks and equipment failures, and takes timely adjustment and optimization measures. The decision-making and issuing module is used to formulate issuing rules, optimize data stream routing, and ensure load balancing of edge nodes.

[0023] In this embodiment, the decision core module is implemented by an intelligent routing algorithm, which includes: setting weight parameters using a random initialization method, the weight parameters including a first weight parameter, a second weight parameter, and a third weight parameter, the value range of the first weight parameter and the second weight parameter is a closed interval [-1,0], and the value range of the third weight parameter is a closed interval [0,1], increasing the number of algorithm training times and calculating the algorithm state every i seconds, the algorithm state including link delay, packet loss rate, and link utilization, when the algorithm state is not a termination state, determining the routing action and calculating the incentive value according to the algorithm state, obtaining the algorithm state at i+1 seconds, and storing the algorithm state, the routing action, and the incentive value in the experience pool; when the link delay and link capacity meet the restriction conditions, updating the intelligent routing algorithm.

[0024] In this embodiment, the intelligent routing algorithm can be implemented by the following steps: The network topology is represented by an undirected graph G(V, E, C), where V is a data network node, E is a node connection path between the data network nodes, and C is a link capacity on the node connection path. The node traffic is obtained, and the node traffic includes node sending traffic and node receiving traffic. The node sending traffic is the traffic sent by each data network node, and the node receiving traffic is the traffic received by each data network node. The node time is obtained, and the node time includes node sending time and node receiving time. The node sending time is the time when the data network node starts to send traffic, and the node receiving time is the time when the data network node ends to receive traffic. The host data packet processing time is obtained; Calculate the link delay, the packet loss rate, and the link utilization rate. The calculation formula for the link delay is: , where delay is the link delay, N is the number of data network nodes, Tr j is the node receiving time of the jth data network node, Ts j is the node sending time of the jth data network node, Tc j is the host data packet processing time of the jth data network node; the calculation formula of the packet loss rate is: , where loss is the packet loss rate, Dr j The node receiving traffic for the jth data network node, Ds j The node sends traffic to the jth data network node; the calculation formula of the link utilization is: , where utilization is the link utilization, C j is the link capacity of the jth data network node; The calculation formula of the excitation value is: , where R is the excitation value, α is the first weight parameter, β is the second weight parameter, and γ is the third weight parameter; The constraints are , where TD is the maximum tolerable delay threshold of the link and TC is the maximum capacity of the link.

[0025] In this embodiment, the flow controller generates a reference trajectory based on a given reference input, uses a reference trajectory method to compare the actual system response with the reference trajectory, and adjusts the reference input to approach or achieve the desired system behavior; performs rolling optimization on the reference trajectory, calculates the optimization result, uses the optimization result to adjust the queue length and the incoming flow rate, and uses the least squares method to adjust the system parameters, predicts the outgoing flow rate of the adjusted system, compares the predicted result with the actual output, and calculates the prediction error; passes the prediction error to feedback correction, and the feedback correction performs the flow adjustment based on the cost function to ensure that the system operates stably in high concurrency scenarios and guarantee the performance and service quality of the network.

[0026] An international logistics task status management device based on edge AI technology, including a task creation module, a task synchronization module, and a flow control module; The task creation module is used for the shipping end to upload international logistics information and create international logistics task status. The international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information. The international logistics task status includes task pending confirmation status, task confirmation status, task dispatched vehicle status, task bound power of attorney status, task power of attorney full receipt status, task rejection status, task modification status, and task invalidation status. The task synchronization module is used for the shipping end to ship and synchronize the international logistics information and the international logistics task status to the dispatching end through improved edge AI technology, and the dispatching end updates the international logistics task status; synchronize the international logistics information and the updated international logistics task status to the receiving end; The flow control module is used to adjust the queue length and the incoming flow rate during the process of information synchronization to the scheduling end through the flow controller to achieve flow control.

[0027] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0028] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0029] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0030] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for managing the status of international logistics tasks based on edge AI technology, characterized in that: The implementation of the international logistics task status management method includes the following steps: The shipping end uploads international logistics information and creates international logistics task status; The shipping end ships the goods and synchronizes the international logistics information and the international logistics task status to the scheduling end through improved edge AI technology, and the scheduling end updates the international logistics task status; The flow controller adjusts the queue length and incoming flow rate during the information synchronization process to the dispatch end to achieve flow control; The dispatching end synchronizes the international logistics information and the updated international logistics task status to the receiving end.

2. The international logistics task status management method according to claim 1 is characterized in that: The international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information; the international logistics task status includes task pending confirmation status, task confirmation status, task vehicle dispatched status, task bound to power of attorney status, task power of attorney full receipt status, task rejection status, task modification status, and task invalidation status.

3. The international logistics task status management method according to claim 1 is characterized in that: The improved edge AI technology includes a data plane layer, a control layer, and a knowledge layer.

4. The international logistics task status management method according to claim 3 is characterized in that: The data plane layer is composed of edge nodes, and the hosts communicate with each other through MPI. The control layer includes a node monitoring module and a decision-making module, and the knowledge layer includes a decision-making core module.

5. The international logistics task status management method according to claim 4 is characterized in that: The decision core module is implemented by an intelligent routing algorithm, which includes: using a random initialization method to set weight parameters, the weight parameters include a first weight parameter, a second weight parameter, and a third weight parameter, the value range of the first weight parameter and the second weight parameter is a closed interval [-1,0], and the value range of the third weight parameter is a closed interval [0,1], increasing the number of algorithm training times and calculating the algorithm state every i seconds, the algorithm state includes link delay, packet loss rate, and link utilization rate, when the algorithm state is not a termination state, determining a routing action and calculating an incentive value according to the algorithm state, obtaining the algorithm state at i+1 seconds, and storing the algorithm state, the routing action, and the incentive value in an experience pool; when the link delay and link capacity meet the restriction conditions, updating the intelligent routing algorithm.

6. The international logistics task status management method according to claim 5 is characterized in that: The intelligent routing algorithm specifically includes: The network topology is represented by an undirected graph G(V, E, C), where V is a data network node, E is a node connection path between the data network nodes, and C is the link capacity on the node connection path. The node traffic is obtained, and the node traffic includes the node sending traffic and the node receiving traffic. The node time is obtained, and the node time includes the node sending time and the node receiving time. The host data packet processing time is obtained; The link delay, the packet loss rate, and the link utilization are calculated based on the node traffic, the node time, and the host data packet processing time. The calculation formula for the link delay is: , where delay is the link delay, N is the number of data network nodes, Tr j is the node receiving time of the jth data network node, Ts j is the node sending time of the jth data network node, Tc j is the host data packet processing time of the jth data network node; the calculation formula of the packet loss rate is: , where loss is the packet loss rate, Dr j The node receiving traffic for the jth data network node, Ds j The node sends traffic to the jth data network node; the calculation formula of the link utilization is: , where utilization is the link utilization, C j is the link capacity of the jth data network node; The calculation formula of the excitation value is: , where R is the excitation value, α is the first weight parameter, β is the second weight parameter, and γ is the third weight parameter; The constraints are , where TD is the maximum tolerable delay threshold of the link and TC is the maximum capacity of the link.

7. The international logistics task status management method according to claim 1 is characterized in that: The flow controller generates a reference trajectory based on a given reference input, uses a reference trajectory method to compare the actual system response with the reference trajectory, and adjusts the reference input to approach or achieve a desired system behavior; The reference trajectory is subjected to rolling optimization, and an optimization result is calculated. The queue length and the incoming flow rate are adjusted using the optimization result, and the system parameters are adjusted using the least square method. The outgoing flow rate of the adjusted system is predicted, and the prediction result is compared with the actual output to calculate the prediction error. The prediction error is passed to feedback correction, and the feedback correction performs the flow adjustment based on the cost function.

8. An international logistics task status management device based on edge AI technology, characterized in that: The device is applied to the international logistics task status management method as described in any one of claims 1 to 7, including a task creation module, a task synchronization module, and a flow control module; The task creation module is used for the shipping end to upload international logistics information and create international logistics task status. The international logistics information includes international logistics weight, international logistics item category, international logistics sending and receiving address information, and international logistics remarks information. The international logistics task status includes task pending confirmation status, task confirmation status, task dispatched vehicle status, task bound power of attorney status, task power of attorney full receipt status, task rejection status, task modification status, and task invalidation status. The task synchronization module is used for the shipping end to ship and synchronize the international logistics information and the international logistics task status to the dispatching end through improved edge AI technology, and the dispatching end updates the international logistics task status; synchronize the international logistics information and the updated international logistics task status to the receiving end; The flow control module is used to adjust the queue length and the incoming flow rate during the process of information synchronization to the scheduling end through the flow controller to achieve flow control.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the international logistics task status management method as described in any one of claims 1-7 is implemented.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the international logistics task status management method as described in any one of claims 1-7.

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