Logistics state management method and system based on dynamic routing cooperative transmission

Abstract

The invention relates to a logistics state management method and system based on dynamic routing cooperative transmission, and belongs to the technical field of logistics transportation management. The method comprises the following steps: constructing a logistics transportation network according to routing attributes of logistics node real-time data, and collecting real-time parameters in a transportation process to generate logistics state data; establishing a dynamic routing network optimization model based on the logistics state data, and optimizing a path responsibility node weight through a distributed optimization algorithm to obtain an optimal routing path; the logistics state data is subjected to fragment coding, fragments are distributed to different responsible nodes through a cooperative transmission tracking algorithm, the nodes cooperatively verify data integrity through a point-to-point network, and candidate path node states are analyzed based on a shortest path algorithm; and performing update optimization detection according to the path node state and the package state, and automatically selecting and adjusting a downstream route or activating a standby route for re-fragmentation transmission.

Description

Technical Field

[0001] This invention belongs to the field of logistics transportation management technology, specifically relating to a logistics status management method and system based on dynamic routing collaborative transmission. Background Technology

[0002] With the rapid development of e-commerce and supply chains, logistics and transportation networks are becoming increasingly complex. Parcels flow through multiple nodes and paths, leading to frequent problems such as transportation delays, information loss, and parcel damage. Traditional logistics management methods rely heavily on static route planning and centralized scheduling, which are ill-suited to adapting to dynamic changes in node status, link congestion, and unforeseen events, resulting in low logistics efficiency and poor transportation reliability. Furthermore, existing methods lack sufficient means for real-time collection, transmission, and monitoring of logistics status data, failing to effectively analyze and optimize the load of each node, link health, and anomalies during transportation, thus limiting the intelligent and collaborative management capabilities of the logistics network. In complex transportation environments, how to achieve dynamic monitoring of logistics status data, optimal route selection, reliable fragmented transmission, and real-time updates of node status has become a critical issue that logistics information management technology urgently needs to address. Therefore, there is an urgent need for a dynamic routing and collaborative transmission management method that integrates real-time status of logistics nodes, link health information, and multi-source data to improve logistics transportation efficiency, ensure data transmission reliability, and achieve dynamic optimization and intelligent scheduling of the logistics network. This method should be able to analyze node and link status in real time during parcel transportation, dynamically generate the optimal path and support backup path switching, while ensuring the integrity and continuity of logistics status data in fragmented transmission, thus providing technical support for the efficient operation of the logistics network. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, this invention provides a logistics status management method based on dynamic routing and cooperative transmission. The objective of this invention can be achieved through the following technical solutions: S1: Construct a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtain the transportation routes of logistics packages, and generate logistics status data based on real-time parameters collected from multi-source sensors during the logistics transmission process. S2: Based on the logistics status data, perform collaborative link status analysis, establish a dynamic routing network optimization model, input real-time data and constraints during cargo transmission as parameters into the dynamic routing network optimization model, optimize the weights of the dynamic path responsibility nodes through a distributed optimization algorithm, and obtain the optimal routing path in collaborative transmission. S3: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. S4: Based on the logistics status management solution, update optimization detection is set according to the status of path nodes and package status. By detecting different update requirements, it automatically selects to record and adjust the downstream routing path or activates the backup routing path to re-segment and transmit the data.

[0004] Specifically, the logistics transportation network uses logistics storage points as logistics network nodes, and establishes the logistics transportation network based on the logistics load of each logistics network node.

[0005] Specifically, the collaborative transmission tracking algorithm establishes a path tracking table during the fragment transmission process based on the unique fragment identifier of the logistics status data. Each responsible node performs signature verification on the received fragments according to the path tracking table and stores the transmission path based on a hash chain structure.

[0006] Specifically, the method for segmenting and encoding the logistics status data is as follows: The logistics status data is formatted, divided into data streams, and fragmented according to a preset maximum data stream length, with a unique identifier generated for each fragment; The fragmented data is encoded using erasure coding to generate redundant parity fragments based on the original fragmented data. When a fragment is damaged, the original fragmented data is restored based on the remaining fragments. After the fragment encoding is completed, the unique identifier of each fragment is distributed to different dynamic path responsibility nodes through a collaborative transmission tracing algorithm; and the integrity of the received fragment is verified based on the hash check value, and the path information and status of the fragment are recorded in the tracing table.

[0007] Specifically, the method for cooperative link state analysis is as follows: The system monitors each link and node in the logistics network to obtain node parameters and link parameters. The node parameters include available bandwidth and package status. The link parameters include road information and transportation costs. The acquired data is normalized, the transportation status of the link is analyzed by a multi-index evaluation method, and the transportation data is weighted according to the logistics task requirements to form a logistics status data table. Using link status as a grid and the real-time status of adjacent nodes as points, a node-link collaboration matrix is ​​formed. Candidate paths are sorted according to the node-link collaboration matrix, a path priority table is output, and dynamic routing is input.

[0008] Specifically, the method for recording the status of the candidate path nodes is as follows: the transmission efficiency of each candidate path node is calculated based on the shortest path algorithm, and the real-time status of the nodes is recorded; the node status is stored as an immutable log using blockchain technology to generate a candidate path status database, the status of each candidate node is saved, and the priority of the candidate paths is dynamically updated according to the status database.

[0009] Specifically, the method for generating the optimal routing path using the dynamic routing network optimization model includes: All transportation routes are generated based on the logistics transportation network topology and node-link status data, and each route is marked. Paths with high node transportation delays are eliminated based on the marked information, and candidate routes are selected. The candidate paths are weighted according to multidimensional indicators, and node status and link health are incorporated into the algorithm constraints according to the distributed optimization algorithm. The path weights are calculated, and the candidate paths are sorted according to the logistics management weights to select the optimal route path, while reserving backup paths. The optimal path and backup path information are output to the optimized transmission layer of the dynamic routing network optimization model. The load and link delay status changes of path nodes are monitored in real time. When the status does not meet the threshold set according to the node status and package status in the logistics status management scheme, the backup path is switched or the optimal path is recalculated.

[0010] Specifically, the different update requests include package status changes, route status changes, and user-defined requests. Based on the triggering conditions defined in the logistics status management scheme, update requests are automatically detected and executed, including recording status change logs, pushing real-time notifications, or adjusting transmission strategies.

[0011] Specifically, the method for adjusting the downstream routing path is as follows: based on the path node status and package status, analyze the transmission efficiency and cost of the current path; recalculate the downstream path through the dynamic routing network optimization model, select a low-latency, low-cost path, update the data fragmentation transmission strategy, and adjust the path weight using the weighted average method.

[0012] Specifically, the activation method for the backup routing path is as follows: when a failure of the primary routing path node is detected, the highest priority backup path is extracted from the candidate path status database; the data is re-fragmented and encoded using a multipath transmission protocol, the running status of the backup path is recorded in the path tracking table, and a new backup routing path is activated.

[0013] Specifically, the logistics status management scheme is based on node status and package status. When package receipt information or vehicle arrival information is received, it is pushed to the logistics management terminal through the logistics transmission network.

[0014] Specifically, a logistics status management system based on dynamic routing and collaborative transmission is characterized by comprising: Status data acquisition module: Constructs a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtains the transportation routes included in the logistics, and generates logistics status data based on real-time parameters of the logistics transmission process collected by multi-source sensors. Dynamic optimization analysis module: Based on the logistics status data, it performs collaborative link status analysis, establishes a dynamic routing network optimization model, and inputs real-time data and constraints during the cargo transmission process as parameters into the dynamic routing network optimization model. The weights of the dynamic path responsibility nodes are optimized through a distributed optimization algorithm to obtain the optimal routing path in collaborative transmission. Coding Collaborative Verification Module: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. Logistics routing control module: Based on the logistics status management scheme, update optimization detection is set according to the status of path nodes and package status. By detecting different update needs, it automatically selects to record and adjust downstream routes or activates backup routes to re-segment and transmit data.

[0015] The beneficial effects of this invention are as follows: This invention presents a logistics status management method based on dynamic routing and collaborative transmission, which significantly improves the intelligence, collaboration, and reliability of logistics networks. By collecting multi-source data from logistics nodes and links in real time, it performs collaborative analysis on node load, transportation delays, and link health status, achieving dynamic monitoring of logistics status. It utilizes a dynamic routing network optimization model to generate optimal paths and combines distributed optimization algorithms and weighted shortest path algorithms to dynamically adjust the weights of candidate paths, ensuring optimal transmission schemes for logistics packages under multi-dimensional indicators. The method employs fragmentation coding technology for reliable transmission of logistics status data and uses collaborative transmission tracking algorithms and node status records to achieve real-time monitoring and integrity verification of responsible nodes, ensuring the continuity and accuracy of data transmission. Simultaneously, it automatically activates backup paths when nodes or links malfunction, achieving dynamic closed-loop scheduling and improving the robustness and emergency response capabilities of the logistics system. Compared to existing technologies, this invention effectively reduces logistics transportation delays, lowers the risk of node and link congestion, improves data transmission reliability, and achieves collaborative management of multiple nodes and paths, providing technical support for the efficient operation of complex logistics networks. It has significant application value and promising prospects for widespread adoption. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of the structure of a logistics status management method and system based on dynamic routing and collaborative transmission according to the present invention.

[0018] Figure 2 This is a schematic diagram of logistics node data in a logistics status management method and system based on dynamic routing and collaborative transmission, as described in this invention. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0020] Please see Figure 1 A logistics status management method based on dynamic routing and cooperative transmission: S1: Construct a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtain the transportation routes of logistics packages, and generate logistics status data based on real-time parameters collected from multi-source sensors during the logistics transmission process. S2: Based on the logistics status data, perform collaborative link status analysis, establish a dynamic routing network optimization model, input real-time data and constraints during cargo transmission as parameters into the dynamic routing network optimization model, optimize the weights of the dynamic path responsibility nodes through a distributed optimization algorithm, and obtain the optimal routing path in collaborative transmission. S3: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. S4: Based on the logistics status management solution, update optimization detection is set according to the status of path nodes and package status. By detecting different update requirements, it automatically selects to record and adjust the downstream routing path or activates the backup routing path to re-segment and transmit the data.

[0021] Specifically, the logistics transportation network uses logistics storage points as logistics network nodes, and establishes the logistics transportation network based on the logistics load of each logistics network node.

[0022] Specifically, the collaborative transmission tracking algorithm establishes a path tracking table during the fragment transmission process based on the unique fragment identifier of the logistics status data. Each responsible node performs signature verification on the received fragments according to the path tracking table and stores the transmission path based on a hash chain structure.

[0023] Specifically, the method for segmenting and encoding the logistics status data is as follows: The logistics status data is formatted, divided into data streams, and fragmented according to a preset maximum data stream length, with a unique identifier generated for each fragment; The fragmented data is encoded using erasure coding to generate redundant parity fragments based on the original fragmented data. When a fragment is damaged, the original fragmented data is restored based on the remaining fragments. After the fragment encoding is completed, the unique identifier of each fragment is distributed to different dynamic path responsibility nodes through a collaborative transmission tracing algorithm; and the integrity of the received fragment is verified based on the hash check value, and the path information and status of the fragment are recorded in the tracing table.

[0024] This embodiment verifies the integrity of received fragments using hash checksums. The logistics status data is fragmented into fixed-size fragments, each denoted as Si, numbered IDi, and with a length of Li bytes. A timestamp Ti is appended to each fragment, recording the standard date and time. The following calculations are performed for each fragment: , S1="Package ID=1234, Status=Transiting", Segment ID IDi=0001, Timestamp Ti=1698356400 (corresponding to 2023-10-27 15:00:00). , where || represents data concatenation.

[0025] Calculate the dynamic weight W for the responsible node of each fragment transmission path. j Based on node load, link latency, and historical transmission reliability, the specific calculation is as follows: , Given node ALoad=0.6, Delay=0.1, Reliability=0.9, weight coefficients α=0.4, β=0.3, γ=0.3, then: Where α, β, γ are weighting coefficients, W j For dynamic weights.

[0026] The receiving end receives fragment S i Afterwards, extract the fragment number and timestamp, recalculate the hash value of the received fragment, and compare the result with the hash value of the sender. If the verification is successful, mark the fragment status as "complete" and write it to the cache or distributed repository; if the verification fails, trigger retransmission or resend via an alternative path.

[0027] Each fragment verification result, path node number, node weight, and timestamp are written into the dynamic path responsibility node status table; the collaborative transmission tracing algorithm updates the node reliability score based on the node status and fragment verification results, supporting subsequent fragment scheduling and path optimization; lost fragments can be reconstructed by combining erasure coding or linear network coding.

[0028] The logistics status management system in this embodiment adopts a collaborative link architecture to achieve unified support for batch calculation and online real-time stream processing. The execution process and the technology stack used are as follows: like Figure 1 As shown, during the logistics transportation process, each logistics node collects transportation status parameters in real time through multi-source sensors, including vehicle location, speed, temperature and humidity, transportation delay, and node load. The system constructs a logistics transportation network based on the routing attributes of the logistics nodes, obtains the transportation route of each logistics package, and generates complete logistics status data from the real-time parameters collected during transmission, providing basic data support for subsequent optimization analysis. Based on the collected logistics status data, collaborative link status analysis is performed to establish a dynamic routing network optimization model. The model takes real-time data and constraints during cargo transportation as input, and optimizes the weights of dynamic path responsibility nodes through a distributed optimization algorithm, thereby calculating the optimal routing path in collaborative transmission. The optimization process considers node load, link delay, and transmission reliability to ensure that packages are transmitted efficiently and reliably in the logistics network. The logistics status data is segmented and encoded. Each segment is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Nodes verify data integrity through a point-to-point network, analyze node status based on the shortest path algorithm, record the real-time status of candidate path nodes, and continuously monitor the status of path nodes and packages according to the logistics status management scheme, setting update optimization checks. By detecting different update requests, the system can automatically adjust downstream routes or activate backup routes to achieve fragment retransmission and path optimization. This module ensures that logistics packages can reach their destination safely, reliably, and on time, even in the event of node anomalies or link congestion.

[0029] Hash algorithms and data integrity verification: SHA-3 or BLAKE2 Erasure coding and redundant sharding: Reed-Solomon Coding Log collection module: Fluentd or Logstash Transfer queues: RabbitMQ or Apache Pulsar Model synchronization: Redis Streams or etcd Distributed storage: Ceph or Amazon S3 The specific implementation process is as follows: Figure 2As shown: Specifically, the method for collaborative link status analysis is as follows: order system > data collection > feature encoding > Fluentd real-time analysis > transmission to Apache Pulsar > synchronization model etcd for update and optimization > redundancy sharding based on Reed-SolomonCoding > management platform.

[0030] The system monitors each link and node in the logistics network to obtain node parameters and link parameters. The node parameters include available bandwidth and package status. The link parameters include road information and transportation costs. The acquired data is normalized, the transportation status of the link is analyzed by a multi-index evaluation method, and the transportation data is weighted according to the logistics task requirements to form a logistics status data table. Using link status as a grid and the real-time status of adjacent nodes as points, a node-link collaboration matrix is ​​formed. Candidate paths are sorted according to the node-link collaboration matrix, a path priority table is output, and dynamic routing is input.

[0031] Specifically, the method for recording the status of the candidate path nodes is as follows: the transmission efficiency of each candidate path node is calculated based on the shortest path algorithm, and the real-time status of the nodes is recorded; the node status is stored as an immutable log using blockchain technology to generate a candidate path status database, the status of each candidate node is saved, and the priority of the candidate paths is dynamically updated according to the status database.

[0032] Specifically, the method for generating the optimal routing path using the dynamic routing network optimization model includes: All transportation routes are generated based on the logistics transportation network topology and node-link status data, and each route is marked. Paths with high node transportation delays are eliminated based on the marked information, and candidate routes are selected. The candidate paths are weighted according to multidimensional indicators, and node status and link health are incorporated into the algorithm constraints according to the distributed optimization algorithm. The path weights are calculated, and the candidate paths are sorted according to the logistics management weights to select the optimal route path, while reserving backup paths. The optimal path and backup path information are output to the optimized transmission layer of the dynamic routing network optimization model. The load and link delay status changes of path nodes are monitored in real time. When the status does not meet the threshold set according to the node status and package status in the logistics status management scheme, the backup path is switched or the optimal path is recalculated.

[0033] Specifically, the different update requests include package status changes, route status changes, and user-defined requests. Based on the triggering conditions defined in the logistics status management scheme, update requests are automatically detected and executed, including recording status change logs, pushing real-time notifications, or adjusting transmission strategies.

[0034] Specifically, the method for adjusting the downstream routing path is as follows: based on the path node status and package status, analyze the transmission efficiency and cost of the current path; recalculate the downstream path through the dynamic routing network optimization model, select a low-latency, low-cost path, update the data fragmentation transmission strategy, and adjust the path weight using the weighted average method.

[0035] Specifically, the activation method for the backup routing path is as follows: when a failure of the primary routing path node is detected, the highest priority backup path is extracted from the candidate path status database; the data is re-fragmented and encoded using a multipath transmission protocol, the running status of the backup path is recorded in the path tracking table, and a new backup routing path is activated.

[0036] Specifically, the logistics status management scheme is based on node status and package status. When package receipt information or vehicle arrival information is received, it is pushed to the logistics management terminal through the logistics transmission network.

[0037] Specifically, a logistics status management system based on dynamic routing and collaborative transmission is characterized by comprising: Status data acquisition module: Constructs a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtains the transportation routes included in the logistics, and generates logistics status data based on real-time parameters of the logistics transmission process collected by multi-source sensors. Dynamic optimization analysis module: Based on the logistics status data, it performs collaborative link status analysis, establishes a dynamic routing network optimization model, and inputs real-time data and constraints during the cargo transmission process as parameters into the dynamic routing network optimization model. The weights of the dynamic path responsibility nodes are optimized through a distributed optimization algorithm to obtain the optimal routing path in collaborative transmission. Coding Collaborative Verification Module: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. Logistics routing control module: Based on the logistics status management scheme, update optimization detection is set according to the status of path nodes and package status. By detecting different update needs, it automatically selects to record and adjust downstream routes or activates backup routes to re-segment and transmit data.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A logistics status management method based on dynamic routing and cooperative transmission, characterized in that, include: S1: Construct a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtain the transportation routes of logistics packages, and generate logistics status data based on real-time parameters collected from multi-source sensors during the logistics transmission process. S2: Based on the logistics status data, perform collaborative link status analysis, establish a dynamic routing network optimization model, input real-time data and constraints during cargo transmission as parameters into the dynamic routing network optimization model, optimize the weights of the dynamic path responsibility nodes through a distributed optimization algorithm, and obtain the optimal routing path in collaborative transmission. S3: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. S4: Based on the logistics status management solution, update optimization detection is set according to the status of path nodes and package status. By detecting different update requirements, it automatically selects to record and adjust the downstream routing path or activates the backup routing path to re-segment and transmit the data.

2. The method according to claim 1, characterized in that, The logistics transportation network uses logistics storage points as logistics network nodes, and establishes the logistics transportation network based on the logistics load of each logistics network node.

3. The method according to claim 1, characterized in that, The collaborative transmission tracking algorithm establishes a path tracking table during the fragment transmission process based on the unique fragment identifier of the logistics status data. Each responsible node verifies the received fragments according to the path tracking table and stores the transmission path based on a hash chain structure.

4. The method according to claim 1, characterized in that, The method for segmenting and encoding the logistics status data is as follows: The logistics status data is formatted, divided into data streams, and fragmented according to a preset maximum data stream length, with a unique identifier generated for each fragment; The fragmented data is encoded using erasure coding to generate redundant parity fragments based on the original fragmented data. When a fragment is damaged, the original fragmented data is restored based on the remaining fragments. After the fragment encoding is completed, the unique identifier of each fragment is distributed to different dynamic path responsibility nodes through a collaborative transmission tracing algorithm; and the integrity of the received fragment is verified based on the hash check value, and the path information and status of the fragment are recorded in the tracing table.

5. The method according to claim 2, characterized in that, The method for analyzing the cooperative link status is as follows: The system monitors each link and node in the logistics network to obtain node parameters and link parameters. The node parameters include available bandwidth and package status. The link parameters include road information and transportation costs. The acquired data is normalized, the transportation status of the link is analyzed by a multi-index evaluation method, and the transportation data is weighted according to the logistics task requirements to form a logistics status data table. Using link status as a grid and the real-time status of adjacent nodes as points, a node-link collaboration matrix is ​​formed. Candidate paths are sorted according to the node-link collaboration matrix, a path priority table is output, and dynamic routing is input.

6. The method according to claim 5, characterized in that, The method for recording the status of candidate path nodes is as follows: the transmission efficiency of each candidate path node is calculated based on the shortest path algorithm, and the real-time status of the node is recorded; the node status is stored as an immutable log using blockchain technology to generate a candidate path status database, the status of each candidate node is saved, and the priority of the candidate path is dynamically updated according to the status database.

7. The method according to claim 4, characterized in that, The method for generating the optimal routing path using the dynamic routing network optimization model includes: All transportation routes are generated based on the logistics transportation network topology and node-link status data, and each route is marked. Paths with high node transportation delays are eliminated based on the marked information, and candidate routes are selected. The candidate paths are weighted according to multidimensional indicators, and node status and link health are incorporated into the algorithm constraints according to the distributed optimization algorithm. The path weights are calculated, and the candidate paths are sorted according to the logistics management weights to select the optimal route path, while reserving backup paths. The optimal path and backup path information are output to the optimized transmission layer of the dynamic routing network optimization model. The load and link delay status changes of path nodes are monitored in real time. When the status does not meet the threshold set according to the node status and package status in the logistics status management scheme, the backup path is switched or the optimal path is recalculated.

8. The method according to claim 2, characterized in that, The different update requests include changes in package status, changes in route status, and user-defined requests. Based on the triggering conditions defined in the logistics status management scheme, the system automatically detects update requests and performs actions such as recording status change logs, pushing real-time notifications, or adjusting transmission strategies.

9. The method according to claim 4, characterized in that, The method for adjusting the downstream routing path is as follows: based on the path node status and package status, analyze the transmission efficiency and cost of the current path; recalculate the downstream path through the dynamic routing network optimization model, select a low-latency, low-cost path, update the data fragmentation transmission strategy, and adjust the path weight using the weighted average method.

10. The method according to claim 4, characterized in that, The activation method for the backup routing path is as follows: when a failure of the primary routing path node is detected, the highest priority backup path is extracted from the candidate path status database; the data is re-fragmented and encoded using a multipath transmission protocol, the running status of the backup path is recorded in the path tracking table, and a new backup routing path is activated.

11. The method according to claim 7, characterized in that, The logistics status management scheme is based on node status and package status. When package receipt information or vehicle arrival information is received, it is pushed to the logistics management terminal through the logistics transmission network.

12. A logistics status management system based on dynamic routing and collaborative transmission, characterized in that, include: Status data acquisition module: Constructs a logistics transportation network based on the routing attributes of real-time data from logistics nodes, obtains the transportation routes included in the logistics, and generates logistics status data based on real-time parameters of the logistics transmission process collected by multi-source sensors. Dynamic optimization analysis module: Based on the logistics status data, it performs collaborative link status analysis, establishes a dynamic routing network optimization model, and inputs real-time data and constraints during the cargo transmission process as parameters into the dynamic routing network optimization model. The weights of the dynamic path responsibility nodes are optimized through a distributed optimization algorithm to obtain the optimal routing path in collaborative transmission. Coding Collaborative Verification Module: The logistics status data is segmented and encoded, and the encoded data is distributed to different dynamic path responsibility nodes through a collaborative transmission tracking algorithm. Each dynamic path responsibility node verifies the data integrity through a point-to-point network, and analyzes the dynamic path responsibility node based on the shortest path algorithm, recording the status of candidate path nodes in collaborative transmission. Logistics routing control module: Based on the logistics status management scheme, update optimization detection is set according to the status of path nodes and package status. By detecting different update needs, it automatically selects to record and adjust downstream routes or activates backup routes to re-segment and transmit data.

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