Logistics supply chain dynamic management method and platform based on Internet of Things

Abstract

The invention discloses a logistics supply chain dynamic management method and platform based on the Internet of Things, and belongs to the technical field of logistics network state awareness, and the method comprises the steps: obtaining the real-time state parameters of each logistics node, including the number of tasks, the queuing length, the processing speed and the node connection frequency; a dynamic weighted directed graph is constructed based on the parameters, nodes in the graph represent processing units, and edge weights reflect transportation efficiency and delay; performing coupling analysis on the graph structure, extracting a transmission incidence matrix, and calculating a node coupling degree; constructing a fragile node set based on the coupling degree, and calculating the node fault-tolerant capability in combination with the task flow pressure and the abnormal frequency; task transfer, path reconstruction and temporary resource adjustment are carried out on the nodes with the fault-tolerant capability lower than a set threshold value, and real-time identification and optimization of logistics network bottlenecks are achieved; according to the invention, the scheduling stability and anti-pressure capability of the logistics network can be improved, and the method is suitable for intelligent logistics management in a multi-source order and high-concurrency scene.

Description

Technical Field

[0001] This invention relates to the field of logistics network status perception technology, specifically to a dynamic management method and platform for logistics supply chain based on the Internet of Things. Background Technology

[0002] In recent years, with the rise of cross-regional e-commerce platforms and the surge in demand for instant delivery, logistics supply chain systems are facing multiple challenges, including "multi-source orders, high-frequency fluctuations, complex links, and dynamic changes in nodes." Traditional logistics supply chain management systems are mostly based on static path planning and manual scheduling models, lacking real-time performance and adaptability. Especially when facing emergencies (such as delivery delays, traffic control, and a surge in temporary orders), they cannot adjust the status of logistics nodes at all levels in a timely manner, leading to imbalances in the supply chain and, in severe cases, even causing partial logistics paralysis. While some systems currently incorporate IoT technology to monitor transportation nodes, the following problems remain: The status data of each logistics node cannot form a globally visible link diagram, making it impossible to accurately predict resource coordination between upstream and downstream nodes; when a node suddenly becomes congested or fails, the system cannot quickly identify the critical impact path, nor can it reallocate resources; in scenarios with multiple order flows running in parallel, some nodes become "breakpoints" in the system due to geographical location or task redundancy, but the existing system is unable to dynamically identify and reinforce them. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic management method and platform for logistics supply chain based on the Internet of Things (IoT) to address the shortcomings of the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a dynamic management method for logistics supply chain based on the Internet of Things, comprising: Obtain the real-time status parameters Si of each logistics node in the logistics system, including the current number of tasks, queue length, processing speed, and connection frequency information of the node and its adjacent nodes; Based on Si, construct a dynamic connection graph G for the logistics network, where nodes in G represent logistics processing units and edge weights represent the transportation frequency and delay cost between nodes. Dynamic coupling analysis is performed on graph G to extract the transmission correlation matrix M between nodes and calculate the coupling degree Ci of each node in the logistics network. A vulnerable node set W is constructed based on the Ci value. Node-level risk prediction is performed on each node in W, and its fault tolerance capability Ti is calculated. Nodes with Ti below a set threshold are marked as the target node set for adjustment. ; Dynamic resource adjustment is performed on the nodes in W′, including task transfer, path reconstruction, and temporary node computation.

[0005] Preferably, the dynamic connection graph G of the logistics network constructed based on Si includes: The real-time status parameters Si of each logistics node are obtained and used as node attribute inputs to initialize the node set V in the graph structure. Based on the connection frequency information between adjacent nodes, construct the edge set E between nodes, and set an edge weight for each edge to represent the stability and delay of the transportation path. A weighted directed graph G containing a set of nodes V and a set of edges E is generated using a graph structure modeling algorithm. The graph G reflects the current operational topology and dynamic load status of the logistics network.

[0006] Preferably, the step of generating a weighted directed graph G containing a set of nodes V and a set of edges E using a graph structure modeling algorithm includes: Based on the processing speed and current number of tasks in the node status parameters, calculate the unit processing time index for each node and add it as a node attribute value to the node set V. Based on the connection frequency information between nodes and historical transportation path data, a set of directed edges E is constructed, and each edge is assigned a comprehensive edge weight, which comprehensively considers transportation delay, path stability and average task pass rate. A weighted graph modeling algorithm based on graph theory is used to fuse the node set V and the edge set E to generate a weighted directed graph G that reflects the current structural characteristics and load status of the logistics network.

[0007] Preferably, the step of performing dynamic coupling analysis on graph G to extract the transmission correlation matrix M between nodes includes: Based on the unit processing time index and edge weight of each node in the weighted directed graph G, a task flow probability model is constructed to represent the transfer tendency of order flow between nodes. Calculate the transition probability Pij from node vi to node vj. The transition probability is determined based on the historical task path frequency and the current edge weight. The higher the transition probability, the stronger the traffic coupling between the two nodes. Construct a transmission correlation matrix M using the transition probabilities Pij between all nodes as elements, where each element Mij in the matrix represents the actual task flow coupling strength between node vi and node vj.

[0008] Preferably, calculating the coupling degree Ci of each node in the logistics network includes: Based on the constructed transmission correlation matrix M, the sum of the transition probabilities of node vi on all inbound and outbound paths is extracted as the task flow convergence index of the node. A node intermediation index is introduced to calculate the frequency of node vi as a transit node in all shortest paths, which reflects the degree of centrality of the node in the task transmission process. The task flow convergence index and the intermediary index of the node are weighted and fused to construct the calculation model of coupling degree Ci, where coupling degree Ci is used to characterize the degree of influence of node vi on the stability of the logistics network. Normalize the coupling degree Ci of all nodes to a set range and output the coupling degree result.

[0009] Preferably, the process involves constructing a vulnerable node set W based on the Ci value, performing node-level risk prediction on each node in W, and calculating its fault tolerance capability Ti, including: Set a coupling threshold θ, and add all nodes with coupling Ci greater than θ to the fragile node set W; For each node in the vulnerable node set W, its task flow convergence index and historical anomaly frequency data are extracted to construct node risk assessment factors. Based on task flow pressure and historical stability parameters, the fault tolerance capability Ti of node vi is calculated. Ti is used to characterize the ability of a node to maintain its service capability under load fluctuations. The fault tolerance capability Ti is compared with the preset tolerance threshold η. If Ti is less than η, the node is marked as a high-risk node.

[0010] Preferably, the dynamic resource adjustment for the nodes in W′ includes: For each node vi∈W′, based on its task queue length and processing speed, assess its current load saturation and determine whether it exceeds the set load tolerance threshold. For nodes that exceed the load tolerance threshold, select the adjacent idle node vj with the highest connection frequency as the task transfer target, and transfer some of the tasks to be processed to node vj under the condition of meeting the processing capacity constraints. Based on the load changes between nodes after task transfer, the edge weights in the graph structure are dynamically adjusted, and the logistics path topology is updated to achieve path reconstruction. If node vi is still overloaded after task transfer and path reconstruction, then allocate schedulable redundant computing resources to the node or introduce a temporary cooperating node.

[0011] This invention also provides an IoT-based dynamic logistics supply chain management platform, comprising: Status awareness module: acquires real-time status parameters Si of each logistics node in the logistics system, including the current number of tasks, queue length, processing speed and connection frequency information of the node and its neighboring nodes; Graph modeling module: Constructs a dynamic connection graph G of the logistics network based on Si, where nodes in G represent logistics processing units and edge weights represent the transportation frequency and delay cost between nodes; Coupling Analysis Module: Performs dynamic coupling analysis on graph G, extracts the transmission correlation matrix M between nodes, and calculates the coupling degree Ci of each node in the logistics network; Vulnerability identification module: Constructs a vulnerable node set W based on Ci values, performs node-level risk prediction on each node in W, calculates its fault tolerance capability Ti, and marks nodes with Ti below a set threshold as the target node set for adjustment. ; Resource adjustment module: Performs dynamic resource adjustment on nodes in W′, including task transfer, path reconstruction and temporary node calculation.

[0012] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention introduces IoT-based data acquisition methods, combined with graph modeling algorithms and coupled measurement mechanisms, to construct a weighted directed graph reflecting the actual operating status of the logistics network in real time. It also dynamically identifies vulnerable nodes with highly concentrated tasks and poor stability, thus solving technical problems in existing logistics supply chains such as the inability to promptly perceive changes in node pressure, delayed scheduling response, and uneven resource allocation. Compared to traditional static scheduling methods, this invention possesses stronger network adaptability and global coordination capabilities.

[0013] 2. This invention achieves precise identification and dynamic adjustment of high-risk nodes by constructing a task flow probability model, a transmission correlation matrix, and node fault tolerance indicators. It also optimizes key nodes locally through task transfer, path reconstruction, and the introduction of redundant resources, effectively improving the stability and service continuity of the logistics network in multi-source orders and high-concurrency scenarios, and has high engineering practicality. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1 This is a flowchart of the method of the present invention.

[0016] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown in this embodiment, a dynamic logistics supply chain management method based on the Internet of Things includes: Obtain the real-time status parameters Si of each logistics node in the logistics system, including the current number of tasks, queue length, processing speed, and connection frequency information of the node and its adjacent nodes; In a preferred embodiment of the present invention, regarding the operational status of logistics nodes at various levels in the logistics supply chain, firstly, multi-dimensional data collection of the real-time operational status of the nodes is performed using IoT data acquisition devices deployed at the nodes to obtain a set of basic state parameters Si for dynamic management. Si is the set of real-time state parameters for the i-th logistics node, and includes at least the following: Node current task count (TaskSi): refers to the number of orders currently queued and waiting to be processed at this logistics node, reflecting the node's current workload.

[0019] Queuei: refers to the length of the physical or logical queue sequence of unprocessed orders. Combined with the node resource scale, it can be used to assess the potential congestion of a node.

[0020] Processing speed (Ratei): refers to the number of orders a node can process per unit of time, representing the node's service capacity, and is related to the node's hardware configuration and the efficiency of the operators.

[0021] Connection frequency information (Freqij): refers to the number of communication or order interactions between the node and its neighboring nodes per unit time, used to quantify the actual business interaction frequency between nodes.

[0022] The above parameters are obtained through the integration of IoT data acquisition terminals and node operation systems. For example, the current number of tasks and queue length can be reported in real time by the node's internal order management system (OMS), the processing speed can be calculated from the statistical data of orders completed by the node over a period of time, and the connection frequency information is obtained by recording the order flow path through the edge server or by order jump records generated by the transportation scheduling system.

[0023] Based on Si, construct a dynamic connection graph G for the logistics network, where nodes in G represent logistics processing units and edge weights represent the transportation frequency and delay cost between nodes. First, based on the real-time state parameters Si of each logistics node obtained in the preceding steps, the node set V in the graph structure is initialized. Each logistics node corresponds to a node vi in ​​the graph structure, and the node attributes are composed of its state parameters Si. The state parameters Si include: The current task count Ti represents the number of orders currently pending processing at the node; Queue length Qi represents the number of orders waiting to be dispatched in the node's task buffer; Processing speed Ri represents the number of orders that a node can process per unit of time; The connection frequency Fij with adjacent nodes represents the number of order interactions between node vi and its adjacent node vj per unit time.

[0024] The aforementioned state parameters are embedded in each node vi as key-value pairs to complete the initialization of the node set V, ensuring that each node has resolvable business attributes in the graph structure.

[0025] Based on the node set V and edge set E, a weighted directed graph G of the logistics network is constructed using a graph structure modeling algorithm. The graph modeling algorithm includes the following steps: First, using the processing speed and current number of tasks from the acquired logistics node status parameters, a unit processing time index Pi is calculated for each node. This index quantifies the average time required for the node to process a single task under the current load. The calculation method is: Unit processing time index Pi = Current number of tasks Ti ÷ Processing speed Ri; where the current number of tasks Ti represents the number of orders currently waiting to be processed at logistics node vi, and the processing speed Ri represents the number of orders that can be processed per unit time, in units of pieces / minute. The larger the obtained unit processing time index Pi value, the more strained the node's processing capacity is, reflecting a higher probability that it will become a potential bottleneck. Subsequently, Pi is added as an attribute field of node vi to the node set V, so that the node set not only contains the node's identification information but also has quantifiable attribute characteristics of the node's operational capabilities.

[0026] After constructing the node set V, a directed edge set E is constructed based on the connection frequency information between nodes and historical transportation path data. Each edge eij represents an order transmission path between logistics node vi and logistics node vj.

[0027] To reflect the overall transmission efficiency and stability of the transportation path, each edge eij is assigned a comprehensive edge weight Wij. This weight comprehensively considers the following three indicators: Transportation delay Dij: The average time required to transfer an order from node vi to node vj, in seconds; Path stability Sij: Represents the frequency of path interruption within a historical period; the smaller the value, the more stable the path. Average task throughput Rij: The amount of tasks transmitted through this path per unit of time, reflecting the path's efficiency.

[0028] The weighted addition model is used to calculate the overall edge weight Wij, and its calculation formula is as follows: Where α, β, and γ are the weight coefficients of the three dimensions, satisfying... In this embodiment, preferably, α is set to 0.5, β to 0.3, and γ to 0.2, ensuring that transportation delay is the primary consideration while also taking into account path stability and task throughput. After calculating the edge weights, all edges and their weights are added to the edge set E, forming a quantifiable and analyzable path data foundation.

[0029] After the node set V and edge set E are constructed, a weighted graph modeling algorithm based on graph theory is used to merge them and construct a weighted directed graph G of the logistics network. The algorithm mainly includes the following steps: initializing the graph structure G as a directed graph structure; traversing the node set V, adding each logistics node vi and its attributes (including the unit processing time index Pi) to the graph structure; traversing the edge set E, adding each edge eij and its corresponding comprehensive edge weight Wij to the graph structure; verifying the integrity of the graph structure, including verifying that all edges connect to valid nodes, that edge weights are real and non-negative, and that there are no isolated nodes in the graph. If the verification is successful, the modeling is complete. The resulting weighted directed graph G has clear node connection relationships and attributes that reflect the node operating status, and can comprehensively express the topology and operating load distribution of the current logistics network.

[0030] Dynamic coupling analysis is performed on graph G to extract the transmission correlation matrix M between nodes and calculate the coupling degree Ci of each node in the logistics network.

[0031] Based on the established weighted directed graph G, and combining the unit processing time index of each node in the graph with the weight of the edges in the graph, a task flow probability model is constructed to describe the tendency of orders to flow from a certain node to adjacent nodes.

[0032] Among them, the unit processing time index represents the load capacity of a node to process tasks per unit of time, and the edge weight comprehensively reflects path latency, stability, and task throughput efficiency. The tendency of node vi to transfer to its neighboring node vj is influenced by the state of both nodes and the path performance.

[0033] The transition probability Pij is calculated to quantify the probability of an order flowing from node vi to node vj. The calculation method comprehensively considers the historical path frequency and the current edge weight, and is defined as follows: Transition probability Pij = Historical path frequency fij ÷ Weighted reciprocal of edge weight Wij; where historical path frequency fij represents the number of times an order is transmitted from node vi to node vj per unit time, and edge weight Wij is the current weight of eij. To maintain numerical stability, the transition probability results can be normalized so that the sum of the transition probabilities originating from each node is 1.

[0034] After calculating the transition probabilities between all node pairs, the transition probability Pij from node vi to node vj is used as matrix element Mij to construct the transmission correlation matrix M. Matrix M is an n×n real matrix, where n is the total number of nodes in the network.

[0035] Each element Mij in the matrix represents the order task coupling strength from node vi to node vj. This matrix serves as the basic structure for quantifying task transmission behavior in the network and is used for subsequent coupling degree calculation.

[0036] For each node vi, based on the transmission correlation matrix M, the sum of its transition probabilities in all incoming and outgoing edges is calculated and used as the task flow aggregation index Fi for that node. This index represents the total participation of node vi as a transmission start and end point in the network. The higher the value, the higher the frequency of the node's participation in task scheduling, and the greater the coupling risk.

[0037] The intermediation metric quantifies the centrality of a node as a transit node in the overall network. Its value equals the percentage of times node vi appears as an intermediate node in all shortest paths. It is defined as follows: Intermediation , where s≠i≠t; Where σst is the number of shortest paths from node vs to node vt, and σst(vi) is the number of paths that contain node vi.

[0038] The higher the degree of mediation, the more significant the role of node vi as a key transit channel in the logistics path, and the greater the impact on the overall scheduling when a blockage occurs.

[0039] The coupling degree Ci is calculated using a weighted superposition model, taking into account both the node's task flow convergence index Fi and the intermediation index Bi. The definition is as follows: Coupling Wherein, λ and μ are preset weight parameters used to adjust the contribution ratio of task density and path centrality to coupling degree. Preferably, λ is 0.6 and μ is 0.4 to reflect the dominance of task flow participation.

[0040] To ensure comparability of coupling values ​​between different nodes, the coupling degree Ci of all nodes is normalized to map it to the interval [0,1]. The normalization formula is as follows: Where Cmin and Cmax represent the minimum and maximum coupling values ​​among all nodes, respectively. The final output standardized coupling degree C′i will be used as the criterion for identifying vulnerable nodes.

[0041] A vulnerable node set W is constructed based on the Ci value. Node-level risk prediction is performed on each node in W, and its fault tolerance capability Ti is calculated. Nodes with Ti below a set threshold are marked as the target node set for adjustment. .

[0042] First, in order to identify nodes that pose a high risk to the overall network stability in high-concurrency logistics task scenarios, a coupling degree judgment threshold θ is set based on the distribution characteristics of node coupling degree Ci in historical logistics operation data.

[0043] Preferably, the threshold θ is the 80th percentile value of the coupling degree Ci of all nodes in the network, used to filter out nodes with high coupling degree. The specific setting method is as follows: Threshold θ = the value corresponding to the 0.8×nth position in the sorted coupling set C, where n is the total number of nodes.

[0044] Add all nodes vi that satisfy Ci>θ to the vulnerable node set W to construct the initial target set to be evaluated.

[0045] For each node in the set of vulnerable nodes W, extract its two core feature variables: Task flow convergence metric Fi: This metric has been defined in the aforementioned coupling degree calculation step and is the sum of the task transition probabilities of a node in all inbound and outbound edge directions; Historical anomaly frequency Ei: This represents the frequency with which node vi experiences abnormal states such as service interruption, processing delay, or queue overload within a fixed period in the past, expressed in terms of the number of anomalies per hour.

[0046] The two variables mentioned above are used to construct the risk assessment factor Ri of the node, which is defined as follows: Risk assessment factor Ri = Fi × Ei; This factor reflects the instability risk of the node under high traffic conditions and is used to assist in assessing its fault tolerance capability.

[0047] The fault tolerance capability Ti of a node is used to measure the node's ability to maintain normal task processing under abnormal conditions such as a sudden surge in tasks, path changes, or partial service interruptions.

[0048] The calculation of Ti takes into account the following two aspects: Node processing capacity redundancy (defined as the ratio of maximum processing capacity to current average load). Node historical stability (represented by the reciprocal of the historical anomaly frequency Ei).

[0049] The calculation formula is defined as: fault tolerance. Where Rmax,i is the theoretical maximum processing rate of node vi, and Ravg,i is the actual average processing rate of the node in the most recent period. A higher fault tolerance value Ti indicates that the node has a stronger ability to absorb and recover tasks. To further screen out nodes that require priority adjustment, a tolerance threshold η is set as the minimum acceptable fault tolerance standard.

[0050] Preferably, the tolerance threshold η is the median of all Ti values ​​in the network multiplied by the coefficient κ, and the coefficient κ ranges from 0.8 to 0.95, which is used to adjust the risk tolerance according to the business scenario.

[0051] If the fault tolerance capability Ti of a certain node vi is less than η, then the node is marked as a high-risk node and added to the set of target adjustment nodes W′, where W′ is a subset of W, i.e., the set of target adjustment nodes. The final set of W′ will serve as the basis for subsequent task transfers, path reconstruction, or resource weighting.

[0052] Dynamic resource adjustment is performed on the nodes in W′, including task transfer, path reconstruction, and temporary node computation.

[0053] For any node vi in ​​the target node set W′, extract its real-time task queue length Qi and processing speed Ri. Based on this, calculate the node's load saturation Si, which measures the node's current task pressure level, defined as follows: Load Saturation Where Qi is the number of tasks per unit, Ri is the number of tasks that can be processed per unit time, and Si is in time, representing the estimated queuing delay of tasks under the current processing capacity.

[0054] Set a load tolerance threshold ψ, preferably ψ is the 90th percentile of the maximum load saturation under the historical normal operating conditions of this type of node. If Si>ψ, then node vi is determined to have entered an over-limit state and adjustment operations need to be performed.

[0055] For a node vi that meets the overload condition, traverse its neighbor set Ni and select the node vj with the highest connection frequency Fij and current task load below its set processing limit as the transfer target node. The target node selection satisfies the following two conditions: Fij = max(Fik), k ∈ Ni; current load saturation Sj ≤ φ, where φ is the maximum tolerable load limit that the target node can accept. The number of tasks nij transferred from node vi to node vj is determined by the following formula: Where ρ is the task mitigation ratio (e.g., 0.2, indicating an attempt to transfer 20% of the tasks), and τ is the idle available time window of the target node (unit time), ensuring that the task transfer is executed without overloading the target node.

[0056] After the task transfer is completed, the real-time load status and task flow direction between nodes change. In order to reflect the new scheduling graph form, the edge weights Wij in the weighted directed graph G need to be updated.

[0057] The update method is as follows: For edge eij where a task transfer occurs, the edge weight is recalculated based on the newly generated task flow rate, path delay, and stability. For other affected edges (such as adjacent paths), adjust their edge weights according to the proportion of their transmission volume change to maintain global consistency of the graph structure; After completing the edge weight update, perform a path update operation on graph G to reconstruct the shortest task transmission path set, providing a basis for subsequent scheduling decisions.

[0058] This process employs an incremental graph update algorithm to avoid full graph reconstruction and improve adjustment efficiency.

[0059] If, after task transfer and path reconstruction, the load saturation Si of node vi still exceeds the load tolerance threshold ψ, it is determined to be a continuously overloaded node.

[0060] At this point, one of the following two mitigation strategies should be implemented: Allocate redundant computing resources: Activate the backup processing unit in the task processing flow of access node vi, and improve the processing rate Ri by expanding its parallel processing capability; Introduce a temporary collaborative node: Schedule a temporary task processing node vi′ within the geographic logistics area where vi is located, and redirect newly enqueued tasks to vi′ for processing. vi′ enters the original graph structure through a short link and participates in this round of scheduling.

[0061] The above strategies can be flexibly selected based on the current network resource availability and node location to ensure rapid relief of node-level pressure and avoid local link congestion or task backlog.

[0062] Example 2, please refer to Figure 2 As shown in this embodiment, a logistics supply chain dynamic management platform based on the Internet of Things includes: Status awareness module: acquires real-time status parameters Si of each logistics node in the logistics system, including the current number of tasks, queue length, processing speed and connection frequency information of the node and its neighboring nodes; Graph modeling module: Constructs a dynamic connection graph G of the logistics network based on Si, where nodes in G represent logistics processing units and edge weights represent the transportation frequency and delay cost between nodes; Coupling Analysis Module: Performs dynamic coupling analysis on graph G, extracts the transmission correlation matrix M between nodes, and calculates the coupling degree Ci of each node in the logistics network; Vulnerability identification module: Constructs a vulnerable node set W based on Ci values, performs node-level risk prediction on each node in W, calculates its fault tolerance capability Ti, and marks nodes with Ti below a set threshold as the target node set for adjustment. ; Resource adjustment module: Performs dynamic resource adjustment on nodes in W′, including task transfer, path reconstruction and temporary node calculation.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A logistics supply chain dynamic management method based on Internet of Things, characterized in that: The method comprises the following steps: Obtain real-time state parameters Si of each logistics node in the logistics system, including the current task number, queue length, processing speed, and connection frequency information with adjacent nodes; Construct a dynamic connection graph G of the logistics network according to Si, wherein the nodes in G represent logistics processing units, and the edge weights represent the transportation frequency and delay cost between nodes; Perform dynamic coupling analysis on the graph G, extract the transmission correlation matrix M between nodes, and calculate the coupling degree Ci of each node in the logistics network; Based on the Ci value, a fragile node set W is constructed, node-level risk estimation is performed on each node in W, the fault tolerance capability Ti of each node is calculated, and nodes with Ti lower than a set threshold are marked as a target node set for adjustment ; Perform resource dynamic adjustment on the nodes in W', including task transfer, path reconstruction, and temporary addition of nodes.

2. The logistics supply chain dynamic management method based on the Internet of Things according to claim 1, characterized in that: Wherein, The step of constructing a dynamic connection graph G of the logistics network according to Si comprises the following steps: Input the obtained real-time state parameters Si of each logistics node as node attribute, and initialize the node set V in the graph structure; Based on the connection frequency information between adjacent nodes, construct the edge set E between nodes, and set the edge weight value representing the transportation path stability and time delay for each edge; Generate a weighted directed graph G containing the node set V and the edge set E by using a graph structure modeling algorithm, wherein the graph G reflects the running topology and dynamic load state of the current logistics network. 3.The logistics supply chain dynamic management method based on the Internet of Things according to claim 2, characterized in that: The step of generating a weighted directed graph G containing the node set V and the edge set E by using a graph structure modeling algorithm comprises the following steps: Based on the processing speed and current task number in the node state parameter, calculate the unit processing time index of each node, and add it as a node attribute value to the node set V; According to the connection frequency information between nodes and historical transportation path data, construct a directed edge set E, and assign a comprehensive edge weight value to each edge, which comprehensively considers transportation delay, path stability and average task passing rate; Fuse the node set V and the edge set E by using a weighted graph modeling algorithm based on graph theory, to generate a weighted directed graph G reflecting the current structure characteristics and load state of the logistics network.

4. The logistics supply chain dynamic management method based on the Internet of Things according to claim 1, characterized in that: Wherein, The step of performing dynamic coupling analysis on the graph G, and extracting the transmission correlation matrix M between nodes comprises the following steps: Based on the unit processing time index of each node in the weighted directed graph G and the edge weight value, construct a task flow transfer probability model for representing the transfer tendency of order flow between nodes; Calculate the transfer probability Pij of node vi to node vj, which is determined according to the historical task path frequency and the current edge weight value, and the higher the transfer probability, the stronger the flow coupling degree between the two nodes; Construct a transmission correlation matrix M with all the transfer probabilities Pij between nodes as elements, wherein each element Mij in the matrix represents the actual task flow coupling strength between node vi and node vj.

5. The logistics supply chain dynamic management method based on the Internet of Things according to claim 4, characterized in that: The step of calculating the coupling degree Ci of each node in the logistics network comprises the following steps: Based on the constructed transmission correlation matrix M, extract the total transfer probability of node vi on all incoming and outgoing paths as the task flow convergence index of the node; Introduce the node betweenness index to calculate the frequency of node vi appearing as a transfer node in all shortest paths, which is used to reflect the centrality degree of the node in the task delivery process; Weight and fuse the task flow convergence index and the betweenness index of the node to construct a calculation model of the coupling degree Ci, wherein the coupling degree Ci is used to represent the influence degree of node vi on the stability of the logistics network; The coupling degrees Ci of all nodes are normalized to a set interval, and the coupling degree results are output.

6. The logistics supply chain dynamic management method based on the Internet of Things according to claim 1, characterized in that: Wherein, Based on the Ci value, a fragile node set W is constructed, and node-level risk estimation is performed on each node in W to calculate its fault tolerance capability Ti, including: Set the coupling degree threshold θ, and add all nodes with coupling degree Ci greater than θ to the fragile node set W; For each node in the fragile node set W, extract its task flow convergence indicators and historical abnormal frequency data to construct node risk assessment factors; Based on the task flow pressure and historical stability parameters, the fault tolerance capability Ti of node vi is calculated, which represents the ability value of the node to continuously maintain service capability under load fluctuation; Compare the fault tolerance capability Ti with the preset tolerance threshold η, if Ti is less than η, mark the node as a high-risk node.

7. The logistics supply chain dynamic management method based on the Internet of Things according to claim 1, characterized in that: Wherein, Resource dynamic adjustment is performed on the nodes in W', including: For each node vi ∈ W', based on its task queue length and processing speed, evaluate its current load saturation and determine whether it exceeds the set load tolerance threshold; For nodes that exceed the load tolerance threshold, select the adjacent idle node vj with the highest connection frequency as the task transfer target, and transfer part of the pending tasks to node vj under the condition of satisfying the processing capability constraint; Based on the load changes between nodes after task transfer, dynamically adjust the edge weights in the graph structure and update the logistics path topology to realize path reconstruction; If node vi is still in an overload state after task transfer and path reconstruction, allocate schedulable redundant computing resources to the node or introduce a temporary collaborative node.

8. A logistics supply chain dynamic management platform based on the Internet of Things, used to implement the logistics supply chain dynamic management method based on the Internet of Things in any one of claims 1-7. Including: State perception module: obtain the real-time state parameters Si of each logistics node in the logistics system, including the current task number, queue length, processing speed, and connection frequency information with adjacent nodes; Graph modeling module: construct a dynamic connection graph G of the logistics network according to Si, where the nodes represent logistics processing units and the edge weights represent the transportation frequency and delay cost between nodes; Coupling analysis module: perform dynamic coupling analysis on graph G, extract the transmission correlation matrix M between nodes, and calculate the coupling degree Ci of each node in the logistics network; Weak identification module: based on the Ci value, a weak node set W is constructed, the node-level risk estimation is performed on each node in W, the fault tolerance capability Ti is calculated, and the node whose Ti is lower than the set threshold is marked as the adjustment target node set ; Resource adjustment module: perform resource dynamic adjustment on the nodes in W', including task transfer, path reconstruction, and temporary addition of nodes.

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