Method for evaluating time-varying cascading failure of highway freight transport network under meteorological impact

Abstract

This invention relates to the field of network security and risk assessment technology for highway freight networks, specifically to a method for assessing time-varying cascading failures of highway freight networks under meteorological shocks, comprising: Step S1: Constructing an analysis network and calibrating the time-varying initial load and dynamic effective capacity of each node in different seasons; Step S2: Introducing a meteorological shock function to correct the effective capacity corresponding to the initially failed node; Step S3: Dividing freight flow into endpoint flow and transit flow, and implementing a three-level allocation rule of endpoint loss-path substitution-neighborhood transit; Step S4: Establishing a load propagation time delay mechanism based on physical geographic mileage and average vehicle speed, and simulating the avalanche hysteresis effect by maintaining a queue of loads to be received for each surviving node; Step S5: Defining the dynamic overload tolerance coefficient of nodes, distinguishing the real-time state of nodes, until the analysis network stabilizes or completely disintegrates; Step S6: Using an entropy weight-TOPSIS model to quantify and assess the comprehensive vulnerability of nodes.

Description

Technical Field

[0001] This invention relates to the field of network security and risk assessment technology for highway freight transport, specifically to a method for assessing time-varying cascading failures of highway freight transport networks under meteorological shocks. Background Technology

[0002] As the core of the national comprehensive three-dimensional transportation network, the stability of the highway freight network is directly related to supply chain security. However, existing methods for studying cascading failures have the following shortcomings: First, most existing cascading failure models are based on static evolution assumptions, assigning nodes fixed initial loads and capacities, making it difficult to reflect the seasonal fluctuations in the freight market. Second, failure triggering mechanisms are often simplified to random or deliberate attacks based on degree and betweenness, lacking accurate simulation of extreme weather events as a direct and real-world cause. Third, the load redistribution process is usually assumed to be instantaneous, ignoring the inevitable time delays in the geographical transfer of goods and the differences in freight flow attributes among different functional roles, leading to simulated collapse speeds that do not match reality. Summary of the Invention

[0003] To address the technical problem that existing technologies struggle to comprehensively capture network failure mechanisms under complex environments and dynamic load coupling, thus affecting the scientific validity of resilience protection strategies, this invention aims to provide a time-varying cascading failure assessment method for highway freight networks under meteorological shocks. The specific technical solution adopted is as follows:

[0004] Step S1: Abstract the road freight network to be evaluated into an analysis network constructed by multiple nodes and edges connected to the nodes. Based on the analysis network, extract historical freight order data to label the time-varying initial load and dynamic effective capacity of each node in different quarters.

[0005] Step S2: Introduce a meteorological impact function to identify surviving nodes and initially failed nodes caused by meteorological interference, and correct the effective capacity corresponding to the initially failed nodes;

[0006] Step S3: Based on the initial failed node, the cargo flow is divided into endpoint flow and transit flow, and a three-level allocation rule of endpoint loss - path substitution - neighborhood transit is executed;

[0007] Step S4: Establish a load propagation time delay mechanism based on physical geographic mileage and average vehicle speed, and simulate the avalanche lag effect by maintaining a queue of loads to be received for each surviving node.

[0008] Step S5: Based on the analysis network corresponding to the avalanche hysteresis effect, define the dynamic overload tolerance coefficient of the nodes, distinguish the real-time state of the nodes, until the analysis network is stable or completely disintegrates.

[0009] Step S6: Use the entropy weight-TOPSIS model to perform dimensionality reduction analysis on the nodes and quantitatively assess the overall vulnerability of the corresponding nodes.

[0010] Preferably, step S1 includes:

[0011] Step S11: Define nodes as cities along the route and edges as intercity freight connections, abstracting the highway freight network to be evaluated into an analysis network;

[0012] Step S12: Based on freight order data, using quarterly time windows, determine the time-varying initial load of nodes;

[0013] Step S13: Determine the initial effective capacity of the node by analyzing the differences in infrastructure redundancy corresponding to the node and combining the time-varying initial load.

[0014] Preferably, in step S12, the time-varying initial load of a node includes the total amount of freight orders flowing into and out of the corresponding node within the current analysis quarter.

[0015] Preferably, step S13 specifically includes:

[0016] By analyzing the differences in infrastructure redundancy among logistics hubs at different levels, a capacity adjustment coefficient based on per capita GDP is introduced to classify node redundancy levels. Based on the time-varying initial load, the historical maximum load of the corresponding node is selected to determine the main stress resistance parameters. The baseline capacity of the node is obtained by combining the capacity adjustment coefficient based on per capita GDP and the main stress resistance parameters, and the baseline capacity is defined as the initial effective capacity.

[0017] Preferably, step S2 includes:

[0018] Step S21: Based on quarterly analysis, obtain the precipitation impact component and the high temperature impact component respectively, establish the meteorological impact function, and identify the surviving nodes and the initial failure nodes caused by meteorological interference.

[0019] Step S22: Introduce the meteorological impact attenuation coefficient and combine it with the meteorological impact function to correct the effective capacity corresponding to each initial failure node.

[0020] Preferably, step S3 includes:

[0021] Step S31: Assess whether the initial failure node is the origin or destination of the goods and determine the endpoint flow loss;

[0022] Step S32: Based on transit traffic, select direct alternative paths for transfer and redistribute transit alternatives.

[0023] Preferably, in step S32, the redistribution for transit substitution is carried out as follows:

[0024] The time-varying initial load corresponding to the current node is transferred to the receiving node in the local neighborhood. The geographical distance between the current node and the receiving node is determined. The redistribution weight is determined in combination with the effective capacity. The node transfer is implemented in the analysis network through the redistribution weight.

[0025] Preferably, step S5 includes:

[0026] Step S51: Obtain the initial overload tolerance coefficient corresponding to the analysis network, and determine the current load rate of the node based on the real-time operation of the analysis network. Combine the initial overload tolerance coefficient and the current load rate to define the dynamic overload tolerance coefficient of the node.

[0027] Step S52: Distinguish the real-time status of nodes based on the dynamic overload tolerance coefficient and effective capacity until the network is stable or completely disintegrated.

[0028] Preferably, step S6 includes:

[0029] Step S61: Construct an initial evaluation matrix based on all nodes in the analysis network;

[0030] Step S62: Analyze the initial evaluation matrix, use the entropy weight method to determine the dynamic weight of each evaluation indicator, and obtain the TOPSIS comprehensive score based on the TOPSIS model;

[0031] Step S63: Filter the optimal and worst solutions using the TOPSIS comprehensive score, and simultaneously quantify the comprehensive vulnerability of the corresponding nodes.

[0032] To solve the above-mentioned technical problems, the present invention also provides: an electronic device, the device comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus, and the processor calls logical instructions in the memory to execute the time-varying cascading failure assessment method for road freight networks under meteorological shocks described in any of the preceding claims.

[0033] The present invention has the following beneficial effects:

[0034] 1. By abstracting the highway freight network to be evaluated into an analytical network, i.e., constructing a simulation framework and introducing time-varying load and physical time delay mechanisms, the evolutionary realism of power network cascading failure simulation is significantly improved. During operation, unlike traditional models that often overestimate the network collapse speed due to neglecting the spatiotemporal attributes of physical processes, the proposed analytical network successfully captures the spatiotemporal hindrance effect of cascading failure in geographic space, i.e., identifying the initial failure node, and can more objectively restore the dynamic characteristics of fault propagation. Furthermore, combined with meteorological impact functions, it deepens the scientific understanding of vulnerability under extreme weather impacts and reveals the topological indiscriminate attack characteristics of such impacts. Based on the dynamic overload tolerance coefficient, the real-time state of nodes is distinguished, and the comprehensive vulnerability of nodes is quantified through the entropy weight-TOPSIS model, accurately locating the hidden trigger nodes that operate poorly in the off-season but may induce network-wide collapse under high load conditions in the peak season, making up for the shortcomings of traditional static topology analysis in identifying dynamic risks.

[0035] Furthermore, the simulation framework built upon the entire analysis network confirmed the seasonal asymmetry of network resilience, providing a decision-making basis for emergency regulation of energy infrastructure. Based on this, the proposed strategy of precise pre-disaster reinforcement and post-disaster topology restoration throughout the entire lifecycle not only enhances the scientific rigor of responding to extreme weather events but also provides systematic theoretical support for building robust networks with high environmental adaptability.

[0036] 2. The electronic device provided by this invention has the same beneficial effects as the time-varying cascade failure assessment method for highway freight networks under meteorological shocks provided by this invention, and will not be described in detail here. Attached Figure Description

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the logical framework of a time-varying cascade failure assessment method for highway freight networks under meteorological shocks, provided in one embodiment of the present invention.

[0039] Figure 2 A comparison diagram of the spatiotemporal evolution of seasonal cascade failure under time delay and high-resolution physical constraints in a time-varying cascade failure assessment method for road freight networks under meteorological shocks, provided in an embodiment of the present invention;

[0040] Figure 3This is a comparison chart of the functional load-weighted seepage evolution under different seasons and removal strategies in a time-varying cascade failure assessment method for road freight networks under meteorological shocks, provided as an embodiment of the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a time-varying cascade failure assessment method for road freight networks under meteorological shocks proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] The following description, in conjunction with the accompanying drawings, details the specific scheme of the time-varying cascade failure assessment method for highway freight networks under meteorological shocks provided by this invention.

[0044] Please see Figure 1 This diagram illustrates the logical framework of a time-varying cascading failure assessment method for highway freight networks under meteorological shocks, provided by an embodiment of the present invention. The method includes:

[0045] Step S1: Abstract the road freight network to be evaluated into an analysis network constructed by multiple nodes and edges connected to the nodes. Based on the analysis network, extract historical freight order data to label the time-varying initial load and dynamic effective capacity of each node in different quarters.

[0046] Step S2: Introduce a meteorological impact function to identify surviving nodes and initially failed nodes caused by meteorological interference, and correct the effective capacity corresponding to the initially failed nodes;

[0047] Step S3: Based on the initial failed node, the cargo flow is divided into endpoint flow and transit flow, and a three-level allocation rule of endpoint loss - path substitution - neighborhood transit is executed;

[0048] Step S4: Establish a load propagation time delay mechanism based on physical geographic mileage and average vehicle speed, and simulate the avalanche lag effect by maintaining a queue of loads to be received for each surviving node.

[0049] Step S5: Based on the analysis network corresponding to the avalanche hysteresis effect, define the dynamic overload tolerance coefficient of the nodes, distinguish the real-time state of the nodes, until the analysis network is stable or completely disintegrates.

[0050] Step S6: Use the entropy weight-TOPSIS model to perform dimensionality reduction analysis on the nodes and quantitatively assess the overall vulnerability of the corresponding nodes.

[0051] To better illustrate that meteorological shocks, as a significant external factor affecting the stable operation of highway freight networks, can lead to a chain reaction of reduced road capacity, disruption of transport nodes, and decreased vehicle efficiency due to their suddenness and uncertainty. Traditional highway freight network assessment methods often focus on static structural analysis or the impact assessment of single disasters, making it difficult to comprehensively capture the dynamic and time-varying cascading failure process caused by meteorological shocks. Therefore, a time-varying cascading failure assessment method for highway freight networks under meteorological shocks is proposed, which can simulate the cascading failure simulation and vulnerability assessment of highway freight networks by simulating the coupling effects of load fluctuations, meteorological shocks, and spatiotemporal delays.

[0052] Further, step S1 includes:

[0053] Step S11: Define nodes as cities along the route and edges as intercity freight connections, abstracting the highway freight network to be evaluated into an analysis network; that is, nodes are cities in the entire highway freight network, denoted as... The corresponding set of nodes is , Indicates the number of nodes; intercity freight connections are denoted as... The corresponding edge set is , Represents the number of edges; abstracted as an analysis network, i.e. .

[0054] Step S12: Based on freight order data, using quarters as the time window, determine the time-varying initial load of the nodes; in this embodiment, the quarter corresponds to... , , and There are four quarters in total, with the year as the basis for dividing quarters, and each quarter consisting of three months.

[0055] Furthermore, in step S12, the time-varying initial load of a node includes the total amount of freight orders flowing into and out of the corresponding node during the current analysis quarter.

[0056] Specifically, the corresponding calculation formula is:

[0057]

[0058] in, Indicates the first Each node in the quarter The time-varying initial load; , They respectively represent the first Each node represents the quarterly destination and departure point. The total number of freight orders.

[0059] Step S13: Determine the initial effective capacity of the node by analyzing the differences in infrastructure redundancy corresponding to the node and combining the time-varying initial load.

[0060] The explanation is that when defining node capacity, a multi-dimensional capacity method that couples historical peak values ​​and macroeconomic carrying capacity is adopted. That is, by considering the highest load level (historical peak value) that the node has experienced during operation, the macroeconomic carrying capacity of the node is incorporated into the evaluation system as a constraint for analysis, which combines historical experience with future prospects as a basis for decision-making.

[0061] Further, in step S13, specifically:

[0062] By analyzing the differences in infrastructure redundancy among logistics hubs at different levels, a capacity adjustment coefficient based on per capita GDP is introduced to classify node redundancy levels. Based on the time-varying initial load, the historical maximum load of the corresponding node is selected to determine the main stress resistance parameters. The baseline capacity of the node is obtained by combining the capacity adjustment coefficient based on per capita GDP and the main stress resistance parameters, and the baseline capacity is defined as the initial effective capacity.

[0063] Specifically, considering the differences in infrastructure redundancy among logistics hubs at different levels, a capacity adjustment coefficient based on per capita GDP (Gross Domestic Product) is introduced, denoted as . Based on the corresponding GDP per capita data, the nodes in the analysis network are sorted in descending order and divided into three equal parts to determine their redundancy levels: high, medium, and low. A capacity adjustment coefficient is then assigned to each redundancy level. Preferably, the capacity adjustment coefficient This refers to redundancy weights, with high, medium, and low redundancy levels corresponding to weights of 0.3, 0.2, and 0.1 respectively. These weights can be adjusted based on actual conditions. Then, the node's historical maximum load throughout the year is extracted as the primary stress resistance parameter, denoted as... The baseline capacity of a node is determined by the following formula:

[0064]

[0065] in, Indicates the first The baseline capacity of each node; Indicates the first Capacity adjustment coefficient for each node; Indicates the first The main compressive parameters of each node.

[0066] Further, step S2 includes:

[0067] Step S21: Based on quarterly analysis, obtain the precipitation impact component and the high temperature impact component respectively, establish the meteorological impact function, and identify the surviving nodes and the initial failure nodes caused by meteorological interference.

[0068] Specifically, the meteorological impact function is established, and the corresponding calculation formula is as follows:

[0069]

[0070] in, Indicates the first Each node in the quarter The meteorological impact index; , All represent weighting coefficients; Indicates the first Each node in the quarter The impact component of precipitation; Indicates the first Each node in the quarter The high-temperature impact component.

[0071] It can be explained that the weakening effect of the external environment on the analysis network is simulated based on the meteorological impact function, and the weight coefficients... and Updated quarterly, this can reflect the shift in core risks across different seasons, for example, in the second quarter. The focus is on the plum rain season, or in the third quarter. Focusing on extreme high temperatures; next, a preset critical threshold is defined as . It is an empirical value based on historical meteorological disaster statistics, and is specifically set according to local historical meteorological statistics and road network disaster resistance standards; in the initial stage of simulation, i.e. At that time, when the meteorological impact index of urban nodes Breaking the critical threshold If the corresponding node is determined to have entered an initial failure state, it is marked as an initial failure node caused by meteorological interference; otherwise, the meteorological impact index... Less than and equal to the critical threshold When the time is specified, it indicates that the corresponding node is a live node.

[0072] Step S22: Introduce the meteorological impact attenuation coefficient and combine it with the meteorological impact function to correct the effective capacity corresponding to each initial failure node.

[0073] Specifically, the meteorological impact attenuation coefficient is denoted as... Assuming the node being analyzed... For each initial failure node, the effective capacity corresponding to that initial failure node is adjusted. A capacity reduction function is constructed, and the corresponding calculation formula is as follows:

[0074]

[0075] in, Indicates the initial failure node In the quarter Corrected effective capacity; Indicates the initial failure node The baseline capacity; Indicates the meteorological impact attenuation coefficient; Indicates the initial failure node In the quarter The meteorological impact index.

[0076] It can be explained that the meteorological shock attenuation coefficient This is used to reduce the effective capacity of a node before subsequent state determination. In real-world scenarios, even if weather disturbances do not immediately cause a node to fail, they will slow down the loading, unloading, and turnover speed of the node, thus classifying the node as an initial failure node. Mapped to the weather impact function, this translates to the compression of the node's effective tolerance space, i.e., its effective capacity. It will be less than the base capacity. A load that could normally function normally is more likely to overflow.

[0077] Further, step S3 includes:

[0078] Step S31: Assess whether the initial failure node is the origin or destination of the goods and determine the endpoint flow loss.

[0079] To accurately depict the adaptive evolution of cargo flow after encountering obstacles, a three-level allocation mechanism is constructed based on the origin-destination (OD) attribute of the cargo flow.

[0080] Specifically, the first level is the determination of endpoint traffic loss. If the initially failed node is the origin or destination of the goods, part of the load of that node is directly removed from the analysis network and included in the absolute freight loss, so as not to cause secondary impact. In freight business, the load of a node consists of two parts: the traffic of the node as the origin or destination and the traffic of the node as a transit point. This part of the load refers to the load belonging to the endpoint traffic attribute, which will be removed.

[0081] Step S32: Based on transit traffic, select direct alternative paths for transfer and redistribute transit alternatives.

[0082] Specifically, the second level is direct alternative path transfer, that is, for transit traffic, global rerouting is performed based on the physical distance of the alternative paths and the proportion of remaining capacity of nodes along the route; where the alternative paths are the paths from the origin to the destination re-acquired based on surviving nodes after a local failure occurs in the analyzed network.

[0083] The third stage is relay replacement redistribution, which means that when global rerouting cannot be completed, the load is transferred to receiving nodes in the local neighborhood.

[0084] Furthermore, in step S32, a redistribution is performed for the transit substitution, specifically as follows:

[0085] The time-varying initial load corresponding to the current node is transferred to the receiving node in the local neighborhood. The geographical distance between the current node and the receiving node is determined. The redistribution weight is determined in combination with the effective capacity. The node transfer is implemented in the analysis network through the redistribution weight.

[0086] Specifically, based on the nodes analyzed above As the current node, the receiving node is denoted as This allows us to determine the geographical distance from the current node to the receiving node, denoted as . , This represents the spatial distance attenuation parameter, which dynamically fluctuates with the average meteorological impact index of the entire analysis network to reflect the forced reduction in the spatial range of freight dispatching under severe weather conditions. , Indicates the base distance attenuation coefficient; Indicates parameters; This represents the average meteorological impact index across the entire network; the redistribution weights are determined using the following formula:

[0087]

[0088] in, Indicates the initial failure node In the quarter The corresponding redistribution weights; This represents the remaining capacity weight index.

[0089] It can be explained that the redistribution of weights It depends on the remaining capacity of the receiving node and the geographical distance; while the remaining capacity weight index It operates during the load redistribution phase after a node becomes overloaded.

[0090] Specifically, in step S4, considering that the spatial transfer of materials depends on the physical operating speed, a delay mechanism based on real geographical mileage is established. First, the time step for the arrival of the transferred load is determined, and the corresponding calculation formula is as follows:

[0091]

[0092] in, This indicates the time step in which the transferred load arrives at the receiving node; This indicates the current simulation moment when the load transfer is triggered by the initial failure node; Indicates the initial failure node to receiving node Geographical distance; This indicates the average speed of freight vehicles; This represents the actual duration of a single simulation time step.

[0093] It can be explained that the time step for the transferred load to reach the receiving node is... Depends on the spatial topological distance between the initial failed node and the receiving node To avoid the simulation model drastically exaggerating the collapse rate in the early stages of a disaster, this mechanism is implemented by maintaining a "waiting load queue" for each surviving node. This means that any node in the queue receives a load during the initial stages of a disaster. The load transfer generated at each time step is first written to the cache pool, and only when the simulation clock advances to the next time step is the load transfer completed. Only then will the load be officially activated and added to the running load of the receiving node.

[0094] Further, step S5 includes:

[0095] Step S51: Obtain the initial overload tolerance coefficient corresponding to the analysis network, and determine the current load rate of the node based on the real-time operation of the analysis network. Combine the initial overload tolerance coefficient and the current load rate to define the dynamic overload tolerance coefficient of the node.

[0096] Specifically, the current load rate of a node is determined based on the real-time operation of the network. Define the dynamic overload tolerance coefficient of the node to measure the elasticity space under different seasons. The corresponding calculation formula is as follows:

[0097]

[0098] in, This represents the upper limit of the basic physical tolerance, i.e., the initial overload tolerance coefficient; Indicates the initial failure node In the quarter The corresponding dynamic overload tolerance coefficient; Indicates the initial failure node In the quarter The corresponding load rate.

[0099] Step S52: Distinguish the real-time status of nodes based on the dynamic overload tolerance coefficient and effective capacity until the network is stable or completely disintegrated.

[0100] Specifically, when the node load is less than or equal to the effective capacity, it indicates that freight is normal and the analysis network is in normal operation. When the node load exceeds the effective capacity but does not exceed the dynamic overload tolerance coefficient, it indicates that the node is congested and queuing, and the overflow traffic is dispersed according to the allocation rules. The node remains in the analysis network, indicating that the analysis network is in a state of partial overload. When the node load exceeds the dynamic overload tolerance coefficient, it indicates that the node management in the entire analysis network is paralyzed and the node is removed from the analysis network, causing a catastrophic secondary impact on the existing load, indicating that the analysis network is in a state of complete failure.

[0101] Preferably, the iteration is terminated when there are no new overloaded or failed nodes in the analysis network, or when the cumulative proportion of collapsed nodes exceeds the preset global collapse threshold. That is, the iteration stops when the analysis network is stable or completely disintegrated. The global collapse threshold is set to 95%, which can be set according to the actual situation.

[0102] Further, step S6 includes:

[0103] Step S61: Construct an initial evaluation matrix based on all nodes in the analysis network.

[0104] Specifically, based on the aforementioned steps, the cities in the entire highway freight network are determined, that is, the set of nodes in the network is analyzed. The set of evaluation indicators is Construct the initial evaluation matrix, i.e. , This represents the evaluation index, where standardized indicators, i.e., positive indicators, are included. negative indicators .

[0105] Step S62: Analyze the initial evaluation matrix, use the entropy weight method to determine the dynamic weight of each evaluation indicator, and obtain the TOPSIS comprehensive score based on the TOPSIS model.

[0106] Specifically, the entropy weight method is used to determine the dynamic weight of each evaluation index, that is, to determine the weight of the first evaluation index. The first item under the indicator The formula for calculating the proportion of each node is as follows: Calculate the first The entropy value of the item index, i.e. , This leads to the determination of the information bias degree, i.e. To obtain the weighting coefficients, i.e. Based on the weighted coefficients and standardized indicators, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution, i.e., the distance method between good and bad solutions) comprehensive score is obtained, and a corresponding weighted standardized matrix is ​​constructed. .

[0107] Step S63: Filter the optimal and worst solutions using the TOPSIS comprehensive score, and simultaneously quantify the comprehensive vulnerability of the corresponding nodes.

[0108] Specifically, the optimal solution and the worst solution are denoted as follows: and Calculate the path from each node to the optimal solution. and worst solution The distance is denoted as . and Assess the overall vulnerability of the corresponding node, i.e. , Represents a node The comprehensive vulnerability; , Representing nodes respectively To the optimal solution and worst solution The distance.

[0109] It can be explained that by using the entropy weight-TOPSIS model to comprehensively analyze the topological connectivity, seasonal load pressure, and meteorological shock risk of nodes, a comprehensive vulnerability score of nodes under different seasons can be obtained, and the dynamic drift of key roles can be identified. In this embodiment, "hidden trigger" nodes can be discovered, that is, some nodes that perform poorly in the off-season topology can rapidly deteriorate into key hubs that trigger network-wide cascading collapses under the coupled pressure of a sharp increase in transit dependence and local severe weather during the peak season. The method for identifying hidden trigger nodes is: if a node has a low comprehensive vulnerability score in the off-season, but its comprehensive vulnerability score rises sharply in the peak season due to meteorological shocks, it is a trigger node. For example, if a city has a low comprehensive vulnerability score in the first quarter, but its comprehensive vulnerability score rises sharply in the peak season due to meteorological shocks, it is a trigger node. Ranked 91st, and in the third quarter It surged to the top spot, defining the node corresponding to that city as a node capable of triggering a cascading collapse across the entire network.

[0110] To better illustrate and clearly explain the implementation of a time-varying cascade failure assessment method for highway freight networks under meteorological shocks, the specific details are shown in Table 1.

[0111] Table 1

[0112]

[0113] Understandably, abstracting the highway freight network to be evaluated into an analytical network, i.e., constructing a simulation framework and introducing time-varying load and physical time delay mechanisms, significantly improves the realism of the evolution of power network cascading failure simulation. During operation, unlike traditional models that often overestimate the network collapse speed due to ignoring the spatiotemporal attributes of physical processes, the proposed analytical network successfully captures the spatiotemporal hindrance effect of cascading failure in geographic space, i.e., identifies the initial failure node, and can more objectively restore the dynamic characteristics of fault propagation. Furthermore, combined with the meteorological impact function, it deepens the scientific understanding of vulnerability under extreme weather impacts and reveals the topological indiscriminate attack characteristics of such impacts. Based on the dynamic overload tolerance coefficient, the real-time state of nodes is distinguished, and the comprehensive vulnerability of nodes is quantified through the entropy weight-TOPSIS model, accurately locating the hidden trigger nodes that operate poorly in the off-season but may induce network-wide collapse under high load conditions in the peak season, thus making up for the shortcomings of traditional static topology analysis in identifying dynamic risks.

[0114] Furthermore, the simulation framework built upon the entire analysis network confirmed the seasonal asymmetry of network resilience, providing a decision-making basis for emergency regulation of energy infrastructure. Based on this, the proposed strategy of precise pre-disaster reinforcement and post-disaster topology restoration throughout the entire lifecycle not only enhances the scientific rigor of responding to extreme weather events but also provides systematic theoretical support for building robust networks with high environmental adaptability.

[0115] The second embodiment of the present invention provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. The processor calls logical instructions in the memory to execute the time-varying cascading failure assessment method for road freight networks under meteorological shocks described in any embodiment of the present invention.

[0116] When it is in operation, it needs to use a time-varying cascading failure assessment method for road freight networks under meteorological impact. Therefore, whether the equipment and program data are integrated or different hardware is configured to produce functions with similar effects to those achieved by this invention, they all fall within the protection scope of this invention. This equipment has the same beneficial effects as the aforementioned time-varying cascading failure assessment method for road freight networks under meteorological impact, and will not be elaborated here.

[0117] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for evaluating time-varying cascading failures of a highway freight network under weather impact, characterized in that, The method includes: Step S1: Abstract the road freight network to be evaluated into an analysis network constructed by multiple nodes and edges connected to the nodes. Based on the analysis network, extract historical freight order data to label the time-varying initial load and dynamic effective capacity of each node in different quarters. Step S2: Introduce a meteorological impact function to identify surviving nodes and initially failed nodes caused by meteorological interference, and correct the effective capacity corresponding to the initially failed nodes; Step S3: Based on the initial failure node, the cargo flow is divided into endpoint flow and transit flow, and a three-level allocation rule of endpoint loss - path substitution - neighborhood transit is executed; Step S4: Establish a load propagation time delay mechanism based on physical geographic mileage and average vehicle speed, and simulate the avalanche lag effect by maintaining a queue of loads to be received for each surviving node. Step S5: Based on the analysis network corresponding to the avalanche hysteresis effect, define the dynamic overload tolerance coefficient of the nodes, distinguish the real-time state of the nodes, until the analysis network is stable or completely disintegrates. Step S6: Use the entropy weight-TOPSIS model to perform dimensionality reduction analysis on the nodes and quantitatively assess the overall vulnerability of the corresponding nodes.

2. The method of claim 1, wherein, Step S1 includes: Step S11: Define nodes as cities along the route and edges as intercity freight connections, abstracting the highway freight network to be evaluated into an analysis network; Step S12: Based on freight order data, using quarterly time windows, determine the time-varying initial load of nodes; Step S13: Determine the initial effective capacity of the node by analyzing the differences in infrastructure redundancy corresponding to the node and combining the time-varying initial load.

3. The method of claim 2, wherein, In step S12, the time-varying initial load of a node includes the total amount of freight orders flowing into and out of the corresponding node during the current analysis quarter.

4. The method for assessing time-varying cascading failures of a highway freight network under meteorological shocks according to claim 2, characterized in that, In step S13, specifically: By analyzing the differences in infrastructure redundancy among logistics hubs at different levels, a capacity adjustment coefficient based on per capita GDP is introduced to classify node redundancy levels. Based on the time-varying initial load, the historical maximum load of the corresponding node is selected to determine the main stress resistance parameters. The baseline capacity of the node is obtained by combining the capacity adjustment coefficient based on per capita GDP and the main stress resistance parameters, and the baseline capacity is defined as the initial effective capacity.

5. The method for assessing time-varying cascading failures of a highway freight network under meteorological shocks according to claim 1, characterized in that, Step S2 includes: Step S21: Based on quarterly analysis, obtain the precipitation impact component and the high temperature impact component respectively, establish the meteorological impact function, and identify the surviving nodes and the initial failure nodes caused by meteorological interference. Step S22: Introduce the meteorological impact attenuation coefficient and combine it with the meteorological impact function to correct the effective capacity corresponding to each initial failure node.

6. The method for assessing time-varying cascading failures of a road freight network under meteorological shocks according to claim 1, characterized in that, Step S3 includes: Step S31: Assess whether the initial failure node is the origin or destination of the goods and determine the endpoint flow loss; Step S32: Based on transit traffic, select direct alternative paths for transfer and redistribute transit alternatives.

7. The method for assessing time-varying cascading failures of a highway freight network under meteorological shocks according to claim 6, characterized in that, In step S32, the transfer replacement is redistributed, specifically as follows: The time-varying initial load corresponding to the current node is transferred to the receiving node in the local neighborhood. The geographical distance between the current node and the receiving node is determined. The redistribution weight is determined in combination with the effective capacity. The node transfer is implemented in the analysis network through the redistribution weight.

8. The method for assessing time-varying cascading failures of a highway freight network under meteorological shocks according to claim 1, characterized in that, Step S5 includes: Step S51: Obtain the initial overload tolerance coefficient corresponding to the analysis network, and determine the current load rate of the node based on the real-time operation of the analysis network. Combine the initial overload tolerance coefficient and the current load rate to define the dynamic overload tolerance coefficient of the node. Step S52: Distinguish the real-time status of nodes based on the dynamic overload tolerance coefficient and effective capacity until the network is stable or completely disintegrated.

9. The method for assessing time-varying cascading failures of a highway freight network under meteorological shocks according to claim 1, characterized in that, Step S6 includes: Step S61: Construct an initial evaluation matrix based on all nodes in the analysis network; Step S62: Analyze the initial evaluation matrix, use the entropy weight method to determine the dynamic weight of each evaluation indicator, and obtain the TOPSIS comprehensive score based on the TOPSIS model; Step S63: Filter the optimal and worst solutions using the TOPSIS comprehensive score, and simultaneously quantify the comprehensive vulnerability of the corresponding nodes.

10. An electronic device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. The processor calls logical instructions in the memory to execute the time-varying cascading failure assessment method for road freight networks under meteorological shocks as described in any one of claims 1 to 9.

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