Harbor group toughness evaluation method, device and equipment, storage medium and program product

Abstract

The invention relates to the technical field of shipping networks, and discloses a harbor group toughness evaluation method, device and equipment, a storage medium and a program product. The method comprises the following steps: collecting port and route information, and constructing a port group shipping network based on a hypergraph; taking each port and each ship as intelligent agents, and constructing a port group shipping ABM model based on the port group shipping network; respectively simulating a response mechanism and a dynamic evolution process of a harbor group shipping network under normal conditions and risk events to obtain network density and the number of strongly connected branches at different moments, the number of ships in a waiting state and cargo delay time corresponding to each ship; and a toughness index is constructed from three dimensions of structure, operation and service, and a harbor group toughness value is calculated. According to the method, the hypergraph theory and the agent simulation are combined, the influence propagation process of the risk event on the port group shipping network is described more truly, the toughness value is calculated based on multiple dimensions, and the port group toughness evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of shipping network technology, and in particular to a port group resilience assessment method, device, equipment, storage medium and program product. Background Art

[0002] A port cluster refers to a network of multiple ports and related facilities that are interdependent and operate collaboratively within a certain geographical area. Natural disasters such as hurricanes, tsunamis and earthquakes are one of the main causes of port service disruptions. These events can cause delays in plans and even major infrastructure damage, inevitably weakening the service capabilities of port clusters. In some cases, they can even lead to partial or complete closure of port clusters. It usually takes a long time to fully restore disrupted shipping services, causing significant losses to the economy and society. Resilience is defined as the ability of a system to predict, absorb, recover and adapt to disturbances. The resilience of a port cluster can be defined as its ability to maintain service integrity and efficiency under specific uncertain conditions. By conducting a resilience assessment of a port cluster, it is possible to provide a reference for the disaster-resistant construction and operation of ports and effectively reduce the losses caused by risk events to port clusters.

[0003] When evaluating the resilience of port clusters in related technologies, complex network theory is usually used to characterize shipping networks. However, complex networks can only characterize binary relationships between nodes and cannot reflect the complex multivariate relationships of multi-port and multi-route interactions between real port clusters. Most of them ignore the heterogeneity, limited rationality, learning and evolutionary characteristics of various entities within the port cluster, and do not consider the complex interactions and behaviors between these entities and the external environment, resulting in the inability to fully describe the complex impact of climate events on port clusters. Resilience indicators are mainly based on network topology or overall system performance, but network topology can only reflect the static characteristics of the system, and the overall system performance can only describe the overall performance loss, and cannot simultaneously quantify the resilience of different dimensions such as network structure, operation and service.

[0004] Therefore, it is urgent to propose a more accurate method for assessing the resilience of port groups. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a port group resilience assessment method, device, equipment, storage medium and program product to solve the problem of low accuracy when performing port group resilience assessment.

[0006] In a first aspect, the present invention provides a method for assessing the resilience of a port group, the method comprising:

[0007] Collect port information and route information, and build a port group shipping network based on the hypergraph;

[0008] Taking each port and each ship as an intelligent entity, a port group shipping ABM model is constructed based on the port group shipping network;

[0009] The port group shipping ABM model is run under normal conditions and risk events respectively, and the response mechanism of each intelligent agent in the port group shipping network under different conditions is simulated, as well as the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the status of cargo corresponding to each ship under different conditions, so as to obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship;

[0010] Based on the network density and the number of strongly connected branches of the port group shipping network at different times under the normal conditions, the number of ships in waiting state, and the cargo delay time corresponding to each ship, as well as the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in waiting state, and the cargo delay time corresponding to each ship, a resilience assessment index including three dimensions of structure, operation, and service is constructed, and the port group resilience value is calculated.

[0011] In an optional implementation, the collecting of port information and route information and constructing a port group shipping network based on a hypergraph includes:

[0012] Obtaining shipping service information; the shipping service information includes the location of ports on each route, the number of ports, and the shipping direction between each port;

[0013] Based on the shipping service information, a port group shipping network is constructed through a directed hypergraph.

[0014] In an optional implementation, each port and each ship is used as an intelligent agent, and a port group shipping ABM model is constructed based on the port group shipping network, including:

[0015] Establishing a port intelligent entity based on each port in the port group shipping network, and setting the status of each port intelligent entity; the status of the port intelligent entity includes a normal operation status, a closed status, and a congested status;

[0016] The port response strategies of each port agent in different states are respectively set; the port response strategies include opening the port, closing the port and increasing the cargo handling capacity;

[0017] Establishing each ship intelligent body based on the ship information and setting the status of each ship intelligent body; the status of the ship intelligent body includes waiting status, port operation status and sea navigation status; the ship information includes planned sailing schedule and ship route information;

[0018] The ship response strategy of each ship agent in different states is set respectively; the ship response strategy includes adjusting the sailing speed and selecting the target port;

[0019] A port group shipping ABM model is established based on each port agent, the status and response strategy of each port agent, each ship agent, the status and response strategy of each ship agent.

[0020] In an optional implementation, the port group shipping ABM model is run under normal conditions and risk events respectively, and the response mechanism of each intelligent agent in the port group shipping network under different conditions is simulated, as well as the dynamic evolution process of the topological structure of the port group shipping network under different conditions, the status of each ship, the status of each port, and the cargo status corresponding to each ship, so as to obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, including:

[0021] The port group shipping ABM model is run under normal conditions to simulate the response mechanism of each agent in the port group shipping network at different times, the topological structure of the port group shipping network, the status of each ship, the status of each port, and the dynamic evolution process of the cargo status corresponding to each ship, and iterate until all ship agents complete the cargo delivery according to the planned ship schedule, and obtain the network density and the number of strongly connected branches, the number of ships in the waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under normal conditions; the network density and the number of strongly connected branches of the port group shipping network are calculated based on the status of each port; the number of ships in the waiting state is calculated based on the status of each ship; the cargo delay time is calculated based on the cargo status corresponding to each ship;

[0022] The impact scope and impact time of the risk event are set, and the port group shipping ABM model is run under the risk event to simulate the response mechanism of each agent in the port group shipping network at different times, as well as the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship. It is iterated until all ship agents complete the cargo delivery, and the network density and the number of strongly connected branches, the number of ships in the waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under the risk event are obtained.

[0023] In an optional implementation, based on the network density and the number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, a resilience evaluation index including three dimensions of structure, operation, and service is constructed, and the resilience value of the port group is calculated, including:

[0024] Based on the network density and the number of strongly connected branches of the port group shipping network under normal conditions and the network density and the number of strongly connected branches of the port group shipping network under risk events, a port group structural resilience index is constructed, and the port group structural resilience index value is calculated;

[0025] Based on the number of ships waiting at each time under normal conditions and the number of ships waiting at each time under risk events, the port group operation resilience index is constructed and the port group operation resilience index value is calculated;

[0026] Based on the cargo delay time of each ship agent at each time under normal conditions and the cargo delay time of each ship agent at each time under risk events, a port group service resilience index is constructed, and the port group service resilience index value is calculated;

[0027] Based on the port group structure resilience index value, the port group operation resilience index value and the port group service resilience index value, the port group resilience value is calculated.

[0028] In an optional implementation, the port group resilience value is calculated based on the port group structural resilience index value, the port group operational resilience index value and the port group service resilience index value, including:

[0029] Set the weights of the port group structure resilience index, the port group operation resilience index and the port group service resilience index respectively;

[0030] The port group resilience value is calculated based on the port group structural resilience index value, the port group operational resilience index value, the port group service resilience index value, the port group structural resilience index weight, the port group operational resilience index weight and the port group service resilience index weight.

[0031] In a second aspect, the present invention provides a port group resilience assessment device, the device comprising:

[0032] The Hypergraph module is used to collect port information and route information and build a port group shipping network based on the Hypergraph;

[0033] The ABM module is used to establish a port group shipping ABM model based on the port group shipping network by taking each port and each ship as an intelligent entity;

[0034] A simulation iteration module is used to run the port group shipping ABM model under normal conditions and risk events respectively, simulate the response mechanism of each intelligent agent in the port group shipping network under different conditions, and the dynamic evolution process of the topological structure of the port group shipping network under different conditions, the status of each ship, the status of each port, and the cargo status corresponding to each ship, and obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship;

[0035] The resilience value calculation module is used to construct a resilience evaluation index including three dimensions of structure, operation and service based on the network density and the number of strongly connected branches of the port group shipping network at different times under the normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and calculate the resilience value of the port group.

[0036] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the port group resilience assessment method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the port group resilience assessment method of the first aspect or any corresponding embodiment thereof.

[0038] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the port group resilience assessment method of the first aspect or any corresponding embodiment thereof.

[0039] The technical solution provided by the present invention may include the following beneficial effects:

[0040] The port group resilience assessment method provided by the present invention establishes a port group shipping network based on a hypergraph, reflects the complex multivariate relationship of the interaction between multiple ports and multiple routes, overcomes the limitation of the related technology that only binary relationships can be portrayed by using complex networks, and describes the topological characteristics of the port group shipping network in more detail and accurately. By taking each port and each ship as an intelligent body, a port group shipping ABM model is established based on the port group shipping network, and the port group shipping ABM model is simulated under normal conditions and risk events respectively, which can more realistically portray the response mechanism and dynamic evolution process of the port group shipping network under risk events. Constructing resilience assessment indicators based on multiple dimensions of structure, operation, and service and calculating the resilience value of the port group can more comprehensively and accurately assess the resilience of the port group. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a schematic flow chart of a method for assessing the resilience of a port group according to an embodiment of the present invention;

[0043] Figure 2 is a flow chart of another method for assessing the resilience of a port group according to an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of the scope of resilience assessment of a port group according to an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of a port group shipping network according to an embodiment of the present invention;

[0046] Figure 5 is a structural block diagram of a port group resilience assessment device according to an embodiment of the present invention;

[0047] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0049] A port cluster refers to a network of multiple ports and related facilities that are interdependent and operate in a collaborative manner within a certain geographical area. A port cluster is not just a simple collection of multiple ports in the region, but a multi-port system with clear division of labor and complementary advantages as its main characteristics. During the operation process, the port cluster exchanges materials, funds, information, etc. with the economic hinterland and other port clusters at any time, and its various elements together constitute a complex system. The dynamic nature of the shipping industry and uncertain external factors (such as natural disasters, economic fluctuations and policy changes) further exacerbate this complexity. Natural disasters such as hurricanes, tsunamis and earthquakes are one of the main causes of port service disruptions. These events will cause delays in plans and even major infrastructure damage, inevitably weakening the service capacity of the port cluster. In some cases, it will even lead to partial or complete closure of the port cluster. It usually takes a long time to fully restore the interrupted shipping services, causing significant losses to the economy and society. Resilience is defined as the ability of a system to predict, absorb, recover and adapt to interference. The resilience of a port cluster can be defined as its ability to maintain service integrity and efficiency under specific uncertain conditions. By conducting a resilience assessment of a port cluster, it can provide a reference for the disaster resistance construction and operation of ports and effectively reduce the losses caused by risk events to the port cluster.

[0050] When conducting risk assessment of port clusters in related technologies, complex network theory is usually used to characterize shipping networks. However, complex networks can only characterize binary relationships between nodes and cannot reflect the complex multivariate relationships of multi-port and multi-route interactions between real port clusters. Most of them ignore the heterogeneity, limited rationality, learning and evolutionary characteristics of various entities within the port cluster, and do not consider the complex interactions and behaviors between these entities and the external environment, resulting in the inability to fully describe the complex impact of climate events on port clusters. Resilience indicators are mainly based on network topology or overall system performance, while network topology can only reflect the static characteristics of the system, and the overall system performance can only describe the overall performance loss, and cannot simultaneously quantify the resilience of different dimensions such as network structure, operation and service.

[0051] Therefore, an embodiment of the present invention provides a port group resilience assessment method, which combines hypergraph and ABM to build a port group model, simulates and obtains the required data for calculating the port group resilience value, so as to achieve the effect of improving the accuracy of port group resilience assessment.

[0052] According to an embodiment of the present invention, an embodiment of a port group resilience assessment method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] In this embodiment, a port group resilience assessment method is provided, which can be used for desktop computers, laptop computers, servers, etc. Figure 1 is a flow chart of a method for evaluating the resilience of a port group according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0054] Step S101, collect port information and route information, and build a port group shipping network based on the hypergraph.

[0055] Port information and route information can be collected according to the actual application scenario. For example, if port group A in a certain area is selected, then the ports included in port group A, the location and distance of each port, etc. are collected. Optionally, shipping service information passing through the above-mentioned ports is collected from the operator, including the location of ports on each route, the number of ports and the shipping direction between each port. Hypergraph is a mathematical model in graph theory. The present invention depicts the selected port group based on the hypergraph to establish a port group shipping network corresponding to the selected port group. The network constructed based on the hypergraph can overcome the limitation that complex networks in related technologies can only describe binary relationships between nodes, more accurately reflect the complex multivariate relationships between each port and each route, establish a more accurate port group model (i.e., port group shipping network), and improve the accuracy of subsequent port group resilience assessments.

[0056] Step S102, taking each port and each ship as an intelligent entity, and establishing a port group shipping ABM model based on the port group shipping network.

[0057] The ABM (Agent-Based Modeling) model is a modeling method for simulating complex systems. It studies and predicts the overall behavior and characteristics of the system by simulating the behavior and interaction of multiple autonomous individuals (agents) in the system. Based on the constructed port group shipping network, the present invention takes each port in the port group and the ships moving between the ports as the agents of the ABM model to construct a port group shipping ABM model. Among them, the activity rules of the ship agent and the port agent can be set according to actual needs. For example, the ship agent may move on the route or may anchor at the port agent. The port agent may normally accommodate the ship agent or may cause congestion due to too many ship agents being accommodated.

[0058] Step S103, respectively running the port group shipping ABM model under normal conditions and risk events, simulating the response mechanism of each intelligent agent in the port group shipping network under different conditions, as well as the dynamic evolution process of the topological structure of the port group shipping network under different conditions, the status of each ship, the status of each port, and the cargo status corresponding to each ship, to obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship.

[0059] Risk events can be events that affect the normal operation of ports and ships, such as hurricanes, droughts, etc. Risk events can affect the actions of port agents and ship agents. For example, the port agent stops operating due to a hurricane, and the ship agent stays at the port agent and cannot travel due to a hurricane. Under normal conditions, there are no risk events, and ports and ships are in normal operation.

[0060] When the port group shipping ABM model is run under normal conditions, the response mechanism of each intelligent agent (including each port intelligent agent and each ship intelligent agent) in the port group shipping network, as well as the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship can be simulated, thereby obtaining the network density and number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in waiting state, and the cargo delay time corresponding to each ship. Among them, the network density of the port group shipping network can measure the compactness, overall connectivity and information dissemination efficiency of the port group shipping network, and the network density of the port group shipping network can be calculated by a function with network density calculation function. The number of strongly connected branches of the port group shipping network can measure the modularity, stability and information dissemination efficiency of the port group shipping network, and the number of strongly connected branches of the port group shipping network can be calculated by a function algorithm with a function for calculating the number of strongly connected branches.

[0061] Furthermore, risk events can be set in the port group shipping ABM model, including the type, impact range, impact time, etc. of risk events. For example, the risk event type is set to a hurricane, the impact range is 200 kilometers in diameter and moves from the southeast to the northwest, and the impact time is 72 hours. The port group shipping ABM model is run under risk events to simulate the response mechanism of each intelligent agent in the port group shipping network, the topological structure of the port group shipping network, the status of each ship, the status of each port, and the dynamic evolution of the cargo status corresponding to each ship, so as to obtain the impact of risk events on the port group shipping network, and then obtain the network density and number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship.

[0062] Step S104, based on the network density and the number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, construct a resilience assessment index including three dimensions of structure, operation, and service, and calculate the resilience value of the port group.

[0063] By comparing the structural resilience, operational resilience and service resilience of the port group under normal conditions and the structural resilience, operational resilience and service resilience of the port group under risk events, we can get the impact of risk events on the port intelligent body and ship intelligent body, and then get the impact of risk events on the port group shipping network, and calculate the port group resilience value corresponding to the port group shipping network according to the preset resilience value calculation method. After obtaining the port group resilience value, relevant technical personnel can evaluate the resilience of the selected port group in the face of risk events according to the situation of the port group resilience value, and then take corresponding measures for the port group. For example, if the port group resilience value is low, it means that the port group has poor resilience in the face of risk events, so it is necessary to increase disaster resistance facilities and set up management regulations for ports and ships during disasters.

[0064] The port group resilience assessment method provided in this embodiment establishes a port group shipping network based on a hypergraph, reflecting the complex multivariate relationship of the interaction between multiple ports and multiple routes, overcoming the limitation of the related technology that only binary relationships can be portrayed using complex networks, and describing the topological characteristics of the port group shipping network in more detail and accuracy. By taking each port and each ship as an intelligent entity, establishing a port group shipping ABM model based on the port group shipping network, and simulating the port group shipping ABM model under normal conditions and risk events respectively, it is possible to more realistically portray the response mechanism and dynamic evolution process of the port group shipping network under risk events. Constructing resilience assessment indicators based on multiple dimensions of structure, operation, and service and calculating the resilience value of the port group can more comprehensively and accurately assess the resilience of the port group.

[0065] In this embodiment, a port group resilience assessment method is provided, which can be used for desktop computers, laptop computers, servers, etc. Figure 2 is a flow chart of a method for evaluating the resilience of a port group according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0066] Step S201, collect port information and route information, and build a port group shipping network based on the hypergraph.

[0067] For example, Figure 3: is a schematic diagram of the scope of resilience assessment of a port group according to an embodiment of the present invention. In the embodiment of the present invention, for the scenario of resilience assessment of a port group, the resilience subject, resilience time scale, resilience interference and resilience scenario are first determined. By describing the current status of the selected port group, the resilience subject is clarified. The resilience subject includes the ports, ships, shipping networks, etc. included in the selected port group. Different ports are equipped with different infrastructures and have different processing capabilities for ships and cargoes. Shipping networks are set up between ports; each ship sails on different routes and has different schedules when transporting cargo or completing other tasks. The resilience time scale is clarified by defining the time range. Resilience measures the ability of a port group to cope with interference occurring within a certain period of time. This ability can be based on events or time. For example, the time range can be determined based on the occurrence and impact duration of a specific interference, or a predetermined time range, such as one year, can be selected. By distinguishing different time periods, resilience can be divided into event-based (short-term) and time-based (medium-term) frameworks. Resilient interference can be a risk event that affects the port group (for example, destroys the operation of the port group), such as hurricanes, heat waves, droughts, etc. Since risk events change over time, it is necessary to set risk events with specific attributes such as frequency, intensity, severity and duration. Resilience scenarios are determined based on the nature and characteristics of resilience disturbances. For example, if the risk event is a weather variable (such as wind speed, solar irradiance, ambient temperature and rainfall rate), it is necessary to determine the upper and lower limits, duration and impact range of the risk event.

[0068] After clarifying the scope of the port cluster resilience assessment, the shipping service information can be obtained, including the location of ports on each route, the number of ports, and the shipping direction between ports. Then, based on the obtained shipping service information, the port cluster shipping network is constructed through a directed hypergraph, for example, using circles with numerical sequences as ports, and lines with arrows as routes and indicating shipping directions. Optionally, the shipping service information is collected from shipping operators.

[0069] For example, Figure 4 is a schematic diagram of a port group shipping network according to an embodiment of the present invention. Figure 4As shown, circles represent ports, yellow circles represent ports within the port group, white circles represent ports outside the port group, lines of different colors represent routes, and the direction of the route is indicated by arrows. Taking the risk event of a hurricane as an example, the impact caused includes factors such as wind speed, impact radius and order. A hurricane can affect the port group in two ways. First, a hurricane may directly affect ports in the port group, such as Port 2. This event will damage the port's infrastructure and cause the port to close. Its impact will continue for a period of time after the hurricane. The port will remain closed until it recovers to a sufficient level to resume operations. During this period, ships on the red and black routes cannot transport goods through Port 2. When Port 2 reopens, the backlog of ships and cargo may exceed its processing capacity, causing congestion; second, a hurricane may directly affect ports outside the port group, such as Port 1. Although Ports 2 and 8 are not directly affected, they are subsequent ports of Port 1 and will be affected by the closure and congestion of Port 1. Therefore, hurricanes can directly or indirectly affect port clusters, causing closures or congestion, making it impossible for ships to sail as planned, causing delays in cargo delivery, and these impacts can spread along the shipping routes, spread to the entire shipping network, and change the network topology.

[0070] Step S202: Taking each port and each ship as an intelligent entity, a port group shipping ABM model is constructed based on the port group shipping network.

[0071] Specifically, the above step S202 includes:

[0072] Step S2021, establishing a port intelligent entity based on each port in the port group shipping network, and setting the status of each port intelligent entity.

[0073] The agent is an individual unit in the ABM model. Each agent has its own behavioral rules, attributes and decision-making capabilities, and influences and promotes the overall dynamics of the system through interaction, environmental feedback and self-adjustment. This embodiment is based on the port group resilience assessment scenario, and sets the port agent and the ship agent. The state of the port agent and the state of the ship agent are set for the aforementioned port group resilience assessment scope. Among them, the state of the port agent includes normal operation state, closed state and congestion state. The normal operation state is used to indicate that there is no risk event, and the number of ship agents arriving at the port is less than the port agent's processing capacity, and the port agent is in a normal working state; the closed state is used to indicate that the port agent is closed and does not work when the risk event interferes with the port agent; the congestion state is used to indicate that the number of ships at the port agent is too large, resulting in the port agent's ability to handle the arrival of ships and transport and unload cargo being less than the port agent's processing capacity.

[0074] Optionally, the closed status includes the risk event impact period and the port recovery period. The risk event impact period is used to indicate the time period from when the port agent is affected by the risk event to when the risk event ends. For example, if the risk event is a hurricane, when it is observed and predicted that the hurricane will land at the port within 48 hours, the port operation will be suspended. When the landing time is shortened to 24 hours, the port will be closed. The port closure time is set to 24 hours before the impact of the hurricane. The risk event end time is the time when the hurricane does not affect the ports in the port group. The port recovery period is the time period from the end of the risk event to the reopening of the port. During the port recovery period, the port agent needs to adopt a recovery strategy to restore the infrastructure and business affected by the risk event as soon as possible, and pre-set the port opening conditions, such as reopening the port agent when it recovers to the processing capacity before the risk event occurs.

[0075] Optionally, the congestion state includes congestion caused by the direct impact of the risk event and congestion caused by the indirect impact of the risk event. Since the entire port group shipping network will be directly or indirectly affected by the risk event, this embodiment assumes that the port agent provides services to the ship agent on a first-come, first-served basis. For a given target port agent, the ship agents in the port are counted at each moment and sorted according to the arrival time of the ship agents. When the number of arriving ship agents exceeds the processing capacity of the target port agent, the target port agent will enter a congested state. If the target port agent is directly affected by the risk event, the target port agent is closed, resulting in the inability to process the ship agents and cargo planned to dock at the target port agent at this time; when the target port agent is reopened, the backlog of ship agents during the closure period and the newly arrived ship agents after the opening may exceed the processing capacity of the target port agent, thereby causing congestion. If the target port agent is indirectly affected by a risk event, the infrastructure is not damaged and the cargo can be handled normally. However, the closure of the upstream port agent will disrupt the planned sailing schedules of some ship agents, causing ship agents to arrive at the port outside the planned sailing schedules. These additional arriving ship agents may exceed the processing capacity of the target port agent, causing congestion.

[0076] Step S2022, respectively setting the port response strategy of each port agent in different states.

[0077] The port response strategies include opening ports, closing ports, and increasing cargo handling capacity. For details, see the description of the actions and decisions of the port agent in each state in step S2021, which will not be repeated here.

[0078] Step S2023, establishing each ship intelligent agent based on the ship information, and setting the status of each ship intelligent agent.

[0079] The ship information includes the planned sailing schedule, ship speed and ship route information, which can be set according to the relevant information corresponding to the ships in the actually selected port group. The planned sailing schedule includes the starting port, passing port, terminal port, and planned time of arrival at each port for the ship intelligent body to transport the loaded cargo. The ship route information includes the sailing route of the ship intelligent body.

[0080] The states of the ship agent include waiting state, port operation state and sea navigation state.

[0081] The waiting state is used to indicate that the ship agent is not working. The waiting state includes the time period when the port agent is closed and the time period when the port agent is waiting for the port agent to reopen, and the time period when the port agent is congested and the time period when the port agent is waiting for other ship agents to complete loading and unloading. Specifically, when the port agent is in a congested state, the ship agents that plan to load and unload cargo at the port agent during this period will not be able to meet the planned ship schedule and will be affected. Most of the affected ship agents will adopt a waiting strategy, but a small number will adopt a port-jumping strategy under certain circumstances. That is, if the planned port agent is closed, the ship agent may skip the port agent and take the next planned port agent as the next target port.

[0082] The port operation status is used to indicate the state of the ship agent loading and unloading cargo in the port when the port agent is in a normal operating state, or the state when the port is in a congested state but the position of the ship agent in the port queue is less than the port agent capacity. Exemplarily, the moment when the ship agent decides the next target port agent and the ship speed is set when the ship agent leaves the current port agent after completing loading and unloading. When making a decision, if the next target port agent in the planned sailing schedule is in a normal operating state or an operating state, the original route is still followed, and the next target port agent in the planned sailing schedule is used as the next destination; if the next target port agent in the planned sailing schedule is in a closed state, it is determined whether to skip the port agent. When determining the ship speed, the planned sailing schedule is used to evaluate whether there is a delay. If there is no delay, the original ship speed is maintained unchanged. If there is a delay, the ship speed needs to be adjusted to minimize the delay.

[0083] Step S2024, respectively setting the ship response strategy of each ship agent in different states.

[0084] The ship response strategy includes adjusting the sailing speed and selecting the target port. The specific contents are described in steps S2021 and S2022 for the actions and decisions of the port agent in each state, and in step S2023 for the actions and decisions of the ship agent in each state, which will not be repeated here.

[0085] Step S2025, based on each port agent, the status and response strategy of each port agent, each ship agent, the status and response strategy of each ship agent, a port group shipping ABM model is established.

[0086] After setting the status and response strategy of each port intelligent agent and the status and response strategy of each ship intelligent agent, a port group shipping ABM model can be established based on the port group shipping network. The function of the port group shipping ABM model is to simulate the response mechanism and dynamic evolution process of the port group shipping network under normal conditions, as well as the impact of risk events on the port group shipping network, and the response mechanism and dynamic evolution process of the port group shipping network under risk events.

[0087] Step S203, running the port group shipping ABM model under normal conditions and risk events respectively, simulating the response mechanism of each intelligent agent in the port group shipping network under different conditions and the dynamic evolution process of the topological structure of the port group shipping network under different conditions, the status of each ship, the status of each port and the cargo status corresponding to each ship, and obtaining the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state and the cargo delay time corresponding to each ship.

[0088] Specifically, the above step S203 includes:

[0089] Step S2031, run the port group shipping ABM model under normal conditions, simulate the response mechanism of each agent in the port group shipping network at different times, and the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship, and iterate until all ship agents complete the cargo delivery according to the planned shipping schedule, and obtain the network density and the number of strongly connected branches, the number of ships in the waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under normal conditions.

[0090] The topological structure of the port group shipping network is used to indicate the existence of nodes in the network and the connection of edges. The status of each ship at different times is used to indicate the state of the ship agent at each time, that is, whether the ship agent is in a waiting state, a port operation state, or a sea voyage state at each time. The status of each port at different times is used to indicate the state of the port agent at each time, that is, whether the port agent is in a normal operation state, a closed state, or a congested state at each time. The network density of the port group shipping network represents the ratio of potential connections to actual connections within the network, and is a measure of the compactness of the network. The number of strongly connected branches of the port group shipping network reflects the number of maximum size subsets in a directed network, in which bidirectional reachability is maintained between nodes. Using network density and the number of strongly connected branches as evaluation indicators can measure the resilience of the port group shipping network from a more comprehensive perspective, which can reflect both the overall connectivity of the port group shipping network and its inherent robustness, thereby revealing the adaptability of the network design to maintain connectivity in the event of interruptions. The network density and the number of strongly connected branches of the port group shipping network are calculated based on the status of each port; the number of ships in waiting state is calculated based on the status of each ship; the cargo delay time is calculated based on the cargo status corresponding to each ship. Specifically, whether the ship intelligent entity complies with the planned shipping schedule at each moment and the specific time difference compared with the time listed in the planned shipping schedule are used as the cargo delay time of the ship intelligent entity.

[0091] Optionally, the network density D of the port group shipping network is calculated using the formula D=L / [N*(N-1)], D∈[0,1], where L represents the number of directed edges actually existing in the port group shipping network, and N represents the number of nodes in the port group shipping network.

[0092] Optionally, the Tarjan algorithm is used to find strongly connected branches in the port group shipping network, and the number of strongly connected branches is obtained.

[0093] Step S2032, set the impact range and impact time of the risk event, and run the port group shipping ABM model under the risk event, simulate the response mechanism of each intelligent agent in the port group shipping network at different times, and the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship, and iterate until all ship intelligent agents complete the cargo delivery, and obtain the network density and the number of strongly connected branches of the port group shipping network at different times under the risk event, the number of ships in the waiting state, and the cargo delay time corresponding to each ship.

[0094] The difference from step S2031 is that a risk event is set, and due to the impact of the risk event, each ship intelligent agent may not complete the cargo delivery completely according to the planned shipping schedule. The other simulation processes are similar and will not be repeated here.

[0095] Step S204, based on the network density and the number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, construct a resilience assessment index including three dimensions of structure, operation, and service, and calculate the port group resilience value.

[0096] Specifically, the above step S204 includes:

[0097] Step S2041, based on the network density and the number of strongly connected branches of the port group shipping network under normal conditions and the network density and the number of strongly connected branches of the port group shipping network under risk events, construct a port group structural resilience index and calculate the port group structural resilience index value.

[0098] The port group structural resilience index is defined as the ability of the port group shipping network to resist and quickly recover from disturbances. Optionally, the port group structural resilience index value R is obtained by the following formula Structural :

[0099]

[0100] Among them, T1 represents the time range of the port group shipping network interruption (that is, there is a port agent in the closed state), w represents the risk event that affects the port group shipping network, m t (w) and M t They represent the network density of the port group shipping network at time t under risk event w and normal conditions, c t (w) and C t They represent the number of strongly connected branches of the port group shipping network at time t under risk event w and normal conditions respectively. α1 and α2 represent the weights of the network density and the number of strongly connected branches of the port group shipping network respectively, satisfying α1+α2=1.

[0101] Step S2042: Based on the number of ships in a waiting state at each moment under normal conditions and the number of ships in a waiting state at each moment under risk events, a port group operation resilience index is constructed, and the port group operation resilience index value is calculated.

[0102] The port group operational resilience index is defined as the ability of the port group shipping network to resist interference and recover quickly during operation. It describes the recovery capacity at the operational level and reflects the ability of the port group shipping network to maintain efficient operation in the event of interruption. Optionally, the port group operational resilience index value R is obtained by the following formula Operational :

[0103]

[0104] Among them, T2 represents the time range in which the state of the ship agent is affected, J represents the set of ship agents, and They represent the state of the ship agent j at time t under risk event w and normal conditions, respectively. Equal to 1 means that the ship agent is in a waiting state under the risk event. Equal to 0 means that the ship agent is in port operation or sea navigation under the risk event. Equal to 1 means that the ship agent is in a waiting state under normal conditions. If it is equal to 0, it means that the ship agent is in port operation or sea navigation under normal conditions. t Represents the set of ship agents that may be affected at time t.

[0105] Step S2043, based on the cargo delay time of each ship intelligent body at each moment under normal conditions and the cargo delay time of each ship intelligent body at each moment under risk events, construct a port group service resilience index and calculate the port group service resilience index value.

[0106] The port group service resilience index is defined as the ability of the port group shipping network to maintain service levels and minimize delays. It emphasizes the continuity and recovery speed of shipping operations, describes the resilience of service levels, and reflects the network's ability to maintain cargo flow and minimize shipping plan disruptions in the event of an interruption. Optionally, the port group service resilience index value R is obtained by the following formula Service :

[0107]

[0108] Among them, T3 represents the time range affected by the cargo delivery of the ship intelligent body. and They represent the cargo delay time of ship agent j at time t under risk event w and normal circumstances, respectively.

[0109] Step S2044, based on the port group structure resilience index value, the port group operation resilience index value and the port group service resilience index value, the port group resilience value is calculated.

[0110] Optionally, the weights of the port group structural resilience index, the port group operational resilience index and the port group service resilience index are set respectively, and then the port group resilience value is calculated based on the port group structural resilience index value, the port group operational resilience index value, the port group service resilience index value, the port group structural resilience index weight, the port group operational resilience index weight and the port group service resilience index weight. Optionally, the port group resilience value R(w) is calculated using the following formula:

[0111] R(w)=β1R Structural (w)+β2R Operational (w)+β3R Service (w)

[0112] Among them, β1, β2 and β3 represent the weight of the port group structure resilience index, the weight of the port group operation resilience index and the weight of the port group service resilience index respectively, satisfying β1+β2+β3=1.

[0113] It should be noted that the port group resilience value under risk event w ranges from (0,1). In the worst case, all port infrastructure is completely damaged, port agents are closed, all ship agents are waiting, and all cargo is delayed, which means that m t (w)→0,c t (w)→+∞, Further, R Structural (w)→0,R Operational (w)→0,R Service (w)→0, we get R(w)→0. In the best case, all port infrastructure is not affected, all ship agents sail as planned, and all goods are delivered on time. Therefore, m t (w) = M t , c t (w) = C t , Further, R Structural (w)=1,R Operational (w)=1,R Service (w) = 1. We get R(w) = 1.

[0114] The above step S204 obtains the resilience value of the port group based on short-term events, while for long-term practical application scenarios, such as obtaining the resilience value of the port group within a whole year, the risk events include all potential risk events within the long-term time range, such as extreme weather, accidental damage to facilities, etc. Each risk event contains various characteristics. In this case, the resilience value of the port group can be obtained based on time. Specifically, the impact of interference on the port group is multifaceted, which may have both direct impacts, that is, the impact of interference within the port group on the port group and its operations, and indirect impacts, that is, the impact on the port group and its operations by affecting other parts with network connections to the port group. Interference may damage port infrastructure or hinder operations through physical damage within the cluster, thereby having a direct impact; interference may also have an indirect impact, affecting other ports in the port group shipping network, causing a chain effect, and adversely affecting the operational continuity of the cluster.

[0115] According to the above event-based assumptions, the port group structural resilience index value can be obtained as follows:

[0116]

[0117] Among them, X represents the set of potential risk events that directly affect the port group shipping network, Y represents the set of potential risk events that indirectly affect the port group shipping network, p(x) represents the probability of event x, and p(y) represents the probability of event y.

[0118] The port group operational resilience index is:

[0119]

[0120] The service resilience index of the port group is:

[0121]

[0122] In summary, the port group resilience value in the long term is:

[0123] R=β1R Structural +β2R Operational +β3R Service

[0124] The time-based port group resilience value ∈(0,1].

[0125] The port group resilience assessment method provided in this embodiment establishes a port group shipping network based on a hypergraph, reflecting the complex multivariate relationship of the interaction between multiple ports and multiple routes, overcoming the limitation of the related technology that only binary relationships can be portrayed using complex networks, and describing the topological characteristics of the port group shipping network in more detail and accuracy. By taking each port and each ship as an intelligent entity, establishing a port group shipping ABM model based on the port group shipping network, and simulating the port group shipping ABM model under normal conditions and risk events respectively, it is possible to more realistically portray the response mechanism and dynamic evolution process of the port group shipping network under risk events. Constructing resilience assessment indicators based on multiple dimensions of structure, operation, and service and calculating the resilience value of the port group can more comprehensively and accurately assess the resilience of the port group.

[0126] As one or more specific application embodiments of the embodiments of the present invention, the optimal implementation scheme or the scheme that the inventor most wants to embody is described below in combination with specific application scenarios.

[0127] This embodiment is based on a target port group, mainly involving nine coastal ports and two inland ports.

[0128] First, a baseline scenario is defined, in which ships travel at planned speeds and follow their scheduled routes to ports. It is assumed that at time zero, all ships are handling cargo at the first port. If congestion occurs, ships adjust their speeds and try to follow the predetermined planned schedule. As the number of ships arriving at the port increases, the port will operate at maximum capacity. In order to fully observe the impact of the hurricane, it is simulated that ships sail according to the planned schedule for one year, which is equivalent to 8760 hours. A specific event is selected based on the probability of a hurricane occurring. In this case, the hurricane makes landfall at the port at time 1000 hours, resulting in the port closure. The hurricane affects all 8 container terminals in the target port group, and each terminal is affected for 36 hours. After 48 hours, these ports reopened and operate at the same capacity as before the closure.

[0129] According to the ABM modeling, the final resilience value of the target port group under hurricanes is 0.64, indicating that its resilience is at a medium level. This result highlights the potential vulnerability of the port group in the face of extreme weather events and the need to take resilience-enhancing measures. The corresponding port group structural resilience index values, port group operational resilience index values, and port group service resilience index values ​​are 0.79, 0.74, and 0.40, respectively. The port group structural resilience index value of 0.79 is relatively high, indicating that the physical and network structures of the target port group have a strong ability to resist the impact of hurricanes and recover quickly. The port group operational resilience index value is slightly lower than the port group structural resilience index value, but it shows that the target port group still has a strong operational adaptability. The long time range may be caused by factors such as continued port congestion. The port group service resilience index value is relatively low and has the longest time range, which reflects the huge challenges faced in fully restoring the service level of the target port group to the pre-interruption state.

[0130] In this embodiment, a port group resilience assessment device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0131] This embodiment provides a port group resilience assessment device, such as Figure 5 As shown, including:

[0132] The hypergraph module 501 is used to collect port information and route information and build a port group shipping network based on the hypergraph;

[0133] ABM module 502, used to construct a port group shipping ABM model based on the port group shipping network by taking each port and each ship as an intelligent entity;

[0134] The simulation iteration module 503 is used to run the port group shipping ABM model under normal conditions and risk events respectively, simulate the response mechanism of each intelligent agent in the port group shipping network under different conditions, and the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship under different conditions, and obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship;

[0135] The resilience value calculation module 504 is used to construct a resilience evaluation index including three dimensions of structure, operation and service based on the network density and the number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and calculate the resilience value of the port group.

[0136] In an optional embodiment, the hypergraph module is also used to: obtain shipping service information; the shipping service information includes the port location, the number of ports and the shipping direction between each port on each route; based on the shipping service information, construct a port group shipping network through a directed hypergraph.

[0137] In an optional embodiment, the ABM module is also used to: establish a port intelligent entity based on each port in the port group shipping network, and set the status of each port intelligent entity; the status of the port intelligent entity includes normal operation status, closed status and congestion status; set the port response strategy of each port intelligent entity in different states; the port response strategy includes opening the port, closing the port and increasing the cargo handling capacity; establish each ship intelligent entity based on ship information, and set the status of each ship intelligent entity; the status of the ship intelligent entity includes waiting status, port operation status and sea navigation status; the ship information includes planned ship schedule and ship route information; set the ship response strategy of each ship intelligent entity in different states; the ship response strategy includes adjusting the sailing speed and selecting the target port; establish a port group shipping ABM model based on each port intelligent entity, the status and response strategy of each port intelligent entity, each ship intelligent entity, and the status and response strategy of each ship intelligent entity.

[0138] In an optional implementation, the simulation iteration module is also used to: run the port group shipping ABM model under normal conditions, simulate the response mechanism of each intelligent agent in the port group shipping network at different times, and the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship, and iterate until all ship intelligent agents complete the cargo delivery according to the planned sailing schedule, and obtain the network density and the number of strongly connected branches of the port group shipping network at different times under normal conditions, the number of ships in the waiting state, and the cargo delay time corresponding to each ship; the network density and the number of strongly connected branches of the port group shipping network are calculated based on the status of each port; The number of ships in waiting state is calculated based on the state of each ship; the cargo delay time is calculated based on the cargo state corresponding to each ship; the impact range and impact time of the risk event are set, and the port group shipping ABM model is run under the risk event to simulate the response mechanism of each intelligent agent in the port group shipping network at different times, as well as the dynamic evolution process of the topological structure of the port group shipping network, the state of each ship, the state of each port, and the cargo state corresponding to each ship, and iterate until all ship intelligent agents complete the cargo delivery, and obtain the network density and the number of strongly connected branches, the number of ships in waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under the risk event.

[0139] In an optional embodiment, the resilience value calculation module is also used to: construct a port group structural resilience index based on the network density and the number of strongly connected branches of the port group shipping network under normal conditions and the network density and the number of strongly connected branches of the port group shipping network under risk events, and calculate the port group structural resilience index value; construct a port group operation resilience index based on the number of ships in a waiting state at each moment under normal conditions and the number of ships in a waiting state at each moment under risk events, and calculate the port group operation resilience index value; construct a port group service resilience index based on the cargo delay time of each ship intelligent body at each moment under normal conditions and the cargo delay time of each ship intelligent body at each moment under risk events, and calculate the port group service resilience index value; calculate the port group resilience value based on the port group structural resilience index value, the port group operation resilience index value and the port group service resilience index value.

[0140] In an optional embodiment, the resilience value calculation module is also used to: respectively set the weights of the port group structure resilience index, the port group operation resilience index and the port group service resilience index; based on the port group structure resilience index value, the port group operation resilience index value, the port group service resilience index value, the port group structure resilience index weight, the port group operation resilience index weight and the port group service resilience index weight, calculate the port group resilience value.

[0141] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0142] The port group resilience assessment device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0143] The embodiment of the present invention also provides a computer device having the above Figure 5 The port cluster resilience assessment device shown.

[0144] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0145] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0146] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0147] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0148] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0149] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0150] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0151] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0152] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0153] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall all fall within the scope of protection of the present invention.

Claims

1. A method for assessing the resilience of a port group, characterized in that: The method comprises: Collect port information and route information, and build a port group shipping network based on the hypergraph; Taking each port and each ship as an intelligent entity, a port group shipping ABM model is constructed based on the port group shipping network; The port group shipping ABM model is run under normal conditions and risk events respectively, and the response mechanism of each intelligent agent in the port group shipping network under different conditions is simulated, as well as the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the status of cargo corresponding to each ship under different conditions, so as to obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship; Based on the network density and the number of strongly connected branches of the port group shipping network at different times under the normal conditions, the number of ships in waiting state, and the cargo delay time corresponding to each ship, as well as the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in waiting state, and the cargo delay time corresponding to each ship, a resilience assessment index including three dimensions of structure, operation, and service is constructed, and the port group resilience value is calculated.

2. The method according to claim 1, characterized in that The collecting of port information and route information and building of a port group shipping network based on a hypergraph include: Obtaining shipping service information; the shipping service information includes the location of ports on each route, the number of ports, and the shipping direction between each port; Based on the shipping service information, a port group shipping network is constructed through a directed hypergraph.

3. The method according to claim 1 or 2, characterized in that: The port group shipping ABM model is constructed based on the port group shipping network by taking each port and each ship as an intelligent entity, including: Establishing a port intelligent entity based on each port in the port group shipping network, and setting the status of each port intelligent entity; the status of the port intelligent entity includes a normal operation status, a closed status, and a congested status; The port response strategies of each port agent in different states are respectively set; the port response strategies include opening the port, closing the port and increasing the cargo handling capacity; Establishing each ship intelligent body based on the ship information and setting the status of each ship intelligent body; the status of the ship intelligent body includes waiting status, port operation status and sea navigation status; the ship information includes planned sailing schedule and ship route information; The ship response strategy of each ship agent in different states is set respectively; the ship response strategy includes adjusting the sailing speed and selecting the target port; A port group shipping ABM model is established based on each port agent, the status and response strategy of each port agent, each ship agent, the status and response strategy of each ship agent.

4. The method according to claim 3, characterized in that The port group shipping ABM model is run under normal conditions and risk events respectively, and the response mechanism of each intelligent agent in the port group shipping network under different conditions is simulated, as well as the dynamic evolution process of the topological structure of the port group shipping network under different conditions, the status of each ship, the status of each port, and the cargo status corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship are obtained, including: The port group shipping ABM model is run under normal conditions to simulate the response mechanism of each agent in the port group shipping network at different times, the topological structure of the port group shipping network, the status of each ship, the status of each port, and the dynamic evolution process of the cargo status corresponding to each ship, and iterate until all ship agents complete the cargo delivery according to the planned ship schedule, and obtain the network density and the number of strongly connected branches, the number of ships in the waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under normal conditions; the network density and the number of strongly connected branches of the port group shipping network are calculated based on the status of each port; the number of ships in the waiting state is calculated based on the status of each ship; the cargo delay time is calculated based on the cargo status corresponding to each ship; The impact scope and impact time of the risk event are set, and the port group shipping ABM model is run under the risk event to simulate the response mechanism of each agent in the port group shipping network at different times, as well as the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship. It is iterated until all ship agents complete the cargo delivery, and the network density and the number of strongly connected branches, the number of ships in the waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under the risk event are obtained.

5. The method according to claim 4, characterized in that Based on the network density and the number of strongly connected branches, the number of ships in waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under normal conditions, and the network density and the number of strongly connected branches, the number of ships in waiting state, and the cargo delay time corresponding to each ship of the port group shipping network at different times under risk events, a resilience evaluation index including three dimensions of structure, operation, and service is constructed, and the resilience value of the port group is calculated, including: Based on the network density and the number of strongly connected branches of the port group shipping network under normal conditions and the network density and the number of strongly connected branches of the port group shipping network under risk events, a port group structural resilience index is constructed, and the port group structural resilience index value is calculated; Based on the number of ships waiting at each time under normal conditions and the number of ships waiting at each time under risk events, the port group operation resilience index is constructed and the port group operation resilience index value is calculated; Based on the cargo delay time of each ship agent at each time under normal conditions and the cargo delay time of each ship agent at each time under risk events, a port group service resilience index is constructed, and the port group service resilience index value is calculated; Based on the port group structure resilience index value, the port group operation resilience index value and the port group service resilience index value, the port group resilience value is calculated.

6. The method according to claim 5, characterized in that The port group resilience value is calculated based on the port group structural resilience index value, the port group operational resilience index value and the port group service resilience index value, including: Set the weights of the port group structure resilience index, the port group operation resilience index and the port group service resilience index respectively; The port group resilience value is calculated based on the port group structural resilience index value, the port group operational resilience index value, the port group service resilience index value, the port group structural resilience index weight, the port group operational resilience index weight and the port group service resilience index weight.

7. A port group resilience assessment device, characterized in that: The device comprises: The Hypergraph module is used to collect port information and route information and build a port group shipping network based on the Hypergraph; The ABM module is used to establish a port group shipping ABM model based on the port group shipping network by taking each port and each ship as an intelligent entity; A simulation iteration module is used to run the port group shipping ABM model under normal conditions and risk events respectively, simulate the response mechanism of each intelligent agent in the port group shipping network under different conditions, and the dynamic evolution process of the topological structure of the port group shipping network, the status of each ship, the status of each port, and the cargo status corresponding to each ship under different conditions, and obtain the network density and the number of strongly connected branches of the port group shipping network at different times, the number of ships in the waiting state, and the cargo delay time corresponding to each ship; The resilience value calculation module is used to construct a resilience evaluation index including three dimensions of structure, operation and service based on the network density and the number of strongly connected branches of the port group shipping network at different times under the normal conditions, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and the network density and the number of strongly connected branches of the port group shipping network at different times under risk events, the number of ships in a waiting state, and the cargo delay time corresponding to each ship, and calculate the resilience value of the port group.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the port group resilience assessment method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the port group resilience assessment method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the port group resilience assessment method according to any one of claims 1 to 6.

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