Method, system, terminal and storage medium for restoring a damaged logistics network

Abstract

The application relates to the technical field of data modeling, and specifically provides a method and system for recovering a damaged logistics network, a terminal and a storage medium, which comprise the following steps: collecting site cargo flow of the logistics network, and constructing a circulation model of the logistics network according to the site cargo flow; obtaining first model data of the circulation model when the circulation model is not damaged, second model data after the circulation model is damaged, and a local loss matrix based on the difference between the first model data and the second model data; constructing a cost objective function based on a recovery cost of a candidate recovery object, a cargo stagnation cost and a connectivity loss cost; solving the cost objective function by using a continuous average algorithm and a simulated annealing particle swarm algorithm based on the circulation model and the local loss matrix, so as to obtain a minimum value of the cost objective function; integrating and outputting the candidate recovery object and a recovery mode corresponding to the minimum value into a recovery scheme; and executing the recovery scheme on the damaged logistics network. The application can minimize the recovery cost of the logistics network.

Description

Technical Field

[0001] This invention belongs to the field of data modeling technology, and specifically relates to a method, system, terminal and storage medium for restoring a damaged logistics network. Background Technology

[0002] Logistics networks, such as those in the express delivery industry, typically consist of numerous stations and distribution routes. If certain stations or routes become unable to handle cargo flow, it negatively impacts the entire regional logistics network. When many items are affected, the order in which to restore those that have lost functionality will influence the overall cost of the damage.

[0003] Existing network cost calculation models primarily consider the impact of network hub failures on network structure and total cost. However, these models are not applicable to all stations and edges in a delivery network. In reality, not only hub stations and roads are damaged, but losses to non-hub stations and roads also warrant attention. This results in numerous errors in existing cost calculation models, preventing the identification of optimal recovery solutions. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, this invention provides a method, system, terminal, and storage medium for restoring a damaged logistics network. Considering the local network structure and traffic volume when a localized damage occurs in the express delivery network, and taking into account both the direct and indirect costs required to restore services, the invention aims to find a more suitable restoration method to ensure normal network operation within time and total cost constraints. Minimizing these costs is a key problem that needs to be addressed.

[0005] In a first aspect, the present invention provides a method for restoring a damaged logistics network, comprising:

[0006] Collect cargo flow data at stations in the logistics network and construct a circulation model of the logistics network based on the cargo flow data at each station;

[0007] Obtain the first model data of the circulation model when it is undamaged, and the second model data after it is damaged, and obtain the local loss matrix based on the difference between the first model data and the second model data;

[0008] Based on the recovery cost of the candidate recovery objects, the cost of cargo stagnation, and the cost of connectivity loss, a cost objective function is constructed.

[0009] Based on the aforementioned circulation model and local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm algorithm to obtain the minimum value of the cost objective function;

[0010] The candidate recovery objects and recovery methods corresponding to the minimum value are integrated and output as a recovery plan, which is then executed on the damaged logistics network.

[0011] In one optional implementation, the logistics network's station cargo flow is collected, and a logistics network circulation model is constructed based on the station cargo flow, including:

[0012] Let G = (V, E, F) define the logistics network, where V = {v1, v2, v3, ..., v i} represents the set of sites, where i is the number of sites; E = (e xy ) i×i Let e ​​be the set of edges of the network, and each edge e xy ∈E represents the path between station x and station y, d xy Representing edge e xy The distance between them, F = f xy Representing edge e xy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the network are collectively referred to as objects n in network G, where n∈N, and N=V∪E is the set of all objects; when a local network is damaged, the locally damaged express delivery network is defined as O=(V o E o ), where the site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths.

[0013] In the case of partial damage to the express delivery network, obtain the transportation path of packages in the local network. The transportation path exists when edge e = (u,v) ∈ E and e ≠ 0. e If the value is greater than 0, then the current path has transport volume.

[0014] Based on the cargo flow of a station and the preset matching rules between cargo flow and status, the station status is set to normal, overloaded, or failed. Based on the cargo flow between stations, the corresponding edges are set to available or unavailable. The station status and path status are aggregated to generate a set S. The status of stations and edges in a normal state is defined as 0, and other states are represented by s∈S. If the function of a station or edge is completely lost, it is represented by q, i.e.

[0015] In an optional implementation, a cost objective function is constructed based on the recovery cost of the candidate object, the cargo stagnation cost, and the connectivity loss cost, including:

[0016] Establish the cost objective function:

[0017]

[0018]

[0019] Where n represents the object to be restored, which is a site or path; δ n,j,t This indicates that if a recovery operation j is performed on object n during time period t, the value is 1; otherwise, it is 0. n,j I(t|x) represents the recovery cost incurred by object n when using recovery operation j; e,t X(t) represents the cost incurred by the package after the extended time period t; X(t) represents the connectivity loss cost caused by the local loss network at time period t. Π(t) represents the link traffic of edge e within time period t; Π(t) represents the total cost within time period t; f od (P) represents the flow rate from point o to point d on path P; x e,t This represents the connection flow on edge e during time period t; j∈J(n|s) indicates that object n adopted recovery type j in state s; Z represents the minimum cost value;

[0020] The constraints for the cost objective function include:

[0021] The model can only select one recovery method for each object within a certain period of time, and executes it within a time interval;

[0022] The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value;

[0023] The flow constraints between all node pairs must meet this requirement;

[0024] This represents a nonnegativity constraint, used to ensure that the solution of the program is meaningful.

[0025] In an optional implementation, based on the circulation model and the local loss matrix, the cost objective function is solved using a continuous averaging algorithm and a simulated annealing particle swarm optimization algorithm to obtain the minimum value of the cost objective function, including:

[0026] The indirect data corresponding to the recovery object is calculated using a continuous averaging algorithm. The indirect data includes return cargo travel time, road traffic flow, vehicle transportation time, vehicle travel distance, and cargo loss. The recovery scheme includes the recovery object and the recovery order of the recovery objects.

[0027] Based on the indirect data, the recovery cost corresponding to the recovery scheme is calculated. The recovery scheme is set as a particle of the simulated annealing particle swarm algorithm. The simulated annealing particle swarm algorithm is used to iteratively select the best particle with the lowest recovery cost.

[0028] In a second aspect, the present invention provides a system for restoring a damaged logistics network, comprising:

[0029] The model building module is used to collect the cargo flow of the logistics network stations and build the circulation model of the logistics network based on the cargo flow of the stations.

[0030] The loss acquisition module is used to acquire the first model data of the circulation model when it is undamaged, the second model data after it is damaged, and to obtain the local loss matrix based on the difference between the first model data and the second model data.

[0031] The function building module is used to construct a cost objective function based on the recovery cost of the candidate recovery object, the cargo stagnation cost, and the connectivity loss cost;

[0032] The function solving module is used to solve the cost objective function based on the circulation model and the local loss matrix, using the continuous averaging algorithm and the simulated annealing particle swarm algorithm, to obtain the minimum value of the cost objective function;

[0033] The solution generation module is used to integrate the candidate recovery objects and recovery methods corresponding to the minimum value and output them as a recovery solution, and execute the recovery solution on the damaged logistics network.

[0034] In one optional implementation, the model building module specifically performs:

[0035] Let G = (V, E, F) define the logistics network, where V = {v1, v2, v3, ..., v i Let} be the set of sites, and i be the number of sites; E = (r xy ) i×i Let e ​​be the set of edges of the network, and each edge e xy ∈E represents the path between station x and station y, d xy Representing edge e xy The distance between them, F = f xy Representing edge e xy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the network are collectively referred to as objects n in network G, where n∈N, and N=V∪E is the set of all objects; when a local network is damaged, the locally damaged express delivery network is defined as O=(V o E o ), where the site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths.

[0036] In the case of partial damage to the express delivery network, obtain the transportation path of packages in the local network. The transportation path exists when edge e = (u,v) ∈ E and e ≠ 0. e If the value is greater than 0, then the current path has transport volume.

[0037] Based on the cargo flow of a station and the preset cargo flow and state matching rules, the station state is set to normal, overloaded, or failed. Based on the cargo flow between stations, the corresponding edges are set to available or unavailable. The station states and path states are aggregated to generate a set S. The state of stations and edges in a normal state is defined as 0, and other states are represented by s∈S. If the function of a station or edge is completely lost, it is represented by g, i.e.

[0038] In one optional implementation, the function construction module specifically executes:

[0039] Establish the cost objective function:

[0040]

[0041]

[0042] Where n represents the object to be restored, which is a site or path; δ n,j,t This indicates that if a recovery operation j is performed on object n during time period t, the value is 1; otherwise, it is 0. n,j I(t|x) represents the recovery cost incurred by object n when using recovery operation j; e,t X(t) represents the cost incurred by the package after the extended time period t; X(t) represents the connectivity loss cost caused by the local loss network at time period t. Π(t) represents the link traffic of edge e within time period t; Π(t) represents the total cost within time period t; f od (P) represents the flow rate from point o to point d on path P; x e,t This represents the connection flow on edge e during time period t; j∈J(n|s) indicates that object n adopted recovery type j in state s; Z represents the minimum cost value;

[0043] The constraints for the cost objective function include:

[0044] The model can only select one recovery method for each object within a certain period of time, and executes it within a time interval;

[0045] The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value;

[0046] The flow constraints between all node pairs must meet this requirement;

[0047] This represents a nonnegativity constraint, used to ensure that the solution of the program is meaningful.

[0048] In one optional implementation, the function solving module specifically performs:

[0049] The indirect data corresponding to the recovery object is calculated using a continuous averaging algorithm. The indirect data includes return cargo travel time, road traffic flow, vehicle transportation time, vehicle travel distance, and cargo loss. The recovery scheme includes the recovery object and the recovery order of the recovery objects.

[0050] Based on the indirect data, the recovery cost corresponding to the recovery scheme is calculated. The recovery scheme is set as a particle of the simulated annealing particle swarm algorithm. The simulated annealing particle swarm algorithm is used to iteratively select the best particle with the lowest recovery cost.

[0051] Thirdly, a terminal is provided, including:

[0052] Processor, memory, among which,

[0053] This memory is used to store computer programs.

[0054] The processor is used to retrieve and run the computer program from memory, causing the terminal to perform the terminal method described above.

[0055] Fourthly, a computer storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the methods described in the above aspects.

[0056] The beneficial effects of this invention are as follows: the method, system, terminal, and storage medium provided by this invention for restoring damaged logistics networks propose a relatively general recovery model for express delivery networks with localized losses. It establishes a local network recovery model that minimizes the cost of the recovery plan, taking into account both direct and indirect costs. The model is solved using a simulated annealing particle swarm optimization algorithm. This invention considers the services, costs, priority available resources, and possible improvements to existing resources required for express delivery network recovery. It provides a detailed design for locally damaged express delivery networks, proposes an optimal recovery plan model for such networks, and designs a solution based on the simulated annealing particle swarm optimization algorithm. Express delivery companies can use this model to obtain information such as expenditures and investments during recovery operations, weigh the relationship between recovery budgets and the quality of the resulting recovery plans, and monitor and manage the reliability and resilience of critical infrastructure within the network to disruptive events.

[0057] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects. Attached Figure Description

[0058] To more clearly illustrate the technical solutions 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic flowchart of a method according to an embodiment of the present invention.

[0060] Figure 2 This is another illustrative flowchart of a method according to an embodiment of the present invention.

[0061] Figure 3 This is a schematic flowchart illustrating the continuous averaging algorithm solution of one embodiment of the present invention.

[0062] Figure 4 This is a schematic flowchart illustrating the simulated annealing particle swarm optimization algorithm solution of one embodiment of the present invention.

[0063] Figure 5 This is a schematic block diagram of a system according to an embodiment of the present invention.

[0064] Figure 6 This is a schematic diagram of the structure of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0065] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0066] 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. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0067] The method for restoring a damaged logistics network provided in this embodiment of the invention is executed by a computer device, and correspondingly, the system for restoring a damaged logistics network runs in the computer device.

[0068] Figure 1 This is a schematic flowchart illustrating a method according to an embodiment of the present invention. Wherein, Figure 1 The implementing entity can be a system for restoring a damaged logistics network. Depending on different needs, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0069] like Figure 1 As shown, the method includes:

[0070] Step 110: Collect the cargo flow of the logistics network stations and construct the logistics network circulation model based on the cargo flow of the stations;

[0071] Step 120: Obtain the first model data of the circulation model when it is undamaged, and the second model data after it is damaged, and obtain the local loss matrix based on the difference between the first model data and the second model data;

[0072] Step 130: Construct a cost objective function based on the recovery cost, cargo stagnation cost, and connectivity loss cost of the candidate recovery objects;

[0073] Step 140: Based on the circulation model and the local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm algorithm to obtain the minimum value of the cost objective function;

[0074] Step 150: Integrate the candidate recovery objects and recovery methods corresponding to the minimum value into a recovery plan, and execute the recovery plan on the damaged logistics network.

[0075] To facilitate understanding of the present invention, the following description further illustrates the method for restoring a damaged logistics network provided by the present invention, based on the principle of the method for restoring a damaged logistics network and in conjunction with the process of restoring a damaged logistics network in the embodiments.

[0076] For details, please refer to Figure 2 The method for restoring the damaged logistics network includes:

[0077] S1. Collect the cargo flow of the logistics network stations and construct the logistics network circulation model based on the cargo flow of the stations.

[0078] Let G = (V, E, F) define the logistics network, where V = {v1, v2, v3, ..., v i Let} be the set of sites, and i be the number of sites; E = (r xy ) i×i Let e ​​be the set of edges of the network, and each edge e xy ∈E represents the path between station x and station y, d xy Representing edge e xy The distance between them, F = f xy Representing edge exy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the network are collectively referred to as objects n in network G, where n∈N, and N=V∪E is the set of all objects; when a local network is damaged, the locally damaged express delivery network is defined as O=(V o E o ), where the site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths.

[0079] In the case of partial damage to the express delivery network, obtain the transportation path of packages in the local network. The transportation path exists when edge e = (u,v) ∈ E and e ≠ 0. e If the value is greater than 0, then the current path has transport volume.

[0080] Based on the cargo flow of a station and the preset matching rules between cargo flow and status, the station status is set to normal, overloaded, or failed. Based on the cargo flow between stations, the corresponding edges are set to available or unavailable. The station status and path status are aggregated to generate a set S. The status of stations and edges in a normal state is defined as 0, and other states are represented by s∈S. If the function of a station or edge is completely lost, it is represented by q, i.e.

[0081] S2. Obtain the first model data of the circulation model when it is undamaged, and the second model data after it is damaged, and obtain the local loss matrix based on the difference between the first model data and the second model data.

[0082] Specifically, the local network consisting of sites with abnormal status and associated paths (edges) with zero traffic is output as a local loss matrix.

[0083] S3. Based on the recovery cost of the candidate recovery object, the cargo stagnation cost, and the connectivity loss cost, construct a cost objective function.

[0084] Establish the cost objective function:

[0085]

[0086]

[0087] Where n represents the object to be restored, which is a site or path; δ n,j,t This indicates that if a recovery operation j is performed on object n during time period t, the value is 1; otherwise, it is 0. n,j I(t|x) represents the recovery cost incurred by object n when using recovery operation j;e,t X(t) represents the cost incurred by the package after the extended time period t; X(t) represents the connectivity loss cost caused by the local loss network at time period t. Π(t) represents the link traffic of edge e within time period t; Π(t) represents the total cost within time period t; f od (P) represents the flow rate from point o to point d on path P; x e,t This represents the connection flow on edge e during time period t; j∈J(n|s) indicates that object n adopted recovery type j in state s; Z represents the minimum cost value;

[0088] The constraints for the cost objective function include:

[0089] The model can only select one recovery method for each object within a certain period of time, and executes it within a time interval;

[0090] The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value;

[0091] The flow constraints between all node pairs must meet this requirement;

[0092] This represents a nonnegativity constraint, used to ensure that the solution of the program is meaningful.

[0093] S4. Based on the aforementioned circulation model and local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm algorithm to obtain the minimum value of the cost objective function.

[0094] Methods for solving intermediate parameters using continuous averaging algorithms, such as Figure 3 As shown, it includes:

[0095] Initialize the parameters of the flow model, the number of iterations, the indirect cost weighting factor θ, and the values ​​of the correction parameters α and β. Set the initial package delay time, initial flow, and initial auxiliary flow. Generate an initial flow scheme and randomly generate a set of recovery sequences. Calculate the auxiliary flow based on the recovery sequences and update the values. Calculate the optimal descent direction based on the auxiliary flow and construct a new flow scheme. Then calculate and update the current path flow, update the edge capacity t_k value, and determine if the edge capacity exceeds the limit. If it does not exceed the limit, calculate and update the current edge capacity t_k value. If it exceeds the limit, calculate the extension time using the cost-flow-time relationship and determine if the termination adjustment has been reached (whether the set number of iterations has been reached). If termination occurs, return the cargo travel time, road flow, vehicle transportation time, vehicle travel distance, and package loss amount. If termination does not occur, regenerate the initial flow scheme and randomly generate a set of recovery sequences.

[0096] The cost-flow-time relationship is shown in the following formula:

[0097]

[0098] Where α and β are correction parameters in the formula, usually set to α = 0.15 and β = 4.

[0099] Specifically, the following steps are included:

[0100] Step 1: Input the express delivery network G, the local loss network OD_G, and the local loss matrix OD_matrix;

[0101] Step 2: Initialize the relevant parameters in the algorithm and set the initial parameters of the edges in the network;

[0102] Step 3: Generate the initial flow scheme F0 and randomly generate a set of recovery sequences D. n ;

[0103] Step 4: Calculate the auxiliary flow help_flow using the mapping function, and construct a minimum cost tree using the current recovery sequence;

[0104] Step 5: Calculate the optimal descent direction based on the mapping function results, and load the locally damaged network matrix into the minimum cost tree to obtain a new flow scheme;

[0105] Step 6: Calculate and update the flow values ​​on each path in the current recovery sequence;

[0106] Step 7: Determine if the termination condition has been met. If it has, proceed to Step 8; otherwise, proceed to Step 3.

[0107] Step 8: End and return parameters.

[0108] Figure 4 The solution process based on the simulated annealing particle swarm optimization algorithm is described. In this solution process, the parameters obtained from the previous stage are used as the basis for the solution in this stage, including the following steps:

[0109] Step 1: Input the express delivery network G, the local loss network OD_G, the local loss matrix OD_matrix, and initialize the loss sequence D;

[0110] Step 2: Initialize the relevant parameters in the algorithm;

[0111] Step 3: Treat each object in the initial loss sequence D as a particle, and randomly initialize the position and velocity of the particles;

[0112] Step 4: Calculate the fitness of each particle and record its current position and global optimal position;

[0113] Step 5: Calculate the initial temperature of the simulated annealing algorithm based on the global optimal fitness value;

[0114] Step 6: Run the MSA algorithm to calculate indirect cost-related parameters;

[0115] Step 7: Run the recovery model to calculate the objective function as the initial solution for the current temperature, and calculate the applicability value of the initial solution at the current temperature;

[0116] Step 8: Use the proportional selection method to update the global optimum from the individual optima and change the state to generate a new loss sequence;

[0117] Step 9: Update the velocity and position of each particle, and update the optimal position for individuals and the population;

[0118] Step 10: Determine whether to continue searching for a solution at the given temperature T. If yes, proceed to Step 5; otherwise, proceed to Step 11.

[0119] Step 11: Determine if the termination condition is met. If it is met, the algorithm ends; otherwise, proceed to Step 8.

[0120] S5. Integrate the candidate recovery objects and recovery methods corresponding to the minimum value and output them as a recovery plan, and execute the recovery plan on the damaged logistics network.

[0121] The minimum cost value is output as a recovery plan by combining the candidate recovery objects and recovery methods. This recovery plan defines the recovery objects, the recovery methods for those objects, and the recovery order of the objects. Applying this recovery plan to the locally damaged network minimizes the cost of the damage.

[0122] In some embodiments, the system 500 for restoring a damaged logistics network may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the system 500 for restoring a damaged logistics network may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality to restore the damaged logistics network.

[0123] In this embodiment, the system 500 for restoring the damaged logistics network can be divided into multiple functional modules based on its functions, such as... Figure 5 As shown. The functional modules may include: a model building module 510, a loss acquisition module 520, a function construction module 530, a function solving module 540, and a solution generation module 550. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and are stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0124] The model building module 510 is used to collect the cargo flow of the logistics network stations and build the circulation model of the logistics network based on the cargo flow of the stations.

[0125] The loss acquisition module 520 is used to acquire the first model data of the circulation model when it is not damaged, the second model data after it is damaged, and to obtain a local loss matrix based on the difference between the first model data and the second model data.

[0126] Function construction module 530 is used to construct a cost objective function based on the recovery cost of the candidate recovery object, the cargo stagnation cost, and the connectivity loss cost;

[0127] The function solving module 540 is used to solve the cost objective function based on the circulation model and the local loss matrix, using the continuous averaging algorithm and the simulated annealing particle swarm algorithm, to obtain the minimum value of the cost objective function;

[0128] The solution generation module 550 is used to integrate the candidate recovery objects and recovery methods corresponding to the minimum value and output them as a recovery solution, and execute the recovery solution on the damaged logistics network.

[0129] Optionally, as an embodiment of the present invention, the model building module specifically performs the following:

[0130] Let G = (V, E, F) define the logistics network, where V = {v1, v2, v3, ..., v i} represents the set of sites, where i is the number of sites; E = (e xy ) i×i Let e ​​be the set of edges of the network, and each edge e xy∈E represents the path between station x and station y, d xy Representing edge e xy The distance between them, F = f xy Representing edge e xy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the network are collectively referred to as objects n in network G, where n∈N, and N=V∪E is the set of all objects; when a local network is damaged, the locally damaged express delivery network is defined as O=(V o E o ), where the site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths.

[0131] In the case of partial damage to the express delivery network, obtain the transportation path of packages in the local network. A transportation path exists when edge e = (u,v) ∈ E and e ≠ 0. When f e If the value is greater than 0, then the current path has transport volume.

[0132] Based on the cargo flow of a station and the preset matching rules between cargo flow and status, the station status is set to normal, overloaded, or failed. Based on the cargo flow between stations, the corresponding edges are set to available or unavailable. The station status and path status are aggregated to generate a set S. The status of stations and edges in a normal state is defined as 0, and other states are represented by s∈S. If the function of a station or edge is completely lost, it is represented by q, i.e.

[0133] Optionally, as an embodiment of the present invention, the model building module specifically performs the following:

[0134] Establish the cost objective function:

[0135]

[0136]

[0137] Where n represents the object to be restored, which is a site or path; δ n,j,t This indicates that if a recovery operation j is performed on object n during time period t, the value is 1; otherwise, it is 0. n,j I(t|x) represents the recovery cost incurred by object n when using recovery operation j; e,t X(t) represents the cost incurred by the package after the extended time period t; X(t) represents the connectivity loss cost caused by the local loss network at time period t. Π(t) represents the link traffic of edge e within time period t; Π(t) represents the total cost within time period t; f od (P) represents the flow rate from point o to point d on path P; x e,t This represents the connection flow on edge e during time period t; j∈J(n|s) indicates that object n adopted recovery type j in state s; Z represents the minimum cost value;

[0138] The constraints for the cost objective function include:

[0139] The model can only select one recovery method for each object within a certain period of time, and executes it within a time interval;

[0140] The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value;

[0141] The flow constraints between all node pairs must meet this requirement;

[0142] This represents a nonnegativity constraint, used to ensure that the solution of the program is meaningful.

[0143] Optionally, as an embodiment of the present invention, based on the circulation model and the local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm optimization algorithm to obtain the minimum value of the cost objective function, including:

[0144] The indirect data corresponding to the recovery object is calculated using a continuous averaging algorithm. The indirect data includes return cargo travel time, road traffic flow, vehicle transportation time, vehicle travel distance, and cargo loss. The recovery scheme includes the recovery object and the recovery order of the recovery objects.

[0145] Based on the indirect data, the recovery cost corresponding to the recovery scheme is calculated. The recovery scheme is set as a particle of the simulated annealing particle swarm algorithm. The simulated annealing particle swarm algorithm is used to iteratively select the best particle with the lowest recovery cost.

[0146] Figure 6 This is a schematic diagram of the structure of a terminal 600 provided in an embodiment of the present invention. The terminal 600 can be used to execute the method for restoring a damaged logistics network provided in the embodiment of the present invention.

[0147] The terminal 600 may include a processor 610, a memory 620, and a communication module 630. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figures does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0148] The memory 620 can be used to store the execution instructions of the processor 610. The memory 620 can be implemented using any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 620 are executed by the processor 610, the terminal 600 is able to perform some or all of the steps in the above method embodiments.

[0149] The processor 610 serves as the control center of the storage terminal, connecting various parts of the electronic terminal via various interfaces and lines. It executes software programs and / or modules stored in the memory 620, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 610 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0150] The communication module 630 is used to establish a communication channel, enabling the storage terminal to communicate with other terminals. It receives user data from other terminals or sends user data to other terminals.

[0151] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps provided in the embodiments of the present invention. The storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0152] Therefore, this invention proposes a relatively general recovery model for express delivery networks experiencing localized losses. It establishes a local network recovery model that minimizes the direct and indirect costs of the recovery plan, and solves this model using simulated annealing particle swarm optimization. This invention considers the services, costs, priority available resources, and possible improvements to existing resources required for express delivery network recovery. It provides a detailed design for locally damaged express delivery networks, proposes an optimal recovery plan model for such networks, and designs a solution based on simulated annealing particle swarm optimization. Express delivery companies can use this model to obtain information such as expenditures and investments during recovery operations, weigh the relationship between recovery budgets and the quality of the resulting recovery plans, and monitor and manage the reliability and resilience of critical infrastructure within the network to disruptive events. The technical effects achieved in this embodiment are described above and will not be repeated here.

[0153] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code. It includes several instructions to cause a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0154] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0155] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0156] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0158] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the invention should also be covered within the protection scope of the invention. Therefore, the protection scope of the invention should be determined by the scope of the claims.

Claims

1. A method for restoring a damaged logistics network, characterized in that, include: Collect cargo flow data at stations in the logistics network and construct a circulation model of the logistics network based on the cargo flow data at each station; Obtain the first model data of the circulation model when it is undamaged, and the second model data after it is damaged, and obtain the local loss matrix based on the difference between the first model data and the second model data; Based on the recovery cost of the candidate recovery objects, the cost of cargo stagnation, and the cost of connectivity loss, a cost objective function is constructed. Based on the aforementioned circulation model and local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm algorithm to obtain the minimum value of the cost objective function; The candidate recovery objects and recovery methods corresponding to the minimum value are integrated and output as a recovery plan, and the recovery plan is executed on the damaged logistics network. Based on the recovery costs of the candidate recovery objects, the costs of cargo stagnation, and the costs of connectivity loss, a cost objective function is constructed, including: Establish the cost objective function: Where n represents the object to be restored, which is a site or path; This indicates that if a recovery operation j is performed on object n during time period t, then... The value is 1 if it is not 0 otherwise; This represents the recovery cost incurred by object n when using recovery operation j; This represents the cost incurred by the package after the extended time period t. This represents the cost of connectivity loss caused by a localized loss logistics network at time t. This represents the link traffic of edge e within time period t; This represents the total cost within time period t; This represents the flow rate from point o to point d on path P; This represents the connection traffic on edge e during time period t; This indicates that object n adopted recovery type j in state s; Z represents the minimum cost value; The constraints for the cost objective function include: The model can only select one recovery method for each object within a certain period of time and executes it within a time interval. The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value; The flow constraints between all node pairs must meet this requirement; , represents a nonnegativity constraint, used to ensure that the solution of the program is meaningful, and the set of sites is . .

2. The method according to claim 1, which involves collecting cargo flow data from stations in a logistics network and constructing a logistics network circulation model based on the cargo flow data, includes: Used to define the logistics network, in which Let i be the set of sites, and i be the number of sites. For the edge set of the logistics network, each edge F represents the path between station x and station y, where F = f xy Representing edge e xy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the logistics network are collectively referred to as objects n in the logistics network G. ,in It is a collection of all objects; When a part of the logistics network is damaged, the locally damaged logistics network is defined as follows: The site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths. In the event of partial damage to the logistics network, obtain the transportation routes of packages within that localized logistics network, when the edge and The transportation route exists at that time. If there is traffic on this path, then the current path ; Based on the cargo flow of the station and the preset cargo flow and status matching rules, the station status is set to normal, overloaded or failed; based on the cargo flow between stations, the corresponding edges are set to available or unavailable, and the station status and edge status are summarized to generate a set S. Define the state of stations and edges in a normal state as 0, and use other states as... If the functionality of a certain station or edge is completely lost, then g is used to represent this, i.e. .

3. The method according to claim 1, characterized in that, Based on the aforementioned circulation model and local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm optimization algorithm to obtain its minimum value, including: The indirect data corresponding to the recovery object is calculated using a continuous averaging algorithm. The indirect data includes return cargo travel time, road traffic flow, vehicle transportation time, vehicle travel distance, and cargo loss. The recovery scheme includes the recovery object and the recovery order of the recovery objects. Based on the indirect data, the recovery cost corresponding to the recovery scheme is calculated. The recovery scheme is set as a particle of the simulated annealing particle swarm algorithm. The simulated annealing particle swarm algorithm is used to iteratively select the best particle with the lowest recovery cost.

4. A system for restoring a damaged logistics network, characterized in that, include: The model building module is used to collect the cargo flow of the logistics network stations and build the circulation model of the logistics network based on the cargo flow of the stations. The loss acquisition module is used to acquire the first model data of the circulation model when it is undamaged, the second model data after it is damaged, and to obtain the local loss matrix based on the difference between the first model data and the second model data. The function building module is used to construct a cost objective function based on the recovery cost of the candidate recovery object, the cargo stagnation cost, and the connectivity loss cost; The function solving module is used to solve the cost objective function based on the circulation model and the local loss matrix, using the continuous averaging algorithm and the simulated annealing particle swarm algorithm, to obtain the minimum value of the cost objective function; The solution generation module is used to integrate the candidate recovery objects and recovery methods corresponding to the minimum value and output them as a recovery solution, and execute the recovery solution on the damaged logistics network. The model building module is executed as follows: Establish the cost objective function: Where n represents the object to be restored, which is a site or path; This indicates that if a recovery operation j is performed on object n during time period t, then... The value is 1 if it is not 0 otherwise; This represents the recovery cost incurred by object n when using recovery operation j; This represents the cost incurred by the package after the extended time period t. This represents the cost of connectivity loss caused by a localized loss logistics network at time t. This represents the link traffic of edge e within time period t; This represents the total cost within time period t; This represents the flow rate from point o to point d on path P; This represents the connection traffic on edge e during time period t; This indicates that object n adopted recovery type j in state s; Z represents the minimum cost value; The constraints for the cost objective function include: The model can only select one recovery method for each object within a certain period of time and executes it within a time interval. The constraint on direct costs is that the cost incurred by the model in selecting recovery measures cannot exceed the currently given total cost value; The flow constraints between all node pairs must meet this requirement; , represents a non-negativity constraint, used to ensure that the solution of the program is meaningful, and the set of sites is . .

5. The system according to claim 4, characterized in that, The model building module is executed as follows: Used to define the logistics network, in which Let i be the set of sites, and i be the number of sites. For the edge set of the logistics network, each edge F represents the path between station x and station y, where F = f xy Representing edge e xy The capacity here refers to the upper limit of the number of packages an edge can hold within a specific time interval; the stations and edges in the logistics network are collectively referred to as objects n in the logistics network G. ,in It is a collection of all objects; When a part of the logistics network is damaged, the locally damaged logistics network is defined as follows: The site set is An edge between stations exists if and only if the amount of data transmitted between them is greater than 0. The set of paths for a package flow is denoted by P, which refers to the set of all non-empty paths. In the event of partial damage to the logistics network, obtain the transportation routes of packages within that localized logistics network, when the edge and The transportation route exists at that time. If there is traffic on this path, then the current path... ; Based on the cargo flow of the station and the preset cargo flow and status matching rules, the station status is set to normal, overloaded or failed; based on the cargo flow between stations, the corresponding edges are set to available or unavailable, and the station status and edge status are summarized to generate a set S. Define the state of stations and edges in a normal state as 0, and use other states as... If the functionality of a certain station or edge is completely lost, then g is used to represent this, i.e. .

6. The system according to claim 4, characterized in that, Based on the aforementioned circulation model and local loss matrix, the cost objective function is solved using the continuous averaging algorithm and the simulated annealing particle swarm optimization algorithm to obtain its minimum value, including: The indirect data corresponding to the recovery object is calculated using a continuous averaging algorithm. The indirect data includes return cargo travel time, road traffic flow, vehicle transportation time, vehicle travel distance, and cargo loss. The recovery scheme includes the recovery object and the recovery order of the recovery objects. Based on the indirect data, the recovery cost corresponding to the recovery scheme is calculated. The recovery scheme is set as a particle of the simulated annealing particle swarm algorithm. The simulated annealing particle swarm algorithm is used to iteratively select the best particle with the lowest recovery cost.

7. A terminal, characterized in that, include: Memory, used to store programs for restoring the damaged logistics network; The processor, when executing the program for restoring the damaged logistics network, implements the steps of the method for restoring the damaged logistics network as described in any one of claims 1-3.

8. A computer-readable storage medium storing a computer program, characterized in that, The readable storage medium stores a program for restoring a damaged logistics network, which, when executed by a processor, implements the steps of the method for restoring a damaged logistics network as described in any one of claims 1-3.

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