Port transportation network risk evaluation method and system based on topology simulation

Abstract

The invention discloses a port transportation network risk evaluation method and system based on topology simulation. The method comprises the following steps: collecting historical data of dangerous cargo transportation in a dangerous cargo concentration area of a port; constructing a dynamic port transportation network model based on the transportation route, and performing topology analysis to construct a vulnerability function; fusing the feature data of the historical data to obtain a fused feature, and obtaining an accident rate based on the accident record; performing topology simulation on the port transportation network model based on the fusion features and the accident rate to obtain a risk function, and obtaining risk evaluation data based on the risk function and the vulnerability function; and constructing a risk evaluation model based on the risk evaluation data, optimizing the risk evaluation model through a saltat optimization algorithm, and inputting the to-be-evaluated data into the risk evaluation model to obtain an evaluation result. The method not only can improve the precision of port transportation network risk evaluation, but also has good interpretability, and can be directly applied to a port transportation network risk evaluation system.

Description

Technical Field

[0001] The present invention relates to the technical field of port transportation network risk assessment, and in particular to a port transportation network risk assessment method and system based on topology simulation. Background Art

[0002] With the rapid development of global trade, ports, as important logistics hubs, undertake an increasing task of transporting dangerous goods. These dangerous goods include flammable, explosive, toxic and corrosive chemicals, etc., and their safety management during transportation is crucial. However, due to the complexity and dynamic changes of port transportation networks, traditional risk assessment methods have been difficult to meet current needs. Existing evaluation methods often rely on static data and empirical judgments, lack the ability to analyze real-time data and adapt to dynamic changes in the network. Therefore, there is an urgent need for a method that can evaluate the risks of port transportation networks in real time and dynamically to improve the safety and efficiency of port transportation. It is based on this background that the present invention proposes a port transportation network risk assessment method and system based on topological simulation, aiming to achieve accurate assessment of port transportation network risks through advanced data analysis technology and network topology theory. Summary of the invention

[0003] The purpose of the present invention is to provide a port transportation network risk assessment method and system based on topology simulation.

[0004] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0005] The present invention comprises the following steps:

[0006] Collect historical data on dangerous goods transportation in the port's dangerous goods concentration area, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records;

[0007] Based on the transportation routes, a dynamic port transportation network model is constructed, and a topological analysis is performed to construct a vulnerability function;

[0008] Fusing the feature data of the historical data to obtain fusion features, and obtaining the accident occurrence rate based on the accident records;

[0009] Performing topological simulation on the port transportation network model based on the fusion characteristics and the accident incidence rate to obtain a risk function, and obtaining risk assessment data based on the risk function and the vulnerability function;

[0010] A risk assessment model is constructed based on the risk assessment data, the risk assessment model is optimized by using a sandcat optimization algorithm, and the data to be evaluated is input into the risk assessment model to obtain an evaluation result.

[0011] Further, the method of collecting historical data on dangerous goods transportation in the dangerous goods concentration area of ​​the port, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records, includes:

[0012] Historical data is obtained through port operation records, maritime department reports and safety supervision archives. The transportation routes include the geographical location, transportation connections and transportation processes of equipment and facilities for the transportation of dangerous goods. The operation data includes the cargo loading and unloading time, equipment usage time, capacity and processing speed of the equipment and facilities, as well as the weather data, transportation time, congestion and transportation capacity of the transportation connections.

[0013] Furthermore, a method for constructing a dynamic port transportation network model based on the transportation route and performing topological analysis to construct a vulnerability function includes:

[0014] The nodes of the dynamic port transportation network model are equipment and facilities. The node attributes include the capacity and processing speed of the nodes. The edges represent the transportation connections between nodes. The attributes of the edges include transportation time and transportation capacity. The nodes, edges and node attributes of the dynamic port transportation network model are dynamically updated as the transportation routes change. The direction of the edges is the same as the flow direction of dangerous goods.

[0015] The node vulnerability function is obtained by analyzing the node vulnerability through the node clustering coefficient, betweenness, degree, degree distribution and node weight:

[0016]

[0017]

[0018] Among them, V i,cr is the vulnerability of node i, δ1, δ2, δ3, δ4 and δ5 are the influence weights of clustering coefficient, betweenness, degree, degree distribution and node weight respectively, C i , B.C. i,v , k i , P(k i ) and W i are the clustering coefficient, betweenness, degree, degree distribution and node weight of node i respectively. and are the influence weights of node capacity and processing speed, respectively, i and S i are the capacity and processing speed of node i, C max and S max are the maximum capacity and maximum processing speed among all nodes respectively;

[0019] The edge vulnerability function is obtained by performing edge vulnerability analysis based on edge attributes:

[0020]

[0021] Among them, V j,cr is the vulnerability of edge j, T , Υ C and i are the impact weights of transportation time, transportation capacity and neighboring node vulnerability, T j , C j 、V j,n1,cr and V j,n2,cr They are the transportation time, transportation capacity, vulnerability of neighboring node 1, and vulnerability of neighboring node 2 of edge j respectively.

[0022] Furthermore, the method of fusing the feature data of the historical data to obtain fused features includes:

[0023] The types of dangerous goods are converted into numerical vectors, and the transport frequency, transport volume and operation data are normalized and preprocessed.

[0024] The types of dangerous goods, transportation frequency, transportation volume and operation data are arranged in chronological order to form time series data that can be processed by signals, and the time series data are decomposed:

[0025]

[0026] in is the Dirac function, u k and ω k are the kth signal component after decomposition and the corresponding center frequency, ω k and u k are the center frequency and signal component of the kth component after decomposition, K is the number of components after decomposition, is the partial derivative of signal f with respect to time step t, * represents convolution operation, π is the ratio of pi, e is the base of natural logarithm, t is the current time step, j is the imaginary unit, st represents the constraint condition, f is the signal to be decomposed, is the time series data,

[0027] By iteratively optimizing the decomposition algorithm, the components and center frequencies are continuously adjusted to minimize the augmented Lagrangian function, which is:

[0028]

[0029] in, is the quadratic penalty factor, λ is the Lagrange multiplier, λ(t) is the Lagrange multiplier of the current time step,

[0030] The alternating direction multiplier method is introduced and combined with Fourier isometry transform to find the solution of augmented Lagrangian function until and are u of the nth and n+1th iterations respectively k , update u k ,ω k and λ, and obtain the optimal solution of the signal component;

[0031] The BiLSTM algorithm is used to extract the temporal features of the signal components, and the weighted temporal features are obtained through the cross-attention mechanism. The weighted temporal features are concatenated to obtain the fusion features.

[0032] Furthermore, the method for obtaining the accident occurrence rate based on the accident record includes:

[0033] The annual historical accident rates of nodes and edges are obtained based on accident records, and an accident prediction model is established using LSTM combined with the SVR algorithm:

[0034] Arrange the transport frequency, transport volume, equipment usage time and historical accident rate of the nodes in chronological order to obtain the input sequence. The objective function of LSTM is:

[0035]

[0036] Where Θ represents all parameters of LSTM, N is the number of feature dimensions of the input sequence, T is the length of the input sequence, and y l,t is the eigenvalue of the lth feature dimension at time step t, is the jth parameter of LSTM, is the LSTM pair y l,t The predicted data, α is the coefficient of the L1 regularization term, ξ is a constant term used to ensure numerical stability, and ξ=1×10 -9 ;

[0037] The LSTM prediction data and the corresponding transportation frequency, transportation volume and equipment usage time form an input vector and are input into the SVR model to predict the LSTM residual. The decision function of SVR is:

[0038] f(x)=ω T τ(x)+b,b∈R

[0039] Among them, f(x) is the linear regression function, x is the input vector, including the transportation frequency, transportation volume and equipment usage time of the node, τ(x) is the mapping function, ω T is the transpose of ω, ω and b are the weight vector and bias to be determined, R represents a real number, slack variables and penalty factors are added to prevent overfitting, and the Lagrangian objective function of SVR is:

[0040]

[0041] in, is the regularization term, ξ and are the slack variables for the upper and lower bounds, respectively. ξ l and x l The corresponding upper and lower bounds of the slack variables, x l is the lth input vector, C is the penalty factor, 0.1≤C≤100, M is the number of input vectors, L is the Lagrangian objective function, and the Lagrangian multiplier μ l , α l , μ l and is the slack multiplier associated with the slack variable, α l and x l The corresponding Lagrange multipliers for the upper and lower boundaries, f(x l ) is based on x l The obtained l The predicted value, r l For x l The corresponding target value is the residual, ε is the tolerance error, 0.01≤ε≤0.2, and the minimum value of the Lagrangian function is solved to obtain the optimal optimization multiplier. The weight vector and bias are obtained according to the optimal optimization multiplier:

[0042]

[0043] Where S u and S d denotes the set of support vectors that violate the upper and lower boundaries, respectively, |S u | and |S d |S u and S d The number of support vectors in r s is the target value corresponding to the support vector s, x s is the support vector s;

[0044] Output of the accident prediction model:

[0045]

[0046] in, is the accident rate predicted by the accident prediction model, is the accident rate predicted by LSTM, f(x sgl ) is the residual of the LSTM prediction of the accident rate predicted by SVR, x sgl for The corresponding input vector takes the historical accident rate and the accident rate of the current time step predicted by the accident prediction model as the accident occurrence rate.

[0047] Furthermore, a method for obtaining a risk function by topologically simulating the port transportation network model based on the fusion feature and the accident occurrence rate includes:

[0048] Get the weather risk index based on weather data:

[0049]

[0050] Among them, WRI is the weather risk index, and They are respectively the impact weights of rainfall, wind speed, temperature, humidity, fog visibility and storm index. The storm index is 0 and 1, indicating no storm and the presence of storm. and are respectively rainfall, wind speed, temperature, humidity, fog visibility and storm index, υ is E J The adjustment parameter, E J is the influencing factor of extreme weather events. When there is no extreme weather, E J =0, in case of mild extreme weather, 0 <E J <0.5, in moderate extreme weather, 0.5≤E J <1, in severe extreme weather, E = 1;

[0051] The category label of whether an accident occurred is used as the target data, and the fusion features, node attributes, edge attributes, node geographical location, accident rate and weather risk index are used as input data to perform topological simulation on the topological structure of the port transportation network model and train the port transportation network topological simulation model. The port transportation network topological simulation model consists of three parts: input layer, function layer and output layer:

[0052] Adaptive adjacency matrix for the input layer:

[0053]

[0054] Among them, W1 and W2 are weight coefficient matrices, ReLU(·) and softmax(·) are activation functions, and A (0) To initialize the adjacency matrix, an adjacency matrix is ​​established based on the correlation between the input data and the labels of the accidents in the accident records.

[0055] The adaptive graph convolution expression of the input layer is:

[0056]

[0057] Among them, Z is the graph matrix after adaptive update, X is the feature matrix of input data, is a diagonal matrix whose diagonal elements are The degree of each row in , W is the weight matrix used in the graph convolution operation;

[0058] The loss function of the functional layer is:

[0059]

[0060] Among them, y is the target vector, which is 0 or 1, P is the predicted probability of an accident, is the weight matrix of the function layer, for The element in the i-th row and j-th column, λ is the regularization coefficient,

[0061] Weight update of the function layer:

[0062]

[0063] in, and are the weight matrices before and after the update in the function layer, β is the learning rate, and X is the feature matrix of the fused features;

[0064] The output formula of the output layer is the risk function:

[0065]

[0066] Among them, P i and P j are the predicted probabilities of accidents occurring at node i and edge j, respectively. σ(·) represents the sigmoid activation function. and are the feature vectors of node i and edge j after hidden layer processing, b′ represents the bias term,

[0067] The predicted probability of an accident occurring is output as the predicted risk probability.

[0068] Furthermore, a method for obtaining risk assessment data based on the risk function and the vulnerability function includes:

[0069] The calculation formula for risk assessment data is:

[0070]

[0071] Among them, F PJ is the risk assessment data for the concentrated area of ​​dangerous goods in the port, δ6V i,cr ·P i and δ7V j,cr ·P j are the risk data of node i and edge j respectively, and are the number of nodes and edges, δ6 and δ7 are the normalization coefficients of the risk assessment data of nodes and edges, respectively. j,cr is the vulnerability of edge j, P i and P j are the predicted risk probabilities of node i and edge j respectively.

[0072] Furthermore, a method for constructing a risk assessment model based on the risk assessment data includes:

[0073] A data set was established using fusion features and risk assessment data, and the risk assessment data was predicted using fusion features. The data set was randomly sampled to form a training set and a test set with a data ratio of 4:1. The training set was used to train the risk assessment model through a neural network algorithm, and the test set was used to optimize the hyperparameters of the risk assessment model.

[0074] Furthermore, the method for optimizing the risk assessment model by using the sandcat optimization algorithm comprises:

[0075] Preset n sand cat populations to search for optimization in the hyperparameter search space of the risk assessment model, and the fitness value is the inverse of the objective function value of the risk assessment model.

[0076] Initialization of the Sand Cat optimization algorithm:

[0077]

[0078] where x ij and x ij+1 are the jth and j+1th dimensions of the i-th sand cat population, R j+1 is the j+1th chaotic parameter, and the chaotic parameter set is

[0079] The sand cat position update formula during the prey search phase:

[0080] Pos(t+1)=e z·l ·cos(2πl)·γ·(Pos bc (t)-rand(0,1)·Pos c (t))

[0081] Where e is the base of natural logarithm, π is the ratio of pi, γ represents the sensitivity range, l is a dynamic parameter, which is a random number between [-1, 1], and Pos bc (t) is the current best candidate position, Pos c (t) is the current position, z is the spiral search factor, rand(0,1) is a random number in (0,1),

[0082] Spiral Search Factor:

[0083]

[0084] Where k is the spiral cycle parameter, t is the current time step, and t max represents the maximum time step,

[0085] The sand cat position update formula in the local development stage is:

[0086]

[0087] Among them, Pos w (t) is the current worst position, Pos b (t) is the current global optimal position, β is the step size control parameter, K is a random number between [-1, 1], and f i 、f g and f w They represent the current individual, global best and worst fitness values ​​of the sand cat, respectively. ε is a constant.

[0088] When the maximum time step is reached, the hyperparameter combination corresponding to the global optimal position is output.

[0089] The port transportation network risk assessment system based on topology simulation is characterized by including:

[0090] The collection module collects historical data on dangerous goods transportation in the dangerous goods concentration area of ​​the port, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records;

[0091] A calculation module, constructing a dynamic port transportation network model based on the transportation route, performing topological analysis to construct a vulnerability function, fusing the characteristic data of the historical data to obtain a fusion feature, obtaining an accident rate based on the accident record, performing topological simulation on the port transportation network model based on the fusion feature and the accident rate to obtain a risk function, obtaining risk assessment data based on the risk function and the vulnerability function, and constructing a risk assessment model based on the risk assessment data;

[0092] An optimization module, which optimizes the risk assessment model by using a sandcat optimization algorithm;

[0093] The output module inputs the data to be evaluated into the risk evaluation model to obtain the evaluation result.

[0094] The beneficial effects of the present invention are:

[0095] The present invention realizes accurate evaluation of port transportation network risks by combining feature fusion, topology simulation technology and sandcat optimization algorithm. The method can dynamically analyze the vulnerability and risk in the transportation network, providing port managers with a scientific and quantitative risk assessment tool. By optimizing the risk assessment model, the present invention not only improves the accuracy of risk prediction, but also enhances the ability of the port transportation network to respond to emergencies, thereby effectively reducing the probability of accidents and ensuring the safety of port operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 It is a flowchart of the steps of the port transportation network risk assessment method and system based on topology simulation of the present invention;

[0097] Figure 2 This is a topological diagram of a loading and unloading ship of the present invention;

[0098] Figure 3 This is a topological diagram of the transfer of goods according to the present invention;

[0099] Figure 4 This is the topological structure diagram of the inverted tank of the present invention. DETAILED DESCRIPTION

[0100] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0101] The port transportation network risk assessment method and system based on topology simulation of the present invention include the following steps:

[0102] like Figure 1 As shown, in this embodiment, the following steps are included:

[0103] Collect historical data on dangerous goods transportation in the port's dangerous goods concentration area, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records;

[0104] In the actual assessment, the type of dangerous goods is crude oil, and the transportation route is: unloading operation: A→B, loading operation: B→C→A, lightering operation: A→A, tank unloading operation: B→C→B, A is the berth where the ship is docked, including A1-A6, B is the storage area for oil tanks, including B1-B6, C is the pump room for transporting oil tanks, including C1-C4, and the geographical location is the longitude and latitude coordinates;

[0105] Based on the transportation routes, a dynamic port transportation network model is constructed, and a topological analysis is performed to construct a vulnerability function;

[0106] In the actual evaluation, the topological structure diagram of the loading and unloading ship of the port transportation network model is as follows: Figure 2As shown, the gray edges represent the loading process, the black edges represent the unloading process, and the transshipment topology diagram is Figure 3 , the topological structure diagram of the inverted tank is Figure 4 , where the black edge points from the pump room to the storage area, and the gray edge is a bidirectional edge, indicating that the storage area transports crude oil to the pump room, and the crude oil is transported to other storage areas through the pump room;

[0107] Fusing the feature data of the historical data to obtain fusion features, and obtaining the accident occurrence rate based on the accident records;

[0108] Performing topological simulation on the port transportation network model based on the fusion characteristics and the accident incidence rate to obtain a risk function, and obtaining risk assessment data based on the risk function and the vulnerability function;

[0109] A risk assessment model is constructed based on the risk assessment data, the risk assessment model is optimized by using a sandcat optimization algorithm, and the data to be evaluated is input into the risk assessment model to obtain an evaluation result.

[0110] In this embodiment, the method for collecting historical data of dangerous goods transportation in the dangerous goods concentration area of ​​the port, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records, includes:

[0111] Historical data is obtained through port operation records, maritime department reports and safety supervision archives. The transportation routes include the geographical location, transportation connections and transportation processes of equipment and facilities for the transportation of dangerous goods. The transportation processes include loading operations, unloading operations, transshipment operations and tank emptying operations. The operating data includes the cargo loading and unloading time of the equipment and facilities, the equipment usage time, capacity and processing speed, as well as the weather data, transportation time, congestion and transportation capacity of the transportation connections.

[0112] In this embodiment, a method for constructing a dynamic port transportation network model based on the transportation route and performing topological analysis to construct a vulnerability function includes:

[0113] The nodes of the dynamic port transportation network model are equipment and facilities. The node attributes include the capacity and processing speed of the nodes. The edges represent the transportation connections between nodes. The attributes of the edges include transportation time and transportation capacity. The nodes, edges and node attributes of the dynamic port transportation network model are dynamically updated as the transportation routes change. The direction of the edges is the same as the flow direction of dangerous goods.

[0114] The node vulnerability function is obtained by analyzing the node vulnerability through the node clustering coefficient, betweenness, degree, degree distribution and node weight:

[0115]

[0116] Among them, V i,cris the vulnerability of node i, δ1, v2, δ3, δ4 and δ5 are the influence weights of clustering coefficient, betweenness, degree, degree distribution and node weight respectively, C i , B.C. i,v , k i , P(k i ) and W i are the clustering coefficient, betweenness, degree, degree distribution and node weight of node i respectively. and are the influence weights of node capacity and processing speed, respectively, i and S i are the capacity and processing speed of node i, C max and S max are the maximum capacity and maximum processing speed among all nodes respectively;

[0117] The edge vulnerability function is obtained by performing edge vulnerability analysis based on edge attributes:

[0118]

[0119] Among them, V j,cr is the vulnerability of edge j, T , Υ C and i are the impact weights of transportation time, transportation capacity and neighboring node vulnerability, T j , C j 、V j,n1,cr and V j,n2,cr They are the transportation time, transportation capacity, vulnerability of neighboring node 1, and vulnerability of neighboring node 2 of edge j respectively.

[0120] In this embodiment, the method of fusing the feature data of the historical data to obtain a fused feature includes:

[0121] The types of dangerous goods are converted into numerical vectors, and the transport frequency, transport volume and operation data are normalized and preprocessed.

[0122] The types of dangerous goods, transportation frequency, transportation volume and operation data are arranged in chronological order to form time series data that can be processed by signals, and the time series data are decomposed:

[0123]

[0124] in is the Dirac function, u k and ω k are the kth signal component after decomposition and the corresponding center frequency, ω k and u kare the center frequency and signal component of the kth component after decomposition, K is the number of components after decomposition, is the partial derivative of signal f with respect to time step t, * represents convolution operation, π is the ratio of pi, e is the base of natural logarithm, t is the current time step, j is the imaginary unit, st represents the constraint condition, f is the signal to be decomposed, is the time series data,

[0125] By iteratively optimizing the decomposition algorithm, the components and center frequencies are continuously adjusted to minimize the augmented Lagrangian function, which is:

[0126]

[0127] in, is the quadratic penalty factor, λ is the Lagrange multiplier, λ(t) is the Lagrange multiplier of the current time step,

[0128] The alternating direction multiplier method is introduced and combined with Fourier isometry transform to find the solution of augmented Lagrangian function until and are u of the nth and n+1th iterations respectively k , update u k ,ω k and λ, and obtain the optimal solution of the signal component;

[0129] The BiLSTM algorithm is used to extract the temporal features of the signal components, and the weighted temporal features are obtained through the cross-attention mechanism. The weighted temporal features are concatenated to obtain the fusion features.

[0130] In this embodiment, the method for obtaining the accident occurrence rate based on the accident record includes:

[0131] The annual historical accident rates of nodes and edges are obtained based on accident records, and an accident prediction model is established using LSTM combined with the SVR algorithm:

[0132] Arrange the transport frequency, transport volume, equipment usage time and historical accident rate of the nodes in chronological order to obtain the input sequence. The objective function of LSTM is:

[0133]

[0134] Where Θ represents all parameters of LSTM, N is the number of feature dimensions of the input sequence, T is the length of the input sequence, and y l,t is the eigenvalue of the lth feature dimension at time step t, is the jth parameter of LSTM, is the LSTM pair y l,tThe predicted data, α is the coefficient of the L1 regularization term, ξ is a constant term used to ensure numerical stability, and ξ=1×10 -9 ;

[0135] The LSTM prediction data and the corresponding transportation frequency, transportation volume and equipment usage time form an input vector and are input into the SVR model to predict the LSTM residual. The decision function of SVR is:

[0136] f(x)=ω T τ(x)+b,b∈R

[0137] Among them, f(x) is the linear regression function, x is the input vector, including the transportation frequency, transportation volume and equipment usage time of the node, τ(x) is the mapping function, ω T is the transpose of ω, ω and b are the weight vector and bias to be determined, R represents a real number, slack variables and penalty factors are added to prevent overfitting, and the Lagrangian objective function of SVR is:

[0138]

[0139] in, is the regularization term, ξ and are the slack variables for the upper and lower bounds, respectively. ξ l and x l The corresponding upper and lower bounds of the slack variables, x l is the lth input vector, C is the penalty factor, 0.1≤C≤100, M is the number of input vectors, L is the Lagrangian objective function, and the Lagrangian multiplier μ l , α l , μ l and is the slack multiplier associated with the slack variable, α l and x l The corresponding Lagrange multipliers for the upper and lower boundaries, f(x l ) is based on x l The obtained l The predicted value, r l For x l The corresponding target value is the residual, ε is the tolerance error, 0.01≤ε≤0.2, and the minimum value of the Lagrangian function is solved to obtain the optimal optimization multiplier. The weight vector and bias are obtained according to the optimal optimization multiplier:

[0140]

[0141] Where Su and S d denotes the set of support vectors that violate the upper and lower boundaries, respectively, |S u | and |S d |S u and S d The number of support vectors in r s is the target value corresponding to the support vector s, x s is the support vector s;

[0142] Output of the accident prediction model:

[0143]

[0144] in, is the accident rate predicted by the accident prediction model, is the accident rate predicted by LSTM, f(x sgl ) is the residual of the LSTM prediction of the accident rate predicted by SVR, x sgl for The corresponding input vector takes the historical accident rate and the accident rate of the current time step predicted by the accident prediction model as the accident occurrence rate, and the accident occurrence rate is the inherent probability of an accident.

[0145] In this embodiment, the method of performing topological simulation on the port transportation network model based on the fusion feature and the accident occurrence rate to obtain a risk function includes:

[0146] Get the weather risk index based on weather data:

[0147]

[0148] Among them, WRI is the weather risk index, and They are respectively the impact weights of rainfall, wind speed, temperature, humidity, fog visibility and storm index. The storm index is 0 and 1, indicating no storm and the presence of storm. and are respectively rainfall, wind speed, temperature, humidity, fog visibility and storm index, υ is E J The adjustment parameter, E J is the influencing factor of extreme weather events. When there is no extreme weather, E J =0, in case of mild extreme weather, 0 <E J <0.5, in moderate extreme weather, 0.5≤E J <1, in severe extreme weather, E = 1;

[0149] The category label of whether an accident occurred is used as the target data, and the fusion features, node attributes, edge attributes, node geographical location, accident rate and weather risk index are used as input data to perform topological simulation on the topological structure of the port transportation network model and train the port transportation network topological simulation model. The port transportation network topological simulation model consists of three parts: input layer, function layer and output layer:

[0150] Adaptive adjacency matrix for the input layer:

[0151]

[0152] Among them, W1 and W2 are weight coefficient matrices, ReLU(·) and softmax(·) are activation functions, and A (0) To initialize the adjacency matrix, an adjacency matrix is ​​established based on the correlation between the input data and the labels of the accidents in the accident records.

[0153] The adaptive graph convolution expression of the input layer is:

[0154]

[0155] Among them, Z is the graph matrix after adaptive update, X is the feature matrix of input data, is a diagonal matrix whose diagonal elements are The degree of each row in , W is the weight matrix used in the graph convolution operation;

[0156] The loss function of the functional layer is:

[0157]

[0158] Among them, y is the target vector, which is 0 or 1, P is the predicted probability of an accident, is the weight matrix of the function layer, for The element in the i-th row and j-th column, λ is the regularization coefficient,

[0159] Weight update of the function layer:

[0160]

[0161] in, and are the weight matrices before and after the update in the function layer, β is the learning rate, and X is the feature matrix of the fused features;

[0162] The output formula of the output layer is the risk function:

[0163]

[0164] Among them, Pi and P j are the predicted probabilities of accidents occurring at node i and edge j, respectively. σ(·) represents the sigmoid activation function. and are the feature vectors of node i and edge j after hidden layer processing, b′ represents the bias term,

[0165] The predicted probability of an accident occurring is output as the predicted risk probability.

[0166] In this embodiment, the method for obtaining risk assessment data based on the risk function and the vulnerability function includes:

[0167] The calculation formula for risk assessment data is:

[0168]

[0169] Among them, F PJ is the risk assessment data for the concentrated area of ​​dangerous goods in the port, δ6V i,cr ·P i and δ7V j,cr ·P j are the risk data of node i and edge j respectively, and are the number of nodes and edges, δ6 and δ7 are the normalization coefficients of the risk assessment data of nodes and edges, respectively. j,cr is the vulnerability of edge j, P i and P j are the predicted risk probabilities of node i and edge j respectively.

[0170] In this embodiment, the method for constructing a risk assessment model based on the risk assessment data includes:

[0171] A data set was established using fusion features and risk assessment data, and the risk assessment data was predicted using fusion features. The data set was randomly sampled to form a training set and a test set with a data ratio of 4:1. The training set was used to train the risk assessment model through a neural network algorithm, and the test set was used to optimize the hyperparameters of the risk assessment model.

[0172] In this embodiment, the method for optimizing the risk assessment model by using the sandcat optimization algorithm includes:

[0173] Preset n sand cat populations to search for optimization in the hyperparameter search space of the risk assessment model, and the fitness value is the inverse of the objective function value of the risk assessment model.

[0174] Initialization of the Sand Cat optimization algorithm:

[0175]

[0176] where x ij and x ij+1 are the jth and j+1th dimensions of the i-th sand cat population, R j+1 is the j+1th chaotic parameter, and the chaotic parameter set is

[0177] The sand cat position update formula during the prey search phase:

[0178] Pos(t+1)=e z·l ·cos(2πl)·γ·(Pos bc (t)-rand(0,1)·Pos c (t))

[0179] Where e is the base of natural logarithm, π is the ratio of pi, γ represents the sensitivity range, l is a dynamic parameter, which is a random number between [-1, 1], and Pos bc (t) is the current best candidate position, Pos c (t) is the current position, z is the spiral search factor, rand(0,1) is a random number in (0,1),

[0180] Spiral Search Factor:

[0181]

[0182] Where k is the spiral cycle parameter, t is the current time step, and t max represents the maximum time step,

[0183] The sand cat position update formula in the local development stage is:

[0184]

[0185] Among them, Pos w (t) is the current worst position, Pos b (t) is the current global optimal position, β is the step size control parameter, K is a random number between [-1, 1], and f i 、f g and f w They represent the current individual, global best and worst fitness values ​​of the sand cat, respectively. ε is a constant.

[0186] When the maximum time step is reached, the hyperparameter combination corresponding to the global optimal position is output.

[0187] The port transportation network risk assessment system based on topology simulation is characterized by including:

[0188] The collection module collects historical data on dangerous goods transportation in the dangerous goods concentration area of ​​the port, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records;

[0189] A calculation module, constructing a dynamic port transportation network model based on the transportation route, performing topological analysis to construct a vulnerability function, fusing the characteristic data of the historical data to obtain a fusion feature, obtaining an accident rate based on the accident record, performing topological simulation on the port transportation network model based on the fusion feature and the accident rate to obtain a risk function, obtaining risk assessment data based on the risk function and the vulnerability function, and constructing a risk assessment model based on the risk assessment data;

[0190] An optimization module, which optimizes the risk assessment model by using a sandcat optimization algorithm;

[0191] The output module inputs the data to be evaluated into the risk evaluation model to obtain the evaluation result.

[0192] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A port transportation network risk assessment method based on topology simulation, characterized in that: The following steps are involved: Collect historical data on dangerous goods transportation in the port's dangerous goods concentration area, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records; Based on the transportation routes, a dynamic port transportation network model is constructed, and a topological analysis is performed to construct a vulnerability function; Fusing the feature data of the historical data to obtain fusion features, and obtaining the accident occurrence rate based on the accident records; Performing topological simulation on the port transportation network model based on the fusion characteristics and the accident occurrence rate to obtain a risk function, and obtaining risk assessment data based on the risk function and the vulnerability function; A risk assessment model is constructed based on the risk assessment data, the risk assessment model is optimized by using a sandcat optimization algorithm, and the data to be evaluated is input into the risk assessment model to obtain an evaluation result.

2. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: Methods for collecting historical data on dangerous goods transport in the port's dangerous goods concentration area, including dangerous goods types, transport frequency, transport volume, transport routes, operating data and accident records, including: Historical data is obtained through port operation records, maritime department reports and safety supervision archives. The transportation routes include the geographical location, transportation connections and transportation processes of equipment and facilities for the transportation of dangerous goods. The operation data includes the cargo loading and unloading time, equipment usage time, capacity and processing speed of the equipment and facilities, as well as the weather data, transportation time, congestion and transportation capacity of the transportation connections.

3. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method of constructing a dynamic port transportation network model based on the transportation route and performing topological analysis to construct a vulnerability function includes: The nodes of the dynamic port transportation network model are equipment and facilities. The node attributes include the capacity and processing speed of the nodes. The edges represent the transportation connections between nodes. The attributes of the edges include transportation time and transportation capacity. The nodes, edges and node attributes of the dynamic port transportation network model are dynamically updated as the transportation routes change. The direction of the edges is the same as the flow direction of dangerous goods. The vulnerability function of the node is obtained by analyzing the vulnerability of the node through the node attributes: Among them, V i,cr is the vulnerability of node i, δ1, δ2, δ3, δ4 and δ5 are the influence weights of clustering coefficient, betweenness, degree, degree distribution and node weight respectively, C i , B.C. i,v , k i , P(k i ) and W i are the clustering coefficient, betweenness, degree, degree distribution and node weight of node i respectively. and are the influence weights of node capacity and processing speed, respectively, i and S i are the capacity and processing speed of node i, C max and S max are the maximum capacity and maximum processing speed among all nodes respectively; The edge vulnerability function is obtained by performing edge vulnerability analysis based on edge attributes: Among them, V j,cr is the vulnerability of edge j, Υ T , Υ C and i are the impact weights of transportation time, transportation capacity and neighboring node vulnerability, T j , C j 、V j,n1,cr and V j,n2,cr They are the transportation time, transportation capacity, vulnerability of neighboring node 1, and vulnerability of neighboring node 2 of edge j respectively.

4. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method of fusing the feature data of the historical data to obtain a fused feature includes: The types of dangerous goods are converted into numerical vectors, and the transport frequency, transport volume and operation data are normalized and preprocessed. The types of dangerous goods, transportation frequency, transportation volume and operation data are arranged in chronological order to form time series data that can be processed by signals, and the time series data are decomposed: in is the Dirac function, u k and ω k are the kth signal component after decomposition and the corresponding center frequency, ω k and u k are the center frequency and signal component of the kth component after decomposition, K is the number of components after decomposition, is the partial derivative of signal f with respect to time step t, * represents convolution operation, π is the ratio of pi, e is the base of natural logarithm, t is the current time step, j is the imaginary unit, st represents the constraint condition, f is the signal to be decomposed, is the time series data, By iteratively optimizing the decomposition algorithm, the components and center frequencies are continuously adjusted to minimize the augmented Lagrangian function, which is: in, is the quadratic penalty factor, λ is the Lagrange multiplier, λ(t) is the Lagrange multiplier of the current time step, The alternating direction multiplier method is introduced and combined with Fourier isometry transform to find the solution of augmented Lagrangian function until and are u of the nth and n+1th iterations respectively k , update u k ,ω k and λ, and obtain the optimal solution of the signal component; The BiLSTM algorithm is used to extract the temporal features of the signal components, and the weighted temporal features are obtained through the cross-attention mechanism. The weighted temporal features are concatenated to obtain the fusion features.

5. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method for obtaining the accident occurrence rate based on the accident record comprises: The annual historical accident rates of nodes and edges are obtained based on accident records, and an accident prediction model is established using LSTM combined with the SVR algorithm: Arrange the transport frequency, transport volume, equipment usage time and historical accident rate of the nodes in chronological order to obtain the input sequence. The objective function of LSTM is: Where Θ represents all parameters of LSTM, N is the number of feature dimensions of the input sequence, and T is the length of the input sequence. For the The eigenvalue of the feature dimension at time step t, is the jth parameter of LSTM, For LSTM The predicted data, α is the coefficient of the L1 regularization term, ξ is a constant term used to ensure numerical stability, and ξ=1×10 -9 ; The LSTM prediction data and the corresponding transportation frequency, transportation volume and equipment usage time form an input vector and are input into the SVR model to predict the LSTM residual. The decision function of SVR is: f(x)=ω T τ(x)+b,b∈R Among them, f(x) is the linear regression function, x is the input vector, including the transportation frequency, transportation volume and equipment usage time of the node, τ(x) is the mapping function, ω T is the transpose of ω, ω and b are the weight vector and bias to be determined, R represents a real number, slack variables and penalty factors are added to prevent overfitting, and the Lagrangian objective function of SVR is: in, is the regularization term, ξ and are the slack variables for the upper and lower bounds, respectively. and They are The corresponding upper and lower bounds of the slack variables, For the input vectors, C is the penalty factor, 0.1≤C≤100, M is the number of input vectors, L is the Lagrangian objective function, and the Lagrangian multiplier and is the slack multiplier associated with the slack variable, and They are The corresponding Lagrange multipliers for the upper and lower boundaries are, Based on Obtained for The predicted value of for The corresponding target value is the residual, ε is the tolerance error, 0.01≤ε≤0.2, and the minimum value of the Lagrangian function is solved to obtain the optimal optimization multiplier. The weight vector and bias are obtained according to the optimal optimization multiplier: Where S u and S d denotes the set of support vectors that violate the upper and lower boundaries, respectively, |S u | and |S d |S u and S d The number of support vectors in r s is the target value corresponding to the support vector s, x s is the support vector s; Output of the accident prediction model: in, is the accident rate predicted by the accident prediction model, is the accident rate predicted by LSTM, f(x sgl ) is the residual of the LSTM prediction of the accident rate predicted by SVR, x sgl for The corresponding input vector takes the historical accident rate and the accident rate of the current time step predicted by the accident prediction model as the accident occurrence rate.

6. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method for obtaining a risk function by topologically simulating the port transportation network model based on the fusion feature and the accident occurrence rate includes: Get the weather risk index based on weather data: Among them, WRI is the weather risk index, and They are respectively the impact weights of rainfall, wind speed, temperature, humidity, fog visibility and storm index. The storm index is 0 and 1, indicating no storm and the presence of storm. and are respectively rainfall, wind speed, temperature, humidity, fog visibility and storm index, υ is E J The adjustment parameter, E J is the influencing factor of extreme weather events. When there is no extreme weather, E J =0, in case of mild extreme weather, 0 <E J <0.5, in moderate extreme weather, 0.5≤E J <1, in severe extreme weather, E = 1; The category label of whether an accident occurred is used as the target data, and the fusion features, node attributes, edge attributes, node geographical location, accident rate and weather risk index are used as input data to perform topological simulation on the topological structure of the port transportation network model and train the port transportation network topological simulation model. The port transportation network topological simulation model consists of three parts: input layer, function layer and output layer: Adaptive adjacency matrix for the input layer: Among them, W1 and W2 are weight coefficient matrices, ReLU(·) and softmax(·) are activation functions, and A (0) To initialize the adjacency matrix, an adjacency matrix is ​​established based on the correlation between the input data and the labels of the accidents in the accident records. The adaptive graph convolution expression of the input layer is: Among them, Z is the graph matrix after adaptive update, X is the feature matrix of input data, is the diagonal moment The elements on the diagonal are The degree of each row in , W is the weight matrix used in the graph convolution operation; The loss function of the functional layer is: Among them, y is the target vector, which is 0 or 1, P is the predicted probability of an accident, is the weight matrix of the function layer, for The element in the i-th row and j-th column, λ is the regularization coefficient, Weight update of the function layer: in, and are the weight matrices before and after the update in the function layer, β is the learning rate, and X is the feature matrix of the fused features; The output formula of the output layer is the risk function: Among them, P i and P j are the predicted probabilities of accidents occurring at node i and edge j, respectively. σ(·) represents the sigmoid activation function. and are the feature vectors of node i and edge j after hidden layer processing, b′ represents the bias term, The predicted probability of an accident occurring is output as the predicted risk probability.

7. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method for obtaining risk assessment data based on the risk function and the vulnerability function includes: The calculation formula for risk assessment data is: Among them, F PJ is the risk assessment data for the concentrated area of ​​dangerous goods in the port, δ6V i,cr ·P i and δ7V j,cr ·P j are the risk data of node i and edge j respectively, and are the number of nodes and edges, δ6 and δ7 are the normalization coefficients of the risk assessment data of nodes and edges, respectively. j,cr is the vulnerability of edge j, P i and P j are the predicted risk probabilities of node i and edge j respectively.

8. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method for constructing a risk assessment model based on the risk assessment data comprises: A data set was established using fusion features and risk assessment data, and the risk assessment data was predicted using fusion features. The data set was randomly sampled to form a training set and a test set with a data ratio of 4:

1. The training set was used to train the risk assessment model through a neural network algorithm, and the test set was used to optimize the hyperparameters of the risk assessment model.

9. The port transportation network risk assessment method based on topology simulation according to claim 1 is characterized in that: The method for optimizing the risk assessment model by using the sandcat optimization algorithm comprises: Preset n sand cat populations to search for optimization in the hyperparameter search space of the risk assessment model, and the fitness value is the inverse of the objective function value of the risk assessment model. Initialization of the Sand Cat optimization algorithm: where x ij and x ij+1 are the jth and j+1th dimensions of the i-th sand cat population, R j+1 is the j+1th chaotic parameter, and the chaotic parameter set is The sand cat position update formula during the prey search phase: Pos(t+1)=e z·l ·cos(2πl)·γ·(Pos bc (t)-rand(0,1)·Pos c (t)) Where e is the base of natural logarithm, π is the ratio of pi, γ represents the sensitivity range, l is a dynamic parameter, which is a random number between [-1, 1], and Pos bc (t) is the current best candidate position, Pos c (t) is the current position, z is the spiral search factor, rand(0,1) is a random number in (0,1), Spiral Search Factor: Where k is the spiral cycle parameter, t is the current time step, and t max represents the maximum time step, The sand cat position update formula in the local development stage is: Among them, Pos w (t) is the current worst position, Pos b (t) is the current global optimal position, β is the step size control parameter, K is a random number between [-1, 1], and f i 、f g and f w They represent the current individual, global best and worst fitness values ​​of the sand cat, respectively. ε is a constant. When the maximum time step is reached, the hyperparameter combination corresponding to the global optimal position is output.

10. A port transportation network risk assessment system based on topology simulation, used to execute the port transportation network risk assessment method based on topology simulation according to any one of claims 1 to 9, characterized in that: include: The collection module collects historical data on dangerous goods transportation in the dangerous goods concentration area of ​​the port, including dangerous goods types, transportation frequency, transportation volume, transportation routes, operation data and accident records; A calculation module, constructing a dynamic port transportation network model based on the transportation route, performing topological analysis to construct a vulnerability function, fusing the characteristic data of the historical data to obtain a fusion feature, obtaining an accident rate based on the accident record, performing topological simulation on the port transportation network model based on the fusion feature and the accident rate to obtain a risk function, obtaining risk assessment data based on the risk function and the vulnerability function, and constructing a risk assessment model based on the risk assessment data; An optimization module, which optimizes the risk assessment model by using a sandcat optimization algorithm; The output module inputs the data to be evaluated into the risk evaluation model to obtain the evaluation result.

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