Transport capacity risk determination method and device based on dynamic Bayesian network

Abstract

The invention relates to a transport capacity risk determination method and device based on a dynamic Bayesian network, and the method comprises the steps: determining a rail transit road network comprising at least one line, and each line comprises at least two stations and an operation interval between the adjacent stations; and determining a station risk attribute of each station in the target time interval and an interval risk attribute of each operation interval. And according to the rail transit road network, the risk attribute of each station and the risk attribute of each interval, constructing a dynamic Bayesian network, and according to joint distribution of the dynamic Bayesian network, establishing a Gaussian mixture model to obtain network parameters. And further solving the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval in the target time interval to obtain the road network transport capacity risk of the rail transit road network. According to the method, the interpretability of the transport capacity risk speculation process is improved by constructing the Bayesian network, and the accuracy of the transport capacity risk prediction result is improved by establishing the Gaussian mixture model.

Description

Technical Field

[0001] The present disclosure relates to the field of rail transit, and in particular to a method and device for determining transportation capacity risk based on a dynamic Bayesian network. Background Art

[0002] Rail transit plays an increasingly important role in modern urban transportation due to its advantages such as large capacity, good punctuality, high safety, environmental friendliness and low cost, and has become the backbone and important support of modern urban transportation. Although rail transit has higher safety than conventional road transportation, due to the large scale of the rail transit network, heavy transportation tasks and close coupling between lines, once a failure or safety accident occurs, it will have a great impact on urban transportation. Therefore, how to effectively evaluate and predict the overall risk of the rail transit network and then reduce this overall risk and its impact on the transportation capacity of the rail transit network has always been one of the key issues in the research field of rail transit. At present, the relevant technologies lack interpretability in predicting the risks of the rail transit network, and the accuracy of the prediction results is low. Summary of the invention

[0003] In view of this, the present disclosure proposes a method and device for determining capacity risk based on a dynamic Bayesian network, aiming to improve the interpretability of the prediction process and the accuracy of the prediction results.

[0004] According to a first aspect of the present disclosure, a method for determining transportation capacity risk based on a dynamic Bayesian network is provided, the method comprising:

[0005] Determine a rail transit network including at least one line, each of the lines including at least two stations, and an operating interval between every two stations;

[0006] Determining the station risk attribute of each of the stations in the rail transit network within the target time interval, and the interval risk attribute of each of the operating intervals;

[0007] Constructing a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes;

[0008] Establishing a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network;

[0009] Based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval, the dynamic Bayesian network is solved to obtain the network capacity risk of the rail transit network.

[0010] In a possible implementation, constructing a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes includes:

[0011] Determine a risk correspondence between each of the station risk attributes and the station capacity risk corresponding to each of the stations in the rail transit network;

[0012] Determine a risk correspondence between each of the interval risk attributes and the interval transport capacity risk corresponding to each of the operating intervals in the rail transit network;

[0013] Determine the risk correspondence between each of the station capacity risks, each of the interval capacity risks, and the line capacity risk of each of the lines in the rail transit network;

[0014] Determine the risk correspondence between each of the line capacity risks and the network capacity risk corresponding to the rail transit network;

[0015] The station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0016] In a possible implementation, the method further includes:

[0017] Determining the station single point risk of each of the stations and the interval single point risk of each of the operating intervals;

[0018] The dynamic Bayesian network for predicting the network capacity risk is constructed according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes, and further includes:

[0019] Determine the risk correspondence between each of the station single point risks and the station risk attributes, and the risk correspondence between each of the interval single point risks and the interval risk attributes;

[0020] The station risk attribute, the section risk attribute, the station capacity risk, the section capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is sequentially connected according to the risk correspondence to obtain a dynamic Bayesian network, including:

[0021] The station single point risk, the interval single point risk, the station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0022] In a possible implementation, establishing a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network includes:

[0023] Determine the station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk and network capacity risk of the rail transit network history;

[0024] The Gaussian mixture model is solved according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network.

[0025] In a possible implementation, solving the Gaussian mixture model according to the historical station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk, and network capacity risk to obtain node parameters of each network node in the dynamic Bayesian network includes:

[0026] Solving the Gaussian mixture model according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk through an expectation maximization algorithm to obtain layer parameters of each node layer in the dynamic Bayesian network;

[0027] The node parameters of the network nodes included in the node layer are calculated according to the layer parameters of each node layer.

[0028] In a possible implementation, the layer parameters include first layer parameters, second layer parameters and third layer parameters, and the node parameters include first node parameters, second node parameters and third node parameters;

[0029] The first node parameter is based on Calculated, where β l is the first node parameter, α l is the first layer parameter of the node layer where the network node is located, x S is the risk value of the underlying node corresponding to the network node, μ lS is the second node parameter of the underlying node corresponding to the network node, ∑ lSSis the autocovariance of the third node parameter of the underlying node corresponding to the network node, and M is the number of first-layer parameters of the node layer where the underlying node corresponding to the network node is located;

[0030] The second node parameter is calculated according to the formula Calculated, where μ lR|S is the conditional probability of the network node corresponding to the second node parameter, μ lR is the second node parameter corresponding to the network node, ∑ lRS is the covariance of the third node parameter of the network node and the third node parameter of the underlying node corresponding to the network node;

[0031] The third node parameter is based on the formula Calculated, where ∑ lR|S is the conditional probability of the network node corresponding to the third node parameter, ∑ lRR is the autocovariance of the third node parameter of the network node, ∑ lSR It is the covariance between the third node parameter of the underlying node corresponding to the network node and the third node parameter of the network node.

[0032] In a possible implementation, solving the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network includes:

[0033] Taking each of the station risk attribute values ​​and each of the interval risk attribute values ​​within the target time interval as input, solving the mean of the distribution value corresponding to each of the network nodes in the dynamic Bayesian network layer by layer;

[0034] The mean of the distribution values ​​corresponding to the top-level network nodes of the Bayesian network is used as the network capacity risk of the rail transit network.

[0035] According to a second aspect of the present disclosure, a transport capacity risk determination device based on a dynamic Bayesian network is provided, the device comprising:

[0036] A network determination module, used to determine a rail transit network including at least one line, each of which includes at least two stations, and an operating interval is between every two stations;

[0037] An attribute determination module, used to determine the station risk attribute of each of the stations in the rail transit network within a target time interval, and the interval risk attribute of each of the operating intervals;

[0038] A network construction module, used to construct a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes;

[0039] A model building module, used to establish a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network;

[0040] The risk prediction module is used to solve the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network.

[0041] In a possible implementation, the network construction module is further used to:

[0042] Determine a risk correspondence between each of the station risk attributes and the station capacity risk corresponding to each of the stations in the rail transit network;

[0043] Determine a risk correspondence between each of the interval risk attributes and the interval transport capacity risk corresponding to each of the operating intervals in the rail transit network;

[0044] Determine the risk correspondence between each of the station capacity risks, each of the interval capacity risks, and the line capacity risk of each of the lines in the rail transit network;

[0045] Determine the risk correspondence between each of the line capacity risks and the network capacity risk corresponding to the rail transit network;

[0046] The station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0047] In a possible implementation manner, the device further includes:

[0048] A single-point risk determination module, used to determine the station single-point risk of each of the stations and the interval single-point risk of each of the operating intervals;

[0049] The network construction module is further used to:

[0050] Determine the risk correspondence between each of the station single point risks and the station risk attributes, and the risk correspondence between each of the interval single point risks and the interval risk attributes;

[0051] The network construction module is further used to:

[0052] The station single point risk, the interval single point risk, the station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0053] In a possible implementation, the model building module is further used to:

[0054] Determine the station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk and network capacity risk of the rail transit network history;

[0055] The Gaussian mixture model is solved according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network.

[0056] In a possible implementation, the model building module is further used to:

[0057] Solving the Gaussian mixture model according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk through an expectation maximization algorithm to obtain layer parameters of each node layer in the dynamic Bayesian network;

[0058] The node parameters of the network nodes included in the node layer are calculated according to the layer parameters of each node layer.

[0059] In a possible implementation, the layer parameters include first layer parameters, second layer parameters and third layer parameters, and the node parameters include first node parameters, second node parameters and third node parameters;

[0060] The first node parameter is based on Calculated, where β l is the first node parameter, α l is the first layer parameter of the node layer where the network node is located, x S is the risk value of the underlying node corresponding to the network node, μ lS is the second node parameter of the underlying node corresponding to the network node, ∑ lSS is the autocovariance of the third node parameter of the underlying node corresponding to the network node, and M is the number of first-layer parameters of the node layer where the underlying node corresponding to the network node is located;

[0061] The second node parameter is calculated according to the formula Calculated, where μ lR|S is the conditional probability of the network node corresponding to the second node parameter, μ lR is the second node parameter corresponding to the network node, ∑ lRS is the covariance of the third node parameter of the network node and the third node parameter of the underlying node corresponding to the network node;

[0062] The third node parameter is based on the formula Calculated, where ∑ lR|S is the conditional probability of the network node corresponding to the third node parameter, ∑ lRR is the autocovariance of the third node parameter of the network node, ∑ lSR It is the covariance between the third node parameter of the underlying node corresponding to the network node and the third node parameter of the network node.

[0063] In a possible implementation, the risk prediction module is further used to:

[0064] Taking each of the station risk attribute values ​​and each of the interval risk attribute values ​​within the target time interval as input, solving the mean of the distribution value corresponding to each of the network nodes in the dynamic Bayesian network layer by layer;

[0065] The mean of the distribution values ​​corresponding to the top-level network nodes of the Bayesian network is used as the network capacity risk of the rail transit network.

[0066] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0067] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0068] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0069] In an embodiment of the present disclosure, a rail transit network including at least one line is determined, wherein each line includes at least two stations and operating intervals between adjacent stations. The station risk attribute of each station within the target time interval and the interval risk attribute of each operating interval are determined. A dynamic Bayesian network is constructed based on the rail transit network, the risk attribute of each station, and the risk attribute of each interval, and a Gaussian mixture model is established based on the joint distribution of the dynamic Bayesian network. The Gaussian mixture model is further solved based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network. The present disclosure improves the interpretability of the capacity risk speculation process by constructing a Bayesian network, and improves the accuracy of the capacity risk prediction results by establishing a Gaussian mixture model.

[0070] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0072] Figure 1 A flow chart showing a method for determining transport capacity risk based on a dynamic Bayesian network according to an embodiment of the present disclosure is shown;

[0073] Figure 2 A schematic diagram showing the structure of a dynamic Bayesian network according to an embodiment of the present disclosure is shown;

[0074] Figure 3 A schematic diagram of a local structure in a dynamic Bayesian network according to an embodiment of the present disclosure is shown;

[0075] Figure 4 A schematic diagram showing a transport capacity risk determination device based on a dynamic Bayesian network according to an embodiment of the present disclosure;

[0076] Figure 5 A schematic diagram showing an electronic device according to an embodiment of the present disclosure;

[0077] Figure 6 A schematic diagram showing another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0078] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0079] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0080] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0081] The capacity risk determination method based on the dynamic Bayesian network of the embodiment of the present disclosure can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the capacity risk determination method based on the dynamic Bayesian network of the embodiment of the present disclosure by a processor calling computer-readable instructions stored in a memory.

[0082] Figure 1 FIG. 1 is a flow chart showing a method for determining transport capacity risk based on a dynamic Bayesian network according to an embodiment of the present disclosure. Figure 1 As shown, the method for determining transportation capacity risk based on a dynamic Bayesian network according to an embodiment of the present disclosure may include the following steps S10-S50.

[0083] Step S10: Determine a rail transit network including at least one line.

[0084] In a possible implementation, the rail transit network for which the transport capacity risk needs to be predicted is determined by an electronic device. The rail transit network may be a network for rail vehicles such as a railway network or a subway network, which includes at least one line, each line also includes at least two stations, and there is a corresponding vehicle operation interval between every two adjacent stations. Exemplarily, in the case where the rail transit network is a subway network, it may include two lines, Line 1 and Line 2 of the subway. Further, Line 1 of the subway may include subway stations 1, 2, 3, and 4 arranged in sequence, wherein there are corresponding operation intervals between subway stations 1 and 2, subway stations 2 and 3, and subway stations 3 and 4. Line 2 of the subway may include subway stations 5, 3, and 6 arranged in sequence, wherein there are corresponding operation intervals between subway stations 5 and 3, and subway stations 3 and 6.

[0085] Step S20: determining the station risk attribute of each station in the rail transit network within the target time interval, and the interval risk attribute of each operating interval.

[0086] In one possible implementation, the electronic device determines the station risk attributes of each station in the rail transit network and the interval risk attributes of each operating interval within the target time interval before the time when the capacity risk needs to be predicted. The target time interval can be determined by a time window of a preset length. Exemplarily, when the electronic device performs periodic risk attribute prediction at a frequency of once per hour, when the preset length of the time window is 3, and the risk attributes of the rail transit network at ten o'clock in the morning need to be predicted, the electronic device can determine the station risk attributes of each station corresponding to the three time points of ten o'clock, nine o'clock and eight o'clock in the morning, as well as the interval risk attributes of each operating interval.

[0087] Optionally, the station risk attribute includes at least one attribute information that affects the station capacity risk of the station, for example, it may include the station passenger flow saturation and the station stranded passenger evacuation time. Among them, the station passenger flow saturation can be obtained by calculating the ratio of the station passenger flow and the station passenger flow capacity. The evacuation time of the stranded passengers at the station can be the time when the passengers stranded in the waiting area of ​​the station completely leave the waiting area. The interval risk attribute includes at least one attribute information that affects the interval capacity risk of the operating interval, for example, it may include the interval passenger flow saturation, which can be obtained by calculating the ratio of the interval passenger flow and the passenger flow capacity.

[0088] Step S30: constructing a dynamic Bayesian network for predicting network capacity risk based on the rail transit network, the risk attribute of each station, and the risk attribute of each interval.

[0089] In a possible implementation, after determining the rail transit network, at least one station risk attribute of each station within the target time interval, and at least one interval risk attribute of each operating interval within the target time interval, the electronic device constructs a dynamic Bayesian network based on the determined information to predict the network capacity risk. The dynamic Bayesian network can be regarded as a directed acyclic graph, that is, the process of the electronic device constructing the dynamic Bayesian network can be to draw a directed acyclic graph using the at least one information as a node.

[0090] Optionally, when the electronic device constructs a dynamic Bayesian network by drawing a directed acyclic graph, it is necessary to first determine the nodes included in the drawing and the connections between different nodes. That is, when a dynamic Bayesian network for predicting the network capacity risk is constructed according to the rail transit network, each station risk attribute, and each interval risk attribute, the risk correspondence between each station risk attribute and the station capacity risk corresponding to each station in the rail transit network can be determined first, the risk correspondence between each interval risk attribute and the interval capacity risk corresponding to each operating interval in the rail transit network can be determined, the risk correspondence between each station capacity risk, each interval capacity risk and the line capacity risk of each line in the rail transit network can be determined, and the risk correspondence between each line capacity risk and the network capacity risk corresponding to the rail transit network can be determined. Then, the station risk attribute, interval risk attribute, station capacity risk, interval capacity risk, line capacity risk, and network capacity risk are used as network nodes, and each network node is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0091] Among them, station capacity risk is used to characterize the possibility of failure or accident at the station, interval capacity risk is used to characterize the possibility of failure or accident at the corresponding operating interval, line capacity risk is used to characterize the possibility of failure or accident at the operating interval, and network capacity risk is used to characterize the possibility of failure or accident in the entire rail transit network. Each station capacity risk is determined by the corresponding station risk attribute, so there is a risk correspondence between station capacity risk and station risk attribute. Each interval capacity risk is determined by the corresponding interval risk attribute, so there is a risk correspondence between interval capacity risk and interval risk attribute. Each line capacity risk is determined by the station capacity risk of the stations included in the line and the interval capacity risk of the operating intervals included, so there is a risk correspondence between line capacity risk and station capacity risk and interval capacity risk. The network capacity risk is determined by the line capacity risk of all lines included in the network, so there is a risk correspondence between the network capacity risk and line capacity risk.

[0092] Furthermore, after obtaining different risk correspondences, the electronic device uses the station single-point risk, interval single-point risk, station risk attribute, interval risk attribute, station capacity risk, interval capacity risk, line capacity risk and road network capacity risk as network nodes, and connects each of the network nodes in order according to the risk correspondence, draws an ordered undirected graph, and obtains a dynamic Bayesian network. The drawn dynamic Bayesian network includes multiple node layers, and each node layer includes at least one network node of the same type. Optionally, different node layers can be sorted according to the node types included therein, from bottom to top, they are station risk attribute and interval risk attribute layer, station capacity risk and interval capacity risk attribute layer, line capacity risk attribute layer and road network capacity risk attribute layer.

[0093] In a possible implementation, the station risk attribute and the interval risk attribute in the embodiment of the present disclosure can also be affected by the corresponding single-point risk, that is, the electronic device can also determine the station single-point risk corresponding to each station and the interval single-point risk corresponding to each operating interval. Further, in the process of constructing a dynamic Bayesian network, network nodes corresponding to the station single-point risk and the interval single-point risk can also be added. In other words, the electronic device can determine the risk correspondence between each station single-point risk and the station risk attribute, and the risk correspondence between each interval single-point risk and the interval risk attribute. Then, the station single-point risk, the interval single-point risk, the station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are used as network nodes, and each network node is connected in order according to the risk correspondence to obtain a dynamic Bayesian network. Different node layers can be sorted according to the node type included therein, from bottom to top, they are the station risk attribute and the interval risk attribute layer, the station capacity risk and the interval capacity risk attribute layer, the line capacity risk attribute layer and the road network capacity risk attribute layer.

[0094] Optionally, the single-point risk of a station may include at least one information affecting the risk attribute of the station, such as single-point risk information, station risk possibility, consequences of station equipment and personnel loss, station risk traffic impact level, and station risk duration, etc. The single-point risk of an interval may include at least one information affecting the risk attribute of an interval, such as single-point risk information, interval risk possibility, consequences of interval equipment and personnel loss, interval risk traffic impact level, and interval risk duration, etc.

[0095] Figure 2 FIG. 2 shows a schematic diagram of a dynamic Bayesian network structure according to an embodiment of the present disclosure. Figure 2As shown, under the consideration of the single-point risk influence of stations and operating intervals, the constructed dynamic Bayesian network includes five node layers from bottom to top, namely, station risk attribute and interval risk attribute layer, station capacity risk and interval capacity risk attribute layer, line capacity risk attribute layer and network capacity risk attribute layer. At least one network node in each node layer is connected to the network node in the previous node layer according to the risk correspondence relationship.

[0096] Figure 3 FIG. 2 shows a schematic diagram of a local structure in a dynamic Bayesian network according to an embodiment of the present disclosure. Figure 3 As shown, when the electronic device determines the station capacity risk and the interval capacity risk at the current moment, it not only considers the station risk attributes and the interval risk attributes corresponding to the station or interval at the current moment, but also considers all station risk attributes and interval risk attributes at at least one previous moment, that is, within the target time interval.

[0097] Step S40: establishing a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network.

[0098] In a possible implementation, after constructing a dynamic Bayesian network, the electronic device uses a linear Gaussian model to model the conditional probability of the Bayesian network. The Gaussian Bayesian network is one of the most classic continuous Bayesian networks. It models the joint distribution of all node variables into a multivariate normal distribution, which is convenient for calculation and derivation. However, there is a certain gap between the normal assumption of the conditional probability distribution and the distribution of the collected data. Therefore, in order to further improve the prediction performance, on the basis of not changing the network topology, the embodiment of the present disclosure enhances the fitting effect of the nonlinear relationship between the network nodes of the dynamic Bayesian network by establishing a Gaussian mixture model, and models the joint distribution to facilitate the calculation of network parameters in the dynamic Bayesian network. Among them, the network parameters include at least one node parameter of each network node in the dynamic Bayesian network.

[0099] Optionally, the electronic device may predetermine the network parameters of the dynamic Bayesian network, or determine the network parameters of the dynamic Bayesian network in real time each time the network capacity risk at the current moment is predicted. When the electronic device determines the network parameters of the dynamic Bayesian network, it may first determine the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk of the rail transit network, and then solve the Gaussian mixture model based on the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network. The above-mentioned historical information such as station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk may include historical information corresponding to multiple moments.

[0100] Furthermore, the electronic device can first calculate the layer parameters of each node layer in the Bayesian network, and then further calculate the node parameters of the network nodes based on the layer parameters. That is, the electronic device can solve the Gaussian mixture model according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and road network capacity risk through the expectation maximization algorithm to obtain the layer parameters of each node layer in the dynamic Bayesian network. Then, the node parameters of the network nodes included in the node layer are calculated based on the layer parameters of each node layer. Among them, the layer parameters of each node layer can also include three parameters: the first layer parameters, the second layer parameters and the third layer parameters, and the node parameters corresponding to each network node also include the first node parameters, the second node parameters and the third node parameters.

[0101] According to the prior art, the Gaussian mixture model (GMM) is a weighted average sum of finite Gaussian distributions, which can approximate any distribution if the number of components is sufficient. Satisfy k π k = 1, where each Gaussian distribution is called a component, K is the number of mixture components, and π k The weight of the corresponding component, and the probability density function for each component is In the case of solving layer parameters based on the expectation maximization algorithm, the first layer parameters, second layer parameters and third layer parameters to be estimated are first initialized, and then the parameters are estimated by multiple iterations. Each iteration process performs the following two steps in sequence: Step E calculates the complete data log-likelihood function based on the existing observed data and the current estimated values ​​of the parameters. Assume that there is a data set Z, Z = {X, Y}, X is called the observed data set, that is, the historical information of the adjacent bottom layer of the node layer for which the layer parameters need to be solved, Y is called the unobserved data set, that is, the historical information of the node layer for which the layer parameters need to be solved, and Z is called the complete data set. The complete data expected log-likelihood function is also called the Q function, that is, The M step selects the parameter that maximizes this expectation as the new estimate, that is, When the algorithm runs to the kth iteration, the first layer parameters are obtained as follows: is the weight of the estimated mixed component of the joint distribution, and the second layer parameter is is the estimated mean of the mixed component of the joint distribution, and the third layer parameter is is the covariance matrix of the estimated mixture components of the joint distribution, where That is, the probability that the i-th sample belongs to each mixed component. Furthermore, in order to avoid overflow and singular values ​​when solving the third-layer parameters, the third-layer parameters can be obtained by modifying the estimation of the covariance matrix: λ is a preset parameter, I d is the d-dimensional identity matrix, and estimating it in this way ensures that the covariance matrix is ​​non-zero and avoids singularity.

[0102] Furthermore, for the Gaussian mixture model constructed in the embodiment of the present application x R is the current network node, x S are the corresponding bottom-level nodes, which are the first-level parameters, the second-level parameters, and the third-level parameters. S The marginal distribution of can be written as p(x S )=∑ xR p(x S ,x R ), according to Bayesian theory, the conditional probability p(x R |x S ) can be expressed as For a multivariate Gaussian distribution density function G(x; μ, ∑), and in That is, the Gaussian mixture model can be further expressed as Based on the Bayesian formula, the conditional probability can be further expressed as That is, the conditional probability of the current network node risk given the underlying node information. Since the first-layer parameters, second-layer parameters, and third-layer parameters have been solved based on the above expectation maximization algorithm, the conditional probability representation can be further obtained to obtain the first node parameter β l The second node parameter μ lR|S and the third node parameter ∑ lR|S .

[0103] Furthermore, the first node parameter is based on Calculated, where β l is the first node parameter, α lis the first layer parameter of the node layer where the network node is located, x S is the risk value of the network node corresponding to the underlying node, μ lS is the second node parameter of the network node corresponding to the underlying node, ∑ lSS is the autocovariance of the third node parameter of the network node corresponding to the bottom node, and M is the number of first-layer parameters of the node layer where the network node corresponds to the bottom node. The second node parameter is calculated according to the formula Calculated, where μ lR|S is the conditional probability of the network node corresponding to the second node parameter, μ lR is the second node parameter corresponding to the network node, ∑ lRS is the covariance of the third node parameter of the network node and the third node parameter of the underlying node corresponding to the network node. The third node parameter is calculated according to the formula Calculated, where ∑ lR|S is the conditional probability of the network node corresponding to the third node parameter, ∑ lRR is the autocovariance of the third node parameter of the network node, ∑ lSR is the covariance of the third node parameter of the underlying node corresponding to the network node and the third node parameter of the network node.

[0104] Step S50: solving the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network.

[0105] In a possible implementation, after the electronic device determines the node parameters of each network node in the dynamic Bayesian network, the dynamic Bayesian network can be solved based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network. Optionally, the electronic device can use the risk attribute value of each station and the risk attribute value of each interval within the target time interval as input, and solve the mean of the distribution value corresponding to each network node in the dynamic Bayesian network layer by layer. Then the mean of the distribution value corresponding to the top-level network node of the Bayesian network is used as the network capacity risk of the rail transit network.

[0106] Optionally, the electronic device may determine the observation value x according to the predetermined risk attribute value of each station and the risk attribute value of each interval. S , to predict the corresponding station capacity risk x R . Further, the station capacity risk is used as the observation value to predict the corresponding interval capacity risk, and then the interval capacity risk is used as the observation value to predict the network capacity risk. Finally, the mean of the distribution value corresponding to the network capacity risk is calculated to obtain the network capacity risk of the rail transit network. In each prediction process, the optimal estimate of the capacity risk is calculated, that is, the mean of the distribution value is The formula can be Calculated.

[0107] Furthermore, in order to grasp the uncertainty of the final calculated distribution value mean and improve the interpretability of the prediction model, the electronic device can simultaneously calculate the conditional variance corresponding to the distribution value mean

[0108] Based on the above technical features, the disclosed embodiment proposes a method for deduce rail transit road network transportation safety situation based on dynamic Bayesian network and Gaussian mixture model. Based on the road network topology structure and according to the interaction relationship between transportation capacity risks, a dynamic Bayesian network structure is constructed. The Gaussian mixture model is used to model the joint probability distribution of the local network, and the EM algorithm is used for parameter learning, which reflects the superiority of the Gaussian mixture model and dynamic network compared to the linear Gaussian Bayesian network in performance. This method of predicting road network transportation capacity risks ensures interpretability and further improves the prediction accuracy, and can migrate abnormal scenarios, further improving the scope of application.

[0109] Figure 4 FIG. 1 is a schematic diagram showing a transport capacity risk determination device based on a dynamic Bayesian network according to an embodiment of the present disclosure. Figure 4 As shown, the transport capacity risk determination device based on the dynamic Bayesian network of the embodiment of the present disclosure may include:

[0110] A network determination module 40 is used to determine a rail transit network including at least one line, each line including at least two stations, and a running interval between every two stations;

[0111] An attribute determination module 41, used to determine the station risk attribute of each of the stations in the rail transit network within a target time interval, and the interval risk attribute of each of the operating intervals;

[0112] A network construction module 42, configured to construct a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes;

[0113] A model building module 43, used to establish a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network;

[0114] The risk prediction module 44 is used to solve the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network.

[0115] In a possible implementation, the network construction module 42 is further configured to:

[0116] Determine a risk correspondence between each of the station risk attributes and the station capacity risk corresponding to each of the stations in the rail transit network;

[0117] Determine a risk correspondence between each of the interval risk attributes and the interval transport capacity risk corresponding to each of the operating intervals in the rail transit network;

[0118] Determine the risk correspondence between each of the station capacity risks, each of the interval capacity risks, and the line capacity risk of each of the lines in the rail transit network;

[0119] Determine the risk correspondence between each of the line capacity risks and the network capacity risk corresponding to the rail transit network;

[0120] The station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0121] In a possible implementation manner, the device further includes:

[0122] A single-point risk determination module, used to determine the station single-point risk of each of the stations and the interval single-point risk of each of the operating intervals;

[0123] The network construction module 42 is further used to:

[0124] Determine the risk correspondence between each of the station single point risks and the station risk attributes, and the risk correspondence between each of the interval single point risks and the interval risk attributes;

[0125] The network construction module 42 is further used to:

[0126] The station single point risk, the interval single point risk, the station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

[0127] In a possible implementation, the model building module 43 is further used to:

[0128] Determine the station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk and network capacity risk of the rail transit network history;

[0129] The Gaussian mixture model is solved according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network.

[0130] In a possible implementation, the model building module 43 is further used to:

[0131] Solving the Gaussian mixture model according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk through an expectation maximization algorithm to obtain layer parameters of each node layer in the dynamic Bayesian network;

[0132] The node parameters of the network nodes included in the node layer are calculated according to the layer parameters of each node layer.

[0133] In a possible implementation, the layer parameters include first layer parameters, second layer parameters and third layer parameters, and the node parameters include first node parameters, second node parameters and third node parameters;

[0134] The first node parameter is based on Calculated, where β l is the first node parameter, α l is the first layer parameter of the node layer where the network node is located, x S is the risk value of the underlying node corresponding to the network node, μ lS is the second node parameter of the underlying node corresponding to the network node, ∑ lSS is the autocovariance of the third node parameter of the underlying node corresponding to the network node, and M is the number of first-layer parameters of the node layer where the underlying node corresponding to the network node is located;

[0135] The second node parameter is calculated according to the formula Calculated, where μ lR|S is the conditional probability of the network node corresponding to the second node parameter, μ lR is the second node parameter corresponding to the network node, ∑ lRS is the covariance of the third node parameter of the network node and the third node parameter of the underlying node corresponding to the network node;

[0136] The third node parameter is based on the formula Calculated, where ∑ lR|Sis the conditional probability of the network node corresponding to the third node parameter, ∑ lRR is the autocovariance of the third node parameter of the network node, ∑ lSR It is the covariance between the third node parameter of the underlying node corresponding to the network node and the third node parameter of the network node.

[0137] In a possible implementation, the risk prediction module 44 is further configured to:

[0138] Taking each of the station risk attribute values ​​and each of the interval risk attribute values ​​within the target time interval as input, solving the mean of the distribution value corresponding to each of the network nodes in the dynamic Bayesian network layer by layer;

[0139] The mean of the distribution values ​​corresponding to the top-level network nodes of the Bayesian network is used as the network capacity risk of the rail transit network.

[0140] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0141] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0142] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0143] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0144] Figure 5 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0145] Reference Figure 5, the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output interface 812 (I / O interface), a sensor component 814 , and a communication component 816 .

[0146] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0147] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device 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 memory, flash memory, magnetic disk or optical disk.

[0148] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0149] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0150] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0151] The input / output interface 812 provides an interface between the processing component 802 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0152] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0153] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0154] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0155] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.

[0156] Figure 6 1 is a schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal device. Figure 6 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0157] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , Mac OS X TM , Unix TM,Linux TM , FreeBSD TM or similar.

[0158] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0159] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0160] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0161] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0162] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0163] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0164] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0165] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0166] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0167] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining transportation capacity risk based on a dynamic Bayesian network, characterized in that: The method comprises: Determine a rail transit network including at least one line, each of the lines including at least two stations, and an operating interval between every two stations; Determining the station risk attribute of each of the stations in the rail transit network within the target time interval, and the interval risk attribute of each of the operating intervals; Constructing a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes; Establishing a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network; Based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval, the dynamic Bayesian network is solved to obtain the network capacity risk of the rail transit network.

2. The method according to claim 1, characterized in that The dynamic Bayesian network for predicting the network capacity risk is constructed according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes, including: Determine a risk correspondence between each of the station risk attributes and the station capacity risk corresponding to each of the stations in the rail transit network; Determine a risk correspondence between each of the interval risk attributes and the interval transport capacity risk corresponding to each of the operating intervals in the rail transit network; Determine the risk correspondence between each of the station capacity risks, each of the interval capacity risks, and the line capacity risk of each of the lines in the rail transit network; Determine the risk correspondence between each of the line capacity risks and the network capacity risk corresponding to the rail transit network; The station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

3. The method according to claim 2, characterized in that The method further comprises: Determining the station single point risk of each of the stations and the interval single point risk of each of the operating intervals; The dynamic Bayesian network for predicting the network capacity risk is constructed according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes, and further includes: Determine the risk correspondence between each of the station single point risks and the station risk attributes, and the risk correspondence between each of the interval single point risks and the interval risk attributes; The station risk attribute, the section risk attribute, the station capacity risk, the section capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is sequentially connected according to the risk correspondence to obtain a dynamic Bayesian network, including: The station single point risk, the interval single point risk, the station risk attribute, the interval risk attribute, the station capacity risk, the interval capacity risk, the line capacity risk and the road network capacity risk are taken as network nodes, and each of the network nodes is connected in order according to the risk correspondence to obtain a dynamic Bayesian network.

4. The method according to any one of claims 1 to 3, characterized in that: The step of establishing a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network includes: Determine the station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk and network capacity risk of the rail transit network history; The Gaussian mixture model is solved according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network.

5. The method according to claim 4, characterized in that Solving the Gaussian mixture model according to the historical station risk attributes, section risk attributes, station capacity risk, section capacity risk, line capacity risk and network capacity risk to obtain the node parameters of each network node in the dynamic Bayesian network includes: Solving the Gaussian mixture model according to the historical station risk attributes, interval risk attributes, station capacity risk, interval capacity risk, line capacity risk and network capacity risk through an expectation maximization algorithm to obtain layer parameters of each node layer in the dynamic Bayesian network; The node parameters of the network nodes included in the node layer are calculated according to the layer parameters of each node layer.

6. The method according to claim 4 or 5, characterized in that: The layer parameters include first layer parameters, second layer parameters and third layer parameters, and the node parameters include first node parameters, second node parameters and third node parameters; The first node parameter is based on Calculated, where β l is the first node parameter, α l is the first layer parameter of the node layer where the network node is located, x S is the risk value of the underlying node corresponding to the network node, μ lS is the second node parameter of the underlying node corresponding to the network node, ∑ lSS is the autocovariance of the third node parameter of the underlying node corresponding to the network node, and M is the number of first-layer parameters of the node layer where the underlying node corresponding to the network node is located; The second node parameter is calculated according to the formula Calculated, where μ lR|S is the conditional probability of the network node corresponding to the second node parameter, μ lR is the second node parameter corresponding to the network node, ∑ lRS is the covariance of the third node parameter of the network node and the third node parameter of the underlying node corresponding to the network node; The third node parameter is based on the formula Calculated, where ∑ lR|S is the conditional probability of the network node corresponding to the third node parameter, ∑ lRR is the autocovariance of the third node parameter of the network node, ∑ lSR It is the covariance between the third node parameter of the underlying node corresponding to the network node and the third node parameter of the network node.

7. The method according to any one of claims 1 to 6, characterized in that: The step of solving the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network includes: Taking the risk attribute value of each station and the risk attribute value of each interval within the target time interval as input, solving the mean value of the distribution value corresponding to each network node in the dynamic Bayesian network layer by layer; The mean of the distribution values ​​corresponding to the top-level network nodes of the Bayesian network is used as the network capacity risk of the rail transit network.

8. A transport capacity risk determination device based on a dynamic Bayesian network, characterized in that: The device comprises: A network determination module, used to determine a rail transit network including at least one line, each of which includes at least two stations, and an operating interval is between every two stations; An attribute determination module, used to determine the station risk attribute of each of the stations in the rail transit network within a target time interval, and the interval risk attribute of each of the operating intervals; A network construction module, used to construct a dynamic Bayesian network for predicting network capacity risk according to the rail transit network, each of the station risk attributes, and each of the interval risk attributes; A model building module, used to establish a Gaussian mixture model according to the joint distribution of the dynamic Bayesian network to determine the network parameters of the dynamic Bayesian network; The risk prediction module is used to solve the dynamic Bayesian network based on the risk attribute value of each station and the risk attribute value of each interval within the target time interval to obtain the network capacity risk of the rail transit network.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method described in any one of claims 1 to 7 when executing the instructions stored in the memory.

10. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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