CN120321129B

CN120321129B - Simulation test method and device, storage medium and computer equipment

Info

Publication number: CN120321129B
Application number: CN202510788563.XA
Authority: CN
Inventors: 邹星; 胡宁; 王晓锋; 周逸菲; 申帅永
Original assignee: Peng Cheng Laboratory
Current assignee: Peng Cheng Laboratory
Priority date: 2025-06-13
Filing date: 2025-06-13
Publication date: 2025-08-19
Anticipated expiration: 2045-06-13
Also published as: CN120321129A

Abstract

The embodiment of the application provides a simulation test method, a device, a storage medium and computer equipment, which comprise the steps of obtaining simulation network configuration information, carrying out simulation network configuration according to the simulation network configuration information to obtain a simulation network, wherein the simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, connection relations among the plurality of network nodes are provided, response to simulation starting operation, determining an initial failure node from the plurality of network nodes according to failure triggering conditions, determining a failure mode of the initial failure node according to the node failure conditions, controlling the initial failure node to fail according to the failure mode, determining a next failure node based on the connection relations and risk propagation modes of the plurality of network nodes, determining the next failure node as the initial failure node, and returning to execute the connection relations based on the plurality of network nodes until the risk propagation modes are met, so as to complete simulation test, and improve the simulation degree.

Description

Simulation test method and device, storage medium and computer equipment

Technical Field

The present application relates to the field of network technologies, and in particular, to a simulation test method, a device, a storage medium, and a computer apparatus.

Background

The key information infrastructure (the gateway for short) is very important for the normal stable operation of cities. Because the related base system relates to the reasons of numerous industries, high requirement on domain knowledge, complex business interaction relationship, high real object reproduction cost, inapplicability of a real system to experiments and the like, the research of safety verification, risk prediction, technical verification and the like is difficult to develop aiming at the real related base system. Therefore, aiming at the network security problem in the off-base industry, the implementation based on the simulation platform is generally needed so as not to cause adverse effect on a real off-base system. The Guanyu industry simulation system generally performs realistic reproduction on core units, business processes, system interconnection, risk propagation mechanisms and the like of key information infrastructure based on technologies such as virtualization, virtual-real combination and the like. By connecting different gateway simulation systems, a gateway system interconnection environment close to a real scene can be provided.

In the related art, a simulation test scheme for cascade failure (CASCADING FAILURE) is lacking, and cascade failure refers to a process that a failure of one or several components causes a sequential failure of other components in an interconnected system. Such a phenomenon may occur in various systems, including power transmission, computer network, financial system, traffic system, and ecological system, so that the simulation scheme does not fully consider the chain reaction caused by the node failure, and the simulation degree is low, so that a simulation test method is needed to be proposed in the related art to solve the above technical problem.

Disclosure of Invention

The application mainly aims to provide a simulation test method, a device, a storage medium and computer equipment, wherein the connection relation between network nodes is simulated through a simulation stage, after an initial failure node is determined, the next failure node affected in the risk propagation is determined according to the connection relation of the network nodes and a risk propagation mode, so that the chain reaction in the cascade failure is simulated, and the simulation degree is improved.

In a first aspect, an embodiment of the present application provides a simulation test method, including:

obtaining simulation network configuration information, and performing simulation network configuration according to the simulation network configuration information to obtain a simulation network, wherein the simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, and the plurality of network nodes have connection relations;

responding to the simulation starting operation, and determining an initial failure node from a plurality of network nodes according to a failure triggering condition;

determining a failure mode of the initial failure node according to a node failure condition, and controlling the initial failure node to fail according to the failure mode;

determining a next failure node based on the connection relation and the risk propagation mode of a plurality of network nodes;

and determining the next failure node as an initial failure node, and returning to execute the step of determining the next failure node based on the connection relation of the plurality of network nodes and the risk propagation mode until the termination condition is met so as to complete the simulation test.

In a second aspect, an embodiment of the present application provides a simulation test apparatus, including:

The simulation network configuration unit is used for acquiring simulation network configuration information, carrying out simulation network configuration according to the simulation network configuration information to obtain a simulation network, wherein the simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, and the plurality of network nodes have connection relations;

the first determining unit is used for responding to the simulation starting operation and determining an initial failure node from a plurality of network nodes according to the failure triggering condition;

the control unit is used for determining the failure mode of the initial failure node according to the node failure condition and controlling the failure of the initial failure node according to the failure mode;

the second determining unit is used for determining the next failure node based on the connection relation and the risk propagation mode of the plurality of network nodes;

And the execution unit is used for determining the next failure node as an initial failure node, and returning to execute the steps of determining the next failure node based on the connection relation of the plurality of network nodes and the risk propagation mode until the termination condition is met so as to complete the simulation test.

In a third aspect, embodiments of the present application provide a storage medium storing a plurality of instructions adapted to be loaded by a processor to perform a simulation test method as described above.

In a fourth aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the simulation test method as any one of the above when executing the computer program.

In the embodiment of the application, simulation network configuration information is acquired, simulation network configuration is carried out according to the simulation network configuration information to obtain a simulation network, the simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, connection relations among the plurality of network nodes are provided, response to simulation starting operation is carried out, an initial failure node is determined from the plurality of network nodes according to failure triggering conditions, a failure mode of the initial failure node is determined according to node failure conditions, failure of the initial failure node is controlled according to the failure mode, a next failure node is determined based on the connection relations and risk propagation modes of the plurality of network nodes, the next failure node is determined to be the initial failure node, the connection relations and the risk propagation modes of the plurality of network nodes are executed in a returning mode, and the next failure node is determined until termination conditions are met.

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the disclosure. The objectives and other advantages of the disclosure will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic view of a scenario of a simulation test system according to an embodiment of the present application.

Fig. 2 is a flow chart of a simulation test method according to an embodiment of the present application.

FIG. 3 is a schematic diagram of a network relationship of a simulation test system according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of a simulation test apparatus according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the solution of the present application, a technical solution of an embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present application, and it is apparent that the described embodiment is only a part of the embodiment of the present application, not all the embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

It should be noted that, in some of the processes described in the specification, claims and drawings above, a plurality of steps appearing in a particular order are included, but it should be clearly understood that the steps may be performed out of order or performed in parallel, the step numbers are merely used to distinguish between the different steps, and the numbers themselves do not represent any order of execution. Furthermore, the description of "first," "second," or "object" and the like herein is for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.

Before proceeding to further detailed description of the disclosed embodiments, the terms and terms involved in the disclosed embodiments are described, which are applicable to the following explanation:

simulation is a technique that simulates real system behavior by modeling. The definition core is that the existing or designed system is researched through experiments on a system model, which is also called simulation. The model covers various types of physical and mathematical, static and dynamic, continuous and discrete, and the like, and the system comprises engineering systems such as electric, mechanical, chemical and the like, and also comprises non-engineering systems such as society, economy, ecology, management and the like. When the research object has the characteristics of high cost, high experimental risk or long time for knowing the result of parameter change, the simulation is an extremely effective research means.

The simulation model is a similar object of the simulated object or a structural form thereof, and can be a physical model (such as a scaled-down airplane model) or a mathematical model (expressing a system relation by a mathematical formula). Sometimes, a mathematical model is first constructed and then converted into a simulation model suitable for computer processing.

And (3) simulating experiments, namely observing the whole process of the change of each variable of the system model. In different development stages of the system, the simulation experiment modes are different. The design stage is multipurpose mathematical simulation, the part development stage can use actual parts or subsystems to replace part of computer simulation models to carry out semi-physical simulation experiments, the system development stage is used for carrying out semi-physical simulation experiments mostly, and all-physical simulation experiments are carried out in individual cases (the models are replaced by physical models or real objects).

The simulation tool mainly comprises simulation hardware and simulation software. The most important of the simulation hardware are analog computers (mainly used for continuous system simulation), digital computers (with high speed, which is a main simulation tool), and hybrid computers (combining the advantages of analog and digital computers, but with high cost). In addition, there are special physical simulators, such as motion simulators, object simulators, and the like. The simulation software comprises a simulation program, a simulation program package, a simulation language and a simulation software system taking a database as a core.

The classification method is as follows:

1. The model types are classified into physical simulation (based on a physical model), mathematical simulation (based on a mathematical model) and semi-physical simulation (partially adopting a physical model).

2. The computer types used are classified into analog simulation (using an analog computer), digital simulation (using a digital computer), and hybrid simulation (using a hybrid computer).

3. The simulation object signal flow is divided into continuous system simulation (signal flow continuous) and discrete system simulation (signal flow discrete).

4. According to the proportional relation between the simulation time and the actual time, the simulation method is divided into real-time simulation (the simulation is the same as the natural time scale), super real-time simulation (the simulation time scale is smaller than the natural time scale) and sub real-time simulation (the simulation time scale is larger than the natural time scale).

5. According to object properties, the method is divided into spacecraft simulation, chemical system simulation, economic system simulation and the like.

Application field:

The method can shorten the design and development period of a large-scale passenger plane, reduce the cost by utilizing a flight simulator to train a pilot, is not limited by weather and other conditions, can reduce the number of live ammunition tests, can be used for debugging, maintaining and barrier removal of a nuclear power station in the electric power industry, and is widely applied to the fields of society, economy, biology and the like, such as traffic control, urban planning, resource utilization, environmental pollution control, production management, market prediction, economic analysis prediction, population control and the like.

The key information infrastructure (the gateway for short) is very important for the normal stable operation of cities. Because the related base system relates to the reasons of numerous industries, high requirement on domain knowledge, complex business interaction relationship, high real object reproduction cost, inapplicability of a real system to experiments and the like, the research of safety verification, risk prediction, technical verification and the like is difficult to develop aiming at the real related base system. Therefore, aiming at the network security problem in the off-base industry, the implementation based on the simulation platform is generally needed so as not to cause adverse effect on a real off-base system. The Guanyu industry simulation system generally performs realistic reproduction on core units, business processes, system interconnection, risk propagation mechanisms and the like of key information infrastructure based on technologies such as virtualization, virtual-real combination and the like. By connecting different gateway simulation systems, a gateway system interconnection environment close to a real scene can be provided. Due to the lack of a simulated test solution for cascading failure (CASCADING FAILURE), cascading failure refers to the process in which failure of one or more components causes sequential failure of other components in an interconnected system. This phenomenon may occur in various systems including power transmission, computer networks, financial systems, traffic systems, and ecosystems, etc., resulting in a simulation scheme that does not adequately take into account the chain reaction caused by node failure, with a low degree of simulation.

In order to solve the problems, the embodiment of the application determines to-be-processed data, acquires simulation network configuration information of the to-be-processed data, carries out simulation network configuration according to the simulation network configuration information to obtain a simulation network, wherein each simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, connection relations among the plurality of network nodes are provided, a start failure node is determined from the plurality of network nodes according to failure triggering conditions in response to simulation starting operation, a failure mode of the start failure node is determined according to node failure conditions, failure of the start failure node is controlled according to the failure mode, a next failure node is determined based on the connection relations among the plurality of network nodes and risk propagation modes, the next failure node is determined to be the start failure node, the connection relations among the plurality of network nodes are executed in a return mode, and the step of determining the next failure node is carried out until termination conditions are met, so that simulation tests are completed. Please refer to the following examples.

Referring to fig. 1, fig. 1 is a schematic view of a scenario of a simulation test system according to an embodiment of the present application. Including terminal 140, internet 130, gateway 120, computer device 110, etc.

The terminals 140 include, but are not limited to, cell phones, computers, intelligent voice interaction devices, intelligent appliances, vehicle terminals, aircraft, etc. In addition, the device can be a single device or a set of a plurality of devices. The terminal 140 may communicate with the internet 130 in a wired or wireless manner, exchanging data.

Computer devices refer to computer systems that can provide certain services to the terminal 140. The computer device 110 is more demanding in terms of stability, security, performance, etc. than the normal terminal 140. The computer device 110 may be a high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a high-performance computer (e.g., a virtual machine), a combination of portions of multiple high-performance computers (e.g., virtual machines), etc.

Gateway 120 is also known as an intersubnetwork connector, protocol converter. The gateway implements network interconnection on the transport layer, and is a computer system or device that acts as a translation. The gateway is a translator between two systems using different communication protocols, data formats or languages, and even architectures that are quite different. At the same time, the gateway may also provide filtering and security functions. A message sent by the terminal 140 to the computer device 110 is to be sent to the corresponding computer device 110 through the gateway 120. A message sent by the computer device 110 to a terminal 140 is also sent to the corresponding terminal 140 through the gateway 120.

The simulation test method of the embodiments of the present disclosure may be implemented in the computer device 110.

It should be noted that, the schematic view of the scenario of the simulation test system shown in fig. 1 is only an example, and the simulation test system and the scenario described in the embodiment of the present application are for more clearly describing the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application, and those skilled in the art can know that, with the evolution of the image processing technology and the appearance of the new service scenario, the technical solution provided by the embodiment of the present application is equally applicable to similar technical problems.

In this embodiment, description will be made in terms of a simulation test apparatus which can be integrated in a computer device having a storage unit and a microprocessor mounted thereon and having arithmetic capability.

Referring to fig. 2, fig. 2 is a flow chart of a simulation test method according to an embodiment of the application. The simulation test method comprises the following steps:

In step 201, simulation network configuration information is obtained, and simulation network configuration is performed according to the simulation network configuration information, so as to obtain a simulation network.

The simulation network configuration information refers to configuration data for describing network structure, parameters and behaviors, and is usually stored in a JSON format. Including network layer attributes, layer number (layer), node number per layer (n), network type (type, e.g., single/multi-layer), model type (model, e.g., ER random network). The connection relation is intra-layer node connection probability (p), inter-layer connection mode (connection, such as adjacent layer interconnection nbr) and connection proportion (perct). Node attributes, load_parameters (load_parameters), capacity parameters (capacity_parameters), weight parameters (weight_parameters). Control parameters, whether to print network information (info) and data save path (save). The inside codes have parameters such as network type, network layer number, network name, how the network is created (whether to read the data set or based on the existing model), network model parameters (different models have different parameters, such as the number of nodes required to be set by the ER random network model, probability of forming a border between nodes), how to connect between layers, how many proportion of nodes are connected at each layer, network load parameters, network capacity parameters, network weight parameters, and the like.

The specific JSON file may be the following example:

{

"net": {

"create": {

"type": multiple "# multilayer network

Layer 2, # layer number

Prefix name of each layer of network is not set, default net is set, and finally 1-layer is added as suffix

"prefix": "er",

"Source": "nx" # read model from file

"Model": "ER" # network model is ER random network

"N": 10, # number of nodes

"P": 0.5, # connection probability

# Interlayer relation setting is valid only in a multi-layer network

"metric": "degree",

Type of connection is all, all layers are interconnected, nbr, adjacent layers are interconnected

"connect": "nbr",

How many proportions of nodes between different layers are interconnected

"perct": 0.5,

"Info": true, # whether to print network information

},

"Save": { }, # save file name

"set": {

Load_param: 0.5, # load parameter

"Capability_parameter": 0.5, # Capacity parameter

Weight_param: 0.5, # weight parameter

},

}

The code is used for setting the related parameters of the multi-layer network, and has the following specific meanings:

network creation parameter (create part)

Network basic attributes

Type: "multiple" indicates that a multi-layer network is to be created.

Layer 2, the number of designated network layers is 2.

Prefix "er" is set for each layer of network name, and finally, the two layers of network names are respectively "er1" and "er2".

Source "nx" means reading a network model from networkx (a Python library used to create, manipulate and study complex network structures, dynamics and functions) files.

Model: "ER" describes that the network model employs an ER random network model.

And n is 10, the number of the nodes of each layer of network is set to be 10.

P 0.5 denotes that the probability of forming a border between nodes in the ER random network model is 0.5.

Interlayer relation set (valid only for multi-layer network)

Metric is defined as node degree, which is the metric index based on which the interlayer connection is determined.

Connect, "nbr" specifies that the inter-layer connection type is that adjacent layers are interconnected, i.e., only the connection relationship exists between adjacent layers.

Perct:0.5 represents a 50% proportion of nodes interconnected between the different layers.

Info, true means that relevant information of the network can be printed, so that the network creation condition can be conveniently known.

Network preservation parameter (save part)

Save { }, the current part is empty and can be used for designating information such as file names for storing network related data, which is not set at present.

Network attribute parameter (set part)

Load_param 0.5 load parameter is set to 0.5, which is used for determining the initial load condition of the network node.

The capacity parameter is 0.5, and the capacity parameter is 0.5, so that the capacity parameter can be used for measuring relevant capacity indexes such as maximum load and the like which can be born by the node.

Weight_param 0.5 weight parameter is 0.5, which is used for setting related attributes such as weight of network edge.

Specifically, the configured simulation network comprises at least one network layer, each network layer comprises a plurality of network nodes, and the plurality of network nodes have connection relations.

For example, two layers of ER random networks (ER 1 and ER 2), 10 nodes in each layer, 0.5 in-layer connection probability, and 50% of nodes between layers are interconnected according to node degree. The network nodes are the basic units in the emulated network, representing entities in the gateway system (e.g. stations, base stations, servers, etc.), with status (normal/invalid) and attributes (load, capacity, weight). The connection relationship is a physical or logical association between nodes, and is represented by an edge, and the connection relationship comprises attributes such as weight, transmission capability and the like and is used for defining a risk propagation path.

In step 202, in response to a simulation start operation, an initiating failure node is determined from a plurality of said network nodes according to a failure triggering condition.

Wherein, the developer triggers simulation through interface or API, and the program loads the constructed network model. Acquiring failure triggering conditions from the configuration, wherein the failure triggering conditions comprise two triggering modes, namely direct triggering and indirect triggering, and determining an initial failure node from a plurality of network nodes according to the specific triggering modes, wherein the initial failure node is the first failure node in the network nodes, and the failure is the failure of the network nodes.

In some embodiments, the determining, according to a failure triggering condition, an initial failure node from a plurality of network nodes includes:

(1) When the failure triggering condition is random triggering, randomly selecting one network node from a plurality of network nodes as an initial failure node;

(2) When the failure triggering condition is a condition triggering, selecting a network node meeting the target condition from the network nodes according to the target condition corresponding to the condition triggering as an initial failure node.

Wherein the trigger condition is randomly triggered for failure, and a part can be randomly selected from a set of all nodes or edges by calling a random function.

For example, an identification set of all nodes is extracted from the constructed simulation network. For example, in a two-tier network, the set of nodes may be { er1_1, er1_2, }, er1_10, er2_1, er2_2, }, er2_10.

A random number generator (e.g., a random or numpy library of Python) using a programming language generates an integer between 0 and the total number of nodes-1. For example, if the total node number is 20, a random integer r E [0,19] is generated.

And aiming at the condition triggering of the failure triggering condition, selecting a network node meeting the target condition from the network nodes according to the target condition as an initial failure node according to the triggering condition (namely the target condition) set by the simulation network configuration information.

For example, if the target condition is a node with the largest degree of selection and the degree of all the nodes needs to be calculated, the node with the largest degree of selection is used as a trigger point, and the degree refers to the number of adjacent edges of the nodes.

Therefore, the random triggering and the conditional triggering are realized through modularized design, new triggering rules can be easily expanded, and the simulation requirements of different industries are met. The target condition triggered by the condition can be dynamically adjusted through a configuration file (such as JSON parameters), and the simulation scene can be switched rapidly without modifying codes. The time complexity of random triggering is O (1) (random selection), and the method is suitable for rapid simulation of a large-scale network, and the conditional triggering can avoid repeated calculation by pre-calculating node indexes (such as degree and medium number) and caching results, so that the simulation efficiency is improved.

In step 203, a failure mode of the initial failure node is determined according to a node failure condition, and the initial failure node is controlled to fail according to the failure mode.

After determining the initial failure node, the node failure condition causing the initial failure node to fail is also needed, which includes two modes of direct failure and indirect failure, so that the initial failure node failure needs to be controlled according to the direct failure or the indirect failure mode included in the node failure condition in the simulation test stage.

In some embodiments, the determining the failure mode of the initial failure node according to the node failure condition, and controlling the failure of the initial failure node according to the failure mode includes:

(1) When the node failure condition is direct failure, changing the state of the initial failure node from an operation state to a failure state;

(2) When the node failure condition is indirect failure, the operation parameter of the initial failure node is modified to the operation parameter meeting the failure condition, and the state of the node failure condition is triggered to be changed from the operation state to the failure state.

The node state is directly modified (namely, the state of the initial failure node is changed from the running state to the failure state) aiming at the condition that the node failure condition is direct failure, complex calculation is not needed, and the method is suitable for scenes such as physical damage, active removal and the like.

Aiming at the condition that the node failure condition is indirect failure, the operation parameters (such as load and capacity) of the node are modified to reach the operation parameters meeting the failure condition, so that the failure of the initial failure node is indirectly triggered, and the method is suitable for progressive failure scenes such as overload and resource exhaustion.

In particular, initial operating parameters, load, traffic currently assumed by the node or resource consumption (e.g., power in the power network, traffic in the communication network) may be read from the node attributes. Capacity (capability) is the maximum load threshold that a node can withstand (typically determined by hardware performance or design criteria). Weight (weight) the importance or transmission capacity of a node in the network (affecting the load distribution ratio).

An example is that the initial load of the node er2_4 is 40, the capacity is 50 (capacity_param=0.5×100), and the load ratio is 40/50=0.8.

Modifying the operating parameters to trigger failure-adjusting the parameters according to the fail rules of the simulation configuration-common ways include load increase-scaling up the load (e.g., doubling the load to 80, exceeding capacity 50). Capacity reduction-node capacity is scaled down (e.g., capacity reduction by 30% to 35, current load 40 exceeds new capacity). The combination of the adjustment is that the load is increased and the capacity is reduced (such as load +20, capacity-10, load ratio is more than 1) simultaneously.

Therefore, the direct failure can quickly reproduce instantaneous failure events such as physical equipment damage, artificial misoperation and the like by directly modifying the node state, the failure can be triggered without complex calculation, and the method is suitable for verifying the basic propagation path of cascade failure. The indirect failure can reproduce progressive failure caused by resource competition, service peak and the like by adjusting parameters such as load, capacity and the like, can determine critical conditions of node failure by fine adjustment of the parameters, and can test the suppression effect of different resource scheduling algorithms on overload risks by indirect failure simulation.

In step 204, a next failure node is determined based on the connection relationships and risk propagation manners of the plurality of network nodes, and the next failure node is controlled to fail.

After the initial failure node fails, the cascade failure effect needs to be simulated, so that the connection relation and the risk propagation mode of a plurality of network nodes are needed, the next failure node is determined, and the cascade failure effect can be simulated.

In some embodiments, the determining the next failure node based on the connection relationships of the plurality of network nodes and the risk propagation manner includes:

(1) When the risk propagation mode is direct propagation, selecting candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes;

(2) And determining each candidate network node as a next failure node.

Wherein, direct propagation refers to unconditionally following failure by the adjacent node of the initiating failure node without computing load or capacity change. This propagation method is suitable for strongly dependent scenarios of physical connections.

For example, disconnection of a transmission line in a power network causes power failure of a downstream substation, disconnection of an optical fiber in a communication network causes disconnection of adjacent base stations, and collapse of a bridge in a traffic network causes closure of adjacent road sections. The key characteristics are that the propagation path of the risk propagation is directly determined by the connection relation of the network topology, the speed is high, the range is wide, and the large-scale cascade failure is easy to cause.

Specifically, the candidate network nodes are network nodes with connection relation with the initial failure node, and each candidate network node is directly transmitted aiming at the risk transmission mode, and is directly determined to be the next failure node.

For example, a regional power network comprises 1 power station (G1) responsible for producing electricity, connected to a substation by transmission lines. 3. G1 is connected with S1 through a power transmission line L1, S1 is connected with S2 through L2, S2 is connected with S3 through L3, and S1 is connected with S3 through L4 (forming a ring topology to enhance reliability). The key connection relation is G1 ↔ S1 (L1), S1 ↔ S2 (L2);

S2 ↔ S3(L3);S1 ↔ S3(L4)。

the power station G1 directly fails due to failure;

step 1, triggering an initial failure node, wherein the triggering condition is that the power station G1 directly fails (direct failure mode) due to equipment faults (such as steam turbine damage), and the state is changed from 'operation' to 'failure', so that the power supply is stopped.

And 2, screening candidate network nodes (direct neighbors), wherein the node directly connected with the G1 is a transformer substation S1 (connected through L1) according to the network connection relation.

And 3, directly transmitting to cause S1 failure.

Propagation rules, direct propagation (strong dependence of physical connection, failure of power station, and no power input of downstream substation).

S1 is the next node loss because S1 loses power and synchronization fails (power cannot be distributed to users).

In this way, the fatal defect of the "single power-single link" topology is exposed by way of direct propagation. The rapid diffusion characteristic can help operation staff to identify single-point fault hidden trouble points (such as unique power stations and key power transmission lines L1) in the network in a visual way, and provide basis for topology optimization (such as adding a standby power supply G2 or a trans-regional power transmission line). The design loopholes caused by neglecting physical connection dependency relationships are avoided, for example, key nodes (such as hub substations) are forced to have dual power supply access in a newly built power grid.

In some embodiments, the method further comprises:

(1) When the risk propagation mode is indirect propagation, selecting candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes;

(2) Acquiring the operation parameters of the initial failure node;

(3) When the connection relation between each candidate network node and the initial failure node is not configured with a weight coefficient, calculating the ratio of the operation parameter of the candidate network node to the number of nodes of the candidate network node to obtain a first distribution parameter;

(4) Determining the sum of the operation parameters of each candidate network node and the first distribution parameters to obtain first distributed operation parameters;

(5) And determining the candidate network node of the operation parameter of which the operation parameter reaches the failure condition after the first distribution as the next failure node.

Wherein, indirect propagation refers to a process that an initiating failure node indirectly causes a neighboring node to fail through load transfer or resource consumption. Unlike direct propagation, it requires calculation of operating parameter changes (e.g., load, capacity), which are applicable to progressive failure scenarios such as overload, resource exhaustion, etc.

The application scene comprises an electric power network, a communication network, a traffic network and a traffic network, wherein the load of the electric power network is transferred to an adjacent transformer substation after the transformer substation fails and possibly causes overload, the traffic is shunted to the adjacent base station after the base station fails to cause bandwidth exhaustion, and the traffic network transfers traffic to surrounding roads after a road is closed to cause congestion diffusion.

Specifically, for the risk propagation mode, since the indirect propagation needs to calculate the change of the operation parameters, after the candidate network nodes with the connection relation with the initial failure node are screened out from the connection relation of a plurality of network nodes, the operation parameters of the initial failure node need to be obtained, if the connection relation between each candidate network node and the initial failure node is not configured with a weight coefficient, the operation parameters of the initial failure node need to be distributed to each candidate network node in average, and the average distributed parameters are the first distribution parameters. The first allocation parameter is calculated by calculating the ratio of the operation parameter of the candidate network node to the number of nodes of the candidate network node. And calculating the sum of the operation parameters of each candidate network node and the first allocation parameters to obtain the total operation parameters required to be born by each candidate network node after the initial failure node fails, namely the first allocated operation parameters, and determining the candidate network node of which the operation parameters after the first allocation reach the operation parameters of the failure condition as the next failure node.

For example, the initial failure node is a, the load of the candidate network nodes is B, C and D, a load of a is originally 12, and the failure load is 10, so that 12 needs to be evenly distributed to the candidate network nodes B, C and D, the first allocation parameter is 12/3=4, the operation parameters of the candidate network nodes B, C and D are 5, 6 and 7, respectively, so that the first allocated operation parameters of the candidate network nodes B, C and D are 9, 10 and 11, respectively, and the candidate network node D is determined as the next failure node because 11 exceeds the failure load 10.

Therefore, the gradual process from small disturbance to parameter shift to linkage failure in the closed base system is repeated through parameter calculation such as load transfer, capacity overload and the like. For example, a substation overload in an electrical power network does not occur instantaneously, but triggers as the load gradually builds up above a capacity threshold. The simulation can accurately capture the critical value of parameter change, such as failure risk mutation when the load rate is increased from 80% to 90%, and provides basis for early warning. Through load distribution calculation, a 'hidden risk node' with normal surface but actual approaching a failure threshold value can be found. For example, in traffic network simulation, the daily load rate of a certain road section is only 70%, but after the adjacent road section is closed, the load rate may rise to 95% to trigger congestion failure. The indirect transmission can expose the nodes with low daily risk and high coupling risk in advance, and guide operation and maintenance important monitoring.

In some embodiments, the method further comprises:

(1) When the connection relation between each candidate network node and the initial failure node is configured with a corresponding weight coefficient, calculating the product of the operation parameter of the candidate network node and the weight coefficient of each candidate network node to obtain a second distribution parameter of each candidate network node;

(2) Determining the sum value of the operation parameter of each candidate network node and the corresponding second allocation parameter to obtain a second allocated operation parameter;

(3) And determining the candidate network node of the operation parameter of which the second allocated operation parameter reaches the failure condition as the next failure node.

If the connection relation (i.e., edge) between each candidate network node and the initial failure node is configured with a corresponding weight coefficient, calculating an allocation parameter (i.e., a second allocation parameter) allocated to each candidate network node according to the weight coefficient of the connection relation between each candidate network node and the initial failure node, wherein a specific calculation mode is to determine a product of the operation parameter of each candidate network node and the weight coefficient of each candidate network node, and determining a candidate network node of the operation parameter of each candidate network node, which needs to be born by each candidate network node after the initial failure node is failed, as a next failure node by calculating a sum value of the operation parameter of each candidate network node and the corresponding second allocation parameter, namely, the second allocated operation parameter.

For example, the initial failure node is a, the load of the candidate network node is B, C and D, the load of a is originally 12, and the failure load reaches 10, so 12 needs to be allocated to the candidate network nodes B, C and D according to the weight coefficients corresponding to each candidate network node B, C and D, the weight coefficient of B is 0.3, the weight coefficient of C is 0.5, the weight coefficient of D is 0.2, the second allocation parameter of B is 12×0.3=3.6, the second allocation parameter of C is 12×0.5= 6,D, the second allocation parameter of C is 12×0.2=2.4, the operation parameters of the candidate network nodes B, C and D are 5, 6 and 7, respectively, such that the second allocated operation parameters of the candidate network nodes B, C and D are 8.6, 12 and 9.4, respectively, and the candidate network node C is determined as the next failure node because 12 exceeds the failure load 10.

Therefore, by simulating the differential connection strength, the weight coefficient can quantify the connection strength, the service dependency degree or the resource transmission capacity among the nodes, and the misleading of average allocation is avoided. According to the simulation result, the connection weight is manually or automatically adjusted to balance the load, so that the target node of the high-weight link is ensured to have enough capacity. In a multi-level, multi-type gateway network (e.g., a "power-communication-traffic" interconnection system), weights may define cross-industry dependencies. For example, the communication base station has a dependency weight of 0.9 (strong dependency) on power and a dependency weight of 0.2 (weak dependency) on a traffic system, and when the power node fails, 90% of the communication base station load fails directly due to power interruption, and only 20% fails indirectly due to traffic data interruption. Such cross-layer weight designs may simulate the "key infrastructure chain reaction" in the real world.

In step 205, the next failure node is determined as an initial failure node, and the step of determining the next failure node based on the connection relationships of the plurality of network nodes and the risk propagation mode is performed in a return manner until a termination condition is met, so as to complete a simulation test.

After determining the next failure node, since risk propagation is not stopped, determining the next failure node as an initial failure node, and returning to execute the step of determining the next failure node based on the connection relation of the plurality of network nodes and the risk propagation mode, so as to realize a circulation effect until the position of termination condition of risk propagation is met, thereby completing simulation test.

In some embodiments, the until the termination condition is met comprises:

(1) When the simulation test duration reaches the preset duration or the propagation step length of the risk propagation reaches the preset propagation step length, determining that the termination condition is met or

(2) When there is no next failed node, it is determined that the termination condition is satisfied.

The simulation termination condition is used for judging when to end the risk propagation process, so that the simulation can cover a complete cascading effect and avoid meaningless infinite circulation. Common application scenarios include:

1. Time/step control, limiting the simulation operation duration (such as the simulation of the power network cascade failure is completed within 1 hour). The step length is a propagation step length of risk propagation, namely, the step length is increased by one every time the next failure node is determined in a circulating manner. And when the simulation test duration reaches the preset duration or the propagation step length of the risk propagation reaches the preset propagation step length, determining that the termination condition is met.

2. And judging the stable state, namely automatically stopping to save computing resources when the network does not generate new failure nodes any more, namely, stopping when the next failure node does not exist, and determining that the state is stable and the stopping condition is met.

Therefore, simulation beyond a reasonable range can be forcibly terminated through time/step limitation, and resource waste caused by slow diffusion of cascade effect or abnormal model is avoided. And (3) identifying the network convergence point (if no new failure node exists) in time through the stable state judgment, and stopping the invalid circulation.

Referring to fig. 3, fig. 3 is a schematic diagram of a network relationship of a simulation test system according to an embodiment of the application. Taking the examples of the related industry range including electric power, traffic and communication industries, the electric power industry and the communication industry are mutually dependent, the electric power industry provides power for the communication industry, and the communication industry provides communication services for power stations in different areas. Network data in the industry, including data of physical connection, basic network topology, business relationship and the like, are collected, preprocessed and error data are removed.

If industry data is not collected, the network may be modeled, for example, considering the power network as a small world network in a complex network theory. This information helps to create a simulated power network directly from an existing network model (networkx libraries provide small world models).

If data can be collected, it can be saved as a network data file in pickle, gml, graphml format, etc. Based on the collected data, a network relation diagram of the power and communication industries is drawn, and an example is shown in fig. 3. And the power stations, the transformer stations and the power distribution stations are connected through physical transmission lines, and the different communication base stations are connected through optical fibers and the like. And the basic network topology is network connection of a plurality of hosts and devices in a local area network in the transformer substation. And the service relation is that a power utilization service exists between the user and the power distribution station, and a service dependency relation of power supply and communication service exists between the communication base station and the power distribution station.

The characteristic of complex expert knowledge in the physical equipment of the power and communication system is considered, so that a simulation technical scheme is selected as a virtualization mode. The specific simulation technology is selected based on networkx network libraries, all physical infrastructures are set as nodes in networkx libraries, and association relations are set as edges in networkx libraries.

And constructing basic networks of different related base industries in the related base simulation platform, namely a weighting-free network consisting of nodes and connected edges.

The concrete method is that based on networkx library functions, an empty network (called G) is directly created, and then network nodes and network edges are added.

And adding attributes such as weight, load and the like to the network according to the importance degree and the function of each node in the Guanyu industry.

The specific method is that based on networkx library functions, attributes are added to the network G, for example, weights are set according to the proportion according to the electric quantity obtained by a user from a power distribution station, for example, the weights corresponding to three sides can be set to be 0.2, 0.3 and 0.5 when the users 1,2 and 3 respectively obtain 20%, 30% and 50% of the total electric quantity of the power distribution station.

In a network where power and communications are connected, failure of one node may affect other nodes, such as a power station, leading to failure of the communication node that relies on it to power, and then to a disruption of communications, which is propagated, leading to a large-scale outage.

The simulation test elements are therefore selected from the group consisting of trigger source (node), risk trigger mode (load final power node failure), propagation mode (propagation to neighboring nodes according to dependency, e.g. node a failure, neighboring nodes dependent on a also fail directly), end condition (natural termination), suppression policy (adding a tie, e.g. node a failure, adding 1 alternative tie for neighboring nodes dependent on a, to node B with similar functionality as a).

Referring to fig. 4, fig. 4 is a schematic structural diagram of a simulation test apparatus according to an embodiment of the present application, where the simulation test apparatus is applied to a computer device. The simulation test apparatus may include a simulation network configuration unit 601, a first determination unit 602, a control unit 603, a second determination unit 604, an execution unit 605, and the like.

A simulation network configuration unit 601, configured to obtain simulation network configuration information, perform simulation network configuration according to the simulation network configuration information, and obtain a simulation network, where the simulation network includes at least one network layer, each network layer includes a plurality of network nodes, and the plurality of network nodes have a connection relationship;

A first determining unit 602, configured to determine, in response to a simulation start operation, an initial failure node from a plurality of network nodes according to a failure triggering condition;

the control unit 603 is configured to determine a failure mode of the initial failure node according to a node failure condition, and control the failure of the initial failure node according to the failure mode;

A second determining unit 604, configured to determine a next failure node based on connection relationships of a plurality of network nodes and risk propagation manners;

And the executing unit 605 is configured to determine the next failure node as an initial failure node, and return to execute the step of determining the next failure node based on the connection relationships of the plurality of network nodes and the risk propagation manner until a termination condition is met, so as to complete a simulation test.

In some embodiments, the first determining unit 602 includes:

a first selecting subunit, configured to randomly select, when the failure triggering condition is random triggering, one network node from a plurality of network nodes as an initial failure node;

and the second selecting subunit is used for selecting a network node meeting the target condition from the network nodes as an initial failure node according to the target condition corresponding to the condition triggering when the failure triggering condition is the condition triggering.

In some embodiments, the control unit 603 comprises:

A change subunit, configured to change, when a node failure condition is a direct failure, a state of the initial failure node from an operation state to a failure state;

and the modification subunit is used for modifying the operation parameters of the initial failure node into the operation parameters meeting the failure conditions when the node failure condition is indirect failure, and triggering the state of the node failure condition to be changed from the operation state to the failure state.

In some embodiments, the second determining unit 604 includes:

The first screening subunit is used for screening candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes when the risk propagation mode is direct propagation;

A first determining subunit, configured to determine each candidate network node as a next failure node.

In some embodiments, the second determining unit 604 further comprises:

The second screening subunit is used for screening candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes when the risk propagation mode is indirect propagation;

the acquisition subunit is used for acquiring the operation parameters of the initial failure node;

A first calculating subunit, configured to calculate, when a weight coefficient is not configured in a connection relationship between each candidate network node and the initial failure node, a ratio of an operation parameter of the candidate network node to a number of nodes of the candidate network node, to obtain a first allocation parameter;

A second determining subunit, configured to determine a sum of the operation parameter of each candidate network node and the first allocation parameter, to obtain a first allocated operation parameter;

And the third determining subunit is used for determining the candidate network node of the operation parameter, the operation parameter of which reaches the failure condition after the first allocation, as the next failure node.

In some embodiments, the second determining unit 604 further comprises:

A second calculating subunit, configured to calculate a product of an operation parameter of each candidate network node and a weight coefficient of each candidate network node when a connection relationship between each candidate network node and the initial failure node is configured with a corresponding weight coefficient, so as to obtain a second allocation parameter of each candidate network node;

A fourth determining subunit, configured to determine a sum of an operation parameter of each candidate network node and a corresponding second allocation parameter, to obtain a second allocated operation parameter;

and a fifth determining subunit, configured to determine, as the next failure node, a candidate network node whose second allocated operation parameter reaches the operation parameter of the failure condition.

In some embodiments, execution unit 605 includes:

A sixth determining subunit for determining that the termination condition is satisfied when the duration of the simulation test reaches a preset duration, or the propagation step length of the risk propagation reaches a preset propagation step length, or

When there is no next failed node, it is determined that the termination condition is satisfied.

The specific implementation of each unit can be referred to the previous embodiments, and will not be repeated here.

From the foregoing, it can be seen that in the embodiment of the present application, simulation network configuration information is obtained through a simulation network configuration unit 601, a simulation network is configured according to the simulation network configuration information, so as to obtain a simulation network, where the simulation network includes at least one network layer, each network layer includes a plurality of network nodes, a first determining unit 602 determines, in response to a simulation start operation, an initial failure node from a plurality of network nodes according to a failure triggering condition, a control unit 603 determines a failure mode of the initial failure node according to a node failure condition, and controls the initial failure node to fail according to the failure mode, a second determining unit 604 determines a next failure node based on a connection relationship and a risk propagation mode of the plurality of network nodes, an executing unit 605 determines the next failure node as the initial failure node, and returns to execute the steps of determining the next failure node based on the connection relationship and the risk propagation mode of the plurality of network nodes until a termination condition is satisfied, so as to complete a simulation test. According to the embodiment of the application, the connection relation between the network nodes is simulated through the simulation stage, and after the initial failure node is determined, the next affected failure node in the risk propagation is determined according to the connection relation of the network nodes and the risk propagation mode, so that the chain reaction in the cascade failure is simulated, and the simulation degree is improved.

Referring to fig. 5, fig. 5 is a block diagram of a portion of a computer device 1000 embodying an embodiment of the present disclosure. The computer device 1000 may vary considerably in configuration or performance and may include one or more central processing units (Central Processing Units, simply CPU) 622 (e.g., one or more processors) and memory 632, one or more storage mediums 630 (e.g., one or more mass storage devices) that store applications 642 or data 644. Wherein memory 632 and storage medium 630 may be transitory or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the server 600. Still further, the central processor 622 may be configured to communicate with a storage medium 630 and execute a series of instruction operations in the storage medium 630 on the server 600.

The computer device 1000 may also include one or more power supplies 626, one or more wired or wireless network interfaces 650, one or more input/output interfaces 658, and/or one or more operating systems 641, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.

The central processor 622 in the computer device 1000 may be used to perform the simulation test method of the embodiments of the present disclosure, for example:

Determining a next failure node based on the connection relation and the risk propagation mode of a plurality of network nodes, and controlling the next failure node to fail;

The embodiments of the present disclosure also provide a computer readable storage medium storing program code for executing the simulation test method of the foregoing embodiments.

The disclosed embodiments also provide a computer program product comprising a computer program. The processor of the computer device reads the computer program and executes it, causing the computer device to execute the simulation test method described above. For example:

Furthermore, the terms "comprises," "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe an association relationship of an associated object, and indicates that three relationships may exist, for example, "a and/or B" may indicate that only a exists, only B exists, and three cases of a and B exist simultaneously, where a and B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b or c may represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

It should be understood that in the description of the embodiments of the present application, plural (or multiple) means two or more, and that greater than, less than, exceeding, etc. are understood to not include the present number, and that greater than, less than, within, etc. are understood to include the present number.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. The storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk.

It should also be appreciated that the various embodiments provided by the embodiments of the present application may be arbitrarily combined to achieve different technical effects.

In the present embodiment, the term "module" or "unit" refers to a computer program or a part of a computer program having a predetermined function and working together with other relevant parts to achieve a predetermined object, and may be implemented in whole or in part by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Also, a processor (or multiple processors or memories) may be used to implement one or more modules or units. Furthermore, each module or unit may be part of an overall module or unit that incorporates the functionality of the module or unit.

The embodiments of the present application have been described in detail, but the present application is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and the equivalent modifications or substitutions are included in the scope of the present application as defined in the appended claims.

Claims

1. A simulation test method, comprising:

2. The method according to claim 1, wherein determining an initial failure node from a plurality of network nodes according to a failure trigger condition comprises:

When the failure triggering condition is random triggering, randomly selecting one network node from a plurality of network nodes as an initial failure node;

When the failure triggering condition is a condition triggering, selecting a network node meeting the target condition from the network nodes according to the target condition corresponding to the condition triggering as an initial failure node.

3. The simulation test method according to claim 2, wherein determining the failure mode of the initial failure node according to the node failure condition, and controlling the failure of the initial failure node according to the failure mode, comprises:

When the node failure condition is direct failure, changing the state of the initial failure node from an operation state to a failure state;

When the node failure condition is indirect failure, the operation parameter of the initial failure node is modified to the operation parameter meeting the failure condition, and the state of the node failure condition is triggered to be changed from the operation state to the failure state.

4. A simulation test method according to claim 3, wherein the determining a next failure node based on the connection relationships of the plurality of network nodes and the risk propagation manner comprises:

when the risk propagation mode is direct propagation, selecting candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes;

and determining each candidate network node as a next failure node.

5. The simulation test method according to claim 4, wherein the method further comprises:

when the risk propagation mode is indirect propagation, selecting candidate network nodes with connection relations with the initial failure node from the connection relations of a plurality of network nodes;

Acquiring the operation parameters of the initial failure node;

when the connection relation between each candidate network node and the initial failure node is not configured with a weight coefficient, calculating the ratio of the operation parameter of the candidate network node to the number of nodes of the candidate network node to obtain a first distribution parameter;

Determining the sum of the operation parameters of each candidate network node and the first distribution parameters to obtain first distributed operation parameters;

And determining the candidate network node of the operation parameter of which the operation parameter reaches the failure condition after the first distribution as the next failure node.

6. The simulation test method according to claim 5, further comprising:

When the connection relation between each candidate network node and the initial failure node is configured with a corresponding weight coefficient, calculating the product of the operation parameter of the candidate network node and the weight coefficient of each candidate network node to obtain a second distribution parameter of each candidate network node;

Determining the sum value of the operation parameter of each candidate network node and the corresponding second allocation parameter to obtain a second allocated operation parameter;

And determining the candidate network node of the operation parameter of which the second allocated operation parameter reaches the failure condition as the next failure node.

7. The simulation test method according to claim 6, wherein the step of until the termination condition is satisfied includes:

When the simulation test duration reaches the preset duration or the propagation step length of the risk propagation reaches the preset propagation step length, determining that the termination condition is met or

8. A simulation test apparatus, comprising:

9. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the simulation test method of any of claims 1 to 7.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the simulation test method of any of claims 1 to 7 when executing the computer program.