A complex system resilience evaluation method, device, medium and product

Abstract

The application discloses a complex system elasticity evaluation method, device, medium and product, relates to the technical field of complex systems and reliability, and comprises the following steps: setting an external damage strategy and an external damage type of a complex system, and performing external damage injection on the complex system to obtain a change value of a state variable of the complex system and a change value of an external damage input variable in a preset time period; then, according to a complex system convolution model, the change value of the state variable and the change value of the external damage input variable in the preset time period, the change value of an elasticity function of the complex system in the preset time period is calculated, and then the elasticity evaluation value of the complex system is determined. The application evaluates the elasticity of the complex system by combining system performance and external damage, effectively improves the accuracy of the elasticity evaluation of the complex system, and expands the application range of the elasticity evaluation of the complex system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of complex systems and reliability, and in particular to a complex system resilience evaluation method, device, medium and product. BACKGROUND

[0002] A complex system is a system composed of many interacting members, and the members usually have interaction relationships such as dependence, conflict, and cooperation. The complexity of these relationships makes the system exhibit different behaviors and changes, and also makes the analysis of the system beyond the dimension of ordinary systems. In today's technological and social development background, the structure of matter and energy controlled by human beings is becoming larger and larger, and complex systems have become a universal form of connection between things, such as urban systems, equipment systems, and transportation systems. There are extensive individual behaviors, individual collaborative behaviors, and collective intelligent behaviors in complex systems, which endow the complex systems with the characteristics of self-organization, self-adaptation, and self-recovery. However, due to the existence of complex dependence, conflict, and cooperation interaction relationships in complex systems, the complex systems become extremely fragile when facing disruptive events. Once a disturbance or destruction occurs, the complex system is likely to have a cascading failure, which greatly reduces its survivability. Complex system research has become a hot and frontier topic in artificial intelligence and complex system research. It not only helps to explain and understand the intelligent characteristics and evolution laws of complex groups emerging in nature and society, but also helps to solve technical difficulties in engineering and military fields, and provides further innovation of theories and methods for the research frontiers of artificial intelligence and swarm intelligence.

[0003] System resilience is a comprehensive concept derived from safety, reliability, and other concepts in the face of recovery after disturbance. It is defined as the ability of a system to absorb and resist the impact of destruction and recovery when the system is destroyed. Adaptation, robustness, and recovery are the key elements that distinguish resilience from other concepts. Resilience theory is being applied in different fields such as psychology, economics, and engineering. Developing analysis and evaluation research on complex system resilience has far-reaching significance and influence on improving the survivability and rapid recovery capability of complex systems in the face of complex and changing tasks, unknown and uncertain environments, and extreme conditions or deliberate attacks. It is becoming an important part of complex system research. Existing resilience evaluation methods are based on the perspective of performance changes of complex systems, considering the time domain characteristics or correlation function characteristics of state response, and starting from the performance of complex systems to evaluate and analyze the resilience of complex systems. However, the existing resilience evaluation methods have limitations such as low accuracy and low applicability. SUMMARY

[0004] The applicant finds that the performance change of a complex system depends not only on the inherent resilience mechanism of the complex system itself, but also on the disturbance or destruction of the complex system by the outside world. In view of this, the present application provides a complex system resilience evaluation method, device, medium and product. The resilience of the complex system is evaluated by combining the performance of the complex system and the external destruction, so that the accuracy of the complex system resilience evaluation can be effectively improved, and the application range of the complex system resilience evaluation is expanded.

[0005] To achieve the above object, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a complex system resilience evaluation method, comprising:

[0007] An external destruction strategy and an external destruction type of the complex system are set. The external destruction strategy is a strategy of randomly causing a node in the complex system to enter a failure state according to a certain probability distribution. The node is a basic unit in the complex system.

[0008] The complex system is externally destroyed according to the external destruction strategy and the external destruction type, to obtain a change value of a state variable of the complex system and a change value of an external destruction input variable in a preset time period. The preset time period is a time from the start of external destruction injection to the time when the complex system reaches a balanced state again after resilience recovery. The state variable is a variable representing the performance state of the complex system. The external destruction input variable is a variable representing the interference of the external destruction to the complex system. The external destruction input variable is determined according to the external destruction type.

[0009] According to a complex system convolution model, the change value of the state variable in the preset time period and the change value of the external destruction input variable in the preset time period, a change value of a resilience function of the complex system in the preset time period is calculated. The complex system convolution model is a model representing that the change amount of the state variable of the complex system is equal to the convolution of the external destruction input variable and the resilience function.

[0010] According to the change value of the resilience function of the complex system in the preset time period, a resilience evaluation value of the complex system is determined.

[0011] In a second aspect, the present application provides a computer device, comprising a memory, a processor, a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the complex system resilience evaluation method of the first aspect.

[0012] In a third aspect, the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps of the complex system elasticity evaluation method of the first aspect.

[0013] In a fourth aspect, the present application provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the complex system elasticity evaluation method of the first aspect.

[0014] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0015] The present application provides a complex system elasticity evaluation method, device, medium and product. The present application establishes the relationship between the system state change of the complex system and the external damage input and the system elasticity through the complex system convolution model, and then in the complex system elasticity evaluation process, the external damage injection is performed on the complex system according to the set external damage strategy and the external damage type, and the change value of the state variable and the change value of the external damage input variable in the preset period are collected. Then, according to the complex system convolution model, the change value of the state variable in the preset period and the change value of the external damage input variable in the preset period, the change value of the elasticity function of the complex system in the preset period is calculated, and according to the change value of the elasticity function of the complex system in the preset period, the elasticity evaluation value of the complex system is determined. The elasticity of the complex system is evaluated in combination with the system performance and the external damage, the accuracy of the complex system elasticity evaluation is effectively improved, and the application range of the complex system elasticity evaluation is expanded. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 A flowchart of a complex system elasticity evaluation method provided by an embodiment of the present application is shown in the figure.

[0018] Figure 2 A flowchart of a state variable determination process provided by an embodiment of the present application is shown in the figure.

[0019] Figure 3 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0020] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without creative effort should fall into the scope of the present application.

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0022] In an exemplary embodiment, as shown in Figure 1 A complex system resilience evaluation method is provided, including the following steps 101 to 104. Wherein:

[0023] Step 101, setting the external damage strategy and the external damage type of the complex system; the external damage strategy is a strategy of randomly making the nodes in the complex system enter a fault state according to a certain probability distribution; the node is a basic unit in the complex system.

[0024] The external damage represents the influence of the external environment on the system state. For a complex system, when natural disasters, natural disturbances, enemy attacks and other external damages affect the system, the system state will degrade to a certain extent. System resilience is based on the damage of the complex system. Only when the complex system is affected by external damage, the system resilience will truly be shown.

[0025] In an exemplary embodiment, the certain probability distribution is determined according to historical failure data of the complex system.

[0026] That is, the external damage strategy (random node damage strategy) refers to the individual failure caused by the random failure of individuals (basic units) in the complex system or the random attack of external factors, and follows a certain probability distribution. When analyzing and simulating such damage forms, the accuracy of the distribution and parameters should be improved by referring to historical failure data, and an exponential distribution can usually be used instead.

[0027] In an exemplary embodiment, the external damage strategy is embodied on a network model as follows: randomly select network nodes and their edges for damage according to the probability distribution, so that the nodes enter a redundant (backup) or fault state. If the node is in a fault state, the edges between this node and other nodes can be disconnected.

[0028] Step 102, according to the external damage strategy and the external damage type, performing external damage injection on the complex system to obtain a change value of a state variable of the complex system and a change value of an external damage input variable within a preset time period; wherein the preset time period is a time from the beginning of the external damage injection to the complex system reaching a balance state again after elastic recovery; the state variable is a variable representing a performance state of the complex system; the external damage input variable is a variable representing an interference situation of the external damage to the complex system; and the external damage input variable is determined according to the external damage type.

[0029] In the present application, the state variable of the complex system is a set of variables used to describe the internal behavior of the complex system. In the following, the determination process of the state variable of the complex system is described by taking a certain unmanned aerial vehicle (UAV) complex system as an example. The certain UAV complex system includes 100 UAVs, and the UAVs interact with each other through a wireless communication mode to form an interactive communication network. In this example, the state variable is used to describe and characterize the current network communication function completion of the UAV complex system, and therefore, the network performance parameters are used as the state variable of the complex system. Specifically, Figure 2 The flowchart of the determination process of the state variable in this embodiment is shown in FIG. 2. Figure 2 The determination process specifically includes the following steps:

[0030] Step 201, analyzing the basic units and the interaction relationship of the complex system.

[0031] In this example, the basic constituent units and the interaction relationship between the units of the certain UAV complex system are analyzed, including the number of UAVs, the communication performance of a single UAV, and the communication relationship between the UAVs, which together constitute a system structure description of the complex system, provide system information for the abstraction of the complex system network model, and support the acquisition of the performance indicators of the UAV complex system network model.

[0032] Step 202, establishing a network model of the complex system.

[0033] In this embodiment, the UAV complex system has obvious network characteristics. Based on the system structure description obtained by step 201, the UAV complex system is abstracted into a network model including a point set and an edge set. As an optional implementation manner, the complex system network model can be represented as a five-tuple G=(K,L,F K ,F L ,P), wherein,

[0034] K={n1,n1,…n N} is the node set of G, and the node represents a basic unit in the complex system network. N is the number of nodes in G.

[0035] L = [l ij ] is a connection set or edge set of G, representing the interaction relationship (connection relationship) between the nodes of the complex system network, ij representing the interaction relationship between node n i and node n j . The connection relationship between the nodes of the complex system network can be represented by an adjacency matrix;

[0036] is an attribute set of node set K, representing the attribute of node n i , is the jth attribute value of node n i , such as node type, node load, node importance, etc.

[0037] is an attribute set of connection set L, representing the attribute of l ij , is the rth, r∈{1,2,...,z} attribute value of l ij , such as wired or wireless connection, connection type, connection distance, etc.

[0038] P = {P1, P2,..., P V} is a community set in G, representing a point-edge set decomposed due to different tasks or attributes in a heterogeneous complex network.

[0039] Step 203, taking the network model performance index as the state variable of the complex system.

[0040] In this embodiment, based on the network model topology, the communication performance of the unmanned aerial vehicle complex system is taken as the state variable, and the communication performance is an index for measuring the communication rate of the unmanned aerial vehicle complex system. The communication performance p(t) of the unmanned aerial vehicle complex system is calculated as follows:

[0041]

[0042] where d i,j (t) represents the topological distance between node i and node j of the complex system network at time t; N is the total number of nodes in the network. That is, the communication performance of the unmanned aerial vehicle complex system is the reciprocal of the topological distance.

[0043] In addition, for other complex systems, for example, a large power grid complex system, the flow entropy E of the nodes and edges is taken as a physical index representing the state variable, and the calculation formula is as follows:

[0044]

[0045] wherein k represents a node or an edge in the network corresponding to the large power grid complex system; I k represents the flow of node or edge k; the node includes a power generation bus, a power transmission bus, a power distribution bus, a substation, a transformer, and the like in the large power grid complex system; and the edge represents a connecting line (transmission cable) between nodes.

[0046] For the equipment complex system, the total number of combat loops is taken as a physical index representing the state variable thereof, and the calculation formula thereof is as follows:

[0047]

[0048] wherein S k represents the total number of combat loops with a length of k; k represents the number of edges in the combat loop; the combat loop network of the equipment complex system can be represented by an adjacency matrix A, and the element represents the number of loops (combat loops) passing through node i and having a length of k in the adjacency matrix A; the node represents an (weapon) equipment in the equipment complex system; and the edge represents a communication and interaction relationship between nodes.

[0049] In the present application, the external damage input variable is determined according to the type of external damage. The external damage input variable can represent the change of the state variable of the complex system under only damage strategy, or the influence (interference) of the external damage on the state variable of the complex system. The function form of the external damage input variable is different for different damage cases or types.

[0050] In an exemplary embodiment, when the type of external damage is single damage, the expression of the external damage input variable is as follows:

[0051]

[0052] wherein d(t) represents the external damage input variable; t represents the independent variable time; δ(t) is a unit impulse function; H is the coefficient of the unit impulse function δ(t); t e is the injection time of the external damage; and ε is the action time length of the external damage, which is a minimum value approaching 0.

[0053] That is, if the external damage is single damage, i.e., only one damage is performed, the external damage input variable d(t) is constructed as a pulse function.

[0054] In an exemplary embodiment, when the type of external damage is multiple continuous stable damage, the expression of the external damage input variable is as follows:

[0055]

[0056] wherein d(t) represents an external damage input variable; t represents an independent variable time; u(t) is a unit step function; p d is a coefficient of the unit step function u(t); t e is an injection time of the external damage.

[0057] That is, if the external damage is a plurality of continuous stable damages, that is, a damage is performed every certain time, and the damage degree is the same (for example, the number of fault nodes generated by each damage is the same), the external damage input variable d(t) is constructed as a step function.

[0058] In an exemplary embodiment, when the external damage type is a plurality of continuous growth damages, the expression of the external damage input variable is:

[0059]

[0060] wherein d(t) represents an external damage input variable; t represents an independent variable time; λ(t) is a unit ramp function; v d is a coefficient of the unit ramp function λ(t); t e is an injection time of the external damage.

[0061] That is, if the external damage is a plurality of continuous growth damages, that is, a damage is performed every certain time, and the damage degree increases (for example, the number of fault nodes generated by each damage is more than before), the external damage input variable d(t) is constructed as a ramp function.

[0062] In addition, a person skilled in the art can determine the type of damage input according to the actual situation. The damage effect can not only be modeled as a linear and continuous signal function, but for more complex situations of damage input variation, actual data can be considered to abstract a more complex nonlinear and discrete signal.

[0063] In step 103, a change value of the resilience function of the complex system in the preset period is calculated according to the complex system convolution model, the change value of the state variable in the preset period, and the change value of the external damage input variable in the preset period; the complex system convolution model is a model representing that a change amount of a state variable of a complex system is equal to a convolution of an external damage input variable and a resilience function.

[0064] In the present application, the external damage is defined as an input of the system, the input causes a change of the state of the complex system, and a change characteristic of the input causing the state to resist, absorb, and recover can be regarded as the resilience of the system. Therefore, the resilience of the complex system is modeled as a transfer function R(s) of an input and a state by using the following formula:

[0065] P(s) = R(s) x D(s);

[0066] where P(s) represents the state of the complex system in the complex domain; R(s) represents the resilience of the complex system in the complex domain; and D(s) represents the external damage input of the complex system in the complex domain.

[0067] Further, a time-domain function relationship among the state response, the external damage and the resilience of the complex system, i.e., a convolution model, is derived.

[0068]

[0069] where △p(t) is a complex system state variable change (decrease), p(t) represents a state variable of the complex system; p0 represents an initial state variable value of the complex system; t represents an independent variable time; r(t) represents a resilience function of the complex system; d(t) represents an external damage input variable of the complex system; and the symbol * represents a convolution operation.

[0070] Based on the above convolution model, the following resilience function of the complex system can be obtained.

[0071] (1) If d(t) is a pulse function, the resilience function is:

[0072] r(t) = p0-p(t);

[0073] (2) If d(t) is a step function, the resilience function is:

[0074]

[0075] (3) If d(t) is a ramp function, the resilience function is:

[0076]

[0077] (4) If d(t) is not the above cases, according to the convolution theorem, i.e., the product of the Fourier transforms of two functions is equal to the Fourier transform of their convolution, and by inverse Fourier transform, the resilience function r(t) is obtained,

[0078]

[0079] where F(p(t)) represents the Fourier transform of the state variable p(t); F(d(t)) represents the Fourier transform of the damage input variable d(t); and F -1 (·) represents the inverse Fourier transform.

[0080] That is, in the case where the damage input variable d(t) and the state variable p(t) are known, the resilience function can be obtained.

[0081] Step 104, determining the elasticity evaluation value of the complex system according to the change value of the elasticity function of the complex system in the preset time period.

[0082] In one exemplary embodiment, the step 104 specifically comprises:

[0083] averaging the change value of the elasticity function of the complex system in the preset time period to obtain the elasticity evaluation value of the complex system.

[0084] In this embodiment, the elasticity of the complex system is evaluated by the following formula:

[0085]

[0086] wherein, Я I is the elasticity index of the complex system; r(t) is the elasticity function of the complex system; t e is the moment of external damage injection; t f is the moment when the complex system is restored to the equilibrium state after elasticity; r i is the discrete value of the elasticity function, and n is the number of the discrete values of the elasticity function r i .

[0087] The following still takes the complex system of a certain unmanned aerial vehicle as an example to further illustrate the method of the present application. In this case, a node in the complex network of the unmanned aerial vehicle is randomly selected every unit time for damage and the edges between the node and its adjacent nodes are removed, and this continues for a certain time, i.e. the damage type is multiple continuous stable damage (the damage input variable is structured as a step function). The external damage is injected into the complex system in the above-mentioned manner, and the change value of the state variable of the complex system and the change value of the damage input variable are monitored. It is assumed that the coefficient p d of the damage input variable = 1.

[0088] The discrete function values of the external damage input function d(t) under the unit time collection frequency are shown in Table 1,

[0089] Table 1 Discrete function value table of external damage input

[0090]

[0091]

[0092] Under the condition of the random node damage strategy, the state variable change trend of the communication performance of the complex system network is monitored by software or hardware at a fixed time interval until the damage strategy ends and the state variable is restored to a certain degree and remains unchanged. All the values of the state variable from before the damage strategy starts to after the state variable is restored to remain unchanged are summarized for use in elasticity analysis. In this embodiment, the discrete values of the state variable of the complex system obtained are shown in Table 2,

[0093] Table 2 Discrete value table of state variables of complex system

[0094] t p 0 1 1 1 2 1 3 0.996711 4 0.966294 5 0.939627 6 0.916248 7 0.895751 8 0.877781 9 0.862027 10 0.848215 11 0.836106 12 0.82549 13 0.816183 14 0.808023 15 0.80087 16 0.794598 17 0.7891 18 0.784279 19 0.780053 20 0.776348

[0095] It can be understood that the above-mentioned complex system performance parameters are only an example provided by the embodiments of the present application, and other performance parameters can be selected by those skilled in the art according to actual conditions to perform elasticity evaluation and verification.

[0096] Based on the system performance (state variable) and system external attack (external damage input variable) data of Table 1 and Table 2, the discrete values of the elasticity function can be obtained, as shown in Table 3:

[0097] Table 3 Discrete value table of elasticity function

[0098] t r 0 0 1 0 2 0 3 0.28542 4 0.25023 5 0.21938 6 0.19233 7 0.16862 8 0.14783 9 0.1296 10 0.11363 11 0.09962 12 0.08733 13 0.07657 14 0.06713 15 0.05885 16 0.0516 17 0.04523 18 0.03966 19 0.03477 20 0.03048

[0099] Based on the elasticity function data of Table 3, the elasticity evaluation value of the complex system of the unmanned aerial vehicle can be calculated as 0.1166.

[0100] The present application also provides an application scenario, which applies the complex system elasticity evaluation method described above. Specifically, the complex system elasticity evaluation method provided by the embodiments can be applied in the scenario of complex system elasticity evaluation.

[0101] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and its internal structure diagram can be as shown in Figure 3 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for related data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a complex system elasticity evaluation method.

[0102] Those skilled in the art can understand that, Figure 3The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0103] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[0104] In an exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0106] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0107] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0108] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0109] The principles and implementation modes of the present application are described by applying specific examples in the present application. The above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for complex system resilience assessment, characterized in that, The complex system elasticity evaluation method comprises: setting an external damage strategy and an external damage type of the complex system; the external damage strategy is a strategy of randomly making a node in the complex system enter a failure state according to a certain probability distribution; the node is a basic unit in the complex system; performing external damage injection on the complex system according to the external damage strategy and the external damage type, to obtain a change value of a state variable of the complex system and a change value of an external damage input variable within a preset time period; wherein the preset time period is a time from the start of external damage injection to the complex system reaching a balance state again after elasticity recovery; the state variable is a variable representing a performance state of the complex system; the external damage input variable is a variable representing an interference situation of the external damage to the complex system; the external damage input variable is determined according to the external damage type; calculating a change value of an elasticity function of the complex system within the preset time period according to a complex system convolution model, the change value of the state variable within the preset time period, and the change value of the external damage input variable within the preset time period; the complex system convolution model is a model representing that a change amount of a state variable of a complex system is equal to a convolution of an external damage input variable and an elasticity function; determining an elasticity evaluation value of the complex system according to the change value of the elasticity function of the complex system within the preset time period.

2. The method of claim 1, wherein, The expression of the complex system convolution model is: p0-p(t)=r(t)*d(t); wherein p(t) represents a state variable of a complex system; t represents an independent variable time; p0 represents an initial state variable value of the complex system; r(t) represents an elasticity function of the complex system; d(t) represents an external damage input variable of the complex system; and the symbol * represents a convolution operation.

3. The method of claim 1, wherein, When the external damage type is single damage, the expression of the external damage input variable is: wherein d(t) represents an external damage input variable; t represents an independent variable time; δ(t) is a unit impulse function; H is a coefficient of the unit impulse function δ(t); t e is an injection time of the external damage; and ε is an action duration of the external damage.

4. The method of claim 1, wherein, When the external damage type is multiple continuous stable damage, the expression of the external damage input variable is: wherein d(t) represents an external damage input variable; t represents an independent variable time; u(t) is a unit step function, p d is a coefficient of the unit step function u(t); t e is an injection time of the external damage.

5. The method of claim 1, wherein, When the external damage type is multiple continuous growth damage, the expression of the external damage input variable is: where d(t) represents an external damage input variable; t represents an independent variable time; λ(t) is a unit ramp function; v d is a coefficient of the unit ramp function λ(t); t e is an injection time of the external damage.

6. The method of claim 1, wherein, The determination of the elasticity evaluation value of the complex system according to the change value of the elasticity function of the complex system within the preset time period specifically comprises: performing an averaging operation on the change value of the elasticity function of the complex system within the preset time period, to obtain the elasticity evaluation value of the complex system.

7. The method of claim 1, wherein, The certain probability distribution is determined according to historical failure data of the complex system.

8. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the complex system elasticity evaluation method in any one of claims 1-7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the complex system elasticity evaluation method in any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the complex system elasticity evaluation method in any one of claims 1-7.

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