A method and system for evaluating the task completion capability of a network entity

Abstract

The present application relates to a kind of network entity task completion ability evaluation method and evaluation system, belong to network capability evaluation technical field, solve the problem of lack of network entity task completion ability evaluation scheme in prior art.The method comprises: network entity is mapped into network digital twin system, obtains all capability states of network digital twin system;Based on all capability states of network digital twin system, construct system state transition probability matrix;Capability state is divided into available state and unavailable state;Based on system state transition probability matrix, obtain the transient probability when network digital twin system is in each available state;Based on the performance index of network digital twin system, obtain the task completion ability value when network digital twin system is in each available state;Based on the transient probability and task completion ability value when network digital twin system is in all available states, obtain the task completion ability evaluation result of network entity.

Description

Technical Field

[0001] This invention relates to the field of network capability assessment technology, and in particular to a method and system for evaluating the task completion capabilities of network entities. Background Technology

[0002] In recent years, the internet industry has flourished, reaching a stage of technological maturity and solidified business models. Various industries have greatly expanded online work methods and business operations, making the integration of the physical and virtual worlds, and digital services based on digital twins, a current development focus. Furthermore, the emergence of the metaverse concept for future application scenarios has gradually led to the formation of integrated future solutions that leverage existing mobile internet development foundations, comprehensively integrate various application models, and fully utilize various key technologies.

[0003] In the future, the metaverse will become a parallel space to the physical world in which people work and live. During its synchronous operation and interaction with the physical world, a quantitative evaluation method is needed to assess how digital twins in the virtual world simulate physical entities and the quality of their simulation. However, physical entities are complex and dynamically changing, making it difficult to evaluate their capabilities in a fixed way. Therefore, exploring a capability evaluation method for digital twin systems used in metaverse applications is particularly important.

[0004] Since the 1960s, researchers in this field have been studying the problem of system capability evaluation, and after many years of development, they have achieved considerable research results. However, existing capability evaluation methods do not address the evaluation of the task completion capabilities of network entities, which is of great guiding significance for the actual operation of network entities. Therefore, it is essential to propose a method for evaluating the task completion capabilities of network entities. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method and evaluation system for evaluating the task completion capabilities of network entities, in order to solve the problem of the lack of evaluation schemes for the task completion capabilities of network entities in the prior art.

[0006] On one hand, the present invention provides a method for evaluating the task completion capability of network entities, including: Map network entities to a network digital twin system and obtain all capability states of the network digital twin system; Based on all capability states of the network digital twin system, a system state transition probability matrix is ​​constructed; the capability states are divided into available states and unavailable states. Based on the system state transition probability matrix, the transient probability of the network digital twin system in each available state is obtained; Based on the performance indicators of the network digital twin system, obtain the task completion capability value of the network digital twin system in each available state; Based on the transient probabilities and task completion capability values ​​of the network digital twin system in all available states, the task completion capability evaluation results of the network entity are obtained.

[0007] Based on the above solution, the present invention makes the following improvements: Furthermore, the evaluation results of the task completion capabilities of the network entities. for: (1) in, This indicates that the network digital twin system is in a usable state. The task completion ability value at that time. This indicates that the network digital twin system is in a usable state. The transient probability at time , Indicates time, This represents the total number of nodes that fail when the network digital twin system completely loses its functionality.

[0008] Furthermore, the performance indicators include several primary indicators, and each primary indicator includes several secondary indicators. The process of obtaining the task completion capability value of the network digital twin system in each available state based on the performance indicators of the network digital twin system includes: Obtain the primary indicators and secondary indicators under each primary indicator for each available state of the network digital twin system; The weight of each primary indicator is obtained based on the values ​​of the secondary indicators under each primary indicator. Based on the weights of all primary and secondary indicators, the task completion capability value of the network digital twin system in the corresponding available state is obtained.

[0009] Furthermore, obtaining the weight of each primary indicator includes: The values ​​of the secondary indicators of the network digital twin system in each available state are obtained and normalized respectively to obtain the normalized values ​​of the corresponding secondary indicators. The weight of each secondary indicator is obtained by taking the normalized values ​​of all secondary indicators under the same primary indicator. The weight of the corresponding primary indicator is obtained by calculating the weights of all secondary indicators under the same primary indicator.

[0010] Furthermore, the first The first available state The first primary indicator Weights of each secondary indicator for: (2) in, Indicates the first The first available state The first primary indicator Normalized values ​​of each secondary indicator; Take 1 to , This indicates the total number of categories of secondary indicators under the current primary indicator.

[0011] Furthermore, the first The first available state Weight of each primary indicator for: (3) in, Indicates the first The first available state The entropy value of the weight of each primary indicator. This indicates the total number of categories for the primary indicators; (4).

[0012] Furthermore, the network digital twin system is in a usable state. Task completion ability value at that time for: (5).

[0013] Furthermore, the performance indicators include two primary indicators: transmission capacity and operation and maintenance capacity; The transmission capability includes three secondary indicators: node coverage, link termination time, and transmission connectivity angle. The operation and maintenance capabilities include three secondary indicators: maintainability, electromagnetic protection capability, reliable transmission capability, and access feedback capability.

[0014] Furthermore, all capability states of the network digital twin system include: Capability Status 0: All nodes in the network digital twin system are in good condition, and all functions of the network digital twin system are functioning normally. Capability Status 1: In the network digital twin system, one node is in a fault state, but the network digital twin system can still meet certain functional requirements; Ability Status The network digital twin system has Even when one node is in a faulty state, the network digital twin system can still meet certain functional requirements; ; Ability Status The network digital twin system has If one or more nodes are in a faulty state, the network digital twin system is in a faulty state and cannot meet any functional requirements; Among them, capability status 0 to All are in an available state, capability status It is in an unavailable state.

[0015] On the other hand, the present invention provides a network entity task completion capability evaluation system, comprising: The capability status acquisition module is used to map network entities into a network digital twin system and acquire all capability statuses of the network digital twin system. The system state transition probability matrix construction module is used to construct a system state transition probability matrix based on all capability states of the network digital twin system; the capability states are divided into available states and unavailable states; The transient probability acquisition module is used to acquire the transient probability of the network digital twin system in each available state based on the system state transition probability matrix. The task completion capability value acquisition module is used to acquire the task completion capability value of the network digital twin system in each available state based on the performance indicators of the network digital twin system. The task completion capability evaluation module is used to obtain the task completion capability evaluation result of the network entity based on the transient probability and task completion capability value when the network digital twin system is in all available states.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: The method and system for evaluating the task completion capabilities of network entities provided by this invention have the following beneficial effects: First, this invention constructs a system state transition probability matrix by analyzing all capability states of the network digital twin system. Based on this matrix, it obtains the transient probability and task completion capability value of the network digital twin system in each available state, ultimately yielding the task completion capability evaluation result of the network digital twin system, i.e., the task completion capability evaluation result of the network entity. This result reflects the task completion capability of the network digital twin system, possesses strong dynamism, and can effectively reflect the changes in the comprehensive capabilities of the network entity and the network digital twin system over time.

[0017] Second, in the technical solution provided by the present invention, the process of obtaining the task completion capability value involves multiple primary and secondary indicators, which can construct corresponding indicator systems from different perspectives to evaluate the task completion capability of network entities from the corresponding perspectives.

[0018] Third, the network entity task completion capability evaluation method and evaluation system provided by the present invention effectively solves the problem of the lack of network entity task completion capability evaluation schemes in the prior art, fills the technical gap, and provides technical guidance for those skilled in the art to evaluate the capabilities of network entities from the perspective of task completion.

[0019] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0021] Figure 1 This is a flowchart of the network entity task completion capability evaluation method provided in Embodiment 1 of the present invention; Figure 2 This is a state transition diagram of the network digital twin system provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the network entity task completion capability evaluation system provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0023] A specific embodiment of the present invention discloses a method for evaluating the task completion capability of network entities, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps: Step S1: Map the network entities into a network digital twin system and obtain all capability states of the network digital twin system; In this embodiment, the nodes and links in the network entity are mapped to the corresponding nodes and links in the network digital twin system, and the failure rate and repair rate of the nodes in the network digital twin system are kept consistent with the failure rate and repair rate of the corresponding nodes in the network entity.

[0024] It should be emphasized that the capability assessment method in this embodiment is based on the Markov birth-death process. Therefore, the following conditions must be met when implementing this method: (1) All nodes in the network digital twin system are of the same type; Here, "nodes of the same type" specifically refers to nodes in a network digital twin system having the same level and function.

[0025] (2) All links in the network digital twin system are working normally; (3) During the operation of the network digital twin system, when a node fails, its functionality can be restored through supplementation or backup replacement, and the probability of a single node being repaired is [missing information]. , exist The probability of repair within a time limit is ; (4) During the operation of the network digital twin system, within a very small time interval, there is one and only one node that fails or is repaired; (5) When some nodes of the network digital twin system fail, the network digital twin system completely loses its functional capabilities, and from then on, no node continues to fail.

[0026] In step S1, it is assumed that the network digital twin system is composed of It consists of nodes, and when the number of nodes that fail is (0< ≤ When this happens, the network digital twin system completely loses its functionality; Right now, This represents the total number of nodes that fail when the network digital twin system completely loses its functionality. Therefore, during the task preparation and execution phases, the capability states of the network digital twin system include the following: Capability Status 0: All nodes in the network digital twin system are in good condition, and all functions of the network digital twin system are working properly; Capability Status 1: Even if one node in the network digital twin system is in a fault state, the network digital twin system can still meet certain functional requirements. Capability Status 2: Two nodes in the network digital twin system are in a fault state, but the network digital twin system can still meet certain functional requirements; ... Ability Status The network digital twin system contains Even if one node is in a faulty state, the network digital twin system can still meet certain functional requirements; ; ... Ability Status -1: The network digital twin system contains Even if one node is in a faulty state, the network digital twin system can still meet certain functional requirements; Ability Status The network digital twin system contains If one or more nodes are in a faulty state, the entire network digital twin system is in a faulty state and cannot meet any functional requirements.

[0027] Step S2: Based on all the capability states of the network digital twin system, construct the system state transition probability matrix; Specifically, step S2 includes: Step S21: Determine the system state transition probability based on all capability states of the network digital twin system; Based on the preconditions set in step S1, it is known that in this embodiment, the network digital twin system includes a total of A capability status; The state space formed by the capability states can be represented as: {0, 1, 2, 3, ..., ...} The available states include {0, 1, 2, 3, ...,}. 1}, the fault state (also known as the "unavailable state") is { }

[0028] Assuming the state transition process of a network digital twin system satisfy It is a homogeneous Markov process that is continuous in time and has finite states, with a state space. I ={0,1,2,3,···, Assume that the failure rate and repair rate of each node in the network digital twin system are respectively... and So, what is the ability status? Transition to ability state State transition probability satisfy: (1) When and hour, ; in, , Indicates time, Numbers that represent infinitesimals; At this time, the ability status changes from Transferred to This indicates that a faulty node has been added to the network digital twin system; (2) When and hour, ; At this time, the ability status changes from Transferred to This indicates that a node in the network digital twin system has been repaired.

[0029] (3) When and hour, ; At this time, the ability status changes from Transferred to This indicates that in a network digital twin system, one node fails and another node is repaired within an infinitesimally short period of time.

[0030] (4) When and hour, ; Because within a very small time interval, there is exactly one node that fails or recovers, therefore, , It is a value with an extremely small probability, expressed as .

[0031] The state transition diagram of a network digital twin system is as follows: Figure 2 As shown.

[0032] Step S22: Construct the system state transition probability matrix based on the system state transition probabilities; In constructing the system state transition probability matrix based on the system state transition probabilities, the influence of time needs to be eliminated first. Also, because... It is an infinitesimal number, here Approximately 0, its influence is not considered during matrix construction. Therefore, capability state Transition to ability state baseline state transition probability satisfy: (1) When and hour, ; (2) When and hour, ; (4) When and hour, ; (4) When and hour, ; According to the homogeneous Markov process, the system state transition probability matrix must satisfy the following condition: The sum of the elements in each row of the system state transition probability matrix is ​​0, the diagonal elements are negative or 0, and the remaining elements are greater than or equal to 0.

[0033] Based on this, the system state transition probability matrix is ​​constructed. for:

[0034] Step S3: Based on the system state transition probability matrix, obtain the transient probability of the network digital twin system in each available state; The transient capability evaluation of a network digital twin system characterizes the evaluation of the system state at a certain instant during operation, given that the system's state at the start of a task is known.

[0035] To evaluate the transient capability of a network digital twin system, it is necessary to first calculate the probability that the system is transiently available. The probability of the network digital twin system being in a transiently available state during operation can be obtained using the Komogorov progression equation. The Komogorov progression equation expressed using the aforementioned mathematical model is as follows: (1) in, , These represent the transient probability matrices for completing the specified task under available conditions at two different time points; where, , ; The above matrix equations can be multiplied and extracted to obtain a system of simultaneous equations:

[0036] Assume that all nodes are in normal working condition when the task starts, that is ; Solving the above system of equations using the Laplace transform yields the results for each available state of the network digital twin system: {0, 1, 2, 3, ...,} Transient probability at time}

[0037]

[0038]

[0039] Step S4: Based on the performance indicators of the network digital twin system, obtain the task completion capability value of the network digital twin system in each available state; In this embodiment, the concept of a task completion capability vector for a network digital twin system is proposed. This vector reflects the capability of the network digital twin system to complete task objectives and is characterized by a weighted value of a certain type of performance index or multiple types of performance indexes.

[0040] Specifically, the task completion capability vector of a network digital twin system It can be represented as: .

[0041] in, This indicates that the network digital twin system is in a usable state. The task completion ability value at any time, each Corresponding to one or more types of system performance indicators, . It is the probability that the network digital twin system will complete its task objective when it is in a faulty state. .

[0042] Specifically, the task completion capability value of the network digital twin system in each available state is obtained by performing the following operations: Step S41: Obtain the primary indicators of the network digital twin system in each available state, as well as the secondary indicators under each primary indicator; For example, the performance metrics include several primary metrics, and each primary metric includes several secondary metrics. Step S42: Based on the values ​​of the secondary indicators under each primary indicator, obtain the weight of each primary indicator; specifically: Step S421: Normalize the values ​​of the secondary indicators of the network digital twin system in each available state to obtain the normalized values ​​of the corresponding secondary indicators. Step S422: Obtain the weight of each secondary indicator based on the normalized values ​​of all secondary indicators under the same primary indicator; Step S422: Based on the weights of all secondary indicators under each primary indicator, obtain the weight of each primary indicator.

[0043] The implementation process of step S42 is explained as follows: For example, capability evaluation indicators can be categorized into multiple types and hierarchical levels while the system is available. The available states are categorized into multiple types and hierarchical levels. Thus, the following system performance metrics are defined: (1) Primary indicators No. The first available state Each primary indicator is represented as , The importance of a network digital twin system in the capability evaluation process is determined by its weight. Characterization; among which, Take 1 to , This indicates the total number of categories for the primary indicators.

[0044] (2) Secondary indicators No. The first available state The first primary indicator Each secondary indicator is represented as , The importance of a network digital twin system in the capability evaluation process is determined by its weight. Characterization; among which, Take 1 to , This indicates the total number of categories of secondary indicators under the current primary indicator.

[0045] Since the units and dimensions of various indicators are different, the secondary indicators are normalized before weighting them to assess the overall system capability. The normalized value is .

[0046] (2) In the process of solving for the weights of the first-level normalized index, it is first necessary to calculate the entropy value of the weights of the first-level index system based on the second-level indicators it contains; among them, the first... The first available state The entropy value of the weight of each primary indicator is: (3) Based on the entropy value of each primary indicator, the amount of information contained in each indicator can be obtained, and the weight can be determined according to the amount of information contained. Therefore, the weights of the primary indicators can be calculated; where, the first... The first available state Weight of each primary indicator for: (4) Step S43: Based on the weights of all primary and secondary indicators, obtain the task completion capability value of the network digital twin system in the corresponding available state.

[0047] Specifically, the network digital twin system is in a usable state. Task completion ability value at that time for: (5) For example, the performance metrics and their values ​​for a network entity in capability state 0 are shown in Table 1: Table 1 Performance indicators and their values ​​when network entities are in capability state 0

[0048] At this point, the task completion capability value when capability state 0 can be obtained in the following way: Calculation of secondary indicator weights: , ,

[0049] , , ,

[0050] Entropy calculation: ,

[0051] Primary indicator weights: ,

[0052] Task completion ability value when ability status is 0

[0053] Similarly, the task completion capability value of the system in other states can be calculated, and then the overall task completion capability value of the system can be obtained.

[0054] The task completion capability evaluation result of the network entity is equal to the task completion capability evaluation result of the network digital twin system.

[0055] Evaluation results of the task completion capabilities of network entities for: (6).

[0056] In summary, Embodiment 1 provides a method for evaluating the task completion capability of a network entity. By analyzing all capability states of the network digital twin system, a system state transition probability matrix is ​​constructed. Based on this matrix, the transient probability and task completion capability value of the network digital twin system in each available state are obtained, ultimately yielding the task completion capability evaluation result of the network digital twin system, i.e., the task completion capability evaluation result of the network entity. This result reflects the task completion capability of the network digital twin system, possesses strong dynamism, and can effectively reflect the changes in the comprehensive capability of the network entity and the network digital twin system over time.

[0057] Meanwhile, the process of calculating the task completion capability value involves multiple primary and secondary indicators, which can construct corresponding indicator systems from different perspectives to evaluate the task completion capability of network entities from the corresponding perspectives.

[0058] Example 2 Embodiment 2 of this invention provides a network entity task completion capability evaluation system, the structural diagram of which is shown below. Figure 3 As shown, it includes: The capability status acquisition module is used to map network entities into a network digital twin system and acquire all capability statuses of the network digital twin system. The system state transition probability matrix construction module is used to construct a system state transition probability matrix based on all capability states of the network digital twin system; the capability states are divided into available states and unavailable states; The transient probability acquisition module is used to acquire the transient probability of the network digital twin system in each available state based on the system state transition probability matrix. The task completion capability value acquisition module is used to acquire the task completion capability value of the network digital twin system in each available state based on the performance indicators of the network digital twin system. The task completion capability evaluation module is used to obtain the task completion capability evaluation result of the network entity based on the transient probability and task completion capability value when the network digital twin system is in all available states.

[0059] The specific implementation process of the system embodiment of the present invention can be found in the above method embodiment, and the system embodiment will not be described again here.

[0060] Since the principles of this system embodiment are the same as those of the above method embodiment, this system also has the corresponding technical effects of the above method embodiment.

[0061] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0062] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the task completion capability of network entities, characterized in that, include: Map network entities to a network digital twin system and obtain all capability states of the network digital twin system; Based on all capability states of the network digital twin system, a system state transition probability matrix is ​​constructed; the capability states are divided into available states and unavailable states. Based on the system state transition probability matrix, the transient probability of the network digital twin system in each available state is obtained; Based on the performance indicators of the network digital twin system, the task completion capability value of the network digital twin system in each available state is obtained; the performance indicators include several primary indicators, and each primary indicator includes several secondary indicators. Based on the transient probabilities and task completion capability values ​​of the network digital twin system when it is in all available states, the task completion capability evaluation results of the network entity are obtained. The acquisition of the transient probability of the network digital twin system in each available state includes: Establish the Komogorov progression equation: （1） in, Here is the system state transition probability matrix; , These represent the transient probability matrices for completing the specified task under available conditions at two different time points; where, in, , , These represent the failure rate and repair rate of each node. The total number of nodes in the network digital twin system; The corresponding available states are {0, 1, 2, 3, ...,} }, The total number of nodes that fail when a network digital twin system completely loses its functionality; ， ； Indicates time; Multiplying equation (1) by its root yields a system of simultaneous equations: （2） Assume that all nodes are in normal working condition when the task starts, that is , ; By performing a Laplace transform on the system of simultaneous equations and solving it, we obtain the state of the network digital twin system in each available state {0,1,2,3,..., The transient probability at time}; The process of obtaining the task completion capability value of the network digital twin system in each available state based on the performance indicators of the network digital twin system includes: Obtain the primary indicators and secondary indicators under each primary indicator for each available state of the network digital twin system; The weight of each primary indicator is obtained based on the values ​​of the secondary indicators under each primary indicator. Based on the weights of all primary and secondary indicators, the task completion capability value of the network digital twin system in the corresponding available state is obtained.

2. The method for evaluating the task completion capability of network entities according to claim 1, characterized in that, The task completion capability evaluation results of the network entities for: （3） in, This indicates that the network digital twin system is in a usable state. The task completion ability value at that time. This indicates that the network digital twin system is in a usable state. The transient probability at time, This represents the total number of nodes that fail when the network digital twin system completely loses its functionality.

3. The method for evaluating the task completion capability of network entities according to claim 2, characterized in that, The process of obtaining the weight of each primary indicator includes: The values ​​of the secondary indicators of the network digital twin system in each available state are obtained and normalized respectively to obtain the normalized values ​​of the corresponding secondary indicators. The weight of each secondary indicator is obtained by taking the normalized values ​​of all secondary indicators under the same primary indicator. The weight of the corresponding primary indicator is obtained by calculating the weights of all secondary indicators under the same primary indicator.

4. The method for evaluating the task completion capability of network entities according to claim 3, characterized in that, No. The first available state The first primary indicator Weights of each secondary indicator for: （4） in, Indicates the first The first available state The first primary indicator Normalized values ​​of each secondary indicator; Take 1 to , This indicates the total number of categories of secondary indicators under the current primary indicator.

5. The method for evaluating the task completion capability of network entities according to claim 4, characterized in that, No. The first available state Weight of each primary indicator for: （5） in, Indicates the first The first available state The entropy value of the weight of each primary indicator. This indicates the total number of categories for the primary indicators; （6）。 6. The method for evaluating the task completion capability of network entities according to claim 5, characterized in that, The network digital twin system is in a usable state. Task completion ability value at that time for: （7）。 7. The method for evaluating the task completion capability of network entities according to claim 3, characterized in that, The performance indicators include two primary indicators: transmission capacity and operation and maintenance capacity. The transmission capability includes three secondary indicators: node coverage, link termination time, and transmission connectivity angle. The operation and maintenance capabilities include four secondary indicators: maintainability, electromagnetic protection capability, reliable transmission capability, and access feedback capability.

8. The method for evaluating the task completion capability of network entities according to any one of claims 1-7, characterized in that, All capability states of the network digital twin system include: Capability Status 0: All nodes in the network digital twin system are in good working order, and all functions of the network digital twin system are functioning normally. Ability Status The network digital twin system has Even if one node is in a faulty state, the network digital twin system can still meet certain functional requirements; ; Ability Status The network digital twin system has If one or more nodes are in a faulty state, the network digital twin system is in a faulty state and cannot meet any functional requirements; Among them, capability status 0 to All are in an available state, capability status It is in an unavailable state.

9. A network entity task completion capability evaluation system, characterized in that, The system is implemented according to any one of claims 1-8, which describes the network entity task completion capability evaluation method. The system includes: The capability status acquisition module is used to map network entities into a network digital twin system and acquire all capability statuses of the network digital twin system. The system state transition probability matrix construction module is used to construct a system state transition probability matrix based on all capability states of the network digital twin system; the capability states are divided into available states and unavailable states; The transient probability acquisition module is used to acquire the transient probability of the network digital twin system in each available state based on the system state transition probability matrix. The task completion capability value acquisition module is used to acquire the task completion capability value of the network digital twin system in each available state based on the performance indicators of the network digital twin system. The task completion capability evaluation module is used to obtain the task completion capability evaluation result of the network entity based on the transient probability and task completion capability value when the network digital twin system is in all available states.

Images