A method for determining network entity capability parameters and its evaluation system

Abstract

This invention relates to a method for determining network entity capability parameters and its evaluation system, belonging to the field of network capability evaluation technology. It solves the problems of poor accuracy and difficulty in obtaining closed-form expressions of the entire system, such as the probability density function and cumulative distribution function, of existing network entity capability evaluation methods. In this method, the network entity capability parameters include the probability density function of the network entity capability. The method includes the following steps: mapping the network entity to a network digital twin; obtaining all task flows from the task start node to the task end node in the network digital twin and their equivalent transfer functions; obtaining the equivalent transfer function of the network digital twin based on the equivalent transfer functions of all task flows; and obtaining the probability density function of the network entity capability based on the equivalent transfer function of the network digital twin.

Description

Technical Field

[0001] This invention relates to the field of network capability evaluation technology, and in particular to a method for determining network entity capability parameters and its evaluation system. Background Technology

[0002] In recent years, the internet and mobile internet industries have flourished, reaching a stage of technological maturity and solidified business models. Currently, 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 key development focus. Furthermore, the emergence of the metaverse concept, geared towards future application scenarios, is gradually becoming a future integrated solution based on existing mobile internet development, comprehensively integrating various application models, and utilizing 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 system capability evaluation, and after many years of development, they have achieved considerable research results. Commonly used capability evaluation methods can be divided into four categories. First, expert evaluation methods, which construct a system capability evaluation index system based on extensive expert opinions and obtain index weights through multi-expert comprehensive evaluation. This method can evaluate and predict indicators that are difficult to quantify due to a lack of data, but it inevitably leads to a certain degree of subjectivity and bias in the evaluation and prediction results. Second, analytical methods, which are effective in overcoming estimation errors caused by coarse quantification, compare the performance of systems with a uniform quantification resolution, and can also utilize the analytic hierarchy process (AHP) and its extensions to evaluate system capability. This method essentially calculates the evaluation values ​​of capability indicators by establishing mathematical models; it is simple to calculate and easy to understand and apply. Its disadvantage is that the factors considered in the model construction process are limited, the results are too abstract, and the system lacks objectivity. Third, simulation methods, representative of which include capability evaluation methods based on system dynamics models or artificial neural networks, as well as evaluation methods based on multi-agent and automatic control system theories. This evaluation method simulates the actual operation or development process of the system, but it suffers from high system complexity and weak representation of the connection relationships between nodes. Fourthly, network analysis, which compensates for the shortcomings of agent-based modeling and other methods in modeling system information interaction, abstracts various entities in complex systems as nodes and various connection relationships as links. It has become an important method for studying system characteristics in recent years. Characterizing system capabilities from the perspective of complex networks is an evaluation method that closely reflects the structural characteristics of the system and can better represent the interaction relationships between nodes.

[0005] The above analysis shows that, in the process of effectively evaluating system capabilities, it is necessary to perform network-based characterization of the system and study its capabilities on this basis.

[0006] Research on the capability evaluation of digital twin systems for metaverse applications is limited and still in its early stages. Relevant studies, based on networked representation techniques, model the connection relationships between nodes and links to evaluate the transmission capabilities of communication networks serving as the framework for the virtual world. Modeling nodes, receiving and transmitting queues allows for the generation of expressions and lower bounds for performance parameters through analysis. Furthermore, some studies equate digital twin systems to a topology composed of nodes, utilizing corresponding topological features (such as degree distribution and clustering coefficients) to evaluate system capabilities. Additionally, the states within the digital twin system can be partitioned, and the network system capability can be solved by analyzing the transition processes between these states.

[0007] The above analysis shows that, in order to measure the capabilities of a digital twin system, it is necessary to characterize its structure and, based on this, further study the modeling and evaluation methods of its capabilities.

[0008] In summary, research on the evaluation of the capabilities (corresponding network entity capabilities) of digital twin systems for metaverse applications, both domestically and internationally, is significantly insufficient, mainly due to the following problems: 1) Existing network modeling for capability evaluation focuses more on the topological properties of the network model, neglecting the practical significance of the connections between entities in the digital twin system; 2) Existing capability solution models for networked systems often only analyze the importance of each link, with insufficient research on tasks and activities that significantly impact system capabilities. Furthermore, the determination of link importance primarily relies on analyzing the impact of the presence or absence of a link on information transmission capabilities, resulting in a rather one-sided evaluation; 3) Existing solution models struggle to obtain closed-form expressions of the entire system's capabilities, such as probability density functions and cumulative distribution functions. Summary of the Invention

[0009] Based on the above analysis, the embodiments of the present invention aim to provide a method for determining network entity capability parameters and an evaluation system thereof, in order to solve the problems of poor accuracy of existing network entity capability evaluation methods and difficulty in obtaining closed-form expressions of the entire system such as probability density function and cumulative distribution function of network capabilities.

[0010] On one hand, the present invention provides a method for determining network entity capability parameters, wherein the network entity capability parameters include a probability density function of the network entity capability; the method includes:

[0011] Map network entities to network digital twins, and obtain all task flows and their equivalent transfer functions from the task start node to the task end node in the network digital twin;

[0012] Based on the equivalent transfer functions of all task flows, the equivalent transfer function of the network digital twin is obtained;

[0013] Based on the equivalent transfer function of the network digital twin, the probability density function of the network entity's capabilities is obtained.

[0014] Based on the above method, the present invention also makes the following improvements:

[0015] Furthermore, the task flow represents the nodes and directed links that are passed sequentially during the transmission of task activities from the task start node to the task end node;

[0016] The probability density function for obtaining the network entity's capabilities includes:

[0017] Based on the equivalent transfer function of the network digital twin, the equivalent characteristic function of the network digital twin is obtained;

[0018] Based on the equivalent feature function of the network digital twin, the probability density function of the network entity's capabilities is obtained.

[0019] Furthermore, the equivalent characteristic function of the network digital twin satisfy:

[0020]

[0021] Where W(t) represents the equivalent transfer function of the network digital twin, t∈R, R represents the real number field, and t takes any real number.

[0022] The probability density function of the network entity's capability is obtained by performing a Fourier cosine series expansion on the equivalent characteristic function of the network digital twin within a preset range of independent variables.

[0023] Furthermore, the equivalent transfer function W(t) of the network digital twin satisfies:

[0024]

[0025] Among them, W l (t) represents the equivalent transfer function of the l-th task flow, where l ranges from 1 to L, and L represents the total number of all task flows.

[0026] Furthermore, the network entity capability parameters also include the cumulative distribution function of the network entity capabilities; the method further includes:

[0027] The cumulative distribution function of the network entity's capability is obtained by integrating the probability density function of the network entity's capability within a preset range of independent variables.

[0028] Furthermore, the task flow represents the nodes and directed links that are passed sequentially during the transmission of task activities from the task start node to the task end node;

[0029] The equivalent transfer function for obtaining all task flows includes:

[0030] Take any two adjacent nodes in the task flow and the directed link between them as a basic capability unit; in the basic capability unit, the node pointed to by the directed link is the receiving node, and the other node is the executing node.

[0031] For each basic capability unit, a transfer function is constructed based on the capability distribution of the execution node's task activities and the activity probability of transmitting task activities from the execution node to the receiving node.

[0032] The product of the transfer functions of all the basic units of capability in each task flow is taken as the equivalent transfer function of the corresponding task flow.

[0033] Furthermore, the transfer function of the basic capability unit:

[0034]

[0035] Where, p ij This represents the probability of transmitting task activities from execution node i to receiving node j. The characteristic function represents the distribution of the execution node's ability to perform tasks, t∈R.

[0036] Furthermore, the sum of the activity probabilities of transmitting task activities from the same execution node to all its target receiving nodes is 1.

[0037] On the other hand, the present invention also provides a network entity capability parameter evaluation system, wherein the network entity capability parameters include a probability density function of the network entity capability; the system includes:

[0038] The network digital twin acquisition module is used to map network entities into network digital twins;

[0039] The equivalent transfer function acquisition module is used to acquire all task flows from the task start node to the task end node in the network digital twin and their equivalent transfer functions; it is also used to obtain the equivalent transfer function of the network digital twin based on the equivalent transfer function of each task flow.

[0040] The capability parameter evaluation module is used to obtain the probability density function of the network entity's capability based on the equivalent transfer function of the network digital twin.

[0041] Based on the above solution, the present invention also makes the following improvements:

[0042] Furthermore, the network entity capability parameters also include the cumulative distribution function of the network entity capabilities;

[0043] The capability parameter evaluation module is also used to integrate the probability density function of the network entity's capability within a preset range of independent variables to obtain the cumulative distribution function of the network entity's capability.

[0044] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0045] The method and evaluation system for determining network entity capability parameters provided by this invention fully consider the practical significance of the connections between entities in a digital twin system in forming basic capability units and the equivalent transfer functions of each task flow. Simultaneously, from the perspective of task activities, it analyzes the relationship between the equivalent transfer functions of task flows and the equivalent transfer functions of the entire network digital twin. Based on the determination of the equivalent transfer functions of the network digital twin, and using knowledge of probability theory, it determines the probability density function and cumulative distribution function of network entity capabilities, thus obtaining closed-form expressions of the entire system, including the probability density function and cumulative distribution function of network capabilities. Furthermore, the resulting network capability evaluation results are more comprehensive and accurate, possessing extremely important practical significance.

[0046] 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

[0047] 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.

[0048] Figure 1 This is a flowchart of the method for determining network entity capability parameters provided in Embodiment 1 of the present invention;

[0049] Figure 2 This is a model architecture diagram of a capability-oriented network digital twin provided in Embodiment 1 of the present invention;

[0050] Figure 3 The structure of the basic capability unit provided in Embodiment 1 of the present invention;

[0051] Figure 4 This is a schematic diagram of the network entity capability parameter determination system provided in Embodiment 2 of the present invention;

[0052] Figure 5 The topology of the network entity provided in Embodiment 3 of the present invention;

[0053] Figure 6 This is a model architecture diagram of the network digital twin provided in Embodiment 3 of the present invention;

[0054] Figure 7 This is a schematic diagram of the probability density function of a network entity provided in Embodiment 3 of the present invention;

[0055] Figure 8This is a schematic diagram of the cumulative distribution function of network entities provided in Embodiment 3 of the present invention. Detailed Implementation

[0056] 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.

[0057] The capabilities of network entities and network digital twins refer to the effectiveness with which the physical world entities simulated by the network digital twin complete tasks. The capabilities of a network digital twin can be used as a measure of the capabilities of the network entities it simulates. The evaluation of system capabilities is based on the effective evaluation of the capabilities of each entity in the system. It uses the performance of each entity as a foundation and modeling and simulation as a means to comprehensively evaluate the system capabilities.

[0058] Example 1

[0059] A specific embodiment of the present invention discloses a method for determining network entity capability parameters, wherein the network entity capability parameters include a probability density function of the network entity capability; the flowchart is as follows. Figure 1 As shown, it includes the following steps:

[0060] Step S1: Map the network entity into a network digital twin, and obtain all task flows from the task start node to the task end node in the network digital twin and their equivalent transfer functions;

[0061] In this embodiment, the network digital twin is obtained by mapping network entities. Exemplarily, the network entities simulated by the network digital twin mainly include computer communication networks. Common computer communication networks are composed of terminal devices, routers, switches, and network equipment connected together. Depending on the network size, they can be divided into local area networks (LANs), metropolitan area networks (MANs), or wide area networks (WANs). Different topologies can be formed based on the different topologies of the computer communication network. In a computer communication network, each device is considered a node, and the connection between devices is considered a link, thus forming a corresponding topology. There are many types of computer communication networks, ranging from small communication networks formed between multiple hosts and printers in an office to large-scale urban rail transit network communication systems. Based on the topology of the computer communication network, nodes and links in the network digital twin can be mapped. Specifically, the nodes and links in the network entity are mapped to the nodes and links in the network digital twin, respectively. The distribution of the ability of nodes in the network digital twin to perform tasks is consistent with the distribution of the ability of corresponding nodes in the network entity to perform tasks. The probability of transmitting tasks from one node to another in the network digital twin is consistent with the probability of transmitting tasks between corresponding nodes in the network entity.

[0062] Nodes in a network digital twin are divided into three categories: task initiation nodes, transmission nodes, and task termination nodes. The process of a network digital twin executing a task can be understood as the application generating and sending information through the task initiation node, transmitting it through one or more transmission nodes, and finally reaching the task termination node. The model architecture diagram of a capability-oriented network digital twin is shown below. Figure 2 As shown. In this embodiment, the task flow represents the nodes and directed links sequentially traversed during the transmission of task activity from the task start node to the task end node. For example, in... Figure 2 In this context, S1→R1→T1 represents a task flow. Within a network digital twin, depending on the different task activities and their transmission paths, a network digital twin may contain several task flows. These task flows form the basis for evaluating the capabilities of the network digital twin.

[0063] To accurately measure network capabilities, it is necessary to establish equivalent transfer functions for each task flow within the network digital twin. The capabilities of each task flow during its transmission through each link are random variables following different probability distributions. Therefore, the equivalent transfer function of the network digital twin is a probability distribution of multiple random variables, making it difficult to solve. Thus, in this embodiment, we consider establishing and solving mathematical models for the basic capability units within the network digital twin.

[0064] Specifically, for each task flow, any two adjacent nodes in the task flow and the directed link between them are taken as a basic unit of capability, such as... Figure 3 As shown, this basic capability unit is the fundamental computational unit in the process of evaluating the capabilities of a digital twin. In this basic capability unit, node j, pointed to by the directed link, serves as the receiving node, and another node i serves as the executing node; wherein, the directed link (i,j) carries the task activities between i and j, and p... ij E represents the probability of transmitting task activities from execution node i to receiving node j. ij This represents the capability of the task activity carried on the directed link (i,j). The activity probability of the task activity represents the likelihood of its execution. It should be noted that the sum of the activity probabilities of transmitting task activities from the same execution node to all its target receiving nodes is 1. There is also a flow of capability between nodes. The capability of a node to transmit task activities through the link is represented by the transfer function of the capability basic unit. For each capability basic unit, the transfer function of the capability basic unit is constructed based on the capability distribution of the execution node to execute task activities and the activity probability of transmitting task activities from the execution node to the receiving node; the transfer function of the capability basic unit is shown in formula (1):

[0065]

[0066] Where, pij This represents the probability of transmitting task activities from execution node i to receiving node j. The characteristic function represents the distribution of the execution node's ability to perform tasks, t∈R.

[0067] In probability theory, closed-form expressions for probabilistic features can be obtained through moment generating functions or characteristic functions. In practical modeling, considering that some random distributions may not have moment generating functions, and that characteristic functions are the Fourier transforms of probability density functions and exist for any distribution, they are introduced into the process of solving for the equivalent transfer function of a network digital twin.

[0068] In the basic unit of capability of a network digital twin, the capability E of the task activities carried on the directed link (i,j) between nodes. ij It is a continuous random variable with probability density function f(E) ij The capability E of each basic capability unit can be calculated. ij The characteristic function is:

[0069]

[0070] Where t∈R, R represents the real number field, and t takes any real number. According to relevant knowledge of probability theory, the characteristic function... The calculation follows these rules:

[0071] First, if E1, E2, ..., E k ,…,E K Let K be K independent random variables, each with a corresponding characteristic function, denoted as . Then the characteristic function of the sum of K independent random variables Y is the product of the characteristic functions of each random variable. Therefore, we have...

[0072] Second, the characteristic function E ij The value of the nth derivative of the random variable E at t=0 is the value of the random variable E. ij The nth order origin moment. This law will be used in steps S2 and S3 to calculate the task flow capability.

[0073] If a task flow contains K basic capability units connected in series, then the probability of that task flow occurring is: p k This represents the probability that the k-th basic capability unit successfully executes the task activity. Combining this with Rule 1, we know that when finding the equivalent transfer function of a task flow, the product of the transfer functions of all basic capability units in each task flow can be used as the equivalent transfer function of that task flow. That is, assuming the l-th task flow includes K cascaded basic capability units, then the equivalent transfer function of the l-th task flow is:

[0074]

[0075] Among them, W k (t) is the transfer function of the k-th basic capability unit in the l-th task flow.

[0076] Step S2: Based on the equivalent transfer function of each task flow, obtain the equivalent transfer function of the network digital twin;

[0077] Considering that different task flows are interconnected, the network digital twin includes multiple task flows, and the capability of each task flow is a random function. When evaluating the network capability, it is necessary to calculate the capability of each task flow separately. According to the probability addition formula, the sum of the equivalent transfer functions of the interconnected task flows can be obtained as the equivalent transfer function W(t) of the network digital twin, as shown in formula (4).

[0078]

[0079] l ranges from 1 to L, where L represents the total number of all task flows.

[0080] Step S3: Based on the equivalent transfer function of the network digital twin, obtain the probability density function of the network entity's capabilities.

[0081] Specifically, including:

[0082] Step S31: Based on the equivalent transfer function of the network digital twin, obtain the equivalent characteristic function of the network digital twin;

[0083] W(t) can be expressed as Besides the form , it can also be expressed in the following forms:

[0084]

[0085] Where p represents the equivalent transit probability of the network digital twin. The equivalent characteristic function representing a network digital twin; where,

[0086]

[0087] therefore,

[0088]

[0089] Step S32: Based on the equivalent feature function of the network digital twin, obtain the probability density function of the network entity's capabilities.

[0090] In this embodiment, the capabilities of the network entity are equal to the capabilities of the network digital twin that simulates the network entity, and their probability density functions are also equal. Therefore, the probability density function of the network digital twin capability obtained by solving can be used as the probability density function of the corresponding network entity capability.

[0091] Specifically, the equivalent characteristic function of the network digital twin is expanded into a Fourier cosine series within a preset range of independent variables to obtain the probability density function of the network entity's capabilities.

[0092] Assuming the range of the independent variable is [a, b], then the probability density function f(x) of the network entity obtained on the defined interval can be expressed as:

[0093]

[0094] in, x represents a random variable on the interval [a, b].

[0095] Among them, R e {} denotes taking the real part of a complex number, where N is a positive integer and represents the series of the cosine expansion.

[0096] Furthermore, the cumulative distribution function is also an important indicator for evaluating the capabilities of network entities. Therefore, the capabilities of network entities can also include the cumulative distribution function of network entity capabilities. In this case, the method can further include the following steps:

[0097] Step S4: Integrate the probability density function of the network entity's capability within a preset range of independent variables to obtain the cumulative distribution function of the network entity's capability.

[0098] That is, the cumulative distribution function F(x) of the network entity capabilities can be expressed as:

[0099]

[0100] In actual implementation, the equivalent characteristic function of the network digital twin is determined. Then, the probability density function and cumulative distribution function of network entities can be obtained directly with the help of software.

[0101] Example 2

[0102] Embodiment 2 of the present invention discloses a network entity capability parameter evaluation system, the structural schematic diagram of which is shown below. Figure 4 As shown, the network entity capability parameters include the probability density function of the network entity capability; the system includes:

[0103] The network digital twin acquisition module is used to map network entities into network digital twins;

[0104] The equivalent transfer function acquisition module is used to acquire all task flows from the task start node to the task end node in the network digital twin and their equivalent transfer functions; the task flow represents the nodes and directed links that are passed sequentially during the transmission of task activities from the task start node to the task end node; it is also used to obtain the equivalent transfer function of the network digital twin based on the equivalent transfer function of each task flow.

[0105] The capability parameter evaluation module is used to obtain the probability density function of the network entity's capability based on the equivalent transfer function of the network digital twin.

[0106] In addition, the network entity capability parameters also include the cumulative distribution function of the network entity capability; at this time, the capability parameter evaluation module is also used to integrate the probability density function of the network entity capability within a preset range of independent variables to obtain the cumulative distribution function of the network entity capability.

[0107] The specific implementation process of the system embodiment of the present invention can be found in the above method embodiment, and will not be repeated here.

[0108] 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.

[0109] Example 3

[0110] Specific embodiment 3 of the present invention discloses a method for determining network entity capability parameters to verify the effectiveness of the method in the embodiment. The specific process is described as follows:

[0111] The task flow chain relationships and work sequence of the network digital twin are determined by the functional task planning of the physical world network entity system simulated by the network digital twin, and generated according to business requirements. Based on this, to verify the correctness and effectiveness of the algorithm, capability calculations are performed using user data generated by the software.

[0112] Assuming the network entity topology is as follows: Figure 5 As shown, in the network entity, terminal node S is the node that issues the task, terminal node T is the node that receives the task, and R1-R3 are the transmission nodes between the two terminal nodes. The physical device entities corresponding to the transmission nodes are routers or switches. The model architecture diagram of the network digital twin corresponding to this network entity is as follows. Figure 6 As shown.

[0113] Based on the capability performance data of each node, the probability of each task activity and the capability distribution of each node are shown in Table 1. It is assumed that the capability of each task activity follows a normal distribution, and the overall capability of the network digital twin needs to be evaluated. In practical applications, the above probability and capability distribution can be obtained through experiments on network entities or simulations of the corresponding network digital twin.

[0114] Table 1. Probability of each task activity and capability distribution of each node.

[0115]

[0116] Based on Table 1, the task flows and their equivalent transfer functions can be obtained, as shown in Table 2:

[0117] Table 2 Task Flow and its Equivalent Transfer Function

[0118]

[0119] Correspondingly, the equivalent transfer function of a network digital twin:

[0120]

[0121] At this point, the equivalent characteristic function of the network digital twin... satisfy:

[0122]

[0123] Given the equivalent characteristic function of a network digital twin. Based on this, the probability density function of network entity capabilities can be obtained. In this embodiment, assuming a = 0, b = 4, N = 1000, the probability density function of network entity capabilities is:

[0124]

[0125] in,

[0126] The cumulative distribution function of network entity capabilities is:

[0127]

[0128] The probability density function and cumulative distribution function obtained by software simulation are as follows: Figure 7 and 8 As shown.

[0129] From the cumulative distribution function Figure 8As can be seen, the probability of a network's capability being below 2.19 is 66%, below 2.64 is approximately 80%, below 3.21 is approximately 90%, and below 4 is approximately 100%. This demonstrates the probabilistic statistical characteristics of network capability under random task flow conditions.

[0130] From the probability density function Figure 7 As can be seen, when the network's capability changes around (2, 2.25), below 0.5, and above 3, its rate of change tends to 0, meaning that the probabilities of these capability ranges are similar. The capability value within the range of (2, 2.25) is moderate and has a wide range, allowing for achievement through a variety of task flow combinations, thus making it a suitable capability target. While the probability of a capability value around 1.5 is relatively high, the range of capabilities with similar values ​​is relatively narrow, making it difficult to achieve as a capability target through task flow combinations. Furthermore, the probabilities in the ranges below 0.5 and above 3 are too small (and capabilities below 0.5 are too low), making it difficult to find a sufficient number of task flow combinations for achievement. Therefore, this method can guide the capability improvement goals of the network digital twin. It is important to emphasize that this capability evaluation result is based on the probability of nodes transmitting tasks and the capability distribution of nodes executing tasks during task activity transmission in the network entity. Therefore, when the above probability and capability distribution parameters in the network entity change, the capability evaluation result will also change accordingly, but it can still be obtained using the above calculation method.

[0131] 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.

[0132] 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 determining network entity capability parameters, characterized in that, The network entity capability parameters include a probability density function of the network entity capability; the method includes: Map network entities to network digital twins, and obtain all task flows and their equivalent transfer functions from the task start node to the task end node in the network digital twin; Based on the equivalent transfer functions of all task flows, the equivalent transfer function of the network digital twin is obtained; Based on the equivalent transfer function of the network digital twin, the probability density function of the network entity's capabilities is obtained; The probability density function for obtaining the network entity's capabilities includes: Based on the equivalent transfer function of the network digital twin, the equivalent characteristic function of the network digital twin is obtained; Based on the equivalent feature function of the network digital twin, the probability density function of the network entity's capabilities is obtained; The equivalent characteristic function of the network digital twin satisfy: （1） in, The equivalent transfer function representing a network digital twin. , Represents the real number field. Take any real number; The equivalent characteristic function of the network digital twin is expanded by Fourier cosine series within a preset range of independent variables to obtain the probability density function of the network entity's capabilities. The equivalent transfer function of the network digital twin satisfy: （2） in, Indicates the first Equivalent transfer functions for each task flow, Take 1 to , This represents the total number of all task flows; The task flow represents the nodes and directed links that are passed sequentially during the transmission of task activities from the task start node to the task end node. The equivalent transfer function for obtaining all task flows includes: Take any two adjacent nodes in the task flow and the directed link between them as a basic capability unit; in the basic capability unit, the node pointed to by the directed link is the receiving node, and the other node is the executing node. For each basic capability unit, a transfer function is constructed based on the capability distribution of the execution node's task activities and the activity probability of transmitting task activities from the execution node to the receiving node. The product of the transfer functions of all the basic units of capability in each task flow is taken as the equivalent transfer function of the corresponding task flow. The transfer function of the basic unit of capability: （3） in, Indicates execution node To the receiving node The activity probability of the transmission task activity. A characteristic function representing the distribution of the execution node's ability to perform tasks. .

2. The method for determining network entity capability parameters according to claim 1, characterized in that, The network entity capability parameters also include a cumulative distribution function of the network entity capabilities; the method further includes: The cumulative distribution function of the network entity's capability is obtained by integrating the probability density function of the network entity's capability within a preset range of independent variables.

3. The method for determining network entity capability parameters according to claim 2, characterized in that, The sum of the activity probabilities of transmitting task activities from the same execution node to all the receiving nodes it points to is 1.

4. A network entity capability parameter evaluation system, characterized in that, The network entity capability parameter evaluation system is implemented based on the network entity capability parameter determination method according to any one of claims 1-3, wherein the network entity capability parameter includes a probability density function of network entity capability; the system includes: The network digital twin acquisition module is used to map network entities into network digital twins; The equivalent transfer function acquisition module is used to acquire all task flows from the task start node to the task end node in the network digital twin and their equivalent transfer functions; it is also used to obtain the equivalent transfer function of the network digital twin based on the equivalent transfer function of each task flow. The capability parameter evaluation module is used to obtain the probability density function of the network entity's capability based on the equivalent transfer function of the network digital twin.

5. The network entity capability parameter evaluation system according to claim 4, characterized in that, The network entity capability parameters also include the cumulative distribution function of the network entity capabilities; The capability parameter evaluation module is also used to integrate the probability density function of the network entity's capability within a preset range of independent variables to obtain the cumulative distribution function of the network entity's capability.

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