Simulation topology node evaluation method and device, computer device and storage medium

Abstract

Embodiments of the present application provide a simulation topology node evaluation method and device, computer equipment and a storage medium. The method comprises: obtaining a plurality of real topology nodes corresponding to a target network system and a plurality of simulation topology nodes corresponding to a simulation network system; obtaining a real index set corresponding to each real topology node and a simulation index set corresponding to each simulation topology node; obtaining an influence threshold value corresponding to each simulation index of the simulation index set, and determining target description information based on the size relationship between the simulation value corresponding to each simulation index and the influence threshold value; calculating the target description information according to the simulation value, the target real value and the influence threshold value to obtain a score value of each simulation index; for each simulation topology node, obtaining a plurality of score values corresponding to a plurality of simulation indexes, and calculating an evaluation result of each simulation topology node through the plurality of score values.

Description

Technical Field

[0001] This application relates to the field of simulation technology, and in particular to a simulation topology node evaluation method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Network topology simulation uses techniques such as mathematical modeling, discrete event simulation, or virtualization to reproduce the connection relationships between devices (such as routing nodes and user nodes) in a real network, thus providing fundamental support for network protocol testing, traffic analysis, security assessment, and new technology verification. Since the reliability of the simulation results directly determines their effectiveness and reliability in verifying new network technologies and assessing security, it is necessary to evaluate the network topology nodes obtained from the simulation to ensure that the network simulation environment constructed from it can realistically reflect the operating state of the actual network.

[0003] In related technologies, methods for evaluating the credibility of node simulations often rely on subjective evaluation, such as expert scoring, where domain experts manually score certain performance or functional indicators of the simulated nodes based on their experience, and then synthesize the scores to arrive at a credibility conclusion. However, because this method relies on manual judgment, it is difficult to comprehensively cover all key indicators and is easily affected by the limitations of experts' subjective cognition and differences in experience, making it difficult to accurately evaluate network topology nodes. Summary of the Invention

[0004] This application proposes a method, apparatus, computer equipment, and storage medium for evaluating simulated topology nodes, which can accurately evaluate simulated nodes.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for evaluating simulated topology nodes, the method comprising:

[0006] Obtain multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system;

[0007] Obtain the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulation indicator set includes multiple simulation indicators.

[0008] Obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold.

[0009] Wherein, the first description information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold; the second description information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold.

[0010] Based on the simulated value, the actual target value, and the influence threshold, the target description information is calculated to obtain a score value for each simulation indicator;

[0011] For each simulation topology node, multiple score values ​​corresponding to the multiple simulation indicators are obtained, and the evaluation result of each simulation topology node is calculated using the multiple score values.

[0012] Accordingly, a second aspect of the embodiments of this application proposes a simulation topology node evaluation device, the device comprising:

[0013] The first acquisition module is used to acquire multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system;

[0014] The second acquisition module is used to acquire the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulation indicator set includes multiple simulation indicators.

[0015] The determination module is used to obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold.

[0016] Wherein, the first description information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold; the second description information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold.

[0017] The first calculation module is used to calculate the target description information based on the simulation value, the target real value and the influence threshold to obtain the score value of each simulation index;

[0018] The second calculation module is used to obtain multiple score values ​​corresponding to the multiple simulation indicators for each simulation topology node, and to calculate the evaluation result of each simulation topology node using the multiple score values.

[0019] In some implementations, the determining module is further configured to:

[0020] Obtain at least one influencing factor for each simulation index, and the index influence value corresponding to each influencing factor;

[0021] Obtain the influence weight corresponding to each of the influencing factors of the indicator, and obtain the sub-threshold item based on the product of the influence value of each indicator and the corresponding influence weight;

[0022] The initial threshold is obtained by adding the sub-threshold items corresponding to the influencing factors of the at least one indicator;

[0023] Obtain the threshold adjustment coefficient corresponding to each simulation index, and obtain the influence threshold corresponding to each simulation index based on the product of the initial threshold and the threshold adjustment coefficient.

[0024] In some embodiments, the simulated topology node evaluation device further includes an update module for:

[0025] When among the multiple simulation indicators, there is a target simulation indicator that is quantified and unreliable, the target simulation value corresponding to the target simulation indicator is determined;

[0026] Obtain the preset conversion constant and exponent factor, and calculate the conversion term based on the target simulation value according to the exponent factor;

[0027] Based on the ratio of the transformation constant to the transformation term, the target simulation value corresponding to the target simulation index is updated.

[0028] In some implementations, the target description information includes first description information, which includes first quantization description information. The first calculation module is further configured to:

[0029] When the simulated value corresponding to each simulation indicator is greater than the influence threshold, the first pair of values ​​corresponding to the simulation value and the second pair of values ​​corresponding to the target real value are obtained, wherein each simulation indicator is a quantitative indicator;

[0030] From the first logarithm and the second logarithm, select the smaller value as the target first logarithm and select the larger value as the target second logarithm;

[0031] The first quantitative description information is calculated based on the ratio of the first logarithmic value of the target to the second logarithmic value of the target, so as to obtain the score value of each simulation index.

[0032] In some implementations, the target description information includes second description information, the second description information including second quantization description information, and the first calculation module is further configured to:

[0033] When the simulated value corresponding to each simulation indicator is less than the influence threshold, the first logarithmic value corresponding to the simulation value, the second logarithmic value corresponding to the target real value, and the third logarithmic value corresponding to the influence threshold are obtained, wherein each simulation indicator is a quantitative indicator.

[0034] The larger value is selected from the first logarithmic value and the second logarithmic value as the target second logarithmic value, and the smaller value is selected from the simulated value and the target real value as the target reference value;

[0035] Based on the ratio of the third logarithm to the target second logarithm, the quantification sub-item is determined;

[0036] Obtain a first difference between the impact threshold and the target reference value, and determine a first penalty term based on the ratio between the first difference and the impact threshold;

[0037] The second quantitative description information is calculated based on the difference between the quantized sub-item and the first penalty item to obtain the score value of each simulation index.

[0038] In some implementations, the target description information includes first description information, which further includes first non-quantitative description information; each simulation index includes simulation primary factor indices and simulation secondary factor indices; each target real index includes real primary factor indices and real secondary factor indices; and the influence threshold includes primary influence thresholds and secondary influence thresholds. The first calculation module is further configured to:

[0039] Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index, wherein the simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value;

[0040] When the value of the simulation primary factor is greater than the primary influence threshold and the value of the simulation secondary factor is greater than the secondary influence threshold, the first primary weight factor corresponding to the simulation primary factor index and the first secondary weight factor corresponding to the simulation secondary factor index are obtained, wherein each simulation index is a non-quantitative index.

[0041] Obtain the second difference between the actual main factor value and the simulated main factor value, and obtain the first ratio based on the ratio of the second difference to the actual main factor value;

[0042] The first product is obtained based on the product of the first primary weighting factor and the first ratio;

[0043] Obtain the third difference between the actual minor factor value and the simulated minor factor value, and based on the ratio of the third difference to the actual minor factor value, obtain the second ratio;

[0044] The second product is obtained based on the product of the first weighting factor and the second ratio;

[0045] A reference benchmark value is obtained, and the first non-quantitative descriptive information is calculated based on the difference between the reference benchmark value, the first ratio, and the second ratio to obtain the score value of each simulation index.

[0046] In some implementations, the target description information includes second description information, which further includes second non-quantitative description information; each simulation index includes simulation primary factor indices and simulation secondary factor indices; each target real index includes real primary factor indices and real secondary factor indices; and the influence threshold includes primary influence thresholds and secondary influence thresholds. The first calculation module is further configured to:

[0047] The step of calculating a score for each simulation index based on the simulated value, the actual target value, and the influence threshold includes:

[0048] Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index, wherein the simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value;

[0049] When the value of the simulation primary factor is less than the primary influence threshold and the value of the simulation secondary factor is less than the secondary influence threshold, the first primary weight factor and the second primary weight factor corresponding to the simulation primary factor, as well as the first primary weight factor and the second primary weight factor corresponding to the simulation secondary factor, are obtained, wherein each simulation index is a non-quantitative index.

[0050] Obtain the fourth difference between the true main factor value and the main influence threshold, and calculate the product with the corresponding first main weight factor based on the third ratio of the fourth difference to the true main factor value to obtain the third product;

[0051] Obtain the fifth difference between the main influence threshold and the value of the main simulation factor, and calculate the product between the fifth difference and the main influence threshold and the corresponding second main weight factor based on the fourth ratio of the fifth difference and the main influence threshold to obtain the fourth product;

[0052] Obtain the sixth difference between the actual secondary factor value and the secondary influence threshold, and calculate the product between the sixth difference and the fifth ratio of the actual secondary factor value and the corresponding primary weighting factor to obtain the second penalty term;

[0053] Obtain the seventh difference between the secondary influence threshold and the simulated secondary factor value, and calculate the product between the sixth ratio of the seventh difference and the secondary influence threshold and the corresponding secondary weight factor to obtain the third penalty term;

[0054] Obtain a reference benchmark value, and use the reference benchmark value, the third product, the fourth product, the second penalty term, and the third penalty term to calculate the score value of the second non-quantitative description information to obtain the score value of each simulation index.

[0055] Accordingly, a third aspect of the embodiments of this application proposes a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the simulation topology node evaluation method of any one of the embodiments of the first aspect of this application.

[0056] Accordingly, a fourth aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the simulation topology node evaluation method of any one of the embodiments of the first aspect of this application.

[0057] This application embodiment obtains multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system; obtains a real indicator set corresponding to each real topology node and a simulated indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulated indicator set includes multiple simulated indicators; obtains an influence threshold corresponding to each simulated indicator, and determines target description information from preset first description information and second description information based on the magnitude relationship between the simulated value corresponding to each simulated indicator and the influence threshold; wherein the first description information is used to perform a positive measurement on each simulated indicator based on the relative relationship between the simulated value and the target real value of the corresponding target real indicator when the simulated value is greater than the influence threshold, and the second description information is used to perform an inhibitory measurement on each simulated indicator based on the deviation between the simulated value and the target real value when the simulated value is less than the influence threshold; calculates the target description information according to the simulated value, the target real value, and the influence threshold to obtain a score value for each simulated indicator; for each simulated topology node, obtains multiple score values ​​corresponding to multiple simulated indicators, and calculates the evaluation result of each simulated topology node through multiple score values. This approach allows for the acquisition of multi-dimensional indicator sets (such as performance and functionality) for both real and simulated topological nodes. By setting scientific impact thresholds for each simulation indicator and dynamically selecting between positive and negative metrics based on the relationship between the simulated value and the threshold, the approach considers both the inherent biases of the indicators and enhances sensitivity to outliers through negative metrics. This improves the accuracy and robustness of the evaluation, eliminating inconsistencies caused by subjectivity and experience, and comprehensively and sensitively capturing subtle deviations between simulated and real nodes, enabling objective quantitative scoring of each indicator. Furthermore, by aggregating the scores of all indicators for each simulated topological node to calculate the overall evaluation result, the approach effectively avoids incomplete coverage and inconsistent judgments caused by subjective cognitive limitations or experience differences. This achieves systematic and traceable analysis from single-point indicators to the entire node, improving the comprehensiveness, consistency, and accuracy of the evaluation. In summary, this application enables accurate evaluation of simulated topological nodes. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the architecture of the simulation topology node evaluation system provided in the embodiments of this application;

[0059] Figure 2 This is a flowchart of the simulation topology node evaluation method provided in the embodiments of this application;

[0060] Figure 3 This is an example diagram of the router node indicator set provided in the embodiments of this application;

[0061] Figure 4 This is an example diagram of the content of the user node indicator set provided in the embodiments of this application;

[0062] Figure 5 This is a flowchart of the simulation topology node evaluation method provided in the embodiments of this application;

[0063] Figure 6 This is a schematic diagram of the functional modules of the simulation topology node evaluation device provided in the embodiments of this application;

[0064] Figure 7 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0068] Network topology simulation uses techniques such as mathematical modeling, discrete event simulation, or virtualization to reproduce the connection relationships between devices (such as routing nodes and user nodes) in a real network, thus providing fundamental support for network protocol testing, traffic analysis, security assessment, and new technology verification. Since the reliability of the simulation results directly determines their effectiveness and reliability in verifying new network technologies and assessing security, it is necessary to evaluate the network topology nodes obtained from the simulation to ensure that the network simulation environment constructed from it can realistically reflect the operating state of the actual network.

[0069] In related technologies, methods for evaluating the credibility of node simulations often rely on subjective evaluation, such as expert scoring, where domain experts manually score certain performance or functional indicators of the simulated nodes based on their experience, and then synthesize the scores to arrive at a credibility conclusion. However, because this method relies on manual judgment, it is difficult to comprehensively cover all key indicators and is easily affected by the limitations of experts' subjective cognition and differences in experience, making it difficult to accurately evaluate network topology nodes.

[0070] Based on this, embodiments of this application provide a method, apparatus, computer device, and storage medium for evaluating simulated topology nodes, which can accurately evaluate simulated nodes.

[0071] The simulation topology node evaluation method, apparatus, computer equipment, and storage medium provided in this application are specifically described through the following embodiments. First, the simulation topology node evaluation system in this application is described.

[0072] Please refer to Figure 1 In some embodiments, this application provides a simulation topology node evaluation system, including a terminal 11 and a server 12.

[0073] In some implementations, terminal 11 may be a computer workstation or a dedicated test terminal with a network interface, equipped with necessary network adapters, data acquisition cards and graphical human-machine interfaces, such as industrial computers, mobile terminals, embedded devices or dedicated data acquisition terminals, and may integrate sensor interfaces, protocol parsing modules and local caches for real-time or offline acquisition of node indicator data and uploading to server 12.

[0074] Furthermore, terminal 11 can acquire multiple real-world metrics (such as router throughput, latency, and packet loss rate) corresponding to the real topology nodes of the target network system, as well as multiple simulated metrics (such as simulated values ​​and configuration parameters) corresponding to the simulated topology nodes of the simulated network system, in real time through sensors, network probes, or interface modules. Then, the collected raw data is cleaned and normalized (e.g., logarithmic normalization), and a standardized data format is generated according to preset rules (e.g., threshold division, reliability index conversion). The preprocessed data is then transmitted to server 12 via wired or wireless networks (e.g., Ethernet, 5G) for subsequent evaluation and calculation.

[0075] In some implementations, server 12 can be a high-performance computing server, a distributed storage system, a network load balancer, or a virtualization platform, equipped with a multi-core processor, large-capacity memory, and a high-speed storage system to support complex mathematical operations (such as logarithmic normalization and weighted calculation) and large-scale data processing. In some implementations, the real and simulated indicator sets can also be directly obtained through server 12.

[0076] For example, server 12 can calculate the score of each simulation indicator based on the real indicator set and / or simulation indicator set uploaded by terminal 11, combined with preset thresholds, weights, and evaluation models (such as logarithmic normalization method, weighted calculation method). Furthermore, server 12 can also generate the final credibility evaluation result of the simulation topology nodes (such as the comprehensive credibility score of router nodes or user nodes) based on the score values ​​of each indicator and their weights.

[0077] Furthermore, the server 12 can also store the evaluation results in a database and feed them back to the terminal 11 or other management systems through an application programming interface (API) or a visual interface to support subsequent optimization of the simulation model or adjustment of network configuration.

[0078] The simulation topology node evaluation method in this application can be illustrated through the following embodiments.

[0079] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent will be obtained first. Furthermore, the collection, use, and processing of this data will comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user will be obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent will the necessary user-related data for the normal operation of the embodiments of this application be obtained.

[0080] In this embodiment, the description will focus on the simulation topology node evaluation device, which can be integrated into a computer device. See also Figure 2 , Figure 2 This is a flowchart illustrating the steps of the simulated topology node evaluation method provided in this application embodiment. Taking the simulated topology node evaluation device specifically integrated into a terminal or server as an example, the specific process when the processor on the terminal or server executes the program instructions corresponding to the simulated topology node evaluation method is as follows:

[0081] Step 101: Obtain multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system.

[0082] In some implementations, in order to accurately assess the credibility of network topology node simulation, multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system can be obtained to provide real benchmark data and simulation model data as input, thereby providing a basic comparison source for the subsequent quantitative evaluation index system.

[0083] The target network system can be a real-world deployed computer network system, used as a benchmark for simulation modeling, such as an internet backbone or enterprise intranet. Data information about the real topology nodes in the target network system can be obtained using monitoring tools (such as network probes or log analysis).

[0084] The real topology nodes can include router nodes and user nodes, which can be physical or virtual device entities in the target network system, such as routers or user terminals, and are used to characterize the actual performance and functional characteristics of network connection points. Their attributes are obtained through real-time measurements (such as throughput and latency) or configuration extraction.

[0085] The simulation network system can be a software simulation environment built on the target network system to reproduce network behavior and node interactions. It can be constructed using a discrete event simulator and generated through mathematical modeling and parameter configuration.

[0086] In this context, simulated topology nodes can be model nodes in a simulated network system, used to simulate the functions and performance indicators of real topology nodes, such as data processing capabilities or protocol support. Their attributes are dynamically generated and adjusted by simulation software (such as virtualization tools). For example, simulated topology nodes can include simulated router nodes and simulated user nodes.

[0087] In some implementations, multiple real topology nodes can be obtained through network probe monitoring, device configuration extraction, or log analysis. These multiple real topology nodes may include router nodes (such as core switches and access layer routers that support throughput / latency measurement) and user terminal nodes (such as servers, personal computers, and industrial control terminals that have processor performance detection capabilities).

[0088] For example, a simulated network system can be built using discrete event simulation tools (such as NS-3 tools) or virtualization platforms (such as GNS3 platforms). The simulated network system can be used to reproduce the topological connections and node behavior characteristics of the target network system.

[0089] For example, multiple simulated topology nodes may include simulated router nodes (simulating throughput / protocol networking functions) and simulated user nodes (simulating processor performance / operating system interaction), etc., whose parameters are generated by modeling real node data of the target network system.

[0090] By employing the above methods, we can ensure the data integrity of both real and simulated topology nodes, and use this as an evaluation input. This reduces subjective bias and improves the reliability of index quantification, thereby providing an accurate basis for subsequent credibility calculations of simulated topology nodes (such as single-index scoring based on logarithmic normalization or weighted calculation methods), and improving the comprehensiveness and accuracy of simulated topology node evaluation.

[0091] Step 102: Obtain the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node. The real indicator set includes multiple real indicators, and the simulation indicator set includes multiple simulation indicators.

[0092] In some implementations, in order to achieve a multi-dimensional quantitative evaluation of the credibility of network topology node simulation, the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node can be obtained to provide multiple types of indicator data of real and simulated topology nodes as input, thereby providing basic comparison data for subsequent quantitative evaluation methods and improving the comprehensiveness and accuracy of the evaluation of simulated topology nodes.

[0093] Among them, the real indicator set can be used to characterize the operating characteristics of real topology nodes, such as the throughput, latency, and packet loss rate of router nodes, or the processor performance and operating system compatibility of user nodes. It can be measured in real time or extracted by configuration through monitoring tools (such as network probes or hardware performance detectors).

[0094] The simulation index set can be a set of simulated parameters of simulated topology nodes, used to reproduce the behavioral characteristics of real nodes, such as the protocol networking function of simulated router nodes or the memory performance data of simulated user nodes.

[0095] Among them, the real indicator can be a single quantitative parameter in the set of real indicators, used to reflect a certain aspect of the actual characteristics of a node, such as the throughput value of a router node or the hard disk read and write speed of a real user node. It can be directly measured and obtained through standardized testing tools (such as iperf or CrystalDiskMark).

[0096] Among them, simulation indicators can be single-dimensional simulated data in the simulation indicator set, used to compare differences with real indicators, such as the simulated packet loss rate of a simulated router or the application response latency of a simulated user node, which are calculated and output by simulation software based on a discrete event model.

[0097] Please refer to Figure 3 and Figure 4In some implementations, the real metric set corresponding to each real topology node may include a real router node metric set and a real user node metric set. The real router node metric set may include a three-dimensional metric set: a performance metric set, a functional metric set, and a stability and reliability metric set. The real user node metric set may include a three-dimensional metric set: a computing power metric set, a functional purpose metric set, and a software metric set.

[0098] In some implementations, the performance metrics set may include multiple real-world metrics such as throughput, latency, and packet loss rate. Specifically, throughput, as an important indicator of a router node's data processing capability, directly reflects the amount of data it can successfully transmit per unit time; latency reflects the time interval from when data enters the router node to when it leaves, and is related to the real-time nature of data transmission; packet loss rate indicates the proportion of data packets lost during transmission, and has a significant impact on network transmission quality.

[0099] Furthermore, the multiple real-world metrics corresponding to the functional metric set can include configuration-related functions, status information-related functions, and protocol networking-related functions. Specifically, resource configuration-related functions can be configuration coverage, which directly determines the connection method and resource coverage of router nodes; status information-related functions can be represented by key status information such as the usage of the Central Processing Unit (CPU); protocol networking-related functions can be represented by the coverage rate of major protocols such as IPv4 / 6 and dynamic routing protocols, as well as other secondary protocols, which are core functions to ensure that router nodes can achieve efficient networking and data transmission in different network environments.

[0100] Furthermore, the stability and reliability metrics set includes several real-world indicators such as routing stability, burst traffic handling capability, and primary / backup switchover time. Specifically, routing stability reflects the ability of router nodes to maintain stable routing paths in complex network environments, which is crucial for ensuring continuous network connectivity; burst traffic handling capability measures the response strategies and processing efficiency of router nodes in dealing with sudden surges of data; and primary / backup switchover time reflects how quickly a backup device can take over when the primary device fails, ensuring uninterrupted network service.

[0101] In some implementations, the actual metrics corresponding to the computing power metric set may include CPU performance, memory performance, and hard disk performance. Specifically, processor performance, as the core of user node computation, directly determines the speed and efficiency with which the node processes various tasks; memory performance is the data transfer station between the CPU and external storage devices, and sufficient memory ensures that data is quickly accessed by the CPU when the system is running programs, avoiding program crashes or slow operation; hard disk performance bears the heavy responsibility of data storage, and its read / write speed, capacity, and other factors play a decisive role in the efficiency of user data storage and retrieval.

[0102] Furthermore, the actual metrics corresponding to the functional purpose metrics set can include communication modules, interaction modules, and security mechanisms. The communication module, as the foundational support of the user node's functional architecture, focuses on data transmission, protocol networking, and inter-node collaborative interaction; the interaction module is responsible for real-time response and resource scheduling between the user node and the external environment (including users, applications, and devices); and security mechanisms protect user nodes from external attacks and data leakage threats, including firewalls, encryption technologies, user authentication, and other methods.

[0103] Furthermore, the actual metrics corresponding to the software metrics set can include the operating system, applications, and feature extensions. The operating system, as the underlying core, is responsible for managing hardware resources (such as CPU, memory, and storage devices), providing basic services (such as file management and process scheduling), and building the runtime environment for upper-layer software. Applications are specific tools developed based on the operating system (such as browsers and office software), which implement specific functions required by users by calling system resources. Feature extensions supplement existing software capabilities (such as plugins, modules, or API interfaces), allowing users to flexibly add new features or optimize performance in specific scenarios without modifying the core program.

[0104] In some implementations, the simulation metric set corresponding to each simulated topology node may include the simulated router node metric set and the simulated user node metric set. The simulated router node metric set may include a three-dimensional metric set: a performance metric set, a functional metric set, and a stability and reliability metric set. The simulated user node metric set may include a three-dimensional metric set: a computing power metric set, a functional purpose metric set, and a software metric set.

[0105] In some implementations, corresponding to the real topology node being a router node, when the simulated topology node is a simulated router node, the multiple simulation indicators corresponding to the performance indicator set may include throughput, latency, and packet loss rate, etc.; the multiple simulation indicators corresponding to the function indicator set may include configuration functions, status information functions, and protocol networking functions; the multiple simulation indicators corresponding to the stability and reliability indicator set may include routing stability, burst traffic handling capability, and primary / backup switchover time.

[0106] In some implementations, the simulation metrics corresponding to the computational capability metric set of the simulated topology nodes, corresponding to the real topology nodes, may include CPU performance, memory performance, and disk performance. The simulation metrics corresponding to the functional purpose metric set may include communication modules, interaction modules, and security mechanisms. The simulation metrics corresponding to the software metric set may include operating systems, applications, and functional extensions.

[0107] In some implementations, certain metrics, such as packet loss rate, can be directly obtained from the corresponding data. However, for communication modules, interaction modules, and security mechanisms, the corresponding influencing factors can be identified first. These factors can be categorized as primary or secondary, facilitating a comprehensive, accurate, and quantifiable evaluation of the simulated topology nodes. For example, factors influencing processor performance might include CPU utilization, clock speed, and instruction throughput; memory performance might include memory utilization, read / write latency, and bandwidth; hard disk performance might include read / write speed (MB / s), input / output operations per second (IOPS), and latency; communication modules might include data transfer rate and protocol coverage; interaction modules might include response time, resource scheduling success rate, and concurrent connections; and security mechanisms might include authentication success rate and firewall rule matching rate. Not all influencing factors for all metrics will be listed here.

[0108] By employing the above methods, we can ensure the integrity of multi-dimensional indicator data for both real and simulated nodes. This avoids subjective evaluation bias and provides accurate input for subsequent indicator quantification, thereby laying the foundation for dynamic evaluation of node credibility and ultimately improving the verification accuracy and optimization efficiency of the network topology simulation model.

[0109] Step 103: Obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold.

[0110] The first descriptive information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold. The second descriptive information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold.

[0111] In some implementations, in order to achieve dynamic quantitative evaluation of the credibility of simulation indicators, target descriptive information can be selected based on the relationship between simulation values ​​and influence thresholds to specifically adapt the quantification rules under different conditions, thereby improving the accuracy of indicator evaluation.

[0112] Among them, the impact threshold can be a pre-set critical value of the indicator, used to classify the strictness of the evaluation criteria.

[0113] The first descriptive information may be the quantization rule used when the simulated value is greater than the threshold, which is used to positively measure the relative relationship between the simulated value and the true value.

[0114] The target real-world metric can be the actual metric corresponding to the current simulation value. For example, if the simulation metric corresponding to the current simulation value is throughput, then the target real-world metric corresponding to that simulation value should also be throughput.

[0115] The second descriptive information can be the quantization rule used when the simulated value is less than the threshold, which is used to suppress the deviation between the simulated value and the real value, and emphasizes the strict deduction when the standard is not met.

[0116] The target description information can be the final evaluation rule after dynamic selection, used to perform single-index credibility calculation. For example, it can call the first or second description information formula based on the threshold comparison result and output the simulation credibility score of the router throughput.

[0117] In some implementations, the impact threshold corresponding to each simulation metric is... It can be calculated using the following formula:

[0118] ;

[0119] in, Here, n is the preset adjustment coefficient (e.g., 1.1), and n is the total number of influencing factors. The weight of the i-th influencing factor (e.g., port bandwidth weight 0.5). The specific value of the i-th influencing factor (e.g., the measured bandwidth value of 10Gbps) can be obtained through network probe monitoring, performance log analysis, or configuration parameter extraction.

[0120] In some implementations, when the simulation value exceeds the impact threshold, a positive metric (rewarding high simulation accuracy) is used; when the value does not reach the threshold, an inhibitory metric (penalizing low simulation accuracy) is used, thereby improving the scenario adaptability and accuracy of the evaluation results.

[0121] In some implementations, when the simulated value is quantifiable (e.g., throughput) and exceeds an impact threshold, the logarithmic transformation results of the simulated value and the corresponding target real value can be compared. The smaller of the two logarithms is divided by the larger value, and a score close to 1 is output (a high score bonus). For example, a simulated throughput of 9Gbps ​​(target real value 11Gbps) results in a score of approximately 0.9, reflecting the lenient principle of high score for closeness to reality.

[0122] In some implementations, when the simulated value is quantifiable and less than the impact threshold, a significantly reduced score can be output by subtracting the proportion of the deviation between the simulated value and the corresponding minimum target real value, based on the logarithm of the impact threshold. For example, a simulated throughput of 7Gbps (threshold 8.69Gbps) might result in a score as low as 0.6, highlighting the scoring principle of severe punishment for non-compliance.

[0123] In some implementations, when the simulated value is not quantifiable (e.g., protocol coverage) and the simulated value exceeds the impact threshold, a weighted calculation method can be used to deduct points based on the relative deviation between the simulated value and the corresponding target real value (the smaller the deviation, the fewer points are deducted). For example, if the protocol coverage reaches 90% (target real value 85%), the deduction ratio is relatively low (e.g., 0.1 points).

[0124] In some implementations, when the simulated value is not quantifiable and is less than the impact threshold, a double penalty term can be applied. Specifically, the baseline deviation can be calculated first by weighting the difference between the corresponding target true value and the impact threshold, and then the actual deviation can be calculated by weighting the difference between the impact threshold and the simulated value.

[0125] By adopting the above methods, the quantification rules for different indicator states (whether they exceed the impact threshold or whether they are quantifiable indicators) can be dynamically adapted, thereby avoiding the bias caused by uniform subjective scoring (such as lenient scoring for high throughput and strict deduction for low throughput). This provides a differentiated scientific basis for subsequent single-indicator credibility calculations, and ultimately improves the accuracy and scenario adaptability of node simulation credibility assessment results.

[0126] In some implementations, to achieve the scientific and dynamic generation of the impact threshold of simulation indicators, the impact threshold corresponding to each simulation indicator can be obtained by weighted calculation based on multiple influencing factors. This systematically improves the accuracy and scenario adaptability of network topology node simulation credibility assessment, achieving comprehensive evaluation indicators. For example, step 103, "obtaining the impact threshold corresponding to each simulation indicator," may include:

[0127] (103.1) Obtain at least one influencing factor for each simulation index, and the index influence value corresponding to each influencing factor;

[0128] (103.2) Obtain the influence weight corresponding to each indicator's influencing factor, and obtain the sub-threshold item based on the product of each indicator's influence value and its corresponding influence weight;

[0129] (103.3) The initial threshold is obtained by adding the sub-threshold terms corresponding to at least one of the influencing factors of the indicator;

[0130] (103.4) Obtain the threshold adjustment coefficient corresponding to each simulation index, and obtain the influence threshold corresponding to each simulation index based on the product of the initial threshold and the threshold adjustment coefficient.

[0131] Among them, the influencing factors of the indicator can be the key parameters that determine the performance of the simulation indicator and are used to describe the characteristics of the business scenario. For example, when the simulation indicator is throughput, its influencing factors can include port bandwidth, network congestion level and device processing capacity.

[0132] The influence value of an indicator can be the numerical value of each influencing factor, such as the measured value of port bandwidth (e.g., 10Gbps). The influence value can also be a normalized value. For example, normalizing the measured values ​​of the three influencing factors of throughput—port bandwidth, network congestion level, and device processing capacity—results in normalized influence values ​​of 0.5, 0.2, and 0.8, respectively.

[0133] The influence weight can be the relative importance coefficient of each indicator's influencing factors, used for weighted calculation. For example, the influence weight of port bandwidth is 0.5, the influence weight of network congestion is 0.3, the influence weight of equipment processing capacity is 0.2, and so on. The influence weight can be allocated through the analytic hierarchy process or business demand analysis.

[0134] Among them, the sub-threshold item can be the weighted contribution value of the factors affecting a single indicator.

[0135] The initial threshold can be an uncorrected evaluation benchmark value, used to initially classify the indicator status, and it can be calculated by summing the sub-threshold terms.

[0136] The threshold adjustment coefficient can be a flexible correction factor for business scenarios, used to adapt to the strictness of the evaluation, and it can be dynamically set through simulation target requirements.

[0137] In some implementations, the impact threshold can be dynamically generated through multi-factor weighted fusion and adjustment coefficient correction. Specifically, multiple factors affecting each simulation indicator (such as port bandwidth, network congestion, etc.) can be comprehensively considered. The influence weights of each indicator's influencing factors are assigned to reflect its importance relative to other influencing factors. All sub-threshold terms obtained by weighted summation are then added together, and finally, an adjustment coefficient is introduced to adapt to the evaluation rigor requirements of the business scenario, thus calculating the final impact threshold of the simulation indicator. For example, the impact threshold is calculated... The formula is as follows:

[0138] ;

[0139] in, This represents the threshold adjustment coefficient, and n represents the number of factors affecting the current simulation index. This represents the influence weight of the i-th influencing factor, used to quantify the relative importance of the influencing factors for this indicator. This represents the influence value of the factor affecting the i-th indicator.

[0140] It should be noted that the impact values ​​of the simulation metrics used to calculate the impact thresholds are not data obtained from the simulation topology nodes, but rather data obtained from the real topology nodes or from the topology node states in other standard scenarios.

[0141] In some implementations, taking the simulated node as the simulated router node and the corresponding simulated metric as the router's throughput metric as an example, the influencing factors include port bandwidth, network congestion level, and device processing capacity, with corresponding weights as follows: , , If the port bandwidth value A value of 0.8 for network congestion. The value of the equipment's processing capacity is 0.8. The corresponding influence weight is 0.8. 0.5 0.3 It is 0.2.

[0142] Therefore, the sub-threshold item corresponding to the port bandwidth can be obtained through... and The product of these values ​​is 0.4; similarly, the sub-threshold corresponding to network congestion is 0.21, and the sub-threshold corresponding to device processing capacity is 0.16. Therefore, the three sub-thresholds can be added together to calculate the initial threshold of 0.77. If the preset threshold adjustment coefficient k is 0.11, then the impact threshold corresponding to the simulation metric (router throughput metric) is 0.847.

[0143] In some implementations, the influence weights of the influencing factors of different simulation indicators and the threshold adjustment coefficients of different simulation indicators are different. These can be set according to the actual situation, and no specific limitation is made.

[0144] By using the above methods, thresholds can be dynamically generated by integrating multi-dimensional business characteristics, avoiding the limitations and inaccuracies of expert-set thresholds. This provides a scientific basis for subsequent scoring quantification and ultimately improves the adaptability and accuracy of credibility assessment results in complex scenarios such as network congestion and heterogeneous devices.

[0145] In some implementations, to unify the evaluation direction of all indicators (higher values ​​are better) and eliminate conflicts in evaluation direction caused by inherent differences in indicators (such as lower latency being better, higher throughput being better), non-reliability indicators in the quantitative indicators can be converted into reliability indicators. This ensures that the subsequent threshold division and credibility calculation process can standardize the processing of all indicators, avoiding logical confusion and result distortion caused by the direct participation of reverse indicators in the calculation. For example, before step 103, that is, before "obtaining the influence threshold corresponding to each simulation indicator and determining the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation indicator and the influence threshold," the following may also be included:

[0146] (A.1) When there is a target simulation index that is quantified and unreliable among multiple simulation indices, determine the target simulation value corresponding to the target simulation index;

[0147] (A.2) Obtain the preset conversion constant and exponent factor, and calculate the conversion term based on the target simulation value according to the exponent factor;

[0148] (A.3) Update the target simulation value corresponding to the target simulation index based on the ratio of the transformation constant to the transformation term.

[0149] Among them, the target simulation index can be a quantitative unreliable index that needs to be converted, used to characterize the characteristic that the lower the value, the better, such as the latency or packet loss rate of a router node, which can be directly obtained through the output of simulation software.

[0150] Among them, quantified and unreliable indicators can be measurable parameters whose values ​​are negatively correlated with credibility. They are used to describe characteristics that need to be reverse-converted, such as lower latency leading to better performance. Their values ​​are extracted through network probes or simulation logs.

[0151] The target simulation value can be a specific numerical value of the target simulation index, used for input conversion calculation. For example, the measured delay value D=10ms can be collected by the tool or generated by the simulation model.

[0152] The conversion constant can be a preset benchmark scalar used to adjust the magnitude of the conversion result, and it can be set empirically according to business needs.

[0153] The exponential factor can be an adjustment parameter that controls the transformation curve and is used to amplify / reduce numerical differences. It can be obtained through sensitivity analysis or experimental calibration.

[0154] The conversion term can be an intermediate value after the index calculation, used to generate an equivalent reliability index.

[0155] In some implementations, to ensure that all indicators meet the unified direction of "higher values ​​are better," so as to evaluate each simulation indicator on the same trend and ensure the rationality and accuracy of the evaluation, when there is a target simulation indicator that is quantitative and unreliable among multiple simulation indicators, that is, when there is a target simulation indicator whose target simulation value is negatively correlated with its reliability (i.e., lower values ​​are better), the target simulation value corresponding to the target simulation indicator can be determined (e.g., the measured value of the latency indicator is 10ms). Then, the converted value (equivalent reliability indicator) can be obtained by calculating the ratio of the conversion constant and the exponential factor. The specific conversion formula is as follows:

[0156] ;

[0157] in, This represents the updated target simulation value; This represents the target simulation value before the update; represents the conversion constant; k represents the exponential factor.

[0158] For example, the target simulation metric is latency. The target simulation value is 10ms (milliseconds) extracted from the simulation log. The exponential factor k is 2 and the conversion constant N is 100. Using the above formula, the corresponding conversion term can be calculated to be 100. The target simulation value after conversion and update is 100 / 100=1.

[0159] In some implementations, the values ​​of the conversion constant and the exponential factor can be set according to the actual situation, and this application does not impose specific limitations on them.

[0160] By using the above methods, unreliable indicators can be converted into equivalent units where higher values ​​are better, thereby eliminating directional differences in indicators and unifying quantitative standards. This provides compatible input for subsequent threshold division and logarithmic normalization confidence calculation, ultimately improving the scientificity and consistency of the node simulation evaluation system under complex indicator scenarios.

[0161] Step 104: Calculate the target description information based on the simulation value, the target's actual value, and the impact threshold to obtain the score value for each simulation index.

[0162] In some implementations, in order to achieve objective quantification of the credibility of a single indicator, a calculation rule (such as logarithmic normalization or weighted calculation) can be dynamically selected based on the simulation value, the target real value and the influence threshold to output a score value with a unified dimension, thereby solving the problem of subjective evaluation bias and improving the comprehensiveness and accuracy of the evaluation.

[0163] The score can be a quantitative result of the simulation credibility of one of the simulation indicators of a single simulated topology node, used to characterize the degree of consistency between the simulated topology node and the real topology node under that simulation indicator.

[0164] In some implementations, differentiated scoring rules can be dynamically selected based on pre-calculated impact thresholds to scientifically quantify the reliability of each simulation indicator. During scoring, the simulated value is first compared to the impact threshold. When the simulated value exceeds the threshold, it indicates that the performance of the current individual simulation indicator meets the standard, and a positive metric rule can be used. The score is calculated based on the logarithmic relationship between the simulated value and the actual value, thus rewarding high-precision simulation indicators with a lenient scoring mechanism. When the simulated value fails to reach the threshold, it indicates that the simulation indicator has a weakness, and an inhibitory metric rule is used. This rule uses the logarithm of the threshold as a benchmark and adds a penalty term for the deviation between the simulated value and the actual value, resulting in a significant reduction in performance. This means that a strict scoring mechanism is used to penalize simulation indicators with performance defects. Finally, a score value for each simulation indicator can be obtained.

[0165] By adopting the above methods, differentiated calculation rules can be adapted to the characteristics (quantifiable / non-quantifiable) and status (exceeding threshold / not meeting standard), thereby avoiding subjective defects and providing standardized input for subsequent node credibility weighted fusion, ultimately improving the credibility and reproducibility of network topology simulation evaluation results.

[0166] In some implementations, to achieve robust scoring of threshold-exceeding indicators, the logarithmic extreme value of the simulated value and the actual value can be dynamically selected as the calculation benchmark to eliminate dimensional differences and constrain the scoring range, thereby improving the stability, relevance, comprehensiveness, and accuracy of the evaluation in high-indicator scenarios. For example, the target description information may include first description information. Where the current simulation indicator is quantifiable, the first description information may include first quantified description information. For example, step 104 may include:

[0167] (104.a1) When the simulated value corresponding to each simulation index is greater than the influence threshold, the first pair of values ​​corresponding to the simulation value and the second pair of values ​​corresponding to the target real value are obtained, wherein each simulation index is a quantitative index.

[0168] (104.a2) Select the smaller value from the first logarithm and the second logarithm as the target first logarithm, and select the larger value as the target second logarithm;

[0169] (104.a3) The first quantitative description information is calculated based on the ratio of the first logarithmic value of the target to the second logarithmic value of the target to obtain the score value of each simulation index.

[0170] The first quantitative description information can be the logarithmic normalization formula when the influence threshold is exceeded, which is used to calculate the score.

[0171] The first logarithmic value can be the result of a logarithmic transformation of the simulated value, used to eliminate dimensional differences.

[0172] The second logarithmic value can be the result of a logarithmic transformation of the target true value, used to construct a comparison benchmark.

[0173] The target first logarithm can be the minimum of the first logarithm and the second logarithm, used to constrain the upper limit of the score.

[0174] The target second logarithm can be the maximum value between the first logarithm and the second logarithm, and is used to set the lower limit of the score.

[0175] In some implementations, when the simulated value corresponding to each simulation index is greater than the influence threshold, it indicates that the performance of that simulation index has reached the expected baseline and has room for optimization. Therefore, logarithmic transformation and extreme value ratio calculation are needed to eliminate dimensional differences and constrain the scoring range to avoid scoring distortion caused by extreme values. Specifically, the score value of each simulation index can be calculated using the first quantitative description information, and the specific calculation formula is as follows:

[0176] ;

[0177] in, This represents the score (range 0-1) of the current simulation metric, used to quantify the reliability of the simulation metric (a higher value indicates better simulation accuracy). Represents the first logarithm With the second logarithm The smaller value in the range is used to constrain the upper limit of the score; This represents the larger of the first and second logarithmic values, used to set the scoring benchmark. The simulated value, such as a router throughput of 9Gbps, can be obtained from the output of the simulation software or from the monitoring logs. This represents the true value of the target, which can be collected using measurement tools on a real network system.

[0178] In some implementations, the simulated value and the corresponding target true value for each simulation indicator can be obtained through measurement tools; alternatively, the simulated value and the corresponding target true value for each simulation indicator can be calculated by combining multiple influencing factors. For example, the influencing factor values ​​corresponding to multiple influencing factors can be normalized and then weighted and summed to calculate the simulated value and the corresponding target true value, and so on.

[0179] It should be noted that by using the first quantitative description information to calculate the score of each simulation indicator, the absolute difference between the simulation value and the real value can be transformed into a relative proportional relationship and compressed to the [0,1] interval. In this way, the score of each quantifiable simulation indicator can be accurately calculated in high-performance simulation scenarios (such as when quantifiable indicators such as throughput and processor performance exceed the impact threshold), and the score deviation caused by numerical magnitude differences (such as a tenfold difference) can be effectively suppressed.

[0180] By employing the above methods, the dimensional differences between simulated and actual values ​​can be eliminated through logarithmic transformation, and the scoring range can be constrained by the extreme value ratio. This avoids scoring distortion caused by extreme values ​​(such as simulated values ​​far exceeding actual values), thereby providing a fair and stable single-index input for subsequent node-level credibility weighted fusion, and ultimately improving the reliability and comparability of evaluation results in high-performance simulation scenarios (such as router throughput exceeding the threshold).

[0181] In some implementations, to achieve suppressive scoring of simulation indicators that do not exceed the impact threshold and to improve the reliability of the simulation credibility assessment system, the score value can be calculated by dynamically constructing the difference between the quantified sub-item and the penalty item. This strengthens the assessment rigor in low indicator value scenarios (since reliability transformation has already been performed, higher simulation values ​​indicate better simulation performance), ensuring that the scoring result is strictly negatively correlated with the actual performance defects, thereby enhancing the reliability and feasibility of the assessment conclusion. For example, the target description information may include second description information, which may include second quantified description information. Step 104 may also include:

[0182] (104.b1) When the simulated value corresponding to each simulation index is less than the influence threshold, the first pair of values ​​corresponding to the simulation value, the second pair of values ​​corresponding to the target real value, and the third pair of values ​​corresponding to the influence threshold are obtained, wherein each simulation index is a quantitative index.

[0183] (104.b2) Select the larger value from the first logarithmic value and the second logarithmic value as the target second logarithmic value, and select the smaller value from the simulated value and the target real value as the target reference value;

[0184] (104.b3) Determine the quantification sub-items based on the ratio of the third logarithm to the target second logarithm;

[0185] (104.b4) Obtain the first difference between the impact threshold and the target reference value, and determine the first penalty term based on the ratio between the first difference and the impact threshold;

[0186] (104.b5) The second quantitative description information is calculated based on the difference between the quantized sub-item and the first penalty item to obtain the score value of each simulation index.

[0187] The second quantitative description information can be a scoring formula for when the threshold is not reached, used to perform inhibitory measures.

[0188] The third logarithmic value can be the result of the logarithmic transformation that affects the threshold, and is used to construct a standardized benchmark.

[0189] The target reference value can be the smaller value between the simulation value and the actual value, used to characterize the actual shortcomings.

[0190] Among them, the quantified sub-item can be a basic rating component used to maintain some credibility, which can be generated by the ratio of the third logarithm to the target second logarithm.

[0191] The first difference can be the absolute difference between the impact threshold and the target reference value, used to quantify the degree of deviation.

[0192] The first penalty item can be a deviation compensation component, used to perform a deduction operation.

[0193] In some implementations, when the simulated value corresponding to each simulation index is less than the impact threshold, it indicates that the performance of the index has not met the expected benchmark and has obvious defects. Therefore, it is necessary to implement strict inhibitory scoring by constructing a difference model between the quantified sub-item and the penalty item. The calculation principle is as follows: First, the dimensions are unified by logarithmic transformation and an extreme value benchmark is selected. Then, the ratio of the threshold logarithm to the maximum logarithm of the simulation / real value is calculated as the basic confidence (quantified sub-item). At the same time, a penalty item is generated based on the deviation ratio between the threshold and the minimum value of the simulation / real value. Finally, the score is forcibly reduced through difference calculation to expose performance shortcomings.

[0194] Specifically, the score for each simulation index can be calculated using the second quantitative description information. The specific calculation formula is as follows:

[0195] ;

[0196] in, The score represents the simulation index (range 0-1), with lower values ​​indicating more severe simulation defects. Indicates the threshold of influence The third logarithm is used to provide a standardized benchmark; Represents simulation values The corresponding first pair of values Compared with the target true value The corresponding second pair of values The larger value in the middle (the second logarithm of the target) is used to amplify performance deviations; This represents the smaller of the simulated value and the actual value (the target reference value), used to pinpoint actual performance shortcomings.

[0197] Furthermore, the logarithmic ratio in the formula is used for calculation, that is... This can generate basic credibility components (quantified sub-items), which can be used to reflect the calculation principle of retaining some reasonableness even when the standard is not met; then, the deviation ratio in the formula is used to calculate... It can quantify performance gaps and generate a first penalty term. Finally, by calculating the difference between the quantified sub-item and the first penalty term (S = quantified sub-item - penalty term), it can accurately calculate the score of each simulation indicator in low-performance simulation scenarios (such as when quantifiable indicators such as throughput and latency do not reach the threshold), and ensure that the score is strictly negatively correlated with the degree of performance defect.

[0198] By using the above methods, a basic score (quantitative sub-item) can be constructed by influencing the threshold logarithmic benchmark and a deviation penalty (first penalty item) can be superimposed. This forces a significant reduction in the score of non-compliant indicators (such as low throughput), thereby providing a rigorous evaluation basis and ultimately improving the accuracy of the simulation credibility judgment of the simulation topology nodes.

[0199] In some implementations, to address the objective scoring problem of non-quantifiable indicators (such as protocol coverage, where precise values ​​cannot be directly obtained and only a general range of coverage can be determined), the deviation ratio of primary / secondary factors can be calculated using a hierarchical weighted approach to achieve the quantitative transformation of semi-structured indicators. This compensates for the subjectivity and limitations of expert scoring methods and improves the accuracy of scoring non-quantifiable simulation indicators. For example, target description information may include first description information, which may also include first non-quantifiable description information. Each simulation indicator may include simulation primary factor indicators and simulation secondary factor indicators. Each target real indicator may include real primary factor indicators and real secondary factor indicators. Influence thresholds may include primary influence thresholds and secondary influence thresholds. For instance, step 104 may further include:

[0200] (104.c1) Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index. The simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value.

[0201] (104.c2) When the value of the main simulation factor is greater than the main influence threshold and the value of the secondary simulation factor is greater than the secondary influence threshold, the first main weight factor corresponding to the main simulation factor index and the first secondary weight factor corresponding to the secondary simulation factor index are obtained, wherein each simulation index is a non-quantitative index.

[0202] (104.c3) Obtain the second difference between the actual main factor value and the simulated main factor value, and obtain the first ratio based on the ratio of the second difference to the actual main factor value;

[0203] (104.c4) The first product is obtained based on the product of the first primary weighting factor and the first ratio;

[0204] (104.c5) Obtain the third difference between the actual minor factor value and the simulated minor factor value, and obtain the second ratio based on the ratio of the third difference to the actual minor factor value;

[0205] (104.c6) Based on the product of the first weighting factor and the second ratio, the second product is obtained;

[0206] (104.c7) Obtain a reference benchmark value, and calculate the first non-quantitative descriptive information based on the difference between the reference benchmark value, the first ratio and the second ratio to obtain the score value of each simulation index.

[0207] The first non-quantitative descriptive information can be a weighted calculation formula of non-quantifiable simulation indicators, used to output a score value.

[0208] Among them, the main simulation factor indicators can be the core influencing items of non-quantifiable indicators, used to determine the scoring subject, such as the number of supported protocol types in protocol coverage (simulation indicator).

[0209] Among them, simulation secondary factor indicators can be auxiliary influence items of non-quantifiable indicators, used to supplement scoring details, such as protocol interaction completeness in protocol coverage.

[0210] Among them, the real key factor indicators can be the core parameter benchmarks in real scenarios, used to compare the simulation key factors, such as the number of protocol types supported by a real router.

[0211] Among them, the real secondary factor indicators can be auxiliary parameter benchmarks in real scenarios, used to compare the simulation secondary factors, such as the response latency of real protocol interactions, which can be obtained through log monitoring and other methods.

[0212] Among them, the main influence threshold can be the critical value of the main factor, which is used to determine the standard status of the factor. It can be calculated by the formula for the influence threshold, which has been listed above and will not be repeated here.

[0213] Among them, the secondary impact threshold can be the critical value of the secondary factor, which is used to determine the compliance status of the factor. For example, the protocol interaction completeness threshold = 80%, which can be calculated by the formula for the impact threshold, as listed above, and will not be repeated here.

[0214] Among them, the simulation key factor values ​​can be the simulation result values ​​of the key factors, used for input deviation calculation, and can be output by the simulation software.

[0215] Among them, the simulation secondary factor value can be the simulation result value of the secondary factor, which is used for input deviation calculation. For example, the simulation protocol interaction completeness = 70%, which can be output by the simulation software.

[0216] The true key factor value can be the true benchmark value of the key factor, used to calculate the deviation. It can be obtained through monitoring tools or other means. This application does not limit the method of obtaining it.

[0217] Among them, the true secondary factor value can be the true benchmark value of the secondary factor, used to calculate the deviation. For example, the true protocol interaction completeness = 90%, which can be obtained through monitoring tools or other means. This application does not limit the method of obtaining it.

[0218] The first primary weighting factor can be the relative importance coefficient of the primary factors, which can be allocated using the entropy weighting method or set by the operator.

[0219] The primary weighting factor can be the relative importance coefficient of the secondary factor, which can be set through the contribution analysis of the secondary factor or by the operator.

[0220] Non-quantitative indicators can be evaluation items that cannot be directly quantified, used to describe qualitative characteristics, such as protocol coverage or the completeness of security mechanisms.

[0221] The second difference can be the absolute difference between the real and simulated main factor values, used to quantify the core deviation.

[0222] The first ratio can be a standardized value of the deviation of the main factors, used to eliminate dimensional differences, and can be generated by the ratio of the second difference to the true value of the main factors.

[0223] The first product can be the weighted deviation score of the main factors, used to integrate the influence of weights, and can be output by multiplying the first main weight factor with the first ratio.

[0224] The third difference can be the absolute difference between the actual and simulated secondary factor values, used to quantify auxiliary bias.

[0225] The second ratio can be a standardized value of the minor factor deviation, used to eliminate dimensional differences.

[0226] The second product can be a weighted deviation score of secondary factors, used to integrate the influence of weights.

[0227] The reference benchmark value can be the ideal full score benchmark, used to constrain the upper limit of the score, for example, set to 1.0 or 100%, which can be preset by business requirements.

[0228] In some implementations, when each simulation metric is not quantifiable (such as protocol coverage) and the corresponding simulation value is greater than the impact threshold, it indicates that the overall performance of the simulation metric meets the standard but there may still be local deviations. Therefore, the relative deviation ratio between the main and secondary factors can be calculated using a hierarchical weighted approach, and compensatory deductions can be implemented based on the reference benchmark value. In this way, overall compliance can be recognized while local defects can be precisely quantified. Specifically, the score value of each simulation metric can be calculated using the first non-quantifiable descriptive information. The specific calculation formula is as follows:

[0229] ;

[0230] in, This represents the simulation reliability score (range 0-1), with a higher value indicating better overall simulation compliance. The first primary weighting factor (e.g., the number of protocol types with a weight of 0.7) represents the main factors in the simulation and is used to quantify the relative importance of the main factors. The first primary weighting factor (e.g., protocol interaction completeness weight 0.3) represents the secondary factor index in the simulation and is used to quantify the contribution of the secondary factor index. Indicates the true value of the primary factor; Indicates the values ​​of the main simulation factors; This represents the true value of secondary factors (such as 90% completeness of the actual protocol interaction), which can be obtained through methods such as network probe measurement. This represents the value of secondary simulation factors (such as 85% completeness of simulation protocol interaction), which can be generated through simulation log statistics or obtained through other methods.

[0231] Furthermore, the deviation ratio in the formula is used for calculation. This process quantifies the gap between the simulation's main factor values ​​and the target's true values. Then, through weighted fusion calculation, the overall deviation level of the main and secondary factors can be integrated. Finally, by subtracting the overall deviation from the reference benchmark value (which could be 1), the score of each simulation indicator can be accurately calculated in scenarios where unquantifiable indicators exceed the threshold (such as when the overall range of protocol coverage, security mechanisms, etc. is available or estimated, but there are detailed deviations). This ensures that the score reflects both the overall performance and the consistency of local details.

[0232] Taking the protocol coverage metric as an example (this example is only for the purpose of understanding the formula and presenting the calculation process), if the protocol coverage metric is a non-quantitative indicator, and the main factor indicator is the number of protocol types, the simulated main factor values ​​are... There are 10 types, and the true main factor values ​​are... There are 12 types, with the secondary factor indicator being protocol interaction completeness and the actual secondary factor value. The value is 90%; the simulation secondary factor value The percentage is 85%. If the primary influence threshold is 10 and the secondary influence threshold is 80%, after comparison, the simulated primary factor values ​​and simulated secondary factor values ​​are both greater than the corresponding influence thresholds. If the first primary weighting factor... The first weighting factor is 0.7. It is 0.3.

[0233] Therefore, the first ratio can be calculated by (12-10) / 12=0.167, and the first product can be calculated by 0.7×0.167=0.117. The second ratio can be calculated by (90%-85%) / 90%=0.056, and the second product can be calculated by 0.3×0.056=0.017. If the reference baseline value is 1, then the score corresponding to this simulation index is 1-(0.117+0.017)=0.866. After converting to a percentage, the score is 86.6.

[0234] It should be noted that in the calculation formula for the first non-quantitative descriptive information mentioned above, it is assumed that the actual primary factor value is greater than the simulated primary factor value, and the actual secondary factor value is greater than the simulated secondary factor value. If the actual primary factor value is less than the simulated primary factor value, then... and Simply swap the positions in the formula; or, if the actual secondary factor value is greater than the simulated secondary factor value, then... and Simply swap the positions in the formula.

[0235] By using the above methods, the reference benchmark can be used as the full score anchor point, and the weighted deviation score of the primary and secondary factors can be deducted in layers. This transforms highly subjective and unquantifiable indicators into objective values, thereby providing standardized input for subsequent node credibility fusion calculations and ensuring that the evaluation results are reproducible and traceable.

[0236] In some implementations, to address the stringent evaluation requirements of non-quantifiable indicators (such as protocol coverage) when both factors fail to meet the standards, a tiered calculation of dual penalty terms for primary and secondary factors can be used. Specifically, when a simulated indicator is non-quantifiable and its simulated value is less than an impact threshold, a second and third penalty term are generated based on the threshold difference and superimposed on the reference benchmark value for deduction, thereby obtaining the corresponding simulated indicator score. This forces a significant reduction in scores for low indicator value scenarios, thus improving the accuracy of the score. For example, the target description information may include second description information, which may also include second non-quantifiable description information. Each simulated indicator may include simulated primary factor indicators and simulated secondary factor indicators. Each target real indicator may include real primary factor indicators and real secondary factor indicators. The impact threshold may include a primary impact threshold and a secondary impact threshold. Step 104 may further include:

[0237] (104.d1) Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index. The simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value.

[0238] (104.d2) When the value of the main simulation factor is less than the main influence threshold and the value of the secondary simulation factor is less than the secondary influence threshold, obtain the first and second main weight factors corresponding to the main simulation factor, and the first and second main weight factors corresponding to the secondary simulation factor. Each simulation index is a non-quantitative index.

[0239] (104.d3) Obtain the fourth difference between the true main factor value and the main influence threshold, and calculate the product with the corresponding first main weight factor based on the third ratio of the fourth difference to the true main factor value to obtain the third product;

[0240] (104.d4) Obtain the fifth difference between the main influence threshold and the simulation main factor value, and calculate the product between the fifth difference and the fourth ratio of the main influence threshold and the corresponding second main weight factor to obtain the fourth product;

[0241] (104.d5) Obtain the sixth difference between the true secondary factor value and the secondary influence threshold, and calculate the product between the sixth difference and the fifth ratio of the true secondary factor value and the corresponding first primary weighting factor to obtain the second penalty term;

[0242] (104.d6) Obtain the seventh difference between the secondary influence threshold and the simulated secondary factor value, and calculate the product between the seventh difference and the sixth ratio of the secondary influence threshold and the corresponding secondary weight factor to obtain the third penalty term;

[0243] (104.d7) Obtain the reference baseline value, and use the reference baseline value, the third product, the fourth product, the second penalty term, and the third penalty term to calculate the second non-quantitative description information to obtain the score value of each simulation index.

[0244] The second non-quantitative descriptive information can be a punitive calculation formula for when two factors fail to meet the standard, used to output the score value under strict scoring scenarios.

[0245] The second primary weighting factor can be a penalty coefficient for primary factors failing to reach a threshold, used to amplify the impact of core bias. It can be dynamically set based on the importance of the factors and the degree of bias.

[0246] The second weighting factor can be the penalty coefficient for minor factors failing to reach the threshold, used to amplify the influence of auxiliary bias. It can be set through minor factor sensitivity analysis.

[0247] The fourth difference can be the gap between the true main factor value and the main influence threshold, used to quantify the benchmark deviation.

[0248] The third ratio can be a standardized value of the benchmark deviation of the main factor, used to eliminate dimensions, and can be generated by the ratio of the fourth difference to the true value of the main factor.

[0249] The third product can be a weighted score of the main factor benchmark deviation, used to perform the first-level deduction.

[0250] Among them, the fifth difference can be the absolute difference between the main influence threshold and the simulation main factor value, which is used to quantify the actual shortcomings.

[0251] The fourth ratio can be a standardized value of the actual deviation of the main factors, used to eliminate dimensions.

[0252] The fourth product can be the weighted score of the actual deviation of the main factors, used to perform the second-level deduction.

[0253] The sixth difference can be the absolute difference between the true minor factor value and the minor influence threshold, used to quantify the auxiliary benchmark deviation.

[0254] The fifth ratio can be the standardized value of the benchmark deviation of the secondary factors.

[0255] The second penalty item can be a weighted score of the benchmark deviation of secondary factors, used to implement the first-level deduction.

[0256] Among them, the seventh difference can be the absolute difference between the secondary influence threshold and the value of the simulated secondary factor.

[0257] The sixth ratio can be the standardized value of the actual deviation of the minor factors.

[0258] The third penalty item can be a weighted score of the actual deviation of secondary factors, used to implement the second-level deduction.

[0259] In some implementations, when the simulated value corresponding to each simulation index is not quantifiable, and the simulated value (including the values ​​of secondary and primary simulation factors) is less than the corresponding impact threshold, it indicates that the simulation index has a performance defect, and the simulation weakness needs to be forcibly exposed. Therefore, it is necessary to calculate a dual penalty term (benchmark deviation penalty + actual deviation penalty) for primary and secondary factors in a hierarchical manner, and implement multiple deductions based on the reference benchmark value (to ensure that low-performance indices receive a significant score reduction through mathematical constraints). Specifically, the score value of each simulation index can be calculated using second non-quantifiable descriptive information, and the specific calculation formula is as follows:

[0260] ;

[0261] in, The score represents the simulation index (range 0-1), with lower values ​​indicating more severe simulation defects. This represents the first primary weighting factor set when the simulated primary factor value is greater than the primary influence threshold. It is used to weight the deviation between the actual primary factor value and the primary influence threshold under real conditions, i.e., the primary baseline deviation. This represents the second primary weighting factor set when the value of the primary simulation factor is less than the primary influence threshold. It is used to weight the deviation between the value of the primary simulation factor and the primary influence threshold under simulation conditions, which is also the primary actual deviation. This represents the first primary weighting factor set when the simulated secondary factor value is greater than the secondary influence threshold. It is used to weight the deviation between the actual secondary factor value and the secondary influence threshold under real conditions, which is also the secondary factor baseline deviation. This represents the second major weighting factor set when the value of the secondary factor in the simulation is less than the threshold of the secondary influence. It is used to weight the deviation between the value of the secondary factor in the simulation and the threshold of the secondary influence, i.e., the actual deviation of the secondary factor in the simulation. Indicates the true value of the primary factor; Indicates the main influencing threshold; Indicates the values ​​of the main simulation factors; Indicates the true value of secondary factors; Indicates the threshold for secondary influence; This represents the value of a secondary factor in the simulation.

[0262] It should be noted that in the calculation formula for the first non-quantitative descriptive information mentioned above, it is assumed that the actual primary factor value is greater than the simulated primary factor value, and the actual secondary factor value is greater than the simulated secondary factor value. If the actual primary factor value is less than the simulated primary factor value, then... and Simply swap the positions in the formula; or, if the actual secondary factor value is greater than the simulated secondary factor value, then... and Simply swap the positions in the formula.

[0263] Furthermore, calculations are made using the benchmark deviation ratio (e.g.) This can quantify the relative gap between the true value and the threshold of key factors; then, it can be calculated using the actual deviation ratio (e.g., This allows for the quantification of the relative gap between the threshold of the main factors and the simulated values. Finally, by subtracting the sum of all weighted deviation terms from the reference benchmark, the score of each simulated indicator can be accurately calculated when the non-quantifiable indicators, main and secondary factors have all failed to reach the threshold, and the score results are strictly negatively correlated with the degree of performance defects.

[0264] Furthermore, taking simulation metrics as a security mechanism as an example, these are non-quantifiable metrics (if one of the primary or secondary factors is non-quantifiable, then the corresponding simulation metric is a non-quantifiable metric). The primary factor is the encryption algorithm key length, and the secondary factor is the success rate of password authentication. If the obtained relevant data contains the true values ​​of the primary factors... The encryption strength of a security system deployed in a real network, specifically 256 bits, and the main impact threshold. 128-bit, simulation of key factor values The encryption strength implemented in the simulation model is specifically 112 bits, which is less than the primary influence threshold of 128 bits; the actual secondary factor value... For the user authentication success rate in a real system, such as 95%, the secondary impact threshold. 90%, simulation secondary factor value The value is 85%, which is less than the secondary impact threshold of 90%.

[0265] Furthermore, weighting factors can be set according to the relative importance of each influencing factor; for example, the first primary weighting factor. It can be set to 0.3, the second primary weighting factor. It can be set to 0.4; the first time, a weighting factor is required. It can be set to 0.1, and the second time a weighting factor is needed. It can be set to 0.2.

[0266] Therefore, the fourth difference can be The third ratio is the difference between the fourth and the true principal factor values. The ratio between them, that is, 128 / 256 = 0.5; the third product is the first primary weighting factor. The product of the third ratio is 0.3 × 0.5 = 0.15; similarly, the fifth difference is calculated to be 16, the fourth ratio to be 0.125, and the fourth product to be 0.05; the sixth difference is calculated to be 5%, the fifth ratio to be 0.0526, and the second penalty to be 0.00526; the seventh difference is calculated to be 5%, the sixth ratio to be 0.0556, and the third penalty to be 0.01112; furthermore, if the reference benchmark is 1, then the score corresponding to the security mechanism = reference benchmark - third product - fourth product - second penalty - third penalty. Finally, the score corresponding to the security mechanism is approximately 0.78362, which, converted to a percentage, is 78.362.

[0267] By using the above methods, the baseline deviation score and the actual deviation score of the primary and secondary factors can be deducted from the reference benchmark value. This can achieve a strong suppression assessment of substandard scenarios, thereby making up for the defect of the expert scoring method in that the tolerance for the shortcomings of key indicators is too high, and ultimately improving the accuracy of the simulation credibility judgment of sensitive scenarios such as network security mechanisms.

[0268] Step 105: For each simulation topology node, obtain multiple score values ​​corresponding to multiple simulation indicators, and calculate the evaluation result of each simulation topology node using multiple score values.

[0269] In some implementations, in order to achieve a comprehensive quantitative evaluation of node-level simulation credibility, a node credibility score (evaluation result) can be output for each simulation topology node by weighted fusion of the scores of multiple simulation indicators, so as to ensure the accuracy and comprehensiveness of the evaluation results.

[0270] The evaluation result can be the comprehensive credibility score of the simulated topology node, which is used to characterize the overall degree of consistency between the node and the real node. For example, the credibility of router node A is 0.85, and the credibility of user node B is 0.92.

[0271] For example, the evaluation result C for each simulated topology node can be calculated using the following formula:

[0272] ;

[0273] in, This represents the weight corresponding to the i-th simulation metric of the current simulation topology node. Let be the score value corresponding to the i-th simulation metric, and n be the total number of simulation metrics used to evaluate the current simulation topology node.

[0274] In some implementations... It can be set by the operator, different It can be used to characterize the importance of different simulation metrics in calculating the nodes of the current simulation topology.

[0275] The above calculation method can be used to calculate the evaluation results for all simulated topology nodes, which will not be elaborated here.

[0276] This application embodiment obtains multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system; obtains a real indicator set corresponding to each real topology node and a simulated indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulated indicator set includes multiple simulated indicators; obtains an influence threshold corresponding to each simulated indicator, and determines target description information from preset first description information and second description information based on the magnitude relationship between the simulated value corresponding to each simulated indicator and the influence threshold; wherein the first description information is used to perform a positive measurement on each simulated indicator based on the relative relationship between the simulated value and the target real value of the corresponding target real indicator when the simulated value is greater than the influence threshold, and the second description information is used to perform an inhibitory measurement on each simulated indicator based on the deviation between the simulated value and the target real value when the simulated value is less than the influence threshold; calculates the target description information according to the simulated value, the target real value, and the influence threshold to obtain a score value for each simulated indicator; for each simulated topology node, obtains multiple score values ​​corresponding to multiple simulated indicators, and calculates the evaluation result of each simulated topology node through multiple score values. This approach allows for the acquisition of multi-dimensional indicator sets (such as performance and functionality) for both real and simulated topological nodes. By setting scientific impact thresholds for each simulation indicator and dynamically selecting between positive and negative metrics based on the relationship between the simulated value and the threshold, the approach considers both the inherent biases of the indicators and enhances sensitivity to outliers through negative metrics. This improves the accuracy and robustness of the evaluation, eliminating inconsistencies caused by subjectivity and experience, and comprehensively and sensitively capturing subtle deviations between simulated and real nodes, enabling objective quantitative scoring of each indicator. Furthermore, by aggregating the scores of all indicators for each simulated topological node to calculate the overall evaluation result, the approach effectively avoids incomplete coverage and inconsistent judgments caused by subjective cognitive limitations or experience differences. This achieves systematic and traceable analysis from single-point indicators to the entire node, improving the comprehensiveness, consistency, and accuracy of the evaluation. In summary, this application enables accurate evaluation of simulated topological nodes.

[0277] Please refer to Figure 5 In some implementations, combined with Figure 5This application provides an overview of its overall process. The simulation topology node evaluation method provided in this embodiment first obtains multiple real topology nodes corresponding to the target network system, and multiple simulated topology nodes in the simulated network system built upon them. Then, it obtains the real indicator set and simulation indicator set corresponding to each node, where both sets contain multiple simulation indicators. Next, for each simulation indicator, its corresponding influence threshold is obtained. This threshold is calculated by weighting multiple influencing factors and their weights with the indicator's influence value, and then corrected by a threshold adjustment coefficient. If the simulation indicator is a quantifiable but non-reliable indicator (such as latency), it needs to be converted into an equivalent reliability indicator with higher values ​​using a preset conversion constant and exponential factor to unify the evaluation direction.

[0278] Subsequently, an evaluation strategy is dynamically selected based on the relationship between the simulated value and the impact threshold: For quantifiable indicators, if the simulated value is greater than the impact threshold, the first descriptive information is used for positive measurement, specifically by calculating the score through log normalization of the simulated value and the actual value, i.e., taking the ratio of the smaller logarithmic value to the larger logarithmic value, to reward high-precision simulation; if the simulated value is less than the impact threshold, the second descriptive information is used for inhibitory measurement, by calculating the ratio of the logarithm of the impact threshold to the larger logarithmic value of the simulated value and the actual value as a quantified sub-item, and then subtracting the first penalty item based on the deviation ratio between the impact threshold and the smaller value of the simulated value and the actual value, thereby severely penalizing the non-compliant indicator.

[0279] Furthermore, for non-quantifiable indicators (such as protocol coverage), they are further broken down into primary and secondary factors, and their deviation ratios from the true values ​​are calculated separately. Weighting factors and reference benchmark values ​​are introduced, and scores are obtained through weighted deviation deduction.

[0280] Finally, for each simulated topology node, the scores of all its simulation indicators are aggregated and weighted according to the preset weights of each indicator to obtain the comprehensive evaluation result of the node, thereby achieving a comprehensive, objective and accurate credibility assessment of the simulated topology node.

[0281] Please see Figure 6 This application also provides a simulated topology node evaluation device, which can implement the above-described simulated topology node evaluation method. The simulated topology node evaluation device includes:

[0282] The first acquisition module 61 is used to acquire multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system.

[0283] The second acquisition module 62 is used to acquire the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulation indicator set includes multiple simulation indicators.

[0284] The determination module 63 is used to obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold.

[0285] The first descriptive information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold. The second descriptive information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold.

[0286] The first calculation module 64 is used to calculate the target description information based on the simulation value, the target real value and the influence threshold, and obtain the score value of each simulation index.

[0287] The second calculation module 65 is used to obtain multiple score values ​​corresponding to multiple simulation indicators for each simulation topology node, and to calculate the evaluation result of each simulation topology node through multiple score values.

[0288] The specific implementation of this simulated topology node evaluation device is basically the same as the specific embodiment of the simulated topology node evaluation method described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this application, the simulated topology node evaluation device may also be equipped with other functional modules to implement the simulated topology node evaluation method in the above embodiments.

[0289] This application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described simulation topology node evaluation method. This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0290] Please see Figure 7 , Figure 7 The hardware structure of a computer device according to another embodiment is illustrated. The computer device includes:

[0291] The processor 71 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0292] The memory 72 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 72 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 72 and called by the processor 71 to execute the simulation topology node evaluation method of the embodiments of this application.

[0293] Input / output interface 73 is used to implement information input and output;

[0294] The communication interface 74 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0295] Bus 75 transmits information between various components of the device (e.g., processor 71, memory 72, input / output interface 73, and communication interface 74);

[0296] The processor 71, memory 72, input / output interface 73, and communication interface 74 are connected to each other within the device via bus 75.

[0297] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described simulation topology node evaluation method.

[0298] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0299] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0300] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0301] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0302] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0303] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0304] It should be understood that in this application, "at least one" and "several" refer to one or more, and "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0305] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0306] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0307] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0308] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0309] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for evaluating simulated topology nodes, characterized in that, The method includes: Obtain multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system; Obtain the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulation indicator set includes multiple simulation indicators. Obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold. Wherein, the first description information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold; the second description information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold. Based on the simulated value, the actual target value, and the influence threshold, the target description information is calculated to obtain a score value for each simulation indicator; For each simulation topology node, multiple score values ​​corresponding to the multiple simulation indicators are obtained, and the evaluation result of each simulation topology node is calculated using the multiple score values.

2. The simulation topology node evaluation method according to claim 1, characterized in that, The step of obtaining the impact threshold corresponding to each simulation index includes: Obtain at least one influencing factor for each simulation index, and the index influence value corresponding to each influencing factor; Obtain the influence weight corresponding to each of the influencing factors of the indicator, and obtain the sub-threshold item based on the product of the influence value of each indicator and the corresponding influence weight; The initial threshold is obtained by adding the sub-threshold items corresponding to the influencing factors of the at least one indicator; Obtain the threshold adjustment coefficient corresponding to each simulation index, and obtain the influence threshold corresponding to each simulation index based on the product of the initial threshold and the threshold adjustment coefficient.

3. The simulation topology node evaluation method according to claim 1, characterized in that, Before obtaining the influence threshold corresponding to each simulation index, and determining the target description information from the preset first description information and second description information based on the magnitude relationship between the simulation value corresponding to each simulation index and the influence threshold, the method further includes: When among the multiple simulation indicators, there is a target simulation indicator that is quantified and unreliable, the target simulation value corresponding to the target simulation indicator is determined; Obtain the preset conversion constant and exponent factor, and calculate the conversion term based on the target simulation value according to the exponent factor; Based on the ratio of the transformation constant to the transformation term, the target simulation value corresponding to the target simulation index is updated.

4. The simulation topology node evaluation method according to claim 1, characterized in that, The target description information includes first description information, which includes first quantitative description information. The step of calculating the target description information based on the simulation value, the target real value, and the influence threshold to obtain a score value for each simulation indicator includes: When the simulated value corresponding to each simulation indicator is greater than the influence threshold, the first pair of values ​​corresponding to the simulation value and the second pair of values ​​corresponding to the target real value are obtained, wherein each simulation indicator is a quantitative indicator; From the first logarithm and the second logarithm, select the smaller value as the target first logarithm and select the larger value as the target second logarithm; The first quantitative description information is calculated based on the ratio of the first logarithmic value of the target to the second logarithmic value of the target to obtain the score value of each simulation index.

5. The simulation topology node evaluation method according to claim 1, characterized in that, The target description information includes second description information, which includes second quantitative description information. The step of calculating the target description information based on the simulation value, the target real value, and the influence threshold to obtain a score value for each simulation indicator further includes: When the simulated value corresponding to each simulation indicator is less than the influence threshold, the first logarithmic value corresponding to the simulation value, the second logarithmic value corresponding to the target real value, and the third logarithmic value corresponding to the influence threshold are obtained, wherein each simulation indicator is a quantitative indicator. The larger value is selected from the first logarithmic value and the second logarithmic value as the target second logarithmic value, and the smaller value is selected from the simulated value and the target real value as the target reference value; Based on the ratio of the third logarithm to the target second logarithm, the quantification sub-item is determined; Obtain a first difference between the impact threshold and the target reference value, and determine a first penalty term based on the ratio between the first difference and the impact threshold; The second quantitative description information is calculated based on the difference between the quantized sub-item and the first penalty item to obtain the score value of each simulation index.

6. The simulation topology node evaluation method according to claim 1, characterized in that, The target description information includes first description information, which further includes first non-quantitative description information. Each simulation index includes simulation primary factor index and simulation secondary factor index. Each target real index includes real primary factor index and real secondary factor index. The influence threshold includes primary influence threshold and secondary influence threshold. The step of calculating the target description information based on the simulated value, the target real value, and the influence threshold to obtain the score value of each simulation index further includes: Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index, wherein the simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value; When the value of the simulation primary factor is greater than the primary influence threshold and the value of the simulation secondary factor is greater than the secondary influence threshold, the first primary weight factor corresponding to the simulation primary factor index and the first secondary weight factor corresponding to the simulation secondary factor index are obtained, wherein each simulation index is a non-quantitative index. Obtain the second difference between the actual main factor value and the simulated main factor value, and obtain the first ratio based on the ratio of the second difference to the actual main factor value; The first product is obtained based on the product of the first primary weighting factor and the first ratio; Obtain the third difference between the actual minor factor value and the simulated minor factor value, and based on the ratio of the third difference to the actual minor factor value, obtain the second ratio; The second product is obtained based on the product of the first weighting factor and the second ratio; A reference benchmark value is obtained, and the first non-quantitative descriptive information is calculated based on the difference between the reference benchmark value, the first ratio, and the second ratio to obtain the score value of each simulation index.

7. The simulation topology node evaluation method according to claim 1, characterized in that, The target description information includes second description information, which further includes second non-quantitative description information. Each simulation index includes simulation primary factor index and simulation secondary factor index. Each target real index includes real primary factor index and real secondary factor index. The influence threshold includes primary influence threshold and secondary influence threshold. The step of calculating the target description information based on the simulated value, the target real value, and the influence threshold to obtain the score value of each simulation index further includes: Obtain the simulation primary factor value corresponding to the simulation primary factor index, the simulation secondary factor value corresponding to the simulation secondary factor index, the real primary factor value corresponding to the real primary factor index, and the real secondary factor value corresponding to the real secondary factor index, wherein the simulation value includes the simulation primary factor value and the simulation secondary factor value, and the target real value includes the real primary factor value and the real secondary factor value; When the value of the simulation primary factor is less than the primary influence threshold and the value of the simulation secondary factor is less than the secondary influence threshold, the first primary weight factor and the second primary weight factor corresponding to the simulation primary factor, as well as the first primary weight factor and the second primary weight factor corresponding to the simulation secondary factor, are obtained, wherein each simulation index is a non-quantitative index. Obtain the fourth difference between the true main factor value and the main influence threshold, and calculate the product with the corresponding first main weight factor based on the third ratio of the fourth difference to the true main factor value to obtain the third product; Obtain the fifth difference between the main influence threshold and the value of the main simulation factor, and calculate the product between the fifth difference and the main influence threshold and the corresponding second main weight factor based on the fourth ratio of the fifth difference and the main influence threshold to obtain the fourth product; Obtain the sixth difference between the actual secondary factor value and the secondary influence threshold, and calculate the product between the sixth difference and the fifth ratio of the actual secondary factor value and the corresponding primary weighting factor to obtain the second penalty term; Obtain the seventh difference between the secondary influence threshold and the simulated secondary factor value, and calculate the product between the sixth ratio of the seventh difference and the secondary influence threshold and the corresponding secondary weight factor to obtain the third penalty term; Obtain a reference benchmark value, and use the reference benchmark value, the third product, the fourth product, the second penalty term, and the third penalty term to calculate the score value of the second non-quantitative description information to obtain the score value of each simulation index.

8. A simulation topology node evaluation device, characterized in that, The device includes: The first acquisition module is used to acquire multiple real topology nodes corresponding to the target network system and multiple simulated topology nodes corresponding to the simulated network system, wherein the simulated network system is established based on the target network system; The second acquisition module is used to acquire the real indicator set corresponding to each real topology node and the simulation indicator set corresponding to each simulated topology node, wherein the real indicator set includes multiple real indicators and the simulation indicator set includes multiple simulation indicators. The determination module is used to obtain the influence threshold corresponding to each simulation index, and determine the target description information from the preset first description information and second description information based on the relationship between the simulation value corresponding to each simulation index and the influence threshold. Wherein, the first description information is used to perform a positive measurement on each simulation index based on the relative relationship between the simulation value and the target real value of the corresponding target real index when the simulation value is greater than the influence threshold; the second description information is used to perform an inhibitory measurement on each simulation index based on the deviation between the simulation value and the target real value when the simulation value is less than the influence threshold. The first calculation module is used to calculate the target description information based on the simulation value, the target real value and the influence threshold to obtain the score value of each simulation index; The second calculation module is used to obtain multiple score values ​​corresponding to the multiple simulation indicators for each simulation topology node, and to calculate the evaluation result of each simulation topology node using the multiple score values.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the simulation topology node evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the simulation topology node evaluation method according to any one of claims 1 to 7.

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