A Multimodal Intelligent Network Simulator Architecture and Simulation Testing Method

Abstract

The present invention belongs to the field of new network technologies, and discloses a multi-modal intelligent network simulator architecture and a simulation test method. The architecture includes: a full-dimensional definable subsystem, an intelligent management subsystem, a basic support function subsystem, a model semantic parsing module connected to the full-dimensional definable subsystem, and an extended interface connected to the basic support function subsystem. By abstractly modeling and finely decomposing the network model, the present invention establishes a general "magic cube" structure, improving the expandability and definability of the simulator; by finely perceiving and intelligently analyzing the network state, the adaptability to diverse network services is enhanced; through the extended interface, communication with external networks and efficient integration with third-party application software are realized, enabling accurate simulation of actual networks and actual service scenarios, thereby improving the accuracy of simulation results and reducing differences.

Description

Technical Field

[0001] The present invention relates to the technical field of new network systems, and in particular to a multi-modal intelligent network emulator architecture and a simulation test method. Background Art

[0002] Currently, designing corresponding network identifiers according to the network usage requirements of specific application scenarios, carrying the functional and performance requirements of personalized services, and realizing the coexistence of various network modalities in the same physical environment, that is, multi-modal networks, has become the trend of future network development. Typical new network identifiers include identity identifiers, content identifiers, geospatial identifiers, etc. With the rapid growth of the application fields and scales of information networks and the rapid integration of network technologies, on the one hand, it is necessary to improve network protocols and algorithms and develop new network protocols and algorithms to enhance the basic technical support of the network; on the other hand, it is also necessary to reasonably allocate and utilize existing network software and hardware resources, and conduct systematic planning and design to solve the problems brought about by network complexity. Network simulation is an important tool for computer-aided overall network design and performance evaluation by running a network model on a computer and analyzing the output results of the operation to obtain the performance prediction of a real network system.

[0003] Although existing simulation software can simulate and analyze networks, network elements, and protocols, and provide functions such as visual simulation, animation demonstration, log analysis, packet capture, and traffic statistics, there are the following three deficiencies:

[0004] 1) The problem of insufficient scalability. It cannot quickly and effectively simulate new networks, new devices, and new protocols, and additional customized development is required;

[0005] 2) The problem of insufficient application compatibility and support. Existing simulation software only realizes the simulation of packets and protocols, cannot reflect the unique characteristics, behaviors, and logics of network applications, and at the same time, application programs need to be ported and developed before they can run on the emulator platform;

[0006] 3) The problem of the difference between simulation results and the operation results in the real environment. It does not provide a communication interface with an external real network, cannot accurately simulate the actual network environment and actual service application scenarios, and there are some differences between the simulation results and the operation results in the real network environment. Summary of the Invention

[0007] In view of the problems existing in the current emulator, such as insufficient scalability, insufficient application compatibility and support, and differences between simulation results and actual operating results in the real environment, as well as potential performance problems of the emulator in the face of diverse services and multi-modal network models in the future, the present invention proposes a multi-modal intelligent network emulator architecture and a simulation test method, which expand the modeling ability for new network models, improve the simulation level of diverse service scenarios, enhance the compatibility and support for applications, thereby reducing the difference between simulation results and the actual network environment, and strengthening the efficient adaptation of network resources and network components, so as to make up for the deficiencies of the current emulator.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] On the one hand, the present invention proposes a multi-modal intelligent network emulator architecture, including: a full-dimensional definable subsystem, an intelligent management subsystem, a basic support function subsystem, a model semantic parsing module connected to the full-dimensional definable subsystem, and an extended interface connected to the basic support function subsystem;

[0010] The model semantic parsing module is used to parse the input model description file, map it into corresponding basic components and the logical relationships between the components, and transmit it to the full-dimensional definable subsystem;

[0011] The full-dimensional definable subsystem is used to model the parsed model into a network model with a three-layer functional structure, including a service layer, a control layer, and a data layer, and establish a network function component pool based on the principle of maximizing function reuse;

[0012] The intelligent management subsystem is used to obtain the status information of the network model, fit a real-time network resource management strategy according to service requirements, and then adapt the basic components in the network function component pool and the network resources in the network resource pool to achieve dynamic adaptation between network resources and service requirements;

[0013] The basic support function subsystem is used to provide general basic services, support communication with external networks and integration with third-party application software through the extended interface, and implement simulation tests in a real network environment and actual business application scenarios.

[0014] Furthermore, it further includes:

[0015] A model abstract description module, which is used to abstractly describe the model function using a formal language and store the description in the form of a file.

[0016] Furthermore, it further includes:

[0017] An input / output interface connected to the intelligent management subsystem, which is used to input topology configuration parameters into the emulator, output simulation results, perform command-line interaction, and provide visual display.

[0018] Furthermore, the full-dimensional definable subsystem includes a network function hierarchical model construction module and a network function component pool construction module;

[0019] The network function hierarchical model construction module is used to model the parsed model into a three-layer network model: a data layer, a control layer, and a service layer;

[0020] The network function component pool construction module is used to decompose network functions into basic function components based on the principle of maximizing function reuse and establish a network function component pool.

[0021] Furthermore, the intelligent management subsystem includes a network status perception module, an intelligent decision-making module, and an adaptation and fitting module;

[0022] The network status perception module is used to perform fine-grained perception and intelligent analysis of the network status, obtain the current network topology and network resource usage, and generate a whole-network view based on a unified description model of high-level perception semantics;

[0023] The intelligent decision-making module is used to generate a fitting decision between network resources and service requirements according to the network status perceived by the network status perception module, and make real-time decisions on resource management strategies in the network;

[0024] The adaptation and fitting module is used to perform top-down intelligent fitting of network functions driven by service requirements and network status according to the strategy generated by the intelligent decision-making module, and perform adaptive adjustment operations on network basic components and network resources.

[0025] On the other hand, the present invention proposes a simulation test method, including:

[0026] Parsing the input model description file through the model semantic parsing module, mapping it into corresponding basic components and the logical relationships between the components, and transmitting it to the full-dimensional definable subsystem;

[0027] Modeling the parsed model into a network model with a three-layer functional structure through the full-dimensional definable subsystem, including a service layer, a control layer, and a data layer, and establishing a network function component pool based on the principle of maximizing function reuse;

[0028] Obtaining the status information of the network model through the intelligent management subsystem, fitting real-time network resource management strategies according to service requirements, and then adapting the basic components in the network function component pool to the network resources in the network resource pool to achieve dynamic adaptation between network resources and service requirements;

[0029] Provide general basic services through the basic support function subsystem, support communication with external networks and integration with third-party application software through extended interfaces, and achieve simulation tests in real network environments and actual business application scenarios.

[0030] Furthermore, it includes:

[0031] Step 1: Use a formal language to abstractly describe the model functions through the model abstraction and description module, and store the description in the form of a file.

[0032] Step 2: Parse the input model description file through the model semantic parsing module, map it to the corresponding basic components and the logical relationships between the components, and transmit it to the full-dimensional definable subsystem.

[0033] Step 3: The network function hierarchical model construction module of the full-dimensional definable subsystem models the parsed model into a three-layer network model: the data layer, the control layer, and the service layer.

[0034] Step 4: Based on the constructed three-layer network model, the network function component pool construction module decomposes the network function into basic function components based on the principle of maximizing function reuse, and establishes a network function component pool.

[0035] Step 5: The intelligent management subsystem finely perceives and intelligently analyzes the network state through the network state perception module, obtains the current network topology and network resource usage situation, and generates a whole-network view based on the unified description model of high-level perception semantics.

[0036] Step 6: The intelligent decision-making module generates a fitting decision between network resources and service requirements according to the network state perceived by the network state perception module, and makes real-time decisions on the resource management strategy in the network.

[0037] Step 7: The adaptation and fitting module performs top-down intelligent fitting of network functions driven by service requirements and network states according to the strategy generated by the intelligent decision-making module, and performs adaptive adjustment operations on network basic components and network resources.

[0038] Step 8: Input the topology configuration parameters into the simulator internally, output the simulation results, perform command-line interaction, and visualize the display through a set of input and output interfaces.

[0039] Step 9: The basic support function subsystem provides general services to ensure the smooth progress of the functions of the full-dimensional definable subsystem, the intelligent management subsystem, and the simulator, and supports communication with external networks and integration with third-party application software through a set of extended interfaces for simulation tests in actual network environments and actual business application scenarios.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] The present invention proposes a multi-modal intelligent network simulator architecture and a simulation test method, which expand the modeling ability for new network models, improve the simulation level of diverse service scenarios, enhance the compatibility and support for applications, thereby reducing the difference between the simulation results and the actual network environment, and strengthening the efficient adaptation of network resources and network components, so as to make up for the deficiencies of current simulators. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of a multi-modal intelligent network simulator architecture according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the process of a simulation test method according to an embodiment of the present invention;

[0044] Figure 3 It is a schematic diagram of the process of a fully dimensionally definable subsystem provided by an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the process of an intelligent management subsystem provided by an embodiment of the present invention;

[0046] Figure 5 It is a schematic diagram of discrete time event-driven simulation adopted by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further explains the present invention with reference to the accompanying drawings and specific embodiments:

[0048] As Figure 1 shown, a multi-modal intelligent network simulator architecture includes: a fully dimensionally definable subsystem, an intelligent management subsystem, a basic support function subsystem, a model semantic parsing module connected to the fully dimensionally definable subsystem, and an extended interface connected to the basic support function subsystem;

[0049] The model semantic parsing module is used to parse the input model description file, map it to the corresponding basic components and the logical relationships between the components, and transmit it to the fully dimensionally definable subsystem;

[0050] The fully dimensionally definable subsystem is used to model the parsed model into a network model with a three-layer functional structure, including a service layer, a control layer, and a data layer, and establish a network function component pool based on the principle of maximizing function reuse;

[0051] The intelligent management subsystem is used to obtain the status information of the network model, fit a real-time network resource management strategy according to service requirements, and then adapt the basic components in the network function component pool and the network resources in the network resource pool to achieve dynamic adaptation between network resources and service requirements;

[0052] The basic support function subsystem is used to provide general basic services, support communication with external networks and integration with third-party application software through extended interfaces, and realize simulation tests in real network environments and actual business application scenarios.

[0053] Furthermore, it also includes:

[0054] The model abstract description module is used to abstractly describe the model functions based on a formal language, provide elements such as module configuration, model definition, interface specifications, parameters, etc., efficiently and accurately describe simulation scenarios, functions, and performance requirements, and store the description in the form of a file.

[0055] Specifically, the model abstract description module abstractly describes the model, simulation scenarios, and some open parameters using a formal language, including elements such as module configuration, model definition, interface specifications, parameters, and functional requirements, and is divided into a meta-model and a composite model. The meta-model stipulates the modeling elements and modeling notations; among them, the entity model represents network entity elements, and the abstract model is used to enhance the description ability. The composite model is a functional template based on the meta-model components, further improving the modeling efficiency.

[0056] Furthermore, it also includes:

[0057] The input / output interface connected to the intelligent management subsystem is used to input topology configuration parameters into the simulator, output simulation results, command-line interaction, and visual display, etc.

[0058] Furthermore, the full-dimensional definable subsystem includes a network function hierarchical model construction module and a network function component pool construction module;

[0059] The network function hierarchical model construction module is used to model the parsed model into a three-layer network model: the data layer, the control layer, and the service layer; through a set of programming interfaces, clarify the interface specifications and interaction relationships of each layer, and realize deep programmability of the data layer, multi-modal routing addressing of the control layer, and resource management and function orchestration of the service layer;

[0060] Specifically, the three-layer modeling part is the text parsing of the model abstract description, mapped to the corresponding basic function components and the logical relationships between the components. By analyzing the semantic information of the model, a hierarchical processing method is used to model the three-layer structure of the data layer, the control layer, and the service layer, and realize deep programmability of the data layer, multi-modal routing addressing of the control layer, and resource management and function orchestration of the service layer through a set of programming interfaces; then based on the analysis of the semantic information, map the network functions to the corresponding basic function components and establish the logical relationships between the components.

[0061] The network function component pool construction module is used to decompose network functions into basic function components based on the principle of maximizing function reuse, and establish a network function component pool.

[0062] Specifically, the function component pool decomposes network functions into basic function components according to the parsing of the model description text and the principle of maximizing function reuse, and establishes a network component pool, including node class function components and process class function components. The node class function components are targeted at network objects and represent a specific function in node operations, including data generation, queuing, sending, and receiving, etc., which are abstracted from basic computing units, terminals, network devices, etc. connected to the network, and provide various methods for managing network components in the simulator. The process class components mainly describe the logical behavior of network events, i.e., protocols, through states and state transitions; use valid states to describe the logical behavior of processes, and describe the behavior of modules through two aspects of states and transitions in the state transition diagram.

[0063] Furthermore, the intelligent management subsystem includes a network status perception module, an intelligent decision-making module, and an adaptation and fitting module;

[0064] The network status perception module is used to perform fine-grained perception and intelligent analysis of the network status, obtain the current network topology and network resource usage, and generate a whole-network view based on a unified description model of high-level perception semantics, supporting the definability of network perception objects, perception actions, association rules, and function allocation;

[0065] The intelligent decision-making module is used to generate a fitting decision between network resources and service requirements according to the network status perceived by the network status perception module, and make real-time decisions on resource management strategies in the network;

[0066] The adaptation and fitting module is used to perform top-down intelligent fitting of network functions driven by service requirements and network status according to the strategy generated by the intelligent decision-making module, perform adaptive adjustment operations including intelligent adaptation on network basic components and network resources, such as routing scheduling, function reconstruction, resource configuration, service hosting, etc., to enhance the service adaptability and scalability of the network.

[0067] Specifically, the adaptation and fitting module acts on the model instance being simulated through an interface connected to the full-dimensional definable subsystem for the above operations to achieve the goal of optimizing the network model.

[0068] Based on the above embodiments, the present invention also proposes a simulation test method, including:

[0069] Parse the input model description file through the model semantic parsing module, map it to the corresponding basic components and the logical relationships between the components, and send it to the full-dimensional definable subsystem;

[0070] The parsed model is modeled as a three - layer functional structure network model through a full - dimension definable subsystem, including a service layer, a control layer, and a data layer, and a network function component pool is established based on the principle of maximizing functional reuse;

[0071] The intelligent management subsystem obtains the status information of the network model, fits a real - time network resource management strategy according to business requirements, and then adapts the basic components in the network function component pool and the network resources in the network resource pool to achieve dynamic adaptation between network resources and business requirements;

[0072] The basic support function subsystem provides general basic services, supports communication with external networks and integration with third - party application software through extension interfaces, and realizes simulation testing in a real network environment and actual business application scenarios.

[0073] Specifically, as Figure 2 shown, the overall process of simulation testing is as follows:

[0074] Step 1: The model abstraction description module uses a formal language to abstractly describe the network function model to be simulated, including the description of the simulation scenario and the representation of some open parameters, saves it as a text file, and inputs it into the simulator through a set of programming interfaces;

[0075] Step 2: The model semantic parsing module parses the input model description file, maps it to the corresponding basic components and the logical relationships between the components, and transmits it to the full - dimension definable subsystem;

[0076] Step 3: The network function hierarchical model construction module of the full - dimension definable subsystem models the parsed model into a three - layer network model: data layer, control layer, and service layer;

[0077] Step 4: Based on the constructed three - layer network model, the network function component pool construction module decomposes the network function into basic function components based on the principle of maximizing functional reuse and establishes a network function component pool;

[0078] The detailed processing flow of Steps 1 - 4 is as Figure 3 shown, and the specific details are as follows:

[0079] The model description part uses a formal language for model abstract description, simulation scenario description, and the representation of some open parameters, including elements such as module configuration, model definition, interface specification, parameters, and functional requirements, and is divided into a meta - model and a composite model. The meta - model stipulates the modeling elements and modeling notations; among them, the entity model represents network entity elements, and the abstract model is used to enhance the description ability. The composite model is a functional template based on meta - model components, which further improves the modeling efficiency.

[0080] The three-layer modeling part is the text parsing of the abstract description of the model, which is mapped to the corresponding basic functional components and the logical relationships between the components. By analyzing the semantic information of the model, a three-layer structure modeling of the data layer, control layer, and service layer is carried out using a hierarchical processing method. Through a set of programming interfaces, deep programmability of the data layer, multi-modal routing addressing of the control layer, and resource management and function orchestration of the service layer are realized, etc.; then based on the analysis of semantic information, network functions are mapped to the corresponding basic functional components and the logical relationships between the components are established.

[0081] The functional component pool decomposes network functions into basic functional components according to the parsing of the model description text and the principle of maximizing function reuse, and establishes a network component pool, including node-class functional components and process-class functional components. Node-class functional components are targeted at network objects and represent a specific function in node operations, including data generation, queuing, sending, and receiving, etc., which are abstracted from the basic computing units, terminals, network devices, etc. connected to the network, and provide various methods for managing network components in the simulator. Process-class components mainly describe the logical behavior of network events, that is, protocols, through states and state transitions; use valid states to describe the logical behavior of processes, and describe the behavior of modules through the two aspects of states and transitions in the state transition diagram.

[0082] The model instance instantiates relevant basic functional components according to the described functional and performance requirements, and configures component parameters, constraints between components, and connection relationships, etc.

[0083] Step 5: The intelligent management subsystem performs fine-grained perception and intelligent analysis of the network state through the network state perception module, and transmits the perception and analysis results to the intelligent decision-making module;

[0084] Step 6: The intelligent decision-making module combines network resources and service requirements according to the perception results, fits an optimized decision for resource management, and transmits it to the adaptation fitting module;

[0085] Step 7: The adaptation fitting module performs an adaptive adjustment operation of intelligent adaptation by combining the network infrastructure and network resources according to the generated optimization strategy, and realizes the dynamic adaptation between network resources and service requirements.

[0086] The detailed processing flow of Steps 5-7 is as Figure 4 shown, and the specific details are as follows:

[0087] The network information obtained by the network state perception module includes the network topology and the network resource usage status, and generates a whole-network view based on a unified description model of high-level perception semantics, supporting the definability of network perception objects, perception actions, association rules, and function allocation;

[0088] The intelligent decision-making module makes real-time decisions on resource management strategies in the network based on the perception results, combining network resources and service requirements.

[0089] The adaptation and fitting module performs top-down intelligent fitting of network functions driven by service requirements and network status according to the strategy, and intelligently adapts the basic components in the network function component pool and network resources, including adaptive adjustment operations such as routing scheduling, function reconstruction, resource configuration, and service hosting, to enhance the service adaptability and scalability of the network.

[0090] The adaptation and fitting module in the intelligent management subsystem acts on the model instance being simulated through the interface connected to the full-dimensional definable subsystem for these operations, achieving the goal of optimizing the network model.

[0091] Step 8: Input some topology configuration parameters required for simulation, simulation result output, command-line interaction, visualization display, etc. through the input / output control interface;

[0092] Step 9: During the entire simulation process, the basic support function subsystem provides general services including the loading and scheduling of simulation modules, events, information statistics, probes, etc., to ensure the smooth progress of the functions of the other two subsystems and the simulation, and through a set of extended interfaces, supports the efficient integration with external network communication and third-party application software for simulation testing in the actual network environment and actual business application scenarios.

[0093] The detailed function services of Step 9 are as follows:

[0094] To further improve the simulation efficiency, both the loading and invocation of the simulation adopt the discrete event-driven method, as Figure 5 shown. The simulation object registers events into the simulation framework, and when each discrete event arrives, the registered events are triggered for execution. At the same time, new events are registered and triggered for execution at the next moment, meeting the simulation scenario requirements of diverse network services, intensive computing, etc.

[0095] The basic support function subsystem provides a set of extended interfaces, adopts the direct code execution technology, performs Socket interface conversion on the operating system call library, and the application program can directly execute code in the multi-modal simulator without any compatibility modification and replacement to realize the simulation test of the characteristics, behaviors, and logics of network applications; and adopts the virtual network card bridging technology to simulate the virtual network card and IP address of the simulator to achieve efficient integration with third-party software, communicate with the external network, and realize the simulation test in the real network environment and actual business scenarios.

[0096] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A multi-modal intelligent network simulator architecture, characterized in that, it includes: a full-dimensional definable subsystem, an intelligent management subsystem, a basic support function subsystem, a model semantic parsing module connected to the full-dimensional definable subsystem, and an extended interface connected to the basic support function subsystem; the model semantic parsing module is used to parse the input model description file, map it to the corresponding basic components and the logical relationships between the components, and transmit it to the full-dimensional definable subsystem; the full-dimensional definable subsystem is used to model the parsed model into a network model with a three-layer functional structure, including a service layer, a control layer, and a data layer, and establish a network function component pool based on the principle of maximizing function reuse; the intelligent management subsystem is used to obtain the status information of the network model, fit a real-time network resource management strategy according to the service requirements, and then adapt the basic components in the network function component pool and the network resources in the network resource pool to achieve dynamic adaptation between network resources and service requirements; the basic support function subsystem is used to provide general basic services, support communication with external networks and integration with third-party application software through the extended interface, and realize simulation testing in a real network environment and actual business application scenarios.

2. The multi-modal intelligent network simulator architecture according to claim 1, characterized in that, it further includes: a model abstract description module, which is used to abstractly describe the model function in a formal language and store the description in the form of a file.

3. The multi-modal intelligent network simulator architecture according to claim 1, characterized in that, it further includes: an input / output interface connected to the intelligent management subsystem, which is used to input topology configuration parameters into the simulator internally, output simulation results, command-line interaction, and visual display.

4. The multi-modal intelligent network simulator architecture according to claim 1, characterized in that, the full-dimensional definable subsystem includes a network function hierarchical model construction module and a network function component pool construction module; the network function hierarchical model construction module is used to model the parsed model into a three-layer network model: a data layer, a control layer, and a service layer; the network function component pool construction module is used to decompose the network function into basic function components based on the principle of maximizing function reuse and establish a network function component pool.

5. The multi-modal intelligent network simulator architecture according to claim 1, characterized in that, the intelligent management subsystem includes a network status perception module, an intelligent decision-making module, and an adaptation fitting module; the network status perception module is used to perform fine-grained perception and intelligent analysis of the network status, obtain the current network topology and network resource usage, and generate a whole-network view based on a unified description model of high-level perception semantics; the intelligent decision-making module is used to generate a fitting decision between network resources and service requirements according to the network status perceived by the network status perception module, and make real-time decisions on the resource management strategy in the network; The adaptation and fitting module is used to perform top - down intelligent fitting of network functions driven by business requirements and network status according to the strategies generated by the intelligent decision - making module, and perform adaptive adjustment operations on network basic components and network resources.

6. A simulation test method for a multi - modal intelligent network emulator architecture according to any one of claims 1 - 5, characterized in that, it includes: Parsing the input model description file through the model semantic parsing module, mapping it to the corresponding basic components and the logical relationships between the components, and transmitting it to the fully - definable subsystem; Modeling the parsed model into a network model with a three - layer functional structure, including a service layer, a control layer, and a data layer, through the fully - definable subsystem, and establishing a network function component pool based on the principle of maximizing function reuse; Obtaining the status information of the network model through the intelligent management subsystem, fitting real - time network resource management strategies according to business requirements, and then adapting the basic components in the network function component pool and the network resources in the network resource pool to achieve dynamic adaptation between network resources and business requirements; Providing general basic services through the basic support function subsystem, supporting communication with external networks and integration with third - party application software through extended interfaces, and realizing simulation tests in real network environments and actual business application scenarios.

7. A simulation test method according to claim 6, characterized in that, it includes: Step 1: Abstractly describe the model function in a formal language through the model abstraction description module and store the description in the form of a file; Step 2: Parse the input model description file through the model semantic parsing module, map it to the corresponding basic components and the logical relationships between the components, and transmit it to the fully - definable subsystem; Step 3: The network function hierarchical model construction module of the fully - definable subsystem models the parsed model into a three - layer network model: data layer, control layer, and service layer; Step 4: Based on the constructed three - layer network model, the network function component pool construction module decomposes network functions into basic function components according to the principle of maximizing function reuse and establishes a network function component pool; Step 5: The intelligent management subsystem finely perceives and intelligently analyzes the network status through the network status perception module, obtains the current network topology and network resource usage situation, and generates a whole - network view based on a unified description model of high - level perception semantics; Step 6: The intelligent decision - making module generates a fitting decision between network resources and business requirements according to the network status perceived by the network status perception module, and makes real - time decisions on resource management strategies in the network; Step 7: The adaptation and fitting module performs top - down intelligent fitting of network functions driven by business requirements and network status according to the strategies generated by the intelligent decision - making module, and performs adaptive adjustment operations on network basic components and network resources; Step 8: Input topology configuration parameters into the emulator internally, output simulation results, command - line interaction, and visual display through a set of input - output interfaces. Step 9: The basic support function subsystem provides general services to ensure the smooth operation of the full-dimensional definable subsystem, the intelligent management subsystem, and the emulator. Through a set of extended interfaces, it supports communication with external networks and the integration of third-party application software for simulation testing in actual network environments and actual business application scenarios.

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