A Closed-Loop Bandwidth Adjustment Method and System Based on Model-Free Adaptive Control

Abstract

This invention discloses a closed-loop bandwidth adjustment method and system based on model-free adaptive control, belonging to the field of communication operation and maintenance technology. The technical problem it addresses is how to dynamically adjust bandwidth and achieve closed-loop control based on model-free adaptive control. It includes: a data acquisition module for collecting operation and maintenance data of the target network and sending the data to a model-free adaptive controller; a model-free adaptive controller for adjusting the network bandwidth allocation strategy of the target network based on the operation and maintenance data and using a model-free adaptive algorithm; a bandwidth adjustment execution module for adjusting the network bandwidth of the target network based on the adjusted network bandwidth; a performance evaluation feedback module for evaluating the performance of the adjusted target network; and a model-free adaptive controller for optimizing the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

Description

Technical Field

[0001] This invention relates to the field of communication operation and maintenance technology, specifically to a closed-loop bandwidth adjustment method and system based on model-free adaptive control. Background Technology

[0002] With the increasing complexity and diversification of communication networks, network operation and maintenance management faces numerous challenges, including how to efficiently utilize network resources, how to quickly respond to changes in network status, and how to ensure stable network performance. Furthermore, communication environments are often complex and volatile, with significant time delays and strong interference, severely impacting the stability and reliability of communication systems. Traditional operation and maintenance management methods often rely on static rules or experience-based judgments, making it difficult to respond accurately and in real-time to the variability and complexity of network status. Therefore, an intelligent operation and maintenance optimization system capable of analyzing network status in real time and automatically adjusting operation and maintenance strategies is needed. Model-free adaptive control, as an emerging control technology, does not require precise modeling of the controlled object, relying solely on input and output data for control, offering unique advantages.

[0003] Model-Free Adaptive Control (MFAC) is an adaptive control method that does not require building a process model. This method observes the system's input and output data and uses a specific algorithm to learn and update the system's control strategy online to adapt to dynamic changes in the system. The basic principle of MFAC is based on observing the system's input and output data and using a specific algorithm to learn and update the system's control strategy online. The controller compares the system's actual output with the desired output to obtain an error signal, and then uses this error signal to update the system's control strategy to minimize the system error.

[0004] In communication operations and maintenance, effective management and dynamic adjustment of bandwidth resources are crucial to ensuring network communication quality. Model-free adaptive control can achieve closed-loop control by monitoring network bandwidth utilization in real time and dynamically adjusting bandwidth allocation strategies based on preset target bandwidth utilization or network performance indicators.

[0005] How to dynamically adjust the bandwidth and achieve closed-loop control based on model-free adaptive control is a technical problem that needs to be solved. Summary of the Invention

[0006] The technical objective of this invention is to address the above-mentioned shortcomings by providing a closed-loop bandwidth adjustment method and system based on model-free adaptive control, thereby solving the technical problem of how to dynamically adjust bandwidth and achieve closed-loop control based on model-free adaptive control.

[0007] In a first aspect, the present invention provides a closed-loop bandwidth adjustment system based on model-free adaptive control, comprising a data acquisition module, a model-free adaptive controller, a bandwidth adjustment execution module, and a performance evaluation feedback module.

[0008] The data acquisition module is used to monitor the target network in real time, collect the operation and maintenance data of the target network, and send the operation and maintenance data to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network.

[0009] The model-free adaptive controller is configured with a model-free adaptive algorithm with lag time constraints. It is used to adjust the network bandwidth allocation strategy of the target network based on operation and maintenance data and through the model-free adaptive algorithm, output the adjusted service bandwidth, and send the adjusted network bandwidth to the bandwidth adjustment execution module.

[0010] The bandwidth adjustment execution module is used to adjust the network bandwidth of the target network based on the adjusted network bandwidth, thereby increasing or decreasing the bandwidth resources of specific links in the target network.

[0011] The performance evaluation feedback module is used to obtain the operation and maintenance data of the adjusted target network from the data acquisition module, perform performance evaluation on the adjusted target network based on the operation and maintenance data, and feed the evaluation results back to the model-free adaptive controller; correspondingly, the model-free adaptive controller is used to receive the operation and maintenance data of the adjusted target network from the data acquisition module, and optimize the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

[0012] Preferably, the performance evaluation feedback module evaluates the improvement in bandwidth utilization and the reduction in network congestion in the target network through performance evaluation, and obtains the evaluation results.

[0013] Preferably, the model-free adaptive controller is used to perform the following adjustment of the network bandwidth allocation strategy for the target network:

[0014] Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidth conditions;

[0015] When the network load changes more frequently than predetermined, the service bandwidth is adjusted based on a model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

[0016] Preferably, for the model-free adaptive control algorithm with time lag constraints, its input and output directions are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows:

[0017]

[0018] Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100).

[0019]

[0020] Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10);

[0021] When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through the model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

[0022] In a second aspect, the present invention provides a closed-loop bandwidth adjustment method based on model-free adaptive control, which achieves closed-loop bandwidth adjustment through a closed-loop bandwidth adjustment system based on model-free adaptive control as described in any one of the first aspects, the method comprising the following steps:

[0023] The target network is monitored in real time through the data acquisition module, and the operation and maintenance data of the target network is collected and sent to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network.

[0024] Based on operation and maintenance data, the network bandwidth allocation strategy of the target network is adjusted by a modelless adaptive algorithm configured in the modelless adaptive controller, the adjusted service bandwidth is output and the adjusted network bandwidth is sent to the bandwidth adjustment execution module.

[0025] Based on the adjusted network bandwidth, the network bandwidth of the target network is adjusted to increase or decrease the bandwidth resources of specific links in the target network.

[0026] The system acquires the adjusted target network's operation and maintenance data from the data acquisition module, performs a performance evaluation on the adjusted target network based on the operation and maintenance data, and feeds the evaluation results back to the model-free adaptive controller. Correspondingly, the model-free adaptive controller receives the adjusted target network's operation and maintenance data from the data acquisition module, and optimizes the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

[0027] As a preferred approach, performance evaluation is conducted to assess the improvement in bandwidth utilization and network congestion in the target network, and the evaluation results are obtained.

[0028] As a preferred approach, the network bandwidth allocation strategy for the target network is adjusted by executing the following steps using a model-free adaptive controller:

[0029] Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidth conditions;

[0030] When the network load changes more frequently than predetermined, the service bandwidth is adjusted based on a model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

[0031] Preferably, for the model-free adaptive control algorithm with time lag constraints, its input and output directions are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows:

[0032]

[0033] Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100).

[0034]

[0035] Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10);

[0036] When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through the model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

[0037] The closed-loop bandwidth adjustment system and method based on model-free adaptive control of the present invention has the following advantages: it can monitor network bandwidth utilization in real time, analyze operation and maintenance data through model-free adaptive control algorithms, automatically adjust bandwidth allocation strategies, and realize closed-loop optimization control of bandwidth resources, thereby improving operation and maintenance quality in the following aspects: dynamically adjusting bandwidth resource allocation, improving the overall utilization of network bandwidth; timely detection and resolution of network bottleneck problems, alleviating network congestion, improving communication quality, and enhancing user satisfaction and loyalty; automated bandwidth adjustment strategies can reduce manual intervention, improve operation and maintenance efficiency, and reduce operation and maintenance costs; model-free adaptive control can achieve rapid response and adaptive adjustment to changes in network status, enhancing network stability. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] The invention will be further described below with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart of a closed-loop bandwidth adjustment method based on model-free adaptive control, as shown in Example 2. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments are not intended to limit the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0042] This invention provides a closed-loop bandwidth adjustment system and method based on model-free adaptive control, which solves the technical problem of how to dynamically adjust bandwidth and achieve closed-loop control based on model-free adaptive control.

[0043] Example 1:

[0044] The present invention discloses a closed-loop bandwidth adjustment system based on model-free adaptive control, comprising a data acquisition module, a model-free adaptive controller, a bandwidth adjustment execution module, and a performance evaluation feedback module.

[0045] The data acquisition module is used to monitor the target network in real time, collect the operation and maintenance data of the target network, and send the operation and maintenance data to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network.

[0046] The model-free adaptive controller is configured with a model-free adaptive algorithm with lag time constraints. It is used to adjust the network bandwidth allocation strategy of the target network based on operation and maintenance data and output the adjusted service bandwidth. The adjusted network bandwidth is then sent to the bandwidth adjustment execution module.

[0047] As a specific implementation of a model-free adaptive controller, this controller is used to perform the following adjustments to the network bandwidth allocation strategy for the target network:

[0048] (1) Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidths;

[0049] (2) When the network load changes more than the predetermined frequency, the service bandwidth is adjusted based on the model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

[0050] For the model-free adaptive control algorithm with time lag constraints disclosed in this embodiment, its input and output directions are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows:

[0051]

[0052] Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100).

[0053]

[0054] Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10);

[0055] In this embodiment, the cyclic control of the model-free adaptive algorithm controller is described as follows: Using equation (2), the characteristic parameters at time k are obtained from the service bandwidth at times k-1 and k-2-τ, the actual bandwidth utilization rate fed back by the acquisition module at times k and k-1, and the characteristic parameters at time k-1. Equation (1) calculates the control input, i.e., the current service bandwidth u(k), using the service bandwidth at times k-1 and k-1-τ, the actual bandwidth utilization rate fed back by the data acquisition module at time k, the characteristic parameters at time k, and the expected output at time k+1, i.e., the set value of the optimal bandwidth utilization rate. u(k) is then passed to the bandwidth adjustment execution module to dynamically adjust the bandwidth value. The acquisition module collects the actual bandwidth utilization rate after bandwidth adjustment and obtains the output of the controlled system, i.e., the actual bandwidth utilization rate y(k+1). The obtained data serves as the input data for the model-free adaptive algorithm controller in the next cycle of the control loop.

[0056] When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through a model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

[0057] The bandwidth adjustment execution module is used to adjust the network bandwidth of the target network based on the adjusted network bandwidth, thereby increasing or decreasing the bandwidth resources of specific links in the target network.

[0058] The performance evaluation feedback module is used to obtain the operation and maintenance data of the adjusted target network from the data acquisition module, perform performance evaluation on the adjusted target network based on the operation and maintenance data, and feed the evaluation results back to the model-free adaptive controller; correspondingly, the model-free adaptive controller is used to receive the operation and maintenance data of the adjusted target network from the data acquisition module, and optimize the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

[0059] In this embodiment, the performance evaluation feedback module evaluates the improvement in bandwidth utilization and the reduction in network congestion in the target network through performance evaluation, and obtains the evaluation results.

[0060] The closed-loop bandwidth adjustment system disclosed in this embodiment has the following workflow:

[0061] Data Acquisition: Real-time acquisition of operational data such as network bandwidth utilization.

[0062] (1) Data analysis: The model-free adaptive controller uses the collected data to analyze and identify network bandwidth bottlenecks and redundancies.

[0063] (2) Strategy generation: Based on the analysis results, the model-free adaptive controller generates targeted bandwidth adjustment strategies.

[0064] (3) Strategy execution: The bandwidth adjustment execution module adjusts bandwidth resources according to the generated strategy.

[0065] (4) Performance evaluation: Evaluate the adjusted network performance, paying particular attention to the improvement in bandwidth utilization and the reduction in network congestion.

[0066] (5) Feedback adjustment: The evaluation results are fed back to the model-free adaptive controller, and the control algorithm and bandwidth allocation strategy are adjusted according to the feedback results to form closed-loop control.

[0067] Example 2:

[0068] This invention discloses a closed-loop bandwidth adjustment method based on model-free adaptive control. The closed-loop bandwidth adjustment is implemented using the system disclosed in Example 1. The method includes the following steps:

[0069] Step S100: Monitor the target network in real time through the data acquisition module, collect the operation and maintenance data of the target network, and send the operation and maintenance data to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network.

[0070] Step S200: Based on operation and maintenance data, adjust the network bandwidth allocation strategy of the target network through the modelless adaptive algorithm configured in the modelless adaptive controller, output the adjusted service bandwidth, and send the adjusted network bandwidth to the bandwidth adjustment execution module.

[0071] Step S300: Adjust the network bandwidth of the target network based on the adjusted network bandwidth, and increase or decrease the bandwidth resources of specific links in the target network by adjusting the network bandwidth.

[0072] Step S400: Obtain the adjusted target network's operation and maintenance data from the data acquisition module, perform performance evaluation on the adjusted target network based on the operation and maintenance data, and feed the evaluation results back to the model-free adaptive controller; correspondingly, receive the adjusted target network's operation and maintenance data from the data acquisition module through the model-free adaptive controller, and optimize the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and evaluation results.

[0073] As a specific implementation of step S200, the following adjustment of the network bandwidth allocation strategy for the target network is performed:

[0074] (1) Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidths;

[0075] (2) When the network load changes more than the predetermined frequency, the service bandwidth is adjusted based on the model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

[0076] For the model-free adaptive control algorithm with time lag constraints disclosed in this embodiment, its input and output directions are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows:

[0077]

[0078] Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100).

[0079]

[0080] Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10);

[0081] In this embodiment, the cyclic control of the model-free adaptive algorithm controller is described as follows: Using equation (2), the characteristic parameters at time k are obtained from the service bandwidth at times k-1 and k-2-τ, the actual bandwidth utilization rate fed back by the acquisition module at times k and k-1, and the characteristic parameters at time k-1. Equation (1) calculates the control input, i.e., the current service bandwidth u(k), using the service bandwidth at times k-1 and k-1-τ, the actual bandwidth utilization rate fed back by the data acquisition module at time k, the characteristic parameters at time k, and the expected output at time k+1, i.e., the set value of the optimal bandwidth utilization rate. u(k) is then passed to the bandwidth adjustment execution module to dynamically adjust the bandwidth value. The acquisition module collects the actual bandwidth utilization rate after bandwidth adjustment and obtains the output of the controlled system, i.e., the actual bandwidth utilization rate y(k+1). The obtained data serves as the input data for the model-free adaptive algorithm controller in the next cycle of the control loop.

[0082] When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through a model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

[0083] In this embodiment, the improvement of bandwidth utilization and the reduction of network congestion in the target network are evaluated through performance evaluation, and the evaluation results are obtained.

[0084] The closed-loop bandwidth adjustment system and method based on model-free adaptive control provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A closed-loop bandwidth adjustment system based on model-free adaptive control, characterized in that, It includes a data acquisition module, a model-free adaptive controller, a bandwidth adjustment execution module, and a performance evaluation feedback module; The data acquisition module is used to monitor the target network in real time, collect the operation and maintenance data of the target network, and send the operation and maintenance data to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network. The model-free adaptive controller is configured with a model-free adaptive algorithm with lag time constraints. It is used to adjust the network bandwidth allocation strategy of the target network based on operation and maintenance data and through the model-free adaptive algorithm, output the adjusted service bandwidth, and send the adjusted network bandwidth to the bandwidth adjustment execution module. The bandwidth adjustment execution module is used to adjust the network bandwidth of the target network based on the adjusted network bandwidth, thereby increasing or decreasing the bandwidth resources of specific links in the target network. The performance evaluation feedback module is used to obtain the operation and maintenance data of the adjusted target network from the data acquisition module, perform performance evaluation on the adjusted target network based on the operation and maintenance data, and feed the evaluation results back to the model-free adaptive controller; correspondingly, the model-free adaptive controller is used to receive the operation and maintenance data of the adjusted target network from the data acquisition module, and optimize the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

2. The closed-loop bandwidth adjustment system based on model-free adaptive control according to claim 1, characterized in that, The performance evaluation feedback module evaluates the improvement in bandwidth utilization and network congestion in the target network through performance evaluation, and obtains the evaluation results.

3. The closed-loop bandwidth adjustment system based on model-free adaptive control according to claim 1 or 2, characterized in that, The model-free adaptive controller is used to perform the following adjustment of the network bandwidth allocation strategy for the target network: Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidth conditions; When the network load changes more frequently than predetermined, the service bandwidth is adjusted based on a model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

4. The closed-loop bandwidth adjustment system based on model-free adaptive control according to claim 3, characterized in that, For the model-free adaptive control algorithm with time lag constraints, its input and output terms are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows: Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100). Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10); When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through the model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

5. A closed-loop bandwidth adjustment method based on model-free adaptive control, characterized in that, Closed-loop bandwidth adjustment is achieved through a closed-loop bandwidth adjustment system based on model-free adaptive control as described in any one of claims 1-4, the method comprising the following steps: The target network is monitored in real time through the data acquisition module, and the operation and maintenance data of the target network is collected and sent to the model-free adaptive controller. The operation and maintenance data includes the bandwidth utilization and service bandwidth of the target network. Based on operation and maintenance data, the network bandwidth allocation strategy of the target network is adjusted by a modelless adaptive algorithm configured in the modelless adaptive controller, the adjusted service bandwidth is output and the adjusted network bandwidth is sent to the bandwidth adjustment execution module. Based on the adjusted network bandwidth, the network bandwidth of the target network is adjusted to increase or decrease the bandwidth resources of specific links in the target network. The system acquires the adjusted target network's operation and maintenance data from the data acquisition module, performs a performance evaluation on the adjusted target network based on the operation and maintenance data, and feeds the evaluation results back to the model-free adaptive controller. Correspondingly, the model-free adaptive controller receives the adjusted target network's operation and maintenance data from the data acquisition module, and optimizes the current model-free adaptive algorithm and network bandwidth allocation strategy based on the operation and maintenance data and the evaluation results.

6. The closed-loop bandwidth adjustment method based on model-free adaptive control according to claim 5, characterized in that, The performance evaluation assesses the improvement in bandwidth utilization and network congestion in the target network, and the evaluation results are obtained.

7. The closed-loop bandwidth adjustment method based on model-free adaptive control according to claim 5 or 6, characterized in that, The target network bandwidth allocation strategy is adjusted by executing the following steps using a model-free adaptive controller: Based on project experience or experimental methods, obtain the optimal bandwidth utilization under different bandwidth conditions; When the network load changes more frequently than predetermined, the service bandwidth is adjusted based on a model-free adaptive control algorithm with lag time constraints, so that the bandwidth utilization can stably and quickly track the optimal bandwidth utilization.

8. The closed-loop bandwidth adjustment method based on model-free adaptive control according to claim 7, characterized in that, For the model-free adaptive control algorithm with time lag constraints, its input and output directions are defined. The input terms include the actual bandwidth utilization rate y(k) at time k, the actual bandwidth utilization rate y(k-1) at time k-1, the service bandwidth u(k-1) at time k-1, the service bandwidth u(k-2) at time k-2, the service bandwidth u(k-1-τ) at time k-1-τ, the service bandwidth u(k-2-τ) at time k-2-τ, and the optimal bandwidth utilization rate y(k+1) at time k. * (k+1), where τ represents the lag time constant, and the output term is the service bandwidth u(k) at time k. The formula for calculating the service bandwidth u(k) at time k is as follows: Where T represents the sampling time, ρ, η, and λ represent the weighting coefficients, ρ∈(0,1), η∈(0,10), and λ∈(0,100). Where ξ and μ represent weight coefficients, ξ∈(0,2) and μ∈(0,10); When adjusting the bandwidth strategy, the service bandwidth at time k is obtained as the output through the model-free adaptive control algorithm with lag time constraints. Specifically, when adjusting the service bandwidth based on the model-free adaptive control algorithm with lag time constraints, each iteration of the calculation only requires the measurement data from the existing closed-loop test to obtain the characteristic parameters. This then generates a control signal u(k).

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