A network self-healing device based on node state data

Abstract

The application relates to the technical field of network communication, and discloses a network self-healing device based on node state data, which aims to improve the stability and self-healing ability of network communication. By monitoring the network state in real time, combining the extended Kalman filtering algorithm to perform state estimation and filtering, and combining the ant colony algorithm and the simulated annealing algorithm to perform fault prediction, path selection and resource allocation, the potential fault can be predicted and quickly recovered. The device comprises five modules of state monitoring, state estimation and filtering, fault prediction and prevention, fault recovery and resource reallocation, forms a closed-loop optimization mechanism, and is especially suitable for complex large network environments, such as data center networks.

Description

Technical Field

[0001] This invention relates to the field of network communication technology, specifically a network self-healing device based on node status data. It aims to improve the stability and self-healing capability of network communication by real-time monitoring of network status, prediction and detection of potential faults by a status observer, and taking preventive measures for intelligent decision-making path selection and resource allocation. Background Technology

[0002] With the continuous advancement of network technology, the stability and reliability of network communication have become core requirements for various application scenarios. In practical applications, networks face various sudden failures, such as equipment failure and link interruption. Firstly, relying on simple fault detection and recovery mechanisms, such as manual experience-based link-by-link checks and command-line operations, often results in slow response times and difficulty in quickly restoring network communication, severely impacting communication quality and user experience. Secondly, there is a lack of in-depth observation and intelligent analysis capabilities of network status. Network status includes multiple dimensions such as traffic load, link quality, and equipment health, which are crucial for predicting and detecting potential faults. Existing technologies do not comprehensively observe and utilize this status information, making it impossible to take effective preventative measures before network failures occur. Thirdly, path selection and resource allocation are key factors affecting network communication efficiency and stability. Traditional path selection algorithms often struggle to find the optimal path globally, easily getting trapped in local optima, leading to uneven distribution of network resources and reduced communication efficiency. Summary of the Invention

[0003] This invention aims to address key issues in current data center network self-healing technologies, such as high communication latency, low accuracy of monitoring status data, limited path selection, and uneven resource allocation. By designing a network self-healing device and introducing advanced algorithm models and status observation mechanisms, it achieves real-time and efficient communication assurance while improving the network's ability to predict and respond to potential faults.

[0004] The technical solution adopted in this invention is as follows:

[0005] A network self-healing device based on node state data includes:

[0006] The status monitoring module is used to capture the status information of the network and each device node in real time, including the link quality, traffic dynamics and device load information in the network, and transmit the collected device status data to the status estimation and filtering module. At the same time, the resource utilization information and network status information transmitted by the resource reallocation module are also transmitted to the fault prediction and prevention module.

[0007] The state estimation and filtering module is used to receive equipment state data, use the extended Kalman filter algorithm to finely process the equipment state data, obtain filtered equipment state data, and pass it to the fault prediction and prevention module and the fault recovery module.

[0008] The fault prediction and prevention module receives filtered equipment status data from the status estimation and filtering module and network status data from the status monitoring module. For equipment status data, if a fault is detected in the current equipment, a relevant equipment control strategy is generated. For network status data, if the fault data is detected, the fault data is analyzed and diagnosed in conjunction with resource utilization information transmitted by the status monitoring module and historical fault data of network nodes to construct a network time series dataset. A control strategy is generated based on the local control strategy library. If the status data is detected as normal, the existing ARIMA prediction model is used to predict nodes that may fail within a set time period in the future, and a corresponding control strategy is generated and transmitted to the fault recovery module.

[0009] The fault recovery module receives control strategies generated by the fault prediction and prevention module. For network fluctuation faults, it selects different algorithms for path planning based on the complexity of the current network. For equipment faults, it temporarily isolates the relevant equipment and generates equipment alarm signals. The isolation status is lifted after the relevant equipment returns to normal. After taking corresponding measures, it determines whether the measures are effective. If effective, the fault recovery result is passed to the resource reallocation module. If it fails, relevant alarm information is generated.

[0010] The resource reallocation module is used to reallocate network resources based on the fault recovery status and the current network node resource usage, and to feed back the resource utilization information to the status monitoring module in real time, forming a closed-loop optimization mechanism.

[0011] Furthermore, the state estimation and filtering module employs a two-stage Kalman filter model to perform fine processing on the received device state data, including:

[0012] In the first stage, the extended Kalman filter (EKF) is used to conduct a preliminary assessment of the equipment status and obtain the original equipment status data.

[0013] In the second stage, based on the original equipment status data from the first stage, standard Kalman filtering is used for further fine-tuning to achieve data preprocessing, wave-controlled denoising, and normalization, thereby obtaining effective features.

[0014] Furthermore, the specific process of the fault prediction and prevention module is as follows:

[0015] The fault prediction and prevention module first determines whether the data is device status data or network status data. If it is determined to be device status data and fault data, relevant device control strategies are generated for device isolation and recovery. If it is determined to be network status data, it is processed according to whether a fault has occurred. If it is fault data, key fault information is extracted and combined with resource utilization information transmitted by the status monitoring module, which is then fed into the fault judgment vector machine to diagnose the cause of the fault and query the local preset control strategy library to generate a control strategy. The resource utilization information is used to help diagnose environmental anomalies. If it is determined to be normal status data, a time series dataset is constructed based on the data packet transmission status and network latency in the status data. This dataset is used as input to the ARIMA prediction model for prediction. Based on the prediction results, the trend of network fluctuations and potential fault points within a set future time period are determined, and corresponding control strategies are generated.

[0016] Furthermore, when performing path planning, the fault recovery module adopts different path planning strategies based on the complexity of different networks: for networks with fewer than a set number of nodes, fixed network topology, and simple and predictable traffic patterns, the ant colony algorithm is used to dynamically plan the optimal or suboptimal communication path; for networks with more than a set number of nodes, dynamically changing network topology, and complex and unpredictable traffic patterns, the simulated annealing algorithm is enabled to accept non-optimal paths based on probability.

[0017] Compared with the prior art, the effective gains achieved by this invention are as follows:

[0018] (1) The original data is denoised by a two-stage EKF algorithm, which greatly improves the accuracy and reliability of the state data.

[0019] (2) Construct relevant time series datasets based on network status data continuously collected by the monitoring module, and use them as inputs to the ARIMA model for modeling and predicting network status, so as to predict potential faults.

[0020] (3) By combining the ant colony algorithm and the simulated annealing algorithm, the optimal path selection and resource allocation of the global and local systems were realized, the network fluctuation faults were recovered quickly, and the network communication efficiency was improved.

[0021] (4) By combining the state observation mechanism with the intelligent algorithm model, the network state changes can be predicted in real time, potential faults can be detected and prevented in a timely manner, and the network's self-healing ability and service continuity can be significantly improved. Attached Figure Description

[0022] Figure 1 This is a diagram of a network self-healing system device according to an embodiment of the present invention.

[0023] Figure 2This is a structural diagram of the status monitoring module in an embodiment of the present invention.

[0024] Figure 3 This is a flowchart illustrating the process of the fault prediction and prevention module in an embodiment of the present invention.

[0025] Figure 4 This is a flowchart illustrating the fault recovery module processing procedure in an embodiment of the present invention. Detailed Implementation

[0026] The following is in conjunction with the appendix Figure 1-4 The present invention will be further explained and described below.

[0027] This invention employs a state-detection-based network self-healing mechanism, primarily aiming to provide a novel automation solution for network control systems. It integrates Extended Kalman Filter (EKF), ant colony optimization, and simulated annealing algorithms to achieve efficient denoising of raw network state data, potential fault prediction, intelligent path selection, and globally optimized resource allocation. This solution can monitor network state in real time, design a network self-healing device capable of predicting and detecting potential faults, and execute intelligent decision-making for path selection and resource allocation, thereby improving the stability and self-healing capabilities of network communication. It not only significantly enhances the stability and self-healing capabilities of network communication but is also particularly suitable for complex, large-scale network environments, such as data center networks.

[0028] like Figure 1 As shown, a network self-healing device based on node state data includes:

[0029] In the network control system described in this invention, several key components are deployed for status monitoring, such as... Figure 2 As shown, the condition monitoring module includes sensors (or data acquisition units), controllers, actuators, and the communication network between them. The specific process is as follows:

[0030] Sensors capture real-time status information of the network and each device node (controlled device), such as link quality, traffic dynamics, device load, and physical parameters such as temperature and pressure.

[0031] State monitoring modules deployed in sensor networks periodically collect data from sensors using a controller to construct state vector data. The collected raw equipment status data D0 is transmitted to the status estimation and filtering module via the communication network, and the resource utilization information and network status data D1 transmitted by the resource reallocation module are transmitted to the fault prediction and prevention module.

[0032] As the core processing unit in the device, the state estimation and filtering module employs a two-stage Kalman filter model to finely process the received equipment state data. Combining the state transition function and observation function defined by the system dynamic model, and the Kalman gain calculated based on observation noise and process noise, it performs a preliminary estimate of the current state, obtaining filtered state information. It is passed to the fault prediction and prevention module and the fault recovery module.

[0033] The embodiments of the present invention employ a two-stage Kalman filter model to perform fine processing on the received data, specifically as follows:

[0034] Construct a two-stage model: In the first stage, use EKF to conduct a preliminary assessment of the system state, expressed by the formula:

[0035]

[0036] In the formula, For the current state estimate, f(x) is the state transition function, and K is the state transition function. k For Kalman gain, z k h(x) is the observation value (from the sensor), and h(x) is the observation function (mapping the system state to the observation space);

[0037] In the second stage, based on the estimation in the first stage, standard Kalman filtering is used for further fine-tuning to perform preprocessing steps such as denoising and normalization of the data in order to obtain effective features.

[0038] like Figure 3 As shown, the fault prediction and prevention module receives data from the state estimation and filtering module. After receiving the network status data D1 from the status monitoring module, it determines whether the data is faulty. If it is faulty, the system analyzes the data, extracts key fault information, and inputs it into the fault judgment vector machine to diagnose the cause of the fault. It also queries the locally preset control policy library (dictionary) StD to generate a control policy S. If the data is normal, a time series dataset TSDS is constructed based on the packet transmission status and network latency in the status data. This dataset is used as input to the ARIMA model for prediction. Based on the prediction results, the system determines the trend of network fluctuations and potential fault points within a future period T, and generates a control policy S, such as dynamically adjusting the packet sending rate, optimizing route selection, caching key data, and backing up communication paths. The generated control policy S is then sent to the fault recovery module.

[0039] The ARIMA model is used for time series forecasting. This model mainly consists of three parts: autoregressive (AR), differencing (I), and moving average (MA). By integrating different components (such as trend and residuals) from the time series dataset TSDS, the non-stationary time series is transformed into a stationary time series, and then forecasting is performed using the autoregressive and moving average models. Specifically, the autoregressive and moving average models are used to obtain the linear relationship between the current value and historical values ​​and historical error terms.

[0040] like Figure 4 As shown, after obtaining the control policy, the fault recovery module first determines the type of control policy S. If it indicates a device fault, the device is temporarily isolated and an alarm signal is generated. Once the device returns to normal, the isolation is lifted. After taking appropriate measures, the fault recovery module determines whether the measures are effective. If effective, the fault recovery result is sent to the resource reallocation module; otherwise, a relevant alarm message W is generated. For network fluctuation faults, different algorithms are flexibly selected for path planning based on the complexity of the current network. In simple networks, the ant colony algorithm is used to dynamically plan the optimal or suboptimal communication path; while in complex, large networks, the simulated annealing algorithm is used to enhance global search capabilities by probabilistically accepting non-optimal paths.

[0041] When performing path planning, the fault recovery module can adopt different path planning strategies based on the complexity of different networks. For simple networks, i.e., with a small number of nodes (e.g., tens to hundreds of nodes), a relatively fixed network topology, and relatively simple and predictable traffic patterns, the ant colony algorithm is used to dynamically plan the optimal or suboptimal communication path. For complex and large networks, i.e., with a large number of nodes (e.g., tens of thousands of nodes), a dynamically changing network topology, and complex and unpredictable traffic patterns, the simulated annealing algorithm is enabled. By accepting non-optimal paths with probability, the global search capability is enhanced, avoiding getting trapped in local optima. In addition, to improve response speed, a parallel construction method is used during the network path query process.

[0042] The ant colony algorithm is used to dynamically plan the optimal or suboptimal communication path. The specific process is as follows:

[0043] First, initialize the pheromone τ on each edge of the network. ij (0) = C, where C is a constant, τ ij (0) represents the initial pheromone level; assume ant k is on edge e ij Move upwards, and the pheromones are updated after the move. Where ρ is the pheromone volatility coefficient, representing the proportion of pheromone volatilization over time, m is the number of ants, and Δτ ij For the ant, during the time interval t to t+1, on edge eij The amount of pheromone left on it; and the probability that the ant chooses to move on edge e ij is where is the α -th power of the pheromone concentration on edge e ij ; is the β -th power of the heuristic information (the reciprocal of the length of edge e ij ij ij ) on edge e is the sum over all edges allowed for the k -th ant to choose. Here, allowed k represents the set of nodes that the ant can move to in the current state, is the α -th power of the pheromone concentration on edge e is ; is the β -th power of the heuristic information on edge e is . Here, α and β are algorithm parameters used to control the relative importance of pheromone and heuristic information; then, the path record table Ta is used to record the nodes passed by ant k currently. After t moments, all ants complete one cycle; finally, the path length passed by each ant is calculated, the shortest path length is saved, and it jumps to the pheromone initialization stage at the beginning of the model to prepare for the next cycle;

[0044] However, the ant colony algorithm may fall into the situation of local optimal solution when dealing with complex large - scale networks. Therefore, in order to improve the performance under complex networks, a scheme of ant colony algorithm + simulated annealing algorithm is designed. For the current solution S and the new solution S′, if w(S') < w(S), then S' is accepted as the current solution. Otherwise, with probability S' is accepted. Where w(S') - w(S) is the weight difference between the new solution and the current solution. That is, the way of probabilistically accepting non - optimal paths is used to enhance the global search ability, and a parallel construction method is adopted to reduce the time consumption in the path query process.

[0045] When performing path planning, the simulated annealing algorithm (SA) is enabled. By the way of probabilistically accepting non - optimal paths to enhance the global search ability, to solve the local over - fitting problem in the dynamic programming of the ant colony algorithm, its formula is expressed as:

[0046]

[0047] where ΔE is the path quality difference and T is the current temperature. In addition, in order to reduce the time consumption of path construction, this device adopts a parallel construction method to find paths.

[0048] The resource reallocation module reallocates network resources (such as bandwidth and processor time) based on the fault recovery status and the current network node resource usage, and feeds back the resource utilization information to the status monitoring module in real time, forming a closed-loop optimization mechanism.

Claims

1. A network self-healing device based on node state data, characterized in that, Comprise: The state monitoring module is used for real-time capturing the state information of network and each device node, including the link quality, traffic dynamics and load information of the device in the network, and transmitting the collected device state data to the state estimation and filtering module, and transmitting the resource utilization information and network state information transmitted by the resource reallocation module to the fault prediction and prevention module; The state estimation and filtering module is used for receiving the device state data, adopting the extended Kalman filtering algorithm to finely process the device state data, obtaining the filtered device state data, and transmitting the filtered device state data to the fault prediction and prevention module and the fault recovery module; The fault prediction and prevention module is used for receiving the filtered device state data transmitted by the state estimation and filtering module and the network state data transmitted by the state monitoring module; for the device state data, if it is judged that the current device has a fault, a related device control strategy is generated; For network state data, if it is judged as fault data, the fault data is combined with the resource utilization information transmitted by the state monitoring module and the historical fault data of the network node to analyze and diagnose, construct a network time series data set, and generate a control strategy based on a local control strategy library, if it is judged as normal state data, a node that may have a fault in a future set time period is predicted by using an existing ARIMA prediction model, and a corresponding control strategy is generated and transmitted to the fault recovery module; The fault recovery module is used for receiving the control strategy generated by the fault prediction and prevention module, selecting different algorithms for path planning according to the complexity of the current network for network fluctuation faults; For device faults, the related devices are temporarily isolated and a device alarm signal is generated, and the isolation state is removed after the related devices are normal; And judge whether the measures are effective after taking the corresponding measures, if effective, the fault recovery result is transmitted to the resource reallocation module, if failed, related alarm information is generated; The resource reallocation module is used for reallocating network resources according to the fault recovery situation and the current network node resource usage, and feeding back the resource utilization information to the state monitoring module in real time, forming a closed-loop optimization mechanism.

2. The network self-healing device based on node status data according to claim 1, characterized in that, The state estimation and filtering module adopts a two-stage Kalman filtering model to finely process the received device state data, including: In the first stage, the extended Kalman filter EKF is used to preliminarily evaluate the device state, and the original device state data is obtained; In the second stage, on the basis of the original device state data in the first stage, the standard Kalman filter is used for further fine tuning, realizing the preprocessing, wave control denoising and normalization of the data, and obtaining the effective features therein.

3. The network self-healing device based on node status data according to claim 1, characterized in that, The specific process of the fault prediction and prevention module is: The fault prediction and prevention module first determines whether the data is device state data or network state data; if it is determined that the data is device state data and is fault data, a related device control strategy is generated for isolation and recovery of the device; if it is determined that the data is network state data, processing is performed according to whether a fault has occurred, if it is fault data, fault key information is extracted, and resource utilization information transmitted by the state monitoring module is combined and input into a fault judgment vector machine to diagnose the cause of the fault and query a locally preset control strategy library to generate a control strategy; wherein the resource utilization information is used to help diagnose the abnormality of the environment; if it is determined that the data is normal state data, a time series data set is constructed according to packet transmission and network time delay in the state data, as an input of an ARIMA prediction model for prediction of the model, a trend of network fluctuation and potential fault points in a future set time period are determined according to a prediction result, and a corresponding control strategy is generated.

4. The network self-healing device based on node status data according to claim 1, characterized in that, The fault recovery module adopts different path planning strategies according to the complexity of different networks when planning a path: for a network with a node number less than a set number, a fixed network topology structure, and a simple and predictable traffic mode, an ant colony algorithm is used to dynamically plan an optimal or suboptimal communication path; for a network with a node number greater than a set number, a dynamically changing network topology structure, and a complex and unpredictable traffic mode, a simulated annealing algorithm is enabled to accept a non-optimal path through probability.

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