Networking configuration method and device, electronic equipment and storage medium

Abstract

The invention discloses a networking configuration method and device, electronic equipment and a storage medium, and relates to the technical field of the Internet, and the method comprises the steps: obtaining first state data of networking equipment; the first state data is input into a preset time sequence model, a prediction result is obtained, and the time sequence model is a combined model of an ARIMA model and an LSTM model; and dynamically adjusting the networking configuration of the networking equipment according to the prediction result. According to the invention, the adaptability and resource utilization efficiency of networking configuration can be improved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a networking configuration method, device, electronic device and storage medium. Background Art

[0002] Network configuration refers to the process of planning, setting up and adjusting various devices in the network (such as routers, switches, servers, etc.) and their connection relationships based on specific network requirements, aiming to build a network environment that can meet specific functional and performance requirements.

[0003] Traditional networking configuration methods often fail to effectively respond to complex network requirements when faced with dynamic changes in device status and environment, and can easily lead to problems such as resource waste, network performance degradation, and equipment failure.

[0004] In summary, how to improve the adaptability and resource utilization efficiency of networking configuration has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] The main purpose of this application is to provide a networking configuration method, device, electronic device and storage medium, aiming to improve the adaptability and resource utilization efficiency of the networking configuration.

[0006] To achieve the above purpose, the present application proposes a networking configuration method, which includes:

[0007] Acquire first status data of a networking device;

[0008] Inputting the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model (integrated autoregressive moving average model) and an LSTM model (Long Short-Term Memory).

[0009] The networking configuration of the networking device is dynamically adjusted according to the prediction result.

[0010] In one embodiment, the step of obtaining the first status data of the networking device includes:

[0011] Acquire initial first state data of the networking device;

[0012] The initial first state data is subjected to denoising, anomaly detection and data format standardization processing in sequence to obtain the first state data of the networking device.

[0013] In one embodiment, before the step of inputting the first state data into a preset time series model to obtain a prediction result, the step further includes:

[0014] The ARIMA model and the LSTM model are trained and verified by the historical state data, so that the ARIMA model learns the short-term change characteristics of the historical state data, and the LSTM model learns the long-term change characteristics of the historical state data;

[0015] The trained ARIMA model and the trained LSTM model are combined to obtain the time series model, and the combination method is a forward and backward combination or a weighted combination.

[0016] In one embodiment, the step of dynamically adjusting the networking configuration of the networking device according to the prediction result includes:

[0017] Determining whether resource allocation of the networking device is abnormal according to the prediction result;

[0018] When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm to obtain a configuration optimization strategy;

[0019] The networking configuration of the networking device is dynamically adjusted according to the configuration optimization strategy.

[0020] In one embodiment, the prediction result includes CPU (Central Processing Unit) load, network traffic and device load, and the step of determining whether the resource allocation of the networking device is abnormal according to the prediction result includes:

[0021] When the CPU load exceeds a preset first load threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a first abnormality prompt;

[0022] When the network traffic exceeds a preset traffic threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a second abnormality prompt;

[0023] When the device load exceeds a preset second load threshold, it is determined that there is an abnormality in the resource allocation of the networking device, and a third abnormality prompt is output.

[0024] In one embodiment, when there is an abnormality in the resource allocation, the step of using the prediction result as input data of a preset intelligent optimization algorithm to obtain a configuration optimization strategy includes:

[0025] When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm through selection, crossover and mutation operations;

[0026] or,

[0027] When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm by simulating the behavior of a particle group.

[0028] In one embodiment, the method further comprises:

[0029] Acquire second state data of the networking device, and calculate an error value between the second state data and the prediction result;

[0030] When the error value exceeds a preset error threshold, the time series model is modified, and the networking configuration of the networking device is adjusted according to the second state data.

[0031] In addition, to achieve the above purpose, the present application also proposes a networking configuration device, the networking configuration device comprising:

[0032] A data acquisition module, used to acquire first status data of a networking device;

[0033] A prediction module, used for inputting the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model;

[0034] A configuration adjustment module is used to dynamically adjust the networking configuration of the networking device according to the prediction result.

[0035] In addition, to achieve the above objectives, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the networking configuration method as described above.

[0036] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the networking configuration method as described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the networking configuration method described above are implemented.

[0038] The present application proposes a networking configuration method, which obtains first state data of a networking device and inputs the first state data into a preset time series model. The time series model is a combination model that combines an ARIMA model and an LSTM model. The time series model can utilize the advantages of the ARIMA model in analyzing linear relationships in time series and the advantages of the LSTM model in processing complex nonlinear time series data to more accurately predict the future state of the networking device. Then, the networking configuration of the networking device is dynamically adjusted based on the prediction result of the time series model. The adjustment can specifically include adjusting the network topology structure of the networking device, optimizing resource allocation, working mode, etc., thereby improving the adaptability of the networking configuration, enabling it to better adapt to the dynamic changes of the network environment to optimize resource allocation, improve resource utilization efficiency, and ensure network stability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flowchart of the first embodiment of the network configuration method of the present application is provided;

[0042] Figure 2 A flow chart of the second embodiment of the network configuration method of the present application is provided;

[0043] Figure 3 Another flowchart provided for the second embodiment of the network configuration method of the present application;

[0044] Figure 4 A schematic diagram of the module structure of the networking configuration device according to an embodiment of the present application;

[0045] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the networking configuration method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] An embodiment of the present application provides a solution to obtain first state data of a networking device; input the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and an LSTM model; and dynamically adjust the networking configuration of the networking device according to the prediction result.

[0050] Traditional networking configuration methods often fail to effectively respond to complex network requirements when faced with dynamic changes in device status and environment, and can easily lead to problems such as resource waste, network performance degradation, and equipment failure.

[0051] Specifically, there are a large number of devices in the Internet of Things, and their status and environmental parameters are constantly changing over time. Traditional static networking configurations are mostly based on fixed configurations and cannot adapt to these changes in real time, thus affecting the stability and efficiency of the network. For example, when the CPU utilization of a device suddenly increases, if the networking configuration cannot be adjusted in time, the device may become a network bottleneck, thereby affecting the performance of the entire network.

[0052] In addition, although the existing dynamic networking can cope with changes in device status to a certain extent, it can usually only configure the network based on the current or recent device status, and cannot accurately predict future device status and network requirements. It lacks sufficient predictive capabilities and global optimization strategies, and cannot achieve the optimal networking configuration.

[0053] In summary, the embodiment of the present application obtains the first state data of the networking device and inputs the first state data into a preset time series model. The time series model is a combination model that combines the ARIMA model and the LSTM model. The time series model can utilize the advantages of the ARIMA model in time series linear relationship analysis and the advantages of the LSTM model in processing complex nonlinear time series data to more accurately predict the future state of the networking device; then, the networking configuration of the networking device is dynamically adjusted based on the prediction result of the time series model. The adjustment can specifically be to adjust the network topology structure of the networking device, optimize resource allocation, work mode, etc., thereby improving the adaptability of the networking configuration, enabling it to better adapt to the dynamic changes of the network environment to optimize resource allocation, improve resource utilization efficiency, and ensure the stability and efficiency of the network.

[0054] It should be noted that the execution subject of this embodiment can be an electronic device with data processing, network communication and program running functions, such as a computer, a server, etc., or an electronic device capable of realizing the above functions, etc. The following takes the networking configuration terminal as an example to illustrate this embodiment and the following embodiments.

[0055] Based on this, the present application embodiment provides a networking configuration method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the networking configuration method of the present application.

[0056] In this embodiment, the networking configuration method includes steps S10 to S30:

[0057] Step S10, obtaining first status data of the networking device;

[0058] It should be noted that networking devices are used in various device networking scenarios, such as multi-device management in the Internet of Things, edge computing node collaboration and even distributed cloud computing environments. Networking devices connect different devices and networks to achieve data transmission and resource sharing. Networking devices can specifically be routers, switches, super devices, gateways, etc. The type and quantity of networking devices are not specifically limited in this embodiment.

[0059] The data acquisition module deployed at the network edge computing node acquires the current status data of the networking device, i.e., the first status data, by real-time monitoring or periodic acquisition. The first status data includes:

[0060] Hardware utilization: such as CPU utilization, memory utilization, storage utilization, etc.

[0061] Network status: such as network bandwidth usage, packet loss rate, communication delay, etc.

[0062] Device Energy Consumption: Monitors the power consumption and battery status of networked devices, especially for mobile devices.

[0063] Environmental information: collects parameters such as temperature, humidity, and light intensity of the environment where the networking equipment is located.

[0064] Geographic location information: The geographic location of the networking device is obtained through GPS (Navigation Satellite Timing And Ranging / Global Positioning System) or other positioning technologies.

[0065] It should also be noted that, in this embodiment, the frequency of collecting status data can be adjusted according to the type of networking device and the dynamics of the environment. For example, for mobile networking devices in a highly dynamic environment, a higher frequency of collection is required.

[0066] Step S20, inputting the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model;

[0067] After obtaining the first state data of the networking device, the first state data is input into a pre-built time series model, which is a combination model of the ARIMA model and the LSTM model. The ARIMA model is good at capturing linear relationships and time trends in the data, while the LSTM model can handle more complex nonlinear relationships and long-term dependency problems. Through the combination of the two models, the time series model can more accurately predict the future state of the networking device, so as to prevent potential problems such as possible failures and performance bottlenecks of the networking equipment in advance.

[0068] Step S30: dynamically adjust the networking configuration of the networking devices according to the prediction result.

[0069] After the time series model outputs the prediction results, the networking configuration of the networking equipment is dynamically adjusted according to the prediction results. The specific adjustments can be to adjust the network topology structure of the networking equipment, optimize resource allocation, work mode, etc., to prevent network failures, improve network stability and performance, and maximize resource utilization.

[0070] In summary, the embodiment of the present application obtains the first state data of the networking device and inputs the first state data into a preset time series model. The time series model is a combination model that combines the ARIMA model and the LSTM model. The time series model can utilize the advantages of the ARIMA model in time series linear relationship analysis and the advantages of the LSTM model in processing complex nonlinear time series data to more accurately predict the future state of the networking device; then, the networking configuration of the networking device is dynamically adjusted based on the prediction result of the time series model. The adjustment can specifically be to adjust the network topology structure of the networking device, optimize resource allocation, work mode, etc., thereby improving the adaptability of the networking configuration, enabling it to better adapt to the dynamic changes of the network environment to optimize resource allocation, improve resource utilization efficiency, and ensure the stability and efficiency of the network.

[0071] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, step S10 can include steps S101 to S102:

[0072] Step S101, obtaining initial first state data of a networking device;

[0073] Step S102, performing denoising, anomaly detection and data format standardization processing on the initial first state data in sequence to obtain first state data of the networking device.

[0074] In order to ensure the quality and consistency of the acquired first state data, the collected initial first state data of the networking device is preprocessed, and the preprocessing specifically includes:

[0075] De-noising: Use Kalman filtering, low-pass filtering and other algorithms to remove noise from the initial first state data to ensure the smoothness of the data.

[0076] Anomaly detection: By setting multiple layers of thresholds and rules (such as the Z-score algorithm), possible abnormal data points can be detected and marked, such as a sudden jump in CPU utilization or an abnormal increase in network latency.

[0077] Data format standardization: Standardize the format of initial first-state data from different sources and unify the timestamp, unit and structure of the data.

[0078] In a feasible embodiment, before step S20, as Figure 2 As shown, steps A10 to A20 may also be included:

[0079] Step A10, training and verifying the ARIMA model and the LSTM model through the historical state data, so that the ARIMA model learns the short-term change characteristics of the historical state data, and the LSTM model learns the long-term change characteristics of the historical state data;

[0080] It should be noted that since the status data of the networking equipment has obvious time dependence and trend, two main time series models are selected for combination in this embodiment. One is the ARIMA model, which is mainly used to process linear time series data and is suitable for analyzing and predicting the periodic or trend changes of the equipment. The other is the LSTM model, which is used for the long short-term memory (LSTM) model based on the neural network. It is good at capturing nonlinear changes and long-term dependencies in the equipment status and is particularly suitable for complex dynamic behavior prediction.

[0081] The historical status data of networking equipment is divided into a training set and a validation set to ensure that the time series model can learn the main patterns of the historical status data without overfitting. Then, through cross-validation and grid search, the hyperparameters of the time series model, such as the order (p, d, q) of ARIMA and the number of layers and hidden units of LSTM, are adjusted to minimize the prediction error. For the ARIMA model, the historical status data is used to estimate the autoregressive and moving average parameters; for the LSTM model, the network parameters are trained through feedforward and backpropagation to enable it to accurately predict the future status of the equipment. Then, the historical status data in the validation set is used to evaluate the prediction accuracy of the time series model, and the performance of the time series model is judged by indicators such as MSE (mean square error) and MAE (mean absolute error), and the model is further adjusted based on the verification results.

[0082] Step A20, combining the trained ARIMA model and the trained LSTM model to obtain a time series model, wherein the combination method is a forward and backward combination or a weighted combination.

[0083] The trained ARIMA model and the trained LSTM model are combined to obtain a time series model. In a feasible implementation, the ARIMA model is first used for short-term linear prediction to obtain preliminary prediction results. Then, the results are used as input to the LSTM model. The LSTM model further predicts the nonlinear changes and long-term trends of the equipment based on the ARIMA prediction results.

[0084] In another feasible implementation, the prediction results of ARIMA and LSTM are combined in a weighted manner to obtain the final prediction result. The weights can be adjusted according to the verification accuracy of the historical status data to ensure the prediction accuracy of the combined time series model.

[0085] It is worth mentioning that compared with the use of ARIMA model or LSTM model alone, this embodiment shows better results in prediction accuracy, robustness and adaptability. The combined time series model can not only capture the subtle fluctuations in the short-term status data of the networking equipment, but also accurately predict the long-term development trend, which is beneficial to the status monitoring, fault warning and resource scheduling of the networking equipment. At the same time, this embodiment proposes two strategies: front-to-back combination and weighted combination. The front-to-back combination means first using the ARIMA model for short-term prediction, and then using the prediction results as the input of the LSTM model to correct and predict the long-term trend. This combination method can make full use of the prediction advantages of the two models to form a complementary effect, while the weighted combination dynamically adjusts the weights of the prediction results according to the prediction performance of the two models to achieve the optimization of the prediction results.

[0086] In a possible embodiment, Figure 3 As shown, step S30 may include steps S301 to S303:

[0087] Step S301, determining whether the resource allocation of the networking device is abnormal according to the prediction result;

[0088] Based on the prediction results output by the time series model, a comprehensive assessment is conducted on the resource allocation status of the networking equipment. Specifically, by comparing the predicted data of the networking equipment with the actual resource demand, it is determined whether there are any abnormalities in the resource allocation of the current networking equipment. Abnormalities refer to situations such as excess resources, insufficient resources, or uneven resource allocation.

[0089] Step S302, when there is an abnormality in resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm to obtain a configuration optimization strategy;

[0090] When it is determined that there is an anomaly in resource allocation, the prediction result output by the time series model is used as input data and input into a preset intelligent optimization algorithm. The intelligent optimization algorithm can be a genetic algorithm or a particle swarm optimization algorithm. The intelligent optimization algorithm can automatically search and find the optimal or approximately optimal configuration optimization strategy based on the input data, and optimize resource allocation by executing the configuration optimization strategy.

[0091] Step S303: dynamically adjust the networking configuration of the networking device according to the configuration optimization strategy.

[0092] Dynamically adjust the configuration of networking devices according to the configuration optimization strategy. The adjustment actions may include adjusting the working mode of the networking devices, reallocating resources, optimizing the network topology, etc., so as to adjust the networking configuration of the networking devices in real time according to the changes in the network environment to adapt to the ever-changing network needs.

[0093] In a feasible embodiment, the prediction results include CPU load, network traffic and device load, and step S301 may include steps S3011 to S3013:

[0094] It should be noted that the prediction results based on the time series model include the CPU load, network traffic and device load of the networking equipment. It can identify resource bottlenecks or abnormal situations that the networking equipment may face in advance and make corresponding network configuration adjustments.

[0095] Step S3011, when the CPU load exceeds a preset first load threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a first abnormality prompt;

[0096] When the prediction result shows that the CPU load exceeds the preset first load threshold, it is determined that the computing power of the networking device is about to approach or reach saturation, and there is an abnormal risk of resource allocation. At this time, it is determined that there is an abnormality in resource allocation, and a first abnormal prompt is output to prompt the taking of corresponding measures, such as increasing computing resources, optimizing task scheduling, etc.

[0097] Step S3012, when the network traffic exceeds a preset traffic threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a second abnormality prompt;

[0098] When the prediction results show that the network traffic exceeds the preset traffic threshold, it is determined that the network communication capabilities of the networking equipment cannot meet current or future needs, and there is an abnormal risk in resource allocation. At this time, it is determined that there is an abnormality in resource allocation, and a second abnormal prompt is output to prompt the taking of corresponding measures, such as increasing bandwidth and optimizing the network topology.

[0099] Step S3013: When the device load exceeds a preset second load threshold, it is determined that there is an abnormality in the resource allocation of the networking device, and a third abnormality prompt is output.

[0100] When the prediction result shows that the device load exceeds the preset second load threshold, it is determined that the overall performance of the networking device is close to the limit and there is an abnormal risk in resource allocation. At this time, it is determined that there is an abnormality in resource allocation and a third abnormal prompt is output to prompt the taking of corresponding measures, such as upgrading hardware equipment, adjusting resource allocation strategies, etc.

[0101] In a feasible embodiment, step S302 may include step S3021:

[0102] Step S3021, when there is an abnormality in resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm through selection, crossover and mutation operations;

[0103] After determining that there is an anomaly in resource allocation, the prediction results such as CPU load, network traffic and device load obtained by the time series model are used as input data of the intelligent optimization algorithm. In this embodiment, the intelligent optimization algorithm is preferably a genetic algorithm. The intelligent optimization algorithm uses the prediction results to perform a series of selection, crossover (or hybridization) and mutation operations. These operations simulate natural selection, transmission of genetic information and gene mutation in the process of biological evolution, and aim to gradually optimize the configuration parameters of the networking equipment through iterative search to achieve the optimal state of resource allocation. Finally, the algorithm will output one or more configuration optimization strategies. These configuration optimization strategies can significantly reduce the probability of resource allocation anomalies and improve the overall performance of the network.

[0104] In another feasible embodiment, step S302 may include step S3022:

[0105] Step S3022, when there is an abnormality in resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm by simulating the behavior of the particle group.

[0106] After determining that there is an abnormality in resource allocation, the prediction results such as CPU load, network traffic and device load obtained by the time series model are used as input data of the intelligent optimization algorithm. In this embodiment, the intelligent optimization algorithm is preferably a particle swarm optimization algorithm. The intelligent optimization algorithm simulates the flight behavior of a group of particles in the search space to find the optimal solution. Each particle represents a possible configuration optimization strategy, and the flight direction and speed of the particle are affected by its own historical best position and the historical best position of the entire group. The algorithm updates the position and speed of the particle through continuous iteration until the optimal or approximately optimal configuration optimization strategy is found. These configuration optimization strategies are intended to optimize resource allocation and improve network performance and stability by adjusting the configuration parameters of the networking equipment.

[0107] For example, in a feasible implementation, specific measures for configuring the optimization strategy include:

[0108] Task migration: When it is predicted that the CPU load of a certain networking device is about to reach a bottleneck, some computing tasks are automatically migrated to another networking device or multiple networking devices with lower load to balance network resources.

[0109] Network bandwidth adjustment: Dynamically adjust bandwidth allocation for data transmission based on network traffic forecasts, giving priority to ensuring the communication quality of critical tasks.

[0110] Backup device activation: When it is predicted that some networking devices are about to be overloaded or fail, backup devices are automatically activated to take over tasks or share the load.

[0111] Dynamic adjustment of network topology: Dynamically adjust the network topology according to the prediction results to improve the robustness and efficiency of the network. Specifically include:

[0112] Communication path optimization: adjust the data transmission path between networking devices to reduce transmission delays and avoid network congestion.

[0113] Redefining node roles: Dynamically adjust the roles of nodes in the network (such as core nodes and edge nodes) according to the status and load of networking devices to optimize the load distribution of the entire network.

[0114] In addition, in a feasible embodiment, the networking configuration strategy can be continuously optimized based on the feedback data analysis and historical records of the networking devices, such as dynamically adjusting the priority of tasks according to the status changes of the networking devices to ensure that key tasks are executed first, or adjusting the resource allocation strategy based on the real-time load conditions of the networking devices to ensure optimal resource utilization of the devices in different states.

[0115] In a feasible embodiment, the networking configuration method of the present application may further include steps S40 to S50:

[0116] Step S40, obtaining second state data of the networking device, and calculating an error value between the second state data and the prediction result;

[0117] It should be noted that as time goes by and the status of networking devices changes, the time series model needs to be updated regularly, and the time series model needs to be retrained or fine-tuned based on the newly collected status data of networking devices to maintain its predictive ability.

[0118] Obtain the second state data of the networking device, which is the state data of the networking device after the first state data obtained through real-time monitoring or regular collection after the prediction result, and calculate the error value between the second state data and the prediction result. This error value reflects the degree of deviation between the time series model prediction and the actual state.

[0119] Step S50: when the error value exceeds a preset error threshold, the time series model is corrected, and the networking configuration of the networking device is adjusted according to the second state data.

[0120] If the calculated error value exceeds the preset error threshold, it indicates that the prediction performance of the time series model needs to be corrected. The correction may include adjusting model parameters, updating training data, introducing new feature variables, etc., to improve the prediction accuracy of the time series model. At the same time, the networking configuration of the networking device is fine-tuned according to the second state data to better adapt to the current network environment and needs, and further improve the stability and performance of the network.

[0121] For example, in order to help understand the implementation process of the networking configuration method obtained by combining this embodiment with the above-mentioned embodiment 1, an overall process embodiment is provided:

[0122] 1. Data collection and preprocessing.

[0123] 1.1 Input, that is, obtaining the initial first state data of the networking device:

[0124] Device status data: X t=[CPU usage t, memory usage t, network delay t, device energy consumption t, ambient temperature t, ...] (network device status data at multiple times);

[0125] Time series T = [t1, t2,…, tn].

[0126] 1.2 Processing, that is, performing denoising, anomaly detection and data format standardization processing on the initial first state data in sequence to obtain the first state data of the networking device:

[0127] Data standardization: Normalize the device status data to the same unit and use the mean standard deviation formula to standardize: X tnorm =σX t -μ, where μ is the mean and σ is the standard deviation.

[0128] De-noising: Use denoising methods such as Kalman filtering to ensure data smoothness. The filtering model is as follows: t =x t-1 +K t (z t -x t-1 ) Among them, K t is the Kalman gain, z t is the noise measurement data.

[0129] 1.3 Output:

[0130] High-quality time series data after standardization, denoising and outlier processing: X clean =[X t1norm ,X t2norm ,…,X tnnorm ].

[0131] 2. Time series model construction and training, that is, training and verifying the ARIMA model and LSTM model through historical state data, so that the ARIMA model can learn the short-term change characteristics of the historical state data, and the LSTM model can learn the long-term change characteristics of the historical state data.

[0132] 2.1 Input:

[0133] Preprocessed equipment status time series data: X clean .

[0134] Historical data: H = [H t-n ,H t-(n-1) ,…,H t ](Historical device status data is used to train the model).

[0135] 2.2 Processing:

[0136] ARIMA model: used for linear trend prediction, its mathematical model is: X t =c+φ1X t-1 +φ2X t-2 +…+∈ t Among them, φ1, φ2, … are autoregressive coefficients, ∈ t is the error term.

[0137] LSTM model: used to capture the long-term dependency of networking device states. The model structure is: h t =LSTM(X t-n ,X t-n+1 ,…,X t ), where the calculation of the LSTM unit includes the forget gate, input gate and output gate, which are: t =σ(W f· [h t-1 ,X t ]+b f )i t =σ(W i· [h t-1 ,X t ]+b i ) t =σ(W o· [h t-1 ,X t ]+b o ) where f t ,i t ,o t are the activation values ​​of the forget gate, input gate, and output gate, respectively, W f ,W i ,W o is the weight matrix.

[0138] 2.3 Output:

[0139] The trained ARIMA model and LSTM model are used for short-term linear prediction and long-term complex prediction, respectively.

[0140] Prediction results: Network device status prediction for the future time period X t+1 ,X t+2 ,….

[0141] 3. Optimization of dynamic networking configuration.

[0142] 3.1 Input, that is, input the first state data into the preset time series model to obtain the prediction result:

[0143] Time series forecast results: X t+1 ,X t+2 ,….

[0144] Current networking device status: X t .

[0145] 3.2 Processing, that is, dynamically adjusting the networking configuration of networking devices according to the prediction results:

[0146] Task migration algorithm: When it is predicted that the CPU or network load of the networking device is about to reach a bottleneck, a load balancing algorithm is used to calculate the task allocation between the networking devices. The goal is to minimize the maximum load, and the formula is:

[0147]

[0148] Among them, L i is the load of networking device i, C i is the computing capability of networking device i.

[0149] Network bandwidth adjustment: Based on the predicted traffic demand, adjust the bandwidth allocation between networking devices. The goal is to maximize network utilization. The optimization problem is as follows:

[0150]

[0151] Among them, B i is the bandwidth allocated to networking device i, U(B i ) is the utility function of bandwidth allocation.

[0152] Backup device activation: When a network device is predicted to fail, the backup device is activated. The decision function is:

[0153]

[0154] Among them, P failure is the predicted probability of networking equipment failure.

[0155] 3.3 Output:

[0156] Adjusted network configuration: task allocation plan, bandwidth allocation plan, and list of enabled standby devices.

[0157] 4. Real-time feedback and adaptive optimization

[0158] 4.1 Input:

[0159] Real-time networking device status: X treal ;

[0160] Model predicted status: X tpredict .

[0161] 4.2 Processing, i.e. obtaining the second state data of the networking device and calculating the error value between the second state data and the prediction result; when the error value exceeds the preset error threshold, the time series model is corrected, and the networking configuration of the networking device is adjusted according to the second state data:

[0162] Feedback mechanism: Real-time monitoring of the difference between the current networking device status and the predicted status, and calculation of the error: E t =|X treal -X tpredict ∣, when the error exceeds the threshold, the system retrains the model and updates the network configuration.

[0163] 4.3 Output:

[0164] The adjusted model parameters and optimized configuration ensure the continuous and efficient operation of the device network.

[0165] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the networking configuration method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0166] The present application also provides a network configuration device, please refer to Figure 4 , the networking configuration device comprises:

[0167] A data acquisition module 10, used to acquire first status data of a networking device;

[0168] A prediction module 20 is used to input the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model;

[0169] The configuration adjustment module 30 is used to dynamically adjust the networking configuration of the networking devices according to the prediction results.

[0170] Optionally, the data acquisition module 10 is further used for:

[0171] Acquire initial first state data of the networking device;

[0172] The initial first state data is subjected to denoising, anomaly detection and data format standardization processing in sequence to obtain the first state data of the networking device.

[0173] Optionally, the networking configuration device further includes a model training module (not shown), and the model training module is used to:

[0174] The ARIMA model and LSTM model are trained and verified through historical state data, so that the ARIMA model can learn the short-term change characteristics of the historical state data, and the LSTM model can learn the long-term change characteristics of the historical state data;

[0175] The trained ARIMA model and the trained LSTM model are combined to obtain a time series model. The combination method is forward and backward combination or weighted combination.

[0176] Optionally, the configuration adjustment module 30 is further used to:

[0177] Determine whether the resource allocation of networking devices is abnormal based on the prediction results;

[0178] When there are anomalies in resource allocation, the prediction results are used as input data for the preset intelligent optimization algorithm to obtain the configuration optimization strategy;

[0179] Dynamically adjust the networking configuration of networking devices according to the configuration optimization strategy.

[0180] Optionally, the prediction result includes CPU load, network traffic and device load, and the configuration adjustment module 30 is further used to:

[0181] When the CPU load exceeds a preset first load threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a first abnormality prompt;

[0182] When the network traffic exceeds a preset traffic threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a second abnormality prompt;

[0183] When the device load exceeds a preset second load threshold, it is determined that there is an abnormality in the resource allocation of the networking device, and a third abnormality prompt is output.

[0184] Optionally, the configuration adjustment module 30 is further used to:

[0185] When there are anomalies in resource allocation, the prediction results are used as input data for the preset intelligent optimization algorithm, and the configuration optimization strategy is determined based on the selection, crossover and mutation operations of the intelligent optimization algorithm;

[0186] or,

[0187] When there are anomalies in resource allocation, the prediction results are used as input data for the preset intelligent optimization algorithm. Based on the intelligent optimization algorithm, the configuration optimization strategy is determined by simulating the behavior of the particle group.

[0188] Optionally, the networking configuration device further includes a data feedback module (not shown), and the data feedback module is used to:

[0189] Acquire second state data of the networking device, and calculate an error value between the second state data and the prediction result;

[0190] When the error value exceeds a preset error threshold, the time series model is corrected, and the networking configuration of the networking device is adjusted according to the second state data.

[0191] The networking configuration device provided in the embodiment of the present application adopts the networking configuration method in the above embodiment, which can improve the adaptability and resource utilization efficiency of the networking configuration. Compared with the prior art, the beneficial effects of the networking configuration device provided in the embodiment of the present application are the same as the beneficial effects of the networking configuration method provided in the above embodiment, and other technical features in the networking configuration device are the same as the features disclosed in the networking configuration method in the above embodiment, which will not be described in detail here.

[0192] An embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the networking configuration method in the above-mentioned embodiment one.

[0193] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include but is not limited to mobile terminals such as laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0194] like Figure 5As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0195] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0196] The electronic device provided in the embodiment of the present application adopts the networking configuration method in the above embodiment to improve the adaptability and resource utilization efficiency of the networking configuration. Compared with the prior art, the beneficial effects of the electronic device provided in the embodiment of the present application are the same as the beneficial effects of the networking configuration method provided in the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the networking configuration method in the previous embodiment, which will not be described in detail here.

[0197] It should be understood that the various parts disclosed in the embodiments of the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0198] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0199] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the networking configuration method in the above-mentioned embodiment.

[0200] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0201] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0202] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: obtains the first state data of the networking device; inputs the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model; and dynamically adjusts the networking configuration of the networking device according to the prediction result.

[0203] The computer program code for performing the operation of the embodiment of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0204] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0205] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0206] The readable storage medium provided in the embodiment of the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned networking configuration method, and can improve the adaptability and resource utilization efficiency of the networking configuration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the embodiment of the present application are the same as the beneficial effects of the networking configuration method provided in the above-mentioned embodiment, and will not be repeated here.

[0207] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned networking configuration method when executed by a processor.

[0208] The computer program product provided by the present application can solve the technical problem of network configuration. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the network configuration method provided by the above embodiment, which will not be repeated here.

[0209] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A networking configuration method, characterized in that: The method comprises: Acquire first status data of a networking device; Inputting the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model; The networking configuration of the networking device is dynamically adjusted according to the prediction result.

2. The networking configuration method according to claim 1, characterized in that: The step of obtaining the first status data of the networking device includes: Acquire initial first state data of the networking device; The initial first state data is subjected to denoising, anomaly detection and data format standardization processing in sequence to obtain the first state data of the networking device.

3. The networking configuration method according to claim 1, characterized in that: Before the step of inputting the first state data into a preset time series model to obtain a prediction result, the method further includes: The ARIMA model and the LSTM model are trained and verified by the historical state data, so that the ARIMA model learns the short-term change characteristics of the historical state data, and the LSTM model learns the long-term change characteristics of the historical state data; The trained ARIMA model and the trained LSTM model are combined to obtain the time series model, and the combination method is a forward and backward combination or a weighted combination.

4. The networking configuration method according to claim 1, characterized in that: The step of dynamically adjusting the networking configuration of the networking device according to the prediction result includes: Determining whether resource allocation of the networking device is abnormal according to the prediction result; When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm to obtain a configuration optimization strategy; The networking configuration of the networking device is dynamically adjusted according to the configuration optimization strategy.

5. The networking configuration method according to claim 4, characterized in that: The prediction result includes CPU load, network traffic and device load, and the step of determining whether the resource allocation of the networking device is abnormal according to the prediction result includes: When the CPU load exceeds a preset first load threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a first abnormality prompt; When the network traffic exceeds a preset traffic threshold, determining that there is an abnormality in resource allocation of the networking device, and outputting a second abnormality prompt; When the device load exceeds a preset second load threshold, it is determined that there is an abnormality in the resource allocation of the networking device, and a third abnormality prompt is output.

6. The networking configuration method according to claim 5, characterized in that: When there is an abnormality in the resource allocation, the step of using the prediction result as input data of a preset intelligent optimization algorithm to obtain a configuration optimization strategy includes: When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm through selection, crossover and mutation operations; or, When there is an anomaly in the resource allocation, the prediction result is used as input data of a preset intelligent optimization algorithm, and a configuration optimization strategy is determined based on the intelligent optimization algorithm by simulating the behavior of a particle group.

7. The networking configuration method according to claim 1, characterized in that: The method further comprises: Acquire second state data of the networking device, and calculate an error value between the second state data and the prediction result; When the error value exceeds a preset error threshold, the time series model is modified, and the networking configuration of the networking device is adjusted according to the second state data.

8. A network configuration device, characterized in that: The networking configuration device comprises: A data acquisition module, used to acquire first status data of a networking device; A prediction module, used for inputting the first state data into a preset time series model to obtain a prediction result, wherein the time series model is a combination model of an ARIMA model and a LSTM model; A configuration adjustment module is used to dynamically adjust the networking configuration of the networking device according to the prediction result.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the networking configuration method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the networking configuration method according to any one of claims 1 to 7 are implemented.

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