Adaptive network environment parameter adjustment method and device and electronic equipment

Abstract

The invention relates to a self-adaptive network environment parameter adjusting method and device and electronic equipment, and the method comprises the steps: obtaining the real-time data of a network environment, and carrying out the standardization processing and instant analysis of the data, so as to capture the key features of a network state; identifying a network state based on the preprocessed network environment data, and outputting a network state trend prediction result in a short time in the future; and adaptively adjusting network parameters by using an SAC model according to a prediction result. According to the parameter adjustment method and device adaptive to the network environment and the electronic equipment, the network state is predicted through the network environment data collected in real time and by adopting an advanced data analysis technology, so that a network state trend prediction result in a short time in the future is obtained; therefore, the network environment parameters can be conveniently and accurately adjusted in real time according to the dynamic change of the network state, so that the network performance is optimized, and the stability and efficiency of the network are improved.

Description

Technical Field

[0001] The present invention relates to the field of network environment parameter adjustment, and in particular to a parameter adjustment method, device and electronic equipment for an adaptive network environment. Background Art

[0002] Traditional network environment parameter adjustment technologies often rely on manual settings, which is not only time-consuming but also unable to respond to dynamic changes in network status in real time, resulting in unstable network performance. For example, under changing environmental conditions, such as temperature changes, electromagnetic interference, etc., network equipment often cannot automatically adapt to these changes, thus affecting the speed and stability of the network. Traditional network environment parameter adjustment technologies have limitations in data collection, processing and analysis, making it difficult to achieve real-time and accurate environment parameter adjustment. Therefore, they need to be improved. Summary of the invention

[0003] Based on this, it is necessary to provide a parameter adjustment method, device and electronic device for an adaptive network environment to address the problem that traditional network environment parameter adjustment technology has limitations in data collection, processing and analysis, and it is difficult to achieve real-time and accurate environment parameter adjustment.

[0004] The present invention provides a parameter adjustment method for an adaptive network environment, the method comprising:

[0005] Obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status;

[0006] Based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future;

[0007] According to the prediction results, the SAC model is used to adaptively adjust the network parameters.

[0008] In one embodiment, the real-time data of the network environment is obtained, and the data is standardized and analyzed in real time to capture key features of the network status, including:

[0009] Obtain data collected by temperature sensors and electromagnetic interference detectors on network nodes;

[0010] Clean and format the acquired data;

[0011] Use high-precision timestamps to record data collection time, and adjust the data collection frequency to multiple times per second;

[0012] Apache Flink is used to process streaming data in real time, and sliding windows and aggregation functions are set to analyze data instantly.

[0013] In one embodiment, the use of a high-precision timestamp to record the data collection time and adjusting the data collection frequency to multiple times per second includes:

[0014] Add a high-precision timestamp to each data collected by the temperature sensor and electromagnetic interference detector;

[0015] Adjust the data collection frequency to multiple times per second;

[0016] Sort the data by timestamp;

[0017] Align data sources with different frequencies;

[0018] In response to missing data or data of different frequencies being difficult to align completely, missing values ​​are supplemented based on interpolation or sampling techniques;

[0019] Correct timestamp precision based on missing values.

[0020] In one embodiment, the real-time processing of streaming data according to Apache Flink and setting of sliding windows and aggregation functions for real-time analysis of data include:

[0021] Build a stream data processing pipeline based on Apache Flink and configure data sources to receive data transmitted in real time by temperature sensors and electromagnetic interference detectors;

[0022] Dividing the data stream into specific sliding windows that cover the time range required for analysis;

[0023] In each sliding window, the data in the window is summarized based on the aggregation function to capture the key features of the network status;

[0024] In response to abnormal network status, an early warning signal is output and corresponding adjustment measures are triggered.

[0025] In one of the embodiments, the warning signal is used to push to users in the form of text messages and emails through the cloud platform, and is connected to the big data platform in real time through a data interface for management personnel to conduct on-site inspections and disposal.

[0026] In one embodiment, the identifying the network status based on the preprocessed network environment data and outputting the prediction result of the network status trend in the short future time includes:

[0027] Obtain preprocessed network environment data;

[0028] The network environment data is input into a CNN model to output a prediction result of the network status trend in a short period of time in the future. The CNN model is trained using normal network environment data samples and abnormal network environment data samples as training data.

[0029] In one embodiment, the method of adaptively adjusting network parameters using the SAC model according to the prediction result includes:

[0030] Receive the prediction results of the CNN model;

[0031] Construct an input vector based on the prediction results and the current network parameter configuration;

[0032] Input the input vector into the SAC model and output the action strategy, wherein the SAC model is trained based on a reinforcement learning algorithm using network data anomaly samples and corresponding action strategy samples as training data;

[0033] Based on the action strategy, adjust the network parameters.

[0034] The present invention also provides a parameter adjustment device for an adaptive network environment, comprising:

[0035] The acquisition module is used to obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status;

[0036] The identification module is used to identify the network status based on the pre-processed network environment data and output the network status trend prediction results in the short term in the future;

[0037] The adjustment module is used to adaptively adjust network parameters using the SAC model according to the prediction results.

[0038] The present invention also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the above-mentioned parameter adjustment methods for an adaptive network environment when executing the computer program.

[0039] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the method for adjusting parameters of an adaptive network environment as described in any one of the above is implemented.

[0040] The above-mentioned parameter adjustment method, device and electronic device for adaptive network environment, by collecting network environment data in real time and using advanced data analysis technology to predict network status, obtain the prediction results of network status trend in the short future time, and then facilitate real-time and accurate adjustment of network environment parameters according to dynamic changes in network status, so as to optimize network performance and improve network stability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A schematic diagram of a method for adjusting parameters in an adaptive network environment in an embodiment;

[0043] Figure 2 A schematic diagram of a data preprocessing process in one embodiment;

[0044] Figure 3 It is a schematic diagram of a process of using a high-precision timestamp to record data collection time in an embodiment;

[0045] Figure 4 It is a flowchart of real-time processing of streaming data according to Apache Flink in one embodiment;

[0046] Figure 5 A schematic diagram of a network status identification process in one embodiment;

[0047] Figure 6 A schematic diagram of a process of adaptively adjusting network parameters using a SAC model in an embodiment;

[0048] Figure 7 A schematic diagram of the structure of a parameter adjustment device for an adaptive network environment in an embodiment;

[0049] Figure 8 The figure is a diagram showing the internal structure of an electronic device according to an embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Combine the following Figure 1-Figure 8 The present invention describes a parameter adjustment method, device and electronic device for an adaptive network environment.

[0052] like Figure 1As shown, in one embodiment, a parameter adjustment method for an adaptive network environment includes the following steps:

[0053] Step S100, acquiring real-time data of the network environment, and performing standardized processing and real-time analysis on the data to capture key features of the network status.

[0054] Temperature sensors and electromagnetic interference detectors are deployed at key locations in the network environment. These sensors can continuously monitor temperature changes and electromagnetic interference levels in the environment. By establishing a wireless data transmission link, the sensors transmit the collected data in real time to the data processing center, so that real-time information flow about the network environment status can be obtained, laying a solid foundation for further data analysis and parameter adjustment.

[0055] Step S200, based on the pre-processed network environment data, identify the network status and output the network status trend prediction result in the short future time.

[0056] It is convenient to predict the network status trend in the short term based on the real-time monitored network status, and then adjust the network environment parameters in advance.

[0057] Step S300: Adaptively adjust network parameters using the SAC model according to the prediction results.

[0058] The SAC (Soft Actor-Critic) model is a reinforcement learning algorithm that is mainly used to solve control problems in continuous action spaces. The SAC algorithm combines the Actor-Critic method and the maximum entropy reinforcement learning theory, aiming to optimize the strategy while maximizing the entropy of the strategy to improve exploration capabilities and learning efficiency.

[0059] Based on the prediction results, the SAC model is used to adaptively adjust network parameters such as baud rate, frame length, TCP window size, etc., to optimize network performance.

[0060] This method for adjusting parameters of an adaptive network environment collects network environment data in real time and uses advanced data analysis technology to predict network status, thereby obtaining prediction results of network status trends in the short term in the future. This makes it convenient to adjust network environment parameters in real time and accurately according to dynamic changes in network status, so as to optimize network performance and improve network stability and efficiency.

[0061] In this embodiment, see Figure 2 , obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture the key characteristics of the network status, including the following steps:

[0062] Step S110, acquiring data collected by temperature sensors and electromagnetic interference detectors on network nodes.

[0063] By installing temperature sensors and electromagnetic interference detectors at locations where network equipment may be affected by environmental factors, and configuring wireless communication modules for the temperature sensors and electromagnetic interference detectors, real-time data transmission is ensured. Then the temperature sensors and electromagnetic interference detectors start working, monitoring environmental data in real time, and sending them to the data processing center through a preset data transmission protocol.

[0064] Among them, deploying temperature sensors and deploying electromagnetic interference detectors are the starting points for collecting data. Real-time data transmission ensures the real-time nature of the data, and data analysis and parameter adjustment are the next steps to use these data to optimize network performance.

[0065] Step S120, cleaning and formatting the acquired data.

[0066] The data processing center receives the data and performs preliminary cleaning and preprocessing to prepare for subsequent analysis and adjustment.

[0067] Step S130, using a high-precision timestamp to record the data collection time, and adjusting the data collection frequency to multiple times per second.

[0068] Each temperature sensor and electromagnetic interference detector will synchronously record a high-precision timestamp when collecting data, which provides an accurate time reference for subsequent data analysis. In addition, in order to improve the resolution of the data, the sampling frequency of the temperature sensor and electromagnetic interference detector is adjusted to ensure that it can collect data at a rate of multiple times per second. Such a collection frequency can capture rapid changes in the environment and provide a more refined data basis for subsequent network status analysis and parameter adjustment.

[0069] Step S140, real-time processing of streaming data is performed according to Apache Flink, and sliding windows and aggregation functions are set to perform real-time analysis of the data.

[0070] Apache Flink is an open source stream processing framework that is specifically designed to process real-time data streams and batch data. It can efficiently process massive amounts of data and provide low latency and high throughput processing capabilities. The working principle is as follows:

[0071] Stream processing: Data streams (unbounded streams) are continuously injected into Apache Flink, which processes the data in real time according to the defined business logic and window configuration.

[0072] Batch processing: Batch processing (bounded data set) tasks load data at one time and stop after the task is executed.

[0073] State management and fault tolerance: Apache Flink creates checkpoints regularly to save the status of each task. When a failure occurs, Apache Flink can resume execution from the most recent checkpoint.

[0074] Parallel execution: Apache Flink uses the task slots on the TaskManager to execute multiple subtasks in parallel to improve processing efficiency. Each TaskManager can execute multiple task instances, thereby achieving efficient use of the cluster.

[0075] Among them, TaskManager is the node that actually executes tasks in the Apache Flink cluster. Each TaskManager is responsible for executing one or more tasks.

[0076] By using Apache Flink to process streaming data in real time, real-time monitoring of network status can be achieved.

[0077] In this embodiment, see Figure 3 , using high-precision timestamps to record data collection time and adjusting the data collection frequency to multiple times per second, including the following steps:

[0078] Step S131, adding a high-precision timestamp to the data collected by the temperature sensor and the electromagnetic interference detector each time.

[0079] The timestamp can be accurate to the millisecond level, making it easy to accurately track data trends.

[0080] Step S132, adjusting the data collection frequency to multiple times per second.

[0081] By increasing the frequency of data collection, it is easier to capture subtle changes in the network environment.

[0082] Step S133, sorting the data according to the timestamp.

[0083] These data include timestamps and multiple environmental parameters. By sorting the data using timestamps, accurate environmental status analysis is facilitated.

[0084] Step S134, aligning data sources of different frequencies.

[0085] It is convenient to synchronize all data sources at each time point on the same timeline.

[0086] Step S135 , in response to missing data or difficulty in completely aligning data of different frequencies, supplement the missing values ​​using interpolation or sampling techniques.

[0087] When data is missing or data of different frequencies cannot be completely aligned, interpolation or sampling techniques are used to supplement the missing values ​​to maintain the integrity of the time series.

[0088] Step S136, correcting the timestamp accuracy based on the missing value.

[0089] Missing values ​​can represent interruptions or errors in data collection. Timestamps can be effectively corrected through missing value analysis and processing. By correcting the timestamp accuracy, clock drift or synchronization errors can be reduced to ensure the consistency of data timing.

[0090] In this embodiment, see Figure 4 , based on Apache Flink, real-time processing of streaming data, and setting sliding windows and aggregation functions for instant analysis of data, including the following steps:

[0091] Step S141, construct a stream data processing pipeline according to Apache Flink, and configure the data source to receive the data transmitted in real time by the temperature sensor and the electromagnetic interference detector.

[0092] By building a stream data processing pipeline through Apache Flink and configuring the data sources of temperature sensors and electromagnetic interference detectors, it is convenient to realize real-time data processing and low-latency response; data cleaning, aggregation and analysis; anomaly detection and automatic alarm; multi-source data fusion and in-depth analysis; data storage and subsequent analysis.

[0093] Step S142, dividing the data stream into specific sliding windows, where the sliding windows cover the time range required for analysis.

[0094] In the stream data processing pipeline, the data stream is divided into specific sliding windows. The size of these windows can be adjusted according to actual needs so that the network status can be analyzed at different time granularities. By defining the size and sliding step of the sliding window, it can be ensured that the window can cover the time range required for analysis.

[0095] Step S143, within each sliding window, the data within the window is aggregated based on an aggregation function to capture key features of the network status.

[0096] These aggregation functions can calculate statistical indicators such as average, maximum, and minimum values, thereby capturing the key characteristics of the network status. In this way, they can respond quickly to changes in the network environment and take appropriate measures to maintain optimal network performance.

[0097] Step S144, in response to the abnormal network status, output a warning signal and trigger corresponding adjustment measures.

[0098] It should be noted that the early warning signal is pushed to users in the form of text messages and emails through the cloud platform, and is connected to the big data platform in real time through the data interface for management personnel to conduct on-site inspections and disposal.

[0099] Specific application examples are as follows:

[0100] Assume that a sliding window of 1 second is set, and the aggregation function is to calculate the average signal strength within the window. When new data flows in, Apache Flink will process it like this:

[0101] 1. The data enters a sliding window, which contains all signal strength data collected in the last second.

[0102] 2. Apply the aggregation function to calculate the average of these data points.

[0103] 3. Output the average signal strength value and use it for network status monitoring.

[0104] 4. If the average signal strength is lower than the preset threshold, the system will automatically trigger adjustment measures for signal enhancement.

[0105] In this embodiment, see Figure 5 , based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future, including the following steps:

[0106] Step S210, obtaining pre-processed network environment data.

[0107] Before inputting the data into the CNN (Convolutional Neural Network) model, the data is first standardized to ensure that each feature value is on the same scale, which helps the learning and convergence of the CNN model. Data preprocessing also includes removing missing values ​​and outliers, and converting time series data into a format suitable for CNN processing.

[0108] Step S220, input the network environment data into the CNN model, and output the network status trend prediction result in the short term in the future. The CNN model is trained with normal network environment data samples and abnormal network environment data samples as training data.

[0109] In the present invention, a multi-layer convolutional neural network model is designed to effectively extract features from network status data. The model includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer. The convolutional layer is used to extract local features in the data; the pooling layer is used to reduce the dimension of the data while retaining important information; the fully connected layer is used to combine the features extracted by the convolutional layer and the pooling layer to facilitate the final prediction.

[0110] Feature extraction: Use convolutional and pooling layers to automatically learn complex feature representations from raw data.

[0111] Time Series Analysis: CNN models are able to process time series data because they can capture the local dependencies of the data in the time dimension.

[0112] Use the following formula to train the CNN model:

[0113]

[0114] Among them, L(θ) is the loss function, N is the number of training samples, and y i is the true value, f(x i ,θ) is the model prediction value and θ is the model parameter.

[0115] The CNN model is trained by minimizing the loss function and the gradient descent algorithm is used to update the CNN model parameters:

[0116]

[0117] Here, α is the learning rate.

[0118] Prediction: Once the CNN model is trained, it can be used to predict the network status trend in the short term.

[0119] The following are examples of specific implementations:

[0120] Assume that there is a set of continuous network status data, including indicators such as signal strength, bandwidth utilization, and latency.

[0121] Input this data into the trained CNN model.

[0122] The CNN model will output a prediction of the network status for a period of time in the future, for example, predicting whether bandwidth utilization will increase in the next minute.

[0123] In this way, we can monitor the network status in real time and take optimization measures in advance based on the prediction results. Here is an example:

[0124] If the CNN model predicts upcoming high bandwidth usage, network resources can be increased in advance to avoid possible performance degradation.

[0125] If it is predicted that the signal strength will drop, the location or parameters of the wireless access point can be adjusted in time to improve signal coverage.

[0126] The application of the above steps and algorithms can effectively predict the trend of network status and make adaptive adjustments accordingly to maintain stable network performance.

[0127] In this embodiment, see Figure 6 ,According to the prediction results, the SAC model is used to adaptively adjust the network parameters, including the following steps:

[0128] Step S310, receiving the prediction result of the CNN model.

[0129] Analyze the prediction results output by the CNN model, which include estimates of network status in the future, such as traffic change trends, signal strength fluctuations, etc.

[0130] Step S320, constructing an input vector according to the prediction result and the current network parameter configuration.

[0131] The current network parameter configuration includes baud rate, frame length, and TCP window size. The input vector represents the current state of the network and the expected future state.

[0132] Step S330, input the input vector into the SAC model and output the action strategy. The SAC model is based on a reinforcement learning algorithm and is trained using network data anomaly samples and corresponding action strategy samples as training data.

[0133] The SAC model is a reinforcement learning model based on deep learning, which can learn and output the optimal action strategy based on the current input vector.

[0134] Step S340: adjusting network parameters based on the action strategy.

[0135] According to the decision of the SAC model, the network parameters are adaptively adjusted as follows:

[0136] Baud rate adjustment: If the forecast results show that network traffic will increase, the SAC model may recommend increasing the baud rate to handle the larger amount of data.

[0137] Frame length adjustment: According to the changes in network traffic, the SAC model can adjust the frame length to optimize data transmission efficiency and network throughput.

[0138] TCP window size adjustment: In order to control network congestion, the SAC model dynamically adjusts the TCP window size according to the network status.

[0139] Specific implementation steps:

[0140] 1. Receive the prediction results of the CNN model and convert them into an input format recognizable by the SAC model.

[0141] 2. Send the input vector to the SAC model, and the model will output the corresponding action strategy.

[0142] 3. Based on the decision of the SAC model, perform one or more of the following operations:

[0143] If a higher baud rate is recommended, increase the baud rate of the current network device through the network management interface.

[0144] If it is recommended to adjust the frame length, modify the configuration of the network protocol stack to change the frame length of the data packet.

[0145] If TCP window size adjustment is recommended, the window size is adjusted by the network congestion control algorithm.

[0146] 4. Monitor the adjusted network performance indicators to verify the effectiveness of the SAC model decisions and make fine-tuning based on actual conditions.

[0147] Here are some example instructions:

[0148] Suppose the forecast shows that network traffic will increase by 30% in the next 5 minutes.

[0149] After receiving this prediction, the SAC model decides to increase the baud rate by 10% and the TCP window size by 20%.

[0150] The network administrator performs parameter adjustments based on the SAC model's recommendations.

[0151] After the adjustment, network performance indicators showed that network throughput increased by 25% while latency decreased by 15%, verifying the correctness of the SAC model decision.

[0152] In this way, network parameters can be automatically adjusted in real time according to changes in network status to maintain optimal network performance.

[0153] The parameter adjustment device for an adaptive network environment provided by the present invention is described below. The parameter adjustment device for an adaptive network environment described below and the parameter adjustment method for an adaptive network environment described above can be referred to each other.

[0154] like Figure 7 As shown, in one embodiment, a parameter adjustment device for an adaptive network environment includes an acquisition module 710 , an identification module 720 and an adjustment module 730 .

[0155] The acquisition module 710 is used to acquire real-time data of the network environment, and perform standardized processing and real-time analysis on the data to capture key features of the network status.

[0156] The identification module 720 is used to identify the network status based on the pre-processed network environment data and output the network status trend prediction result in the short future time.

[0157] The adjustment module 730 is used to adaptively adjust network parameters using the SAC model according to the prediction results.

[0158] In this embodiment, the acquisition module 710 is specifically used for:

[0159] Obtain data collected by temperature sensors and electromagnetic interference detectors on network nodes;

[0160] Clean and format the acquired data;

[0161] Use high-precision timestamps to record data collection time, and adjust the data collection frequency to multiple times per second;

[0162] Apache Flink is used to process streaming data in real time, and sliding windows and aggregation functions are set to analyze data instantly.

[0163] In this embodiment, a high-precision timestamp is used to record the data collection time, and the data collection frequency is adjusted to multiple times per second, specifically for:

[0164] Add a high-precision timestamp to each data collected by the temperature sensor and electromagnetic interference detector;

[0165] Adjust the data collection frequency to multiple times per second;

[0166] Sort the data by timestamp;

[0167] Align data sources with different frequencies;

[0168] In response to missing data or data of different frequencies being difficult to align completely, missing values ​​are supplemented based on interpolation or sampling techniques;

[0169] Correct timestamp precision based on missing values.

[0170] In this embodiment, Apache Flink is used to process streaming data in real time, and sliding windows and aggregation functions are set to analyze the data in real time, specifically for:

[0171] Build a stream data processing pipeline based on Apache Flink and configure data sources to receive data transmitted in real time by temperature sensors and electromagnetic interference detectors;

[0172] Divide the data stream into specific sliding windows that cover the time range required for analysis;

[0173] In each sliding window, the data in the window is summarized based on the aggregation function to capture the key features of the network status;

[0174] In response to abnormal network status, an early warning signal is output and corresponding adjustment measures are triggered.

[0175] In this embodiment, the identification module 720 is specifically used for:

[0176] Obtain preprocessed network environment data;

[0177] The network environment data is input into the CNN model, and the prediction result of the network status trend in the short term in the future is output. The CNN model is trained with normal network environment data samples and abnormal network environment data samples as training data.

[0178] In this embodiment, the adjustment module 730 is specifically used to:

[0179] Receive the prediction results of the CNN model;

[0180] Construct an input vector based on the prediction results and the current network parameter configuration;

[0181] Input the input vector into the SAC model and output the action strategy. The SAC model is based on the reinforcement learning algorithm and is trained with network data anomaly samples and corresponding action strategy samples as training data.

[0182] Based on the action strategy, adjust the network parameters.

[0183] The parameter adjustment device for the adaptive network environment collects network environment data in real time and uses advanced data analysis technology to predict the network status, obtains the prediction results of the network status trend in the short future, and then facilitates the real-time and accurate adjustment of network environment parameters according to the dynamic changes of the network status, so as to optimize network performance and improve network stability and efficiency.

[0184] Figure 8 An example of a physical structure diagram of an electronic device is shown. The electronic device may be a smart terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a parameter adjustment method for an adaptive network environment is implemented, and the method includes:

[0185] Obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status;

[0186] Based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future;

[0187] According to the prediction results, the SAC model is used to adaptively adjust the network parameters.

[0188] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the electronic device to which the scheme of the present invention is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0189] On the other hand, the present invention also provides a computer storage medium storing a computer program, which implements a parameter adjustment method for an adaptive network environment when the computer program is executed by a processor, the method comprising:

[0190] Obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status;

[0191] Based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future;

[0192] According to the prediction results, the SAC model is used to adaptively adjust the network parameters.

[0193] In another aspect, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, a method for adjusting parameters of an adaptive network environment is implemented, the method comprising:

[0194] Obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status;

[0195] Based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future;

[0196] According to the prediction results, the SAC model is used to adaptively adjust the network parameters.

[0197] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0198] By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0199] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A parameter adjustment method for an adaptive network environment, characterized in that: The method comprises: Obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status; Based on the pre-processed network environment data, identify the network status and output the network status trend prediction results in the short term in the future; According to the prediction results, the SAC model is used to adaptively adjust the network parameters.

2. The parameter adjustment method for an adaptive network environment according to claim 1, characterized in that: The real-time data of the network environment is obtained, and the data is standardized and analyzed in real time to capture the key characteristics of the network status, including: Obtain data collected by temperature sensors and electromagnetic interference detectors on network nodes; Clean and format the acquired data; Use high-precision timestamps to record data collection time, and adjust the data collection frequency to multiple times per second; Apache Flink is used to process streaming data in real time, and sliding windows and aggregation functions are set to analyze data instantly.

3. The method for adjusting parameters of an adaptive network environment according to claim 2, characterized in that: The method of using a high-precision timestamp to record the data collection time and adjusting the data collection frequency to multiple times per second includes: Add a high-precision timestamp to each data collected by the temperature sensor and electromagnetic interference detector; Adjust the data collection frequency to multiple times per second; Sort the data by timestamp; Align data sources with different frequencies; In response to missing data or data of different frequencies being difficult to align completely, missing values ​​are supplemented based on interpolation or sampling techniques; Correct timestamp precision based on missing values.

4. The parameter adjustment method for an adaptive network environment according to claim 3, characterized in that: The real-time processing of streaming data based on Apache Flink and setting of sliding windows and aggregation functions for real-time analysis of data include: Build a stream data processing pipeline based on Apache Flink and configure data sources to receive data transmitted in real time by temperature sensors and electromagnetic interference detectors; Dividing the data stream into specific sliding windows that cover the time range required for analysis; In each sliding window, the data in the window is summarized based on the aggregation function to capture the key features of the network status; In response to abnormal network status, an early warning signal is output and corresponding adjustment measures are triggered.

5. The method for adjusting parameters of an adaptive network environment according to claim 4, characterized in that: The warning signal is used to be pushed to users in the form of text messages and emails through the cloud platform, and is connected to the big data platform in real time through the data interface for management personnel to conduct on-site inspections and disposal.

6. The method for adjusting parameters of an adaptive network environment according to any one of claims 1 to 5, characterized in that: The method of identifying the network status based on the preprocessed network environment data and outputting the network status trend prediction result in the short term in the future includes: Obtain preprocessed network environment data; The network environment data is input into a CNN model to output a prediction result of the network status trend in a short period of time in the future. The CNN model is trained using normal network environment data samples and abnormal network environment data samples as training data.

7. The parameter adjustment method for an adaptive network environment according to claim 6, characterized in that: The method of adaptively adjusting network parameters using the SAC model according to the prediction results includes: Receive the prediction results of the CNN model; Construct an input vector based on the prediction results and the current network parameter configuration; Input the input vector into a SAC model and output an action strategy, wherein the SAC model is trained based on a reinforcement learning algorithm using network data anomaly samples and corresponding action strategy samples as training data; Based on the action strategy, adjust the network parameters.

8. A parameter adjustment device for an adaptive network environment, characterized in that: include: The acquisition module is used to obtain real-time data of the network environment, and perform standardized processing and instant analysis on the data to capture key features of the network status; The identification module is used to identify the network status based on the pre-processed network environment data and output the network status trend prediction results in the short term in the future; The adjustment module is used to adaptively adjust network parameters using the SAC model according to the prediction results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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