Network state prediction method and system for ad hoc network

Abstract

The invention provides a network state prediction method and system for an ad hoc network, and relates to the technical field of wireless communication, and the method comprises the steps: obtaining key parameters of the ad hoc network; according to the key parameters, a first key feature influencing the network condition of the ad hoc network is identified and obtained, and a first network state report of the ad hoc network is generated based on the first key feature; the first network state report comprises at least one of the following items: the current state of the ad hoc network, possible problems of the ad hoc network and possible influences of the ad hoc network; predicting the future network condition of the ad hoc network by using the target prediction network according to the first key feature and the first network state report; the future network condition of the ad hoc network comprises at least one of the following items: link bandwidth, communication delay and data packet loss rate. According to the method, the network state of the ad hoc network is effectively predicted.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for predicting the network status of an ad hoc network. Background Art

[0002] Ad Hoc Network, also known as Self-adaptive Network, is a network form that does not rely on pre-arranged fixed network facilities, but only needs to form a network through terminal devices. Ad Hoc Network can self-configure, self-organize and self-manage, and can quickly establish and dismantle the network. It has great advantages in flexibly handling certain special scenarios (such as temporary meetings, emergency rescue, battlefield communications, etc.).

[0003] However, the dynamic and self-organizing nature of ad hoc networks also brings a series of challenges, especially in terms of network management and performance optimization. Since nodes may move, join or leave the network, and due to various environmental factors (such as signal interference, physical obstacles, background noise, etc.), the topology of the network may change rapidly and unpredictably. This dynamic change may have a significant impact on the communication performance of the network, such as changes in link bandwidth, increased communication delay, and increased packet loss rate.

[0004] Traditional network management strategies, such as routing, flow control, and quality of service assurance, mainly make decisions based on the current network status information. However, due to the high dynamics and uncertainty of ad hoc networks, these decisions based on the current status often fail to achieve optimal network performance. For example, when an important node in the network moves or leaves the network, the previous route selection may become invalid immediately, resulting in communication interruption or performance degradation. In this case, if such changes can be predicted, decisions can be made in advance, such as selecting a new route, thereby avoiding or minimizing communication interruption and performance degradation.

[0005] Therefore, how to effectively predict the network status of ad hoc networks has become a technical problem that urgently needs to be solved in the industry. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for predicting the network status of an ad hoc network, which can effectively predict the network status of the ad hoc network.

[0007] In a first aspect, the present invention provides a method for predicting the network status of an ad hoc network, the method comprising the following steps: Obtain key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, sudden environmental interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; According to the key parameters, a first key feature that affects the network status of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Using the target prediction network, based on the first key feature and the first network status report, the future network status of the self-organizing network is predicted; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0008] According to a method for predicting the network status of an ad hoc network provided by the present invention, the method of identifying a first key feature that affects the network status of the ad hoc network according to the key parameter, and generating a first network status report of the ad hoc network based on the first key feature, comprising: Removing duplicate data from the key parameters to obtain first data; Identifying missing data in the first data, and correcting the missing data to obtain second data; Correcting erroneous data in the second data to obtain cleaned data; Normalizing the cleaned data to obtain third data; Extracting the first key feature based on the third data; Based on the first key feature, a first network status report of the ad hoc network is generated.

[0009] According to a network status prediction method for a self-organizing network provided by the present invention, the target prediction network is obtained after training an initial prediction network using a sample data set and a sample label, the sample data set includes multiple samples, any of the samples includes a second key feature and a second network status report of the self-organizing network, and the sample label includes the future network status corresponding to each of the samples; the target prediction network is a deep learning model.

[0010] According to a method for predicting network status of an ad hoc network provided by the present invention, the method further includes: Based on the future network status, determining a decision suggestion corresponding to the ad hoc network; the decision suggestion includes at least one of the following: route selection, data transmission rate adjustment and service priority determination; The determining, based on the future network status, a decision suggestion corresponding to the ad hoc network includes: Visually displaying the future network status to obtain a visualization result; Based on the visualization result, the decision suggestion is determined.

[0011] According to a method for predicting network status of an ad hoc network provided by the present invention, the method further includes: Comparing the future network status of the ad hoc network with the current network status of the ad hoc network to obtain a comparison result; Based on the comparison result, the model parameters of the target prediction network are optimized.

[0012] In a second aspect, the present invention further provides a network status prediction system for an ad hoc network, the network status prediction system for an ad hoc network comprising a data collection module, a data analysis module, a prediction module, a decision support module and a feedback module; wherein: A data collection module is used to obtain key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance; A data analysis module, configured to identify, according to the key parameters, a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; A prediction module, configured to predict a future network status of the ad hoc network using a target prediction network according to the first key feature and the first network status report; A decision support module, used to determine a decision suggestion corresponding to the ad hoc network based on the future network status; The feedback module is used to compare the future network status of the self-organizing network with the current network status of the self-organizing network to obtain a comparison result; based on the comparison result, the model parameters of the target prediction network are optimized.

[0013] In a third aspect, the present invention further provides a network status prediction device for an ad hoc network, the device comprising the following modules: An acquisition module is used to acquire key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; A prediction module, configured to identify, according to the key parameter, a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; The target prediction network is used to predict the future network status of the ad hoc network according to the first key feature and the first network status report.

[0014] In a fourth aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the network status prediction method for a self-organizing network as described in any one of the above-mentioned methods is implemented.

[0015] In a fifth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a network status prediction method for a self-organizing network as described in any one of the above.

[0016] In a sixth aspect, the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for predicting the network status of a self-organizing network as described in any one of the above described methods.

[0017] The network status prediction method and system of an ad hoc network provided by the present invention firstly obtain key parameters of the ad hoc network; wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, link conditions between nodes and communication distance; then, according to the key parameters, identify a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; wherein the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network and possible impacts on the ad hoc network; then, using the target prediction network, according to the first key feature and the first network status report, predict the future network status of the ad hoc network; the future network status of the ad hoc network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0018] The present invention can collect various key parameters in the ad hoc network in real time (such as the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance, etc.), and use advanced data analysis and target prediction networks to predict the future network conditions of the ad hoc network. Through real-time data collection and analysis and prediction, the communication effect of the ad hoc network can be predicted more accurately, thereby improving network efficiency and communication quality. The predicted future network conditions can provide real-time and accurate network performance predictions for network managers or commanders to assist them in making effective network management decisions. The present invention realizes the effective prediction of the network status of the ad hoc network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces 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 creative work.

[0020] Figure 1 This is one of the flow charts of the network status prediction method of the ad hoc network provided by the present invention.

[0021] Figure 2 It is a structural schematic diagram of the network status prediction system of the ad hoc network provided by the present invention.

[0022] Figure 3 This is the second flow chart of the network status prediction method of the ad hoc network provided by the present invention.

[0023] Figure 4 It is a schematic diagram of the effect of the network status report provided by the present invention.

[0024] Figure 5 This is the third flow chart of the network status prediction method of the ad hoc network provided by the present invention.

[0025] Figure 6 It is a structural schematic diagram of the network status prediction device of the ad hoc network provided by the present invention.

[0026] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0028] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally a class, and the number of objects is not limited. For example, the first node can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally represents that the front and back associated objects are in an "or" relationship.

[0029] Combine the following Figure 1-Figure 6 The present invention describes a method and system for predicting the network status of an ad hoc network.

[0030] Figure 1 FIG. 1 is one of the flow charts of the network status prediction method of the self-organizing network provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101, obtaining key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance; Specifically, it should be noted that the execution subject of the present invention is a network status prediction system of an ad hoc network, which is used to predict the future network status of the ad hoc network.

[0031] Figure 2 Schematic diagram of the network status prediction system of the ad hoc network provided by the present invention. Figure 2 As shown, the network status prediction system 200 of the ad hoc network mainly includes five modules: a data collection module 210, a data analysis module 220, a prediction module 230, a decision support module 240 and a feedback module.

[0032] In this embodiment, the parameters for obtaining the key parameters of the ad hoc network are collected in real time by the data collection module 210. Specifically, the data collection module 210 is connected to the communication interface of each network node to obtain and record the status information of the network node in real time, including the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance, etc. The data collection module can store these data in the form of a database for use by subsequent modules.

[0033] More specifically, Figure 3This is the second flow chart of the network status prediction method of the self-organizing network provided by the present invention, which shows the working principle and sub-module composition of the data collection module. The data collection module includes: access point number collection sub-module, environmental impact collection sub-module, burst interference collection sub-module, burst interference collection sub-module, channel status collection sub-module, background noise collection sub-module, link status collection sub-module, and communication distance collection sub-module; wherein, each sub-module is introduced as follows: a) Access Point Number Collection Submodule: This submodule monitors and records the number of access points in the network in real time. The number of access points directly affects the coverage and service quality of the network, so it is an important parameter.

[0034] b) Environmental impact collection submodule: This submodule is responsible for collecting and recording the impact of environmental factors on the network, including weather conditions, building obstructions, etc. These factors may affect the performance of the network.

[0035] c) Burst Interference Collection Submodule: This submodule is used to detect and record sudden interference in the network, such as sudden electromagnetic interference, hardware failure, etc. These sudden events may seriously affect the stability of the network.

[0036] d) Channel status collection submodule: This submodule is responsible for monitoring and recording the channel status in the network, including channel capacity, utilization, signal-to-noise ratio, etc.

[0037] e) Background noise collection submodule: This submodule is responsible for collecting and recording the background noise of the network to evaluate the signal-to-noise ratio of the network.

[0038] f) Link status collection submodule: This submodule monitors and records the link status between each node in the network, including the quality, stability and bandwidth of the link.

[0039] g) Communication distance collection submodule: This submodule is responsible for collecting and recording the communication distance of each node in the network, which is very important for evaluating the coverage and service quality of the network.

[0040] Each submodule can communicate with the network nodes through the network's hardware and software interfaces to acquire and record these parameters in real time. All collected data is stored in a unified database for subsequent modules to process and analyze.

[0041] By acquiring key parameters in real time, the key parameters can be stored to facilitate subsequent data calls.

[0042] Step 102: Identify a first key feature that affects the network status of the ad hoc network based on the key parameter, and generate a first network status report of the ad hoc network based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Specifically, after collecting the key parameters of the ad hoc network, the raw data may be preliminarily analyzed and processed, using the data analysis module 220 which is responsible for processing and analyzing the data collected by the data collection module.

[0043] It can be further divided into several steps, which include but are not limited to: Data cleaning, data normalization, feature extraction, data analysis, and report generation. The output of this module is a network status report and a set of key features, which will be passed to the prediction module 230 for prediction of future network status.

[0044] Exemplarily, based on characteristics such as the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance, it is necessary to determine the number of access users, calculate (or obtain) the signal-to-noise ratio of each link, and calculate the remaining bandwidth.

[0045] Shannon's formula can be used to obtain the channel capacity when the signal-to-noise ratio is known. In the case of environmental interference, the formula can be optimized to determine the available bandwidth and remaining bandwidth after knowing the interference power. The path loss model is used. The total available bandwidth can be calculated based on the communication distance.

[0046] Combining the above formulas and models, the network status analysis can be performed to obtain the total available bandwidth and the remaining bandwidth. The network topology can be obtained based on the link status between nodes in the ad hoc network itself.

[0047] Step 103: using the target prediction network, predicting the future network status of the ad hoc network according to the first key feature and the first network status report; the future network status of the ad hoc network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0048] Specifically, after obtaining a network status report and a set of key features, the target prediction network can be further used to predict the future network status of the ad hoc network according to the first key feature and the first network status report.

[0049] The future network status of the ad hoc network includes at least one of the following: link bandwidth, communication delay, and packet loss rate. Link bandwidth, communication delay, and packet loss rate are three key indicators of network performance, which have an important impact on the efficiency and reliability of network communication: 1. Link Bandwidth: Link bandwidth refers to the maximum data transmission rate of the communication link between two devices in the network, usually measured in bits per second (bps).

[0050] It determines the fastest speed at which data can be transmitted on a link and is an important parameter for measuring network transmission capacity. Link bandwidth is limited by the technical specifications of physical media (such as optical fiber, copper cable, etc.) and network equipment (such as routers, switches, etc.).

[0051] 2. Communication Latency: Communication latency refers to the time required for data to travel from the sender to the receiver, including propagation delay, transmission delay, processing delay, and queuing delay.

[0052] Propagation delay refers to the time it takes for a signal to propagate in a physical medium, which is related to the length of the medium and the speed at which the signal propagates. Transmission delay refers to the time it takes for data to be transmitted over a link, which is related to the link bandwidth and packet size. Processing delay refers to the time it takes for a network device to process a packet, such as looking up a routing table, classifying packets, etc. Queuing delay refers to the time a packet waits to be processed in a network device, which usually occurs when the network is congested.

[0053] 3. Packet Loss Rate: Packet loss rate refers to the ratio of the number of data packets lost during network transmission to the total number of data packets sent.

[0054] Packet loss may be caused by network congestion, link failure, equipment failure, signal interference, etc. High packet loss rate will lead to degraded communication quality and affect the integrity and reliability of data transmission. In real-time communications (such as voice and video calls), the impact of packet loss rate on user experience is particularly obvious.

[0055] The target prediction network is, for example, a deep learning model, which is a branch of machine learning and simulates the information processing method of the human brain by using a multi-layer neural network. By learning the characteristics of the self-organizing network, based on the first key characteristics and the first network status report, the target prediction network is used to predict the future network status of the self-organizing network.

[0056] This prediction algorithm can also be any prediction model applicable to time series data, such as autoregressive model, moving average model, deep learning model, etc. The prediction module outputs the predicted future network status, which may include link bandwidth, communication delay, packet loss rate, etc.

[0057] The method provided in this embodiment first obtains key parameters of an ad hoc network; wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, link conditions between nodes, and communication distance; then, based on the key parameters, a first key feature that affects the network condition of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; wherein the first network status report includes at least one of the following: the current state of the ad hoc network, possible problems of the ad hoc network, and possible impacts on the ad hoc network; then, using the target prediction network, based on the first key feature and the first network status report, the future network condition of the ad hoc network is predicted; the future network condition of the ad hoc network includes at least one of the following: link bandwidth, communication delay, and data packet loss rate.

[0058] The present invention can collect various key parameters in the ad hoc network in real time (such as the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance, etc.), and use advanced data analysis and target prediction networks to predict the future network conditions of the ad hoc network. Through real-time data collection and analysis and prediction, the communication effect of the ad hoc network can be predicted more accurately, thereby improving network efficiency and communication quality. The predicted future network conditions can provide real-time and accurate network performance predictions for network managers or commanders to assist them in making effective network management decisions. The present invention realizes the effective prediction of the network status of the ad hoc network.

[0059] According to a network status prediction method for an ad hoc network provided by the present invention, a first key feature affecting the network status of the ad hoc network is identified according to key parameters, and a first network status report of the ad hoc network is generated based on the first key feature, including: Removing duplicate data from key parameters to obtain first data; Identifying missing data in the first data and correcting the missing data to obtain second data; Correcting erroneous data in the second data to obtain cleaned data; Normalizing the cleaned data to obtain third data; Based on the third data, extracting a first key feature; Based on the first key feature, a first network status report of the ad hoc network is generated.

[0060] Specifically, in some embodiments, step 102 may be implemented according to the following steps, including: First, perform data cleaning on key parameters. This step is responsible for removing invalid, incomplete or erroneous data from the collected data to improve data quality. This may include removing duplicate data, processing missing data, correcting erroneous data, etc. The specific steps include: (1) Removing duplicate data from key parameters to obtain first data. (2) Identifying missing data in the first data and correcting the missing data to obtain second data. (3) Correcting erroneous data in the second data to obtain cleaned data.

[0061] Furthermore, the cleaned data is normalized to obtain the third data. Since the collected data may come from different measurement units or magnitudes, normalization is required to facilitate subsequent analysis. This may include maximum and minimum normalization, Z-score normalization, etc.

[0062] Further, based on the third data, the first key feature is extracted. This step is to identify and extract the key features that have the greatest impact on network performance. This may include the number of access points, channel status, link status, etc. This step is to analyze the state of the network based on the extracted features. This may include descriptive statistical analysis, correlation analysis, trend analysis, etc.

[0063] The goal of data analysis in the present invention is to obtain effective bandwidth. The effective bandwidth can be determined by the Shannon formula, and is also related to signal power and signal-to-noise ratio (i.e., background noise). The effective bandwidth may also be affected by the communication environment (e.g., channel spacing, communication distance), and the number of access points. More access users will occupy time slot resources and affect the overall bandwidth. All of the above factors may affect the remaining bandwidth, and the key factors vary depending on the communication environment (i.e., different characteristics). For example, when the signal-to-noise ratio is extremely low, even if other factors are good, a good network effect cannot be obtained.

[0064] Further, based on the first key feature, a first network status report of the ad hoc network is generated. Based on the above data analysis, a detailed network status report is generated. This report may include the current status of the network, possible problems, possible impacts, etc.

[0065] The network status report may contain information such as link bandwidth, communication delay, and packet loss rate. The process of generating a network status report is to calculate the link bandwidth based on the above data (number of access points...), and the communication delay (i.e., latency) and packet loss rate can be calculated based on the link status between nodes or the reported content. The network status report also includes network connectivity and topology.

[0066] Exemplarily, the network status report may be presented in the form of a graph. Figure 4 Schematic diagram of the effect of the network status report provided by the present invention, such as Figure 4As shown in the figure, the network topology, signal-to-noise ratio and node business ratio are included. Environmental impact, channel interference, communication distance and other contents can be presented in a table.

[0067] For example, Figure 5 FIG. 3 is a flow chart of the network status prediction method of the self-organizing network provided by the present invention, which shows the process of data analysis, such as Figure 5 As shown, the method includes: Step 501: Data cleaning.

[0068] Obtain the original data of the networking environment and perform data cleaning on it.

[0069] Step 502: Data normalization.

[0070] Step 503: Feature extraction.

[0071] Step 504: Data analysis.

[0072] Step 505: Generate a networking status report.

[0073] Step 506: Data prediction.

[0074] Data prediction results can provide decision-making support.

[0075] The method provided in this embodiment collects data of the ad hoc network, such as key parameters, and performs preliminary processing and analysis on the data, wherein the data analysis and processing includes steps such as data cleaning, data normalization, and feature extraction. In addition, the data analysis module can also identify key factors that may affect network performance and generate a network status report, which is convenient for subsequent prediction of future network conditions based on key factors that may affect network performance and the network status report, thereby achieving effective prediction of the ad hoc network.

[0076] According to a network status prediction method for a self-organizing network provided by the present invention, a target prediction network is obtained after training an initial prediction network using a sample data set and a sample label, the sample data set includes multiple samples, any sample includes a second key feature and a second network status report of the self-organizing network, and the sample label includes the future network status corresponding to each sample; the target prediction network is a deep learning model.

[0077] Specifically, in some embodiments, the target prediction network is a deep learning model, and the target prediction network is obtained by training an initial prediction network using a sample data set and a sample label. The sample data set includes multiple samples, and any sample includes a second key feature and a second network status report of the self-organizing network. The sample label includes the future network status corresponding to each sample.

[0078] Further, the specific implementation process of using the target prediction network to predict the future network status of the ad hoc network according to the first key feature and the first network status report includes: The target prediction network is a machine learning model, which is used to predict the future network status of the ad hoc network. Data needs to be collected in advance, including the network status report at a certain point in time and the subsequent possible transmission behavior and transmission results between nodes (such as Figure 4 The network status in the subsequent 33 may send broadband data to 0, such as video, resulting in freezes and communication interruption). The prediction algorithm can use machine learning algorithms. For example, RNN, etc. The training data source is the network status report and the data collected in the first step (as the first key feature), and the sequential data transmission behavior and transmission status (evaluated by latency, packet loss rate, and whether it is interrupted).

[0079] When predicting, for a trained model, the input data is the network status report and the data extracted in the first step, and the output data is the network data transmission behavior in the future (in terms of traffic, for example, an ad hoc network reduces 10M of available bandwidth) The factors (features) and structures included in the network status report and the future network status are the same, and the only difference is the internal data and the services that may be generated next (such as voice, video, short message, etc.).

[0080] In the method provided in this embodiment, the target prediction network is obtained by training the initial prediction network using the sample data set and the sample label, the sample data set includes multiple samples, any sample includes the second key feature and the second network status report of the self-organizing network, the sample label includes the future network status corresponding to each sample, the target prediction network is a deep learning model, and the target prediction network is used to predict the future network status of the self-organizing network according to the first key feature and the first network status report, thereby achieving effective prediction of the self-organizing network.

[0081] According to a method for predicting network status of an ad hoc network provided by the present invention, the method further includes: Determine a decision recommendation corresponding to the ad hoc network based on the future network status; the decision recommendation includes at least one of the following: route selection, data transmission rate adjustment and service priority determination; Based on the future network status, determine the decision recommendations for the ad hoc network, including: Visualize the future network status and obtain visualization results; Based on the visualization results, decision recommendations are determined.

[0082] Specifically, in some embodiments, the method further includes: Based on the future network status, determine the decision suggestions corresponding to the ad hoc network. The decision suggestions include at least one of the following: route selection, data transmission rate adjustment, and service priority determination. Specifically, the decision suggestions corresponding to the ad hoc network can be made from the following aspects: 1. Routing selection: The routing protocol must be able to respond quickly to changes in network topology, converge quickly when calculating routes, obtain effective routes, and try to avoid routing loops. In the routing selection process, nodes with slow local topology changes should be selected, and the number of link hops should be reduced by multiplying the node neighbor change rate. Considering the dynamic nature of ad hoc networks, routing algorithms should be able to dynamically adjust paths to adapt to environmental changes and node movement. In scenarios such as military communications, routing selection should support message content of different priorities to ensure extremely low latency for high-priority data.

[0083] 2. Data transmission rate adjustment: Ad hoc networks must have the ability to adaptively change their transmission rates so that they can flexibly provide appropriate transmission rates for a variety of services. Variable rate modulation methods such as variable rate quadrature amplitude modulation (VR-QAM) and variable spreading gain code division multiple access (VSG-CDMA) can dynamically adjust the transmission rate based on link bandwidth and communication delay. Adaptive coding modulation (such as ATCQAM) dynamically matches the channel by changing the code rate and modulation constellation to minimize energy and achieve high spectral efficiency.

[0084] 3. Service priority determination: In military communication scenarios, the content of messages to be sent is divided into several priorities according to the degree of urgency. For example, tactical instructions are usually time-sensitive, while physical environment information can tolerate slightly higher delays. In the design and simulation of distributed ad hoc network systems based on statistical priorities, the statistical priority multiple access protocol (SPMA) is adopted. Compared with the traditional MAC protocol, the highest priority data packet is always selected for transmission. In the case of high traffic volume, the access of low-priority packets is delayed or denied by comparing the load with the threshold, thereby ensuring the success rate of data packet transmission and the stability of the load.

[0085] Based on the above information, the decision-making recommendations for ad hoc networks should include: Routing selection: Use routing protocols that can quickly respond to changes in network topology, select nodes where local topology changes slowly, and dynamically adjust paths to adapt to environmental changes. Data transmission rate adjustment: Dynamically adjust the transmission rate based on link bandwidth and communication delay, and use adaptive modulation and coding modulation technology to adapt to different network conditions. Service priority determination: Determine the priority of different data packets based on the urgency of the message and the type of service, especially in high-load situations, give priority to the transmission of high-priority data.

[0086] Correspondingly, in some embodiments, the specific implementation process of determining the decision suggestion corresponding to the ad hoc network based on the future network status includes the following steps: The future network status is visualized to obtain visualization results; based on the visualization results, decision recommendations are determined. Among them, the decision-making assistance module provides a visualization of the prediction results for commanders to make decisions.

[0087] Exemplarily, determining the decision suggestion corresponding to the ad hoc network is implemented as follows: Assumptions Figure 4 In order to predict the future network status, since the network status of node 33 is poor, if it continues to move forward, it may cause communication interruption, so other nodes are needed to supplement it, such as adjusting the position of node 30 to improve its link quality, and moving node 7 to nodes 30 and 33 to assume the relay function to ensure the link quality and bandwidth of node 33.

[0088] The method provided in this embodiment includes at least one of the following: route selection, data transmission rate adjustment and service priority determination. First, the future network status is visualized to obtain a visualization result, and a decision recommendation is determined based on the visualization result. The future network status predicted by the present invention can provide a basis for network management decisions. Network managers or commanders can make decisions in advance based on the predicted network status, thereby improving network performance, improving service quality, and improving user experience.

[0089] According to a method for predicting network status of an ad hoc network provided by the present invention, the method further includes: Comparing the future network status of the ad hoc network with the current network status of the ad hoc network to obtain a comparison result; Based on the comparison results, the model parameters of the target prediction network are optimized.

[0090] Specifically, in some embodiments, the method further includes: The future network status of the ad hoc network is compared with the current network status of the ad hoc network to obtain a comparison result; based on the comparison result, the model parameters of the target prediction network are optimized. For example, a feedback module of the system is used to compare the actual network performance with the predicted network performance to optimize the prediction performance of the data analysis and prediction module.

[0091] The method provided in this embodiment can optimize the prediction performance of the data analysis and prediction module based on the predicted network performance.

[0092] The present invention also provides a network status prediction system for an ad hoc network. Figure 2As shown, the network status prediction system of the ad hoc network includes a data collection module 210, a data analysis module 220, a prediction module 230, a decision support module 240 and a feedback module 250; wherein, The data collection module 210 is used to obtain key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; The data analysis module 220 is used to identify a first key feature that affects the network status of the ad hoc network according to the key parameter, and generate a first network status report of the ad hoc network based on the first key feature; A prediction module 230, configured to predict a future network status of the ad hoc network using a target prediction network according to the first key feature and the first network status report; A decision support module 240, configured to determine a decision suggestion corresponding to the ad hoc network based on the future network status; The feedback module 250 is used to compare the future network status of the ad hoc network with the current network status of the ad hoc network to obtain a comparison result; based on the comparison result, optimize the model parameters of the target prediction network.

[0093] Specifically, the system described above is only one embodiment of the present invention, and the present invention may also have other embodiments. For example, the data collection module, the data analysis module, the prediction module and the decision support module may exist independently or may be integrated on one or more devices. The data collection module and the data analysis module may be integrated or separated. The prediction module may use various prediction algorithms, including but not limited to regression analysis, time series analysis, machine learning, etc. The decision support module may provide various decision suggestions, including but not limited to routing selection, traffic control, quality of service optimization, etc.

[0094] The data collection module 210 in the system provided in this embodiment is used to obtain key parameters of the self-organizing network; wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, link conditions between nodes and communication distance; the data analysis module 220 is used to identify the first key feature that affects the network status of the self-organizing network according to the key parameters, and generate a first network status report of the self-organizing network based on the first key feature; wherein the first network status report includes at least one of the following: the current status of the self-organizing network, possible problems of the self-organizing network and possible impacts on the self-organizing network; the prediction module 230 is used to use the target prediction network to predict the future network status of the self-organizing network according to the first key feature and the first network status report; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate; the decision-making auxiliary module 240 is used to determine the decision suggestion corresponding to the self-organizing network based on the future network status; the feedback module 250 is used to compare the future network status of the self-organizing network with the current network status of the self-organizing network to obtain a comparison result; based on the comparison result, the model parameters of the target prediction network are optimized.

[0095] The network status prediction system of the ad hoc network provided by the present invention can collect various key parameters in the ad hoc network in real time (such as the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance, etc.), and use advanced data analysis and target prediction networks to predict the future network status of the ad hoc network. Through real-time data collection and analysis and prediction, the communication effect of the ad hoc network can be predicted more accurately, thereby improving network efficiency and communication quality. The predicted future network status can provide real-time and accurate network performance prediction for network managers or commanders to assist them in making effective network management decisions. The present invention realizes the effective prediction of the network status of the ad hoc network.

[0096] The network status prediction device for an ad hoc network provided by the present invention is described below. The network status prediction device for an ad hoc network described below and the network status prediction method for an ad hoc network described above can be referred to each other.

[0097] Figure 6 is a schematic diagram of the structure of the network status prediction device for an ad hoc network provided by the present invention, such as Figure 6 As shown, the network status prediction device 600 of the ad hoc network includes the following modules: an acquisition module 610 and a prediction module 620; wherein: The acquisition module 610 is used to acquire key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; A prediction module 620, configured to identify, according to the key parameter, a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; The target prediction network is used to predict the future network status of the ad hoc network according to the first key feature and the first network status report.

[0098] The device provided in this embodiment includes an acquisition module 610 and a prediction module 620, wherein the acquisition module 610 is used to acquire key parameters of the self-organizing network; wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental burst interference, channel conditions, background noise conditions, link conditions between nodes and communication distance; the prediction module 620 is used to identify a first key feature that affects the network status of the self-organizing network according to the key parameters, and generate a first network status report of the self-organizing network based on the first key feature; wherein the first network status report includes at least one of the following: the current status of the self-organizing network, possible problems of the self-organizing network and possible impacts on the self-organizing network; then, using the target prediction network, the future network status of the self-organizing network is predicted according to the first key feature and the first network status report; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0099] The present invention can collect various key parameters in the ad hoc network in real time (such as the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance, etc.), and use advanced data analysis and target prediction networks to predict the future network conditions of the ad hoc network. Through real-time data collection and analysis and prediction, the communication effect of the ad hoc network can be predicted more accurately, thereby improving network efficiency and communication quality. The predicted future network conditions can provide real-time and accurate network performance predictions for network managers or commanders to assist them in making effective network management decisions. The present invention realizes the effective prediction of the network status of the ad hoc network.

[0100] According to a network status prediction device 600 of an ad hoc network provided by the present invention, the prediction module 620 is specifically used for: Removing duplicate data from the key parameters to obtain first data; Identifying missing data in the first data, and correcting the missing data to obtain second data; Correcting erroneous data in the second data to obtain cleaned data; Normalizing the cleaned data to obtain third data; Extracting the first key feature based on the third data; Based on the first key feature, a first network status report of the ad hoc network is generated.

[0101] According to a network status prediction device 600 for a self-organizing network provided by the present invention, the target prediction network is obtained after training the initial prediction network using a sample data set and a sample label, the sample data set includes multiple samples, any of the samples includes a second key feature and a second network status report of the self-organizing network, and the sample label includes the future network status corresponding to each of the samples; the target prediction network is a deep learning model.

[0102] According to a network status prediction device 600 of an ad hoc network provided by the present invention, the device further includes a decision-making auxiliary module; The decision support module is used for: Based on the future network status, determining a decision suggestion corresponding to the ad hoc network; the decision suggestion includes at least one of the following: route selection, data transmission rate adjustment and service priority determination; The determining, based on the future network status, a decision suggestion corresponding to the ad hoc network includes: Visually displaying the future network status to obtain a visualization result; Based on the visualization result, the decision suggestion is determined.

[0103] According to a network status prediction device 600 of an ad hoc network provided by the present invention, the device further includes a feedback module; The feedback module is used to: Comparing the future network status of the ad hoc network with the current network status of the ad hoc network to obtain a comparison result; Based on the comparison result, the model parameters of the target prediction network are optimized.

[0104] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the network state prediction method of the ad hoc network, and the method includes: Obtain key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, sudden environmental interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; According to the key parameters, a first key feature that affects the network status of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Using the target prediction network, based on the first key feature and the first network status report, the future network status of the self-organizing network is predicted; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0105] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0106] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the network state prediction method of the self-organizing network provided by the above methods, the method includes: Obtain key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, sudden environmental interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; According to the key parameters, a first key feature that affects the network status of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Using the target prediction network, based on the first key feature and the first network status report, the future network status of the self-organizing network is predicted; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0107] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the network state prediction method of the self-organizing network provided by the above methods, the method comprising: Obtain key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, sudden environmental interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; According to the key parameters, a first key feature that affects the network status of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Using the target prediction network, based on the first key feature and the first network status report, the future network status of the self-organizing network is predicted; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

[0108] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the network status of an ad hoc network, characterized in that: include: Obtain key parameters of the ad hoc network; the key parameters include at least one of the following: number of access points, environmental impact, sudden environmental interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; According to the key parameters, a first key feature that affects the network status of the ad hoc network is identified, and a first network status report of the ad hoc network is generated based on the first key feature; the first network status report includes at least one of the following: the current status of the ad hoc network, possible problems of the ad hoc network, and possible impacts of the ad hoc network; Using the target prediction network, based on the first key feature and the first network status report, the future network status of the self-organizing network is predicted; the future network status of the self-organizing network includes at least one of the following: link bandwidth, communication delay and data packet loss rate.

2. The network status prediction method of an ad hoc network according to claim 1, characterized in that: The step of identifying a first key feature that affects the network status of the ad hoc network according to the key parameter, and generating a first network status report of the ad hoc network based on the first key feature, comprises: Removing duplicate data from the key parameters to obtain first data; Identifying missing data in the first data, and correcting the missing data to obtain second data; Correcting erroneous data in the second data to obtain cleaned data; Normalizing the cleaned data to obtain third data; Extracting the first key feature based on the third data; Based on the first key feature, a first network status report of the ad hoc network is generated.

3. The network status prediction method of an ad hoc network according to claim 1, characterized in that: The target prediction network is obtained by training the initial prediction network using a sample data set and a sample label, the sample data set includes multiple samples, any of the samples includes a second key feature and a second network status report of the self-organizing network, and the sample label includes the future network status corresponding to each of the samples; the target prediction network is a deep learning model.

4. The method for predicting the network status of an ad hoc network according to any one of claims 1 to 3, characterized in that: The method further comprises: Based on the future network status, determining a decision suggestion corresponding to the ad hoc network; the decision suggestion includes at least one of the following: route selection, data transmission rate adjustment and service priority determination; The determining, based on the future network status, a decision suggestion corresponding to the ad hoc network includes: Visually displaying the future network status to obtain a visualization result; Based on the visualization result, the decision suggestion is determined.

5. The network status prediction method of an ad hoc network according to any one of claims 1 to 3, characterized in that: The method further comprises: Comparing the future network status of the ad hoc network with the current network status of the ad hoc network to obtain a comparison result; Based on the comparison result, the model parameters of the target prediction network are optimized.

6. A network status prediction system for an ad hoc network, characterized in that: The network status prediction system of the ad hoc network includes a data collection module, a data analysis module, a prediction module, a decision support module and a feedback module; wherein, A data collection module is used to obtain key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions and communication distance; A data analysis module, configured to identify, according to the key parameters, a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; A prediction module, configured to predict a future network status of the ad hoc network using a target prediction network according to the first key feature and the first network status report; A decision support module, used to determine a decision suggestion corresponding to the ad hoc network based on the future network status; The feedback module is used to compare the future network status of the self-organizing network with the current network status of the self-organizing network to obtain a comparison result; based on the comparison result, the model parameters of the target prediction network are optimized.

7. A network status prediction device for an ad hoc network, characterized in that: include: An acquisition module is used to acquire key parameters of the ad hoc network, wherein the key parameters include at least one of the following: the number of access points, environmental impact, environmental sudden interference, channel conditions, background noise conditions, inter-node link conditions, and communication distance; A prediction module, configured to identify, according to the key parameter, a first key feature that affects the network status of the ad hoc network, and generate a first network status report of the ad hoc network based on the first key feature; The target prediction network is used to predict the future network status of the ad hoc network according to the first key feature and the first network status report.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the network status prediction method for an ad hoc network as claimed in any one of claims 1 to 5 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the network status prediction method of an ad hoc network as claimed in any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the network status prediction method of an ad hoc network as claimed in any one of claims 1 to 5 is implemented.

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