CN117614833B

CN117614833B - Automatic regulating method and system for router signals

Info

Publication number: CN117614833B
Application number: CN202410091383.1A
Authority: CN
Inventors: 开军; 周武
Original assignee: Shenzhen Chuangling Zhilian Technology Co ltd
Current assignee: Shenzhen Chuangling Zhilian Technology Co ltd
Priority date: 2024-01-23
Filing date: 2024-01-23
Publication date: 2024-04-05
Anticipated expiration: 2044-01-23
Also published as: CN117614833A

Abstract

The present invention relates to the field of computer networks, and in particular, to a method and a system for automatically adjusting a router signal. The method comprises the following steps: acquiring router log data and performing self-adaptive resource allocation to acquire equipment resource allocation data; acquiring real-time signal strength data, and carrying out deflection path loss analysis according to equipment resource allocation data and the real-time signal strength data to acquire a path loss matrix; carrying out interference intensity prediction on the real-time signal intensity data according to the path loss matrix to obtain interference intensity data; constructing a network environment model according to the equipment resource allocation data, the interference intensity data and the path loss matrix; and performing optimal channel selection on the router log data through the network environment model to obtain channel adjustment data, and sending the channel adjustment data to a router management cloud platform to execute a channel adjustment task. The invention can realize the purpose of automatically adjusting the router signal in the dynamic network environment and improve the overall performance and user experience of the network.

Description

Automatic regulating method and system for router signals

Technical Field

The present invention relates to the field of computer networks, and in particular, to a method and a system for automatically adjusting a router signal.

Background

In modern network environments, routers are one of the key devices for data transmission, responsible for managing and forwarding data packets. However, in practical applications, there are many varying factors in the network, such as device density, signal interference, and network traffic, which may lead to instability and performance degradation of the router signals. Conventional router signal conditioning methods are typically based on fixed configuration parameters and cannot accommodate changes in real-time network environments. Therefore, there is a need for a method that can monitor network conditions in real time and automatically adjust router signals to optimize signal transmission quality and improve network performance.

Disclosure of Invention

Accordingly, the present invention is directed to a method and system for automatically adjusting a router signal, which solve at least one of the above-mentioned problems.

In order to achieve the above object, a method for automatically adjusting a router signal includes the following steps:

step S1: acquiring router log data, and performing self-adaptive resource allocation based on the router log data, so as to acquire equipment resource allocation data;

step S2: acquiring real-time signal strength data, and performing deflection path loss analysis according to equipment resource allocation data and the real-time signal strength data so as to acquire a path loss matrix;

Step S3: carrying out interference intensity prediction on the real-time signal intensity data according to the path loss matrix so as to obtain interference intensity data;

step S4: constructing a network environment model according to the equipment resource allocation data, the interference intensity data and the path loss matrix;

step S5: and performing optimal channel selection on the router log data through the network environment model so as to obtain channel adjustment data, and sending the channel adjustment data to a router management cloud platform to execute a channel adjustment task.

The invention can deeply understand the activity condition and resource allocation condition of the equipment in the network by acquiring the router log data. By analyzing these data, the system is able to obtain real-time device resource allocation data. This helps identify which devices in the network are using network resources, and their usage patterns. Such collection and analysis of data is useful for better understanding of network topology and load conditions. By acquiring real-time signal strength data, the system can know the signal strength distribution conditions of different areas in the network. And combining the equipment resource allocation data, carrying out path loss analysis and establishing a path loss matrix. The path loss matrix reflects the signal strength variations on the various paths in the network, which is critical for subsequent interference strength predictions and channel adjustments. Based on the path loss matrix, the system can predict the interference strength for various areas in the network. Such predictions help identify potential signal interference areas, which provide a basis for subsequent channel adjustment. By considering the interference intensity, the system can more intelligently adjust the channel, reduce the signal interference and improve the signal quality. The system integrates the equipment resource allocation data, the interference intensity data and the path loss matrix to construct a network environment model. The model may provide a comprehensive understanding of the network state, including device distribution, signal strength distribution, and possible sources of interference. The network environment model provides deeper information for the system for formulating more accurate channel adjustment strategies. Based on the constructed network environment model, the system can select an optimal channel in real-time monitoring. By analyzing the router log data and combining the model information, the system can intelligently adjust the frequency, power, etc. parameters of the router signals to optimize channel selection. Such a channel adjustment strategy helps to maximize signal transmission quality, reduce interference, and thus improve overall network performance.

Optionally, step S1 specifically includes:

step S11: acquiring router log data;

step S12: extracting features of the router log data so as to obtain routing table data, network port data and router disconnection data;

step S13: analyzing the network topology structure according to the network port data, thereby obtaining the network topology structure;

step S14: optimizing equipment resource allocation according to the network port data and the router disconnection data, so as to obtain optimized resource allocation data;

step S15: and carrying out parameterization description on the network topology structure based on the optimized resource allocation data so as to obtain the equipment resource allocation data.

By acquiring router log data, the system can acquire detailed records about router operation states, network activities and events. These data are critical to network troubleshooting, performance monitoring, and optimization. By analyzing the router log, potential problems, anomalies, and the like can be detected, providing necessary information for subsequent optimization. By extracting and analyzing the router log, the system can obtain routing table data, network port data and router disconnection data. The routing table data contains routing information about the various devices in the network, the network port data provides information about the router port status and usage, and the disconnection data contains the connection status between the devices. These extracted data provide the basis for network topology and device connectivity for subsequent steps. Based on the extracted network port data, the system may perform network topology analysis. This includes the connection between the devices, the formation of the topology, and the layout of the various parts of the network. By knowing the network topology, the system can better understand the structure of the network, helping to find potential performance bottlenecks and optimize space. Based on the network port data and the disconnection data, the system can optimize the equipment resource allocation. This may include adjusting router port configuration, reassigning IP addresses, optimizing connections between devices, and the like. By optimizing the resource allocation, the system can increase the efficiency of the network, reduce congestion, and improve overall performance. By incorporating the optimized resource allocation data into the network topology, the system can be parameterized. This means that the system can describe the structure and performance of the network in a parameterized manner, so that subsequent monitoring, management and adjustment are more convenient. Such descriptions help to maintain the optimal state of the network and make further analysis and decisions.

Optionally, step S13 specifically includes:

step S131: extracting the characteristics of the network port data so as to obtain the port data of the core router, the equipment MAC identification data and the port data of the switch;

step S132: carrying out port logic connection on the equipment MAC identification data according to the port data of the switch so as to obtain equipment connection data;

step S133: performing topology map conversion based on the equipment connection data, so as to obtain a primary network topology structure;

step S134: and carrying out important node identification on the preliminary network topology structure according to the port data of the core router and the port data of the switch, thereby obtaining the network topology structure.

The invention can identify and record the port information of the core router by extracting the characteristics related to the core router in the network port data. This is critical to determining critical devices and paths in the network. Extracting device MAC identification data helps identify and identify devices in the network. The MAC address is a unique identification of the device, so that this data can be used to track the location, connection and activity of the device. By extracting switch-related features, the system can obtain information about the switch port status and usage. This helps to understand the physical connections of the devices in the network. By logically connecting ports to device MAC identification data according to switch port data, the system can establish a logical connection relationship between devices. Associating device MAC identification data with switch port data can determine through which port a device is connected to a switch, thereby forming a logical connection between devices. This helps build a more accurate network topology. The device connection data provides a connection relationship between devices, and by converting these data into a topology, the system can more clearly represent the network structure. The generation of the topology map facilitates visualization of the network structure, helping to find potential problems and optimize space. The system identifies important nodes in the network through the core router port data and the switch port data. These nodes may be key hubs of the network, having a significant impact on overall performance and stability. Identifying these important nodes facilitates more targeted network optimization and management.

Optionally, step S14 specifically includes:

step S141: calculating network port data and router connection and disconnection data through a connection and disconnection coefficient calculation formula, so as to obtain a connection and disconnection coefficient;

step S142: classifying and calculating the network port data according to the disconnection coefficient, so as to obtain high-frequency use equipment data and low-frequency use equipment data;

step S143: acquiring historical resource allocation data through a router management cloud platform;

step S144: carrying out highest allocation resource calculation on the historical resource allocation data according to the high-frequency use equipment data, thereby obtaining the high-frequency use equipment resource data;

step S145: performing minimum allocation resource calculation on the historical resource allocation data according to the low-frequency use equipment data, so as to obtain the low-frequency use equipment resource data;

step S146: and merging the historical resource allocation data, the high-frequency use equipment resource data and the low-frequency use equipment resource data, thereby obtaining the optimized resource allocation data.

The break-up coefficient in the present invention is typically used to measure the stability and importance of nodes or connections in a network. By calculating the join-break coefficients, key nodes or connections in the network can be identified, which is helpful in understanding the stability and vulnerability of the network. The network port data can be classified according to the disconnection coefficient to distinguish high-frequency use equipment from low-frequency use equipment. This helps to determine which devices or ports are used more frequently in the network, providing a key clue to resource allocation. Acquiring historical resource allocation data can provide a detailed record of past resource usage. Such data can be used to analyze past patterns of resource usage, help predict future demands and optimize resource allocation. By combining the high frequency usage devices with the historical resource allocation data, the highest resource demand for the frequently used devices can be determined. This helps to ensure that the network has adequate resource allocation for the high frequency usage devices, improving network efficiency and performance. Determining the minimum resource requirement of the low frequency usage device may avoid wasting resources. By identifying the resource requirements of the low frequency usage devices, resources can be effectively saved on these devices, and more resources can be used for the high demand devices, thereby optimizing overall resource utilization. Integrating data from different sources can provide a more comprehensive and comprehensive view to formulate resource allocation policies. By combining the historical allocation data and the resource requirements of the high-frequency and low-frequency equipment, a more targeted and optimized resource allocation scheme can be formulated, and the network resource utilization efficiency and performance are improved.

Optionally, the specific formula of the break coefficient calculation in step S141 is:

；

wherein,for the break coefficient, ++>For observing time, < >>For input port power, +.>In order to output the port power,for the link impedance +.>For inputting data quantity->For outputting data quantity->For packet loss rate, < > for>Is the base of natural logarithms.

The invention constructs a joint breaking coefficient calculation formula for calculating the acid of network port data and router joint breaking data. The formula fully considers influencing the co-break coefficientIs +.>Input port power +.>Output port power->Link impedance->Input data volume +.>Output data volume->Packet loss Rate->Base of natural logarithm->A functional relationship is formed:

；

wherein,this part represents the observation time +.>Is applied to +.>And (3) upper part. This involves analysis of the dynamic changes in input power, output power and link impedance, and the effect of these changes on the break-even coefficient. />This part represents the observation time +.>Is applied to>And (3) upper part. This reflects the effect of the change in packet loss rate, amount of input data, and amount of output data on the disconnection coefficient in the observation time. In the art, the interruption factor is generally calculated by adopting a technical means such as a flow analysis tool, a network performance monitoring tool and the like. The method can obtain the co-fracture coefficient more accurately by adopting the co-fracture coefficient calculation formula provided by the invention.

Optionally, step S2 specifically includes:

step S21: acquiring real-time signal intensity data;

step S22: acquiring antenna deflection data of the router, thereby obtaining the antenna deflection data;

step S23: calculating deflection influence according to the antenna deflection data and the real-time signal strength data, so as to obtain deflection influence;

step S24: performing path loss analysis according to the deviation influence degree, the equipment resource allocation data and the real-time signal strength data to obtain path loss data, wherein the path loss data comprises optimized path loss data and historical path loss data;

step S25: constructing a signal propagation model according to the path loss data and the real-time signal strength data;

step S26: a path loss matrix is constructed based on the signal propagation model and the network topology.

The real-time signal strength data in the present invention reflects the signal quality of different areas in the current network. By acquiring these data, the strength of the signals in the network can be known, thereby helping to identify possible signal coverage problems or signal congestion conditions. The antenna bias data provides information about the router antenna orientation. This is important for understanding the signal coverage area, optimizing the signal propagation path, and solving potential signal occlusion problems. The bias influence level calculation may help determine the degree of influence of the antenna orientation on the signal strength. This provides valuable information for determining the direction of antenna adjustment or optimization to improve signal quality and network performance. Path loss analysis combines various aspects of antenna orientation, signal strength, and device resource allocation to quantify the loss experienced by a signal as it propagates through a network. This helps to optimize the signal propagation path, reduce signal loss, and improve network availability and performance. By constructing a signal propagation model, the propagation behavior of a signal in a network can be more accurately simulated. This helps predict signal coverage, identify possible sources of signal interference, and formulate optimization strategies. Constructing the path loss matrix combines the signal propagation model and the network topology, providing a comprehensive view for quantifying signal propagation quality between different devices. This helps to better understand the propagation of signals in the network and provides guidance for further network optimization.

Optionally, step S23 specifically includes:

step S231: extracting characteristics according to the antenna deflection data, so as to obtain antenna signal transmission angle data and antenna pointing data;

step S232: carrying out space feature extraction on the real-time signal intensity data according to the antenna pointing data so as to obtain real-time signal space feature data;

step S233: real-time signal offset analysis is carried out according to the antenna signal transmission angle data and the real-time signal space characteristic data, so that real-time signal offset data are obtained;

step S234: performing offset signal fluctuation simulation on the real-time signal intensity data based on the real-time signal offset data, thereby obtaining offset fluctuation simulation data;

step S235: and calculating the deviation influence degree of the deviation fluctuation simulation data and the antenna deviation data through a deviation influence degree formula, so as to obtain the deviation influence degree.

According to the invention, through extracting the characteristics of the antenna deflection data, the transmission angle data of the signal in space is obtained. This helps to understand the directionality of the signal transmission path and provides the basis information for subsequent signal analysis. And acquiring antenna pointing data, namely the current pointing direction of the antenna, by utilizing a feature extraction technology. This is critical for analysing the direction of origin of the signal and determining the relationship of the antenna pointing to the signal transmission path. And carrying out space feature extraction on the real-time signal strength data by utilizing the antenna pointing data. This helps to capture the distribution pattern of the signal in space, providing finer information for subsequent analysis. And carrying out real-time signal offset analysis by combining the antenna signal transmission angle data and the real-time signal space characteristic data. This helps to understand whether the signal transmission path is offset from the ideal path, thereby evaluating the transmission quality and reliability of the signal. And performing offset signal fluctuation simulation on the real-time signal strength data by using the real-time signal offset data. This helps to simulate the signal strength variations in different directions, providing an understanding of signal fluctuations. And calculating the bias influence degree by combining the bias fluctuation simulation data and the antenna bias data through a bias influence degree formula. This helps to quantify the extent to which signal offset in different directions affects signal quality, providing a reference for optimizing antenna pointing.

Optionally, the bias influence formula in step S234 is specifically:

；

wherein,for biasing influence, let us->Is the base of natural logarithm, +.>For the fluctuation degree coefficient>For the frequency of the wave-motion,for periods of fluctuation>For the directional deflection coefficient of the antenna，/>For signal propagation direction coefficient, +.>Is an antenna environment parameter.

The invention constructs a deflection influence formula for calculating the deflection influence degree of the passive fluctuation simulation data and the antenna deflection data. The formula fully considers the influence deviation influence degreeBase of natural logarithm of>Fluctuation degree coefficient->Frequency of fluctuation->Period of fluctuation->Direction deviation coefficient of antenna->Signal propagation direction coefficient->Antenna environmental parameter->A functional relationship is formed:

；

wherein,part of->The square of (2) plus 1 takes the logarithm of the base 2 to represent a logarithmic transformation for processing the signal strength. />Is to->An exponential transformation, representing the relationship between signal strength and frequency of fluctuation. />For->And->And (3) performing complex operation, and taking a cube root to represent the complex influence of the antenna direction deflection on the signal. />For->And carrying out logarithmic operation and taking the reciprocal thereof. This represents the influence of the period of the fluctuation on the signal. Representing the effect of the antenna environment parameters on the signal. The formula comprehensively considers the influence of a plurality of parameters in the signal strength by carrying out complex mathematical operation on a plurality of parameters of the passive fluctuation simulation data and the antenna deflection data, thereby calculating a comprehensive deflection influence degree. This comprehensive analysis can be used to more fully understand the characteristics of the signal under different conditions. In the art, the bias influence degree is generally calculated by adopting technical means such as ray tracing, machine learning and the like. By adopting the deflection influence degree formula provided by the invention, the deflection influence degree can be obtained more accurately.

Optionally, step S3 specifically includes:

step S31: extracting signal characteristics of the real-time signal intensity data to obtain signal characteristic data;

step S32: extracting interference characteristics and directivity characteristics of the path loss matrix, so as to obtain interference characteristic data and directivity characteristic data;

step S33: acquiring historical signal strength data, and carrying out data combination on the historical signal strength data and the historical path loss data so as to obtain modeling data;

step S34: constructing an interference intensity prediction model based on the modeling data, the interference characteristic data, the directivity characteristic data and the signal characteristic data;

Step S35: and predicting the interference intensity of the real-time signal intensity data and the path loss matrix through an interference intensity prediction model, so as to obtain the interference intensity data.

The invention can capture key characteristics of the real-time signal, such as the variation trend of the signal intensity, the frequency spectrum characteristics and the like through signal characteristic extraction. This helps to understand the dynamic changes in the signals in the network and provides important input data for subsequent modeling. The acquisition of signal characteristic data may be used to analyze signal quality, detect potential interference, and optimize signal transmission performance. Interference feature extraction and directivity feature extraction may help identify sources of interference that may be present in the network as well as directivity features of signal propagation. This is critical to understanding interference conditions in the network, evaluating the stability of signals, and optimizing the network configuration. These characteristic data help to improve understanding of the signal propagation environment and thereby better predict potential signal interference. The acquisition of historical signal strength data and the merging of historical path loss data provides more comprehensive modeling data. This may help identify long-term trends and periodic variations, thereby improving the accuracy of the predictive model. The comprehensive utilization of modeling data helps to more fully understand the evolution of network performance and provide more reliable inputs to the predictive model. The interference intensity prediction model is constructed based on a variety of data sources, including real-time signal characteristics, historical data, and interference and directionality characteristics. This allows the model to more fully take into account various influencing factors, improving the ability to accurately predict the interference strength. The model can be used for monitoring the interference level in real time and helping a network manager to take interference suppression measures in time. Interference levels in the network can be predicted in real time by modeling interference intensity predictions. This helps network administrators to identify and handle interference in time, maintaining network performance. The method can also support the optimal configuration of network resources to reduce the adverse effect of interference on communication quality.

Optionally, the present specification further provides an automatic adjustment system of a router signal, for performing an automatic adjustment method of a router signal as described above, the automatic adjustment system of a router signal comprising:

the resource allocation optimization module is used for acquiring router log data and performing self-adaptive resource allocation based on the router log data so as to acquire equipment resource allocation data;

the path loss analysis module is used for acquiring real-time signal intensity data, and carrying out deflection path loss analysis according to equipment resource allocation data and the real-time signal intensity data so as to acquire a path loss matrix;

the interference intensity prediction module is used for predicting the interference intensity of the real-time signal intensity data according to the path loss matrix so as to obtain the interference intensity data;

the model construction module is used for constructing a network environment model according to the equipment resource allocation data, the interference intensity data and the path loss matrix;

and the channel analysis module is used for carrying out optimal channel selection on the router log data through the network environment model so as to obtain channel adjustment data, and sending the channel adjustment data to the router management cloud platform so as to execute the channel adjustment task.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a flow chart illustrating steps of a method for automatically adjusting a router signal according to the present invention;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above object, referring to fig. 1 to 2, the present invention provides an automatic router signal adjustment method, which includes the following steps:

in this embodiment, the router log data is obtained through a router interface or a remote connection mode, and includes information such as device connection, data transmission, and network status. The logs are parsed and analyzed to extract key information such as device identification, bandwidth utilization, connection duration, etc. And carrying out self-adaptive resource allocation through the data, determining the bandwidth allocation situation among the devices, and generating device resource allocation data, wherein the data is used for constructing a network environment model in the subsequent steps.

in this embodiment, real-time signal strength data, including signal strength between devices, is obtained by a wireless sensor or related devices. And (3) carrying out path loss analysis by combining the equipment resource allocation data obtained in the previous step, and generating a path loss matrix by considering the relative positions among the equipment, signal attenuation and other factors. The matrix reflects the signal transmission loss condition among different devices in the network and provides a basis for subsequent interference intensity prediction.

in this embodiment, the path loss matrix and the real-time signal strength data are used to predict the interference strength. This may be accomplished by mathematical models, machine learning algorithms, or other predictive methods. The resulting interference strength data reflects the degree of signal interference that may exist between different devices in the current network environment.

In this embodiment, data such as equipment resource allocation, path loss, interference strength and the like are comprehensively considered, and a network environment model is constructed. This model may employ statistical models, machine learning models, or simulation tools to model the network environment. For example, in combination with interference strength data and a path loss matrix, a model is created that represents the performance of communications between devices. The purpose of the model is to comprehensively reflect the influence of various factors in the network on signal transmission and network performance.

In the embodiment, the built network environment model is utilized to analyze the router log data, so that the selection of the optimal channel is realized. This includes consideration of factors such as device resource allocation, path loss, interference strength, etc., to optimize channel selection and improve network performance. And finally, sending the channel adjustment data to a router management cloud platform, triggering a corresponding channel adjustment task, and ensuring that the network can keep stable and efficient communication under different conditions.

Optionally, step S1 specifically includes:

step S11: acquiring router log data;

the router system is accessed in this embodiment to acquire log data. This may be accomplished through a management interface of the login router or using a specific command line interface. Once the access router is successful, commands may be executed to derive log files, which typically contain information about system events, routing table changes, port status, etc. For example, for a Cisco router, the command show logging may be used to obtain log information and optionally save it to a local file.

in this embodiment, by analyzing the update information of the routing table in the log, the routing information between different routers, including the routing destination, the next hop, etc., is identified and extracted. Information in the log about the status of the network port, such as port connection status, rate, transmission errors, etc., is analyzed. This can provide real-time information about the router port. The log is parsed to determine possible connection interruption or failure events, identifying loss of connection conditions between routers or network devices.

topology analysis is performed to identify physical connections and topology between network devices based on network port data in this embodiment. This may be accomplished by analyzing the physical connections between routers and port states. By identifying the connections between routers and the network topology, a block diagram of the entire network can be built.

in this embodiment, the network port data and the disconnection data are used to optimize the device resource allocation. This may include reassigning port connections, optimizing routing tables, adjusting connections between routers, etc. to improve network performance and resource utilization. This step typically requires decisions to be made based on the actual needs and operation of the network.

In this embodiment, the network topology structure is parameterized based on the optimized resource allocation data. This may include creating a data model describing routers, ports, and relationships between them. This model may be a graphical, mathematical, or specific format data description that facilitates better understanding and management of the network topology.

Optionally, step S13 specifically includes:

in this embodiment, key features are extracted from network port data to obtain core router port data, device MAC identification data, and switch port data. For core router port data, information identifying a particular interface on the router may be used, including interface status, data transfer rate, etc. The device MAC identification data involves parsing the network packet, extracting the source and destination MAC addresses, and associating them with the particular device. The switch port data contains information about the switch port status, connectivity, and rate. For example, port state and rate information of the core router may be obtained by analyzing SNMP (Simple Network Management Protocol) data of the network device, and MAC address in the data packet may be extracted by a packet capturing technique, so as to obtain MAC identification data of the device, and at the same time, port state information may be obtained through a management interface of the switch.

In this embodiment, the logical connection between the device MAC identification data and the port is established by using the switch port data. This means that the port table of the switch is analyzed, associating MAC addresses with the corresponding ports to determine which port the device is connected to. This step helps establish a physical connection relationship between the devices. The logical connection relationship of the device to the switch port is determined, for example, by parsing the switch's CAM table (Content Addressable Memory) or by SNMP lookup, associating the MAC address with a particular port.

in this embodiment, the device connection data is used to perform topology conversion. Devices and their connection relationships are represented as a graph structure, where devices are nodes and connections are edges. This may be achieved by using graph theory algorithms, such as Depth First Search (DFS) or Breadth First Search (BFS), to establish a preliminary topology of the network. For example, a graph structure is constructed from device connection data, where each device is a node of the graph and the connection is an edge of the graph. And establishing a connection relation between the devices through the DFS or BFS traversal diagram.

In this embodiment, the core router port data and the switch port data are used to identify the important nodes of the preliminary network topology. This may include determining core routers, critical switches, or other important network nodes. This helps identify backbone nodes throughout the network topology, further optimizing the network architecture. For example, critical nodes, such as core routers and critical switches, are identified and determined by analyzing port status, rate, and switch location information of the core routers.

Optionally, step S14 specifically includes:

in the embodiment, a joint-and-break coefficient calculation formula is adopted to process network port data and router joint-and-break data. The break-even coefficient may be an indicator of the tightness of the connection of the devices, typically obtained by calculating the strength and frequency of the connection between the devices. This includes taking into account the frequency of communication between the devices and the durability of the connection.

in this embodiment, the devices are classified into high-frequency use devices and low-frequency use devices according to the calculated joint breaking coefficients. This step aims at identifying active devices and relatively inactive devices in the network, providing a basis for subsequent resource allocation. A threshold is set, for example, devices with a coefficient of disconnection above the threshold are classified as high frequency use devices, and devices with a coefficient of disconnection below the threshold are classified as low frequency use devices. In this way, the devices can be divided into two main categories.

in this embodiment, historical resource allocation data is obtained from a router management cloud platform. This may include data related to resource allocation for each device's bandwidth usage, CPU utilization, memory usage, etc. Historical resource allocation data is obtained, for example, by interacting with the router management cloud platform's API or by log data, in order to learn about the resources consumed by the network device over time.

in this embodiment, the historical resource allocation data is analyzed by using the high-frequency usage device data obtained previously, and the highest resource requirement of the high-frequency usage device in the historical time period is calculated. For example, by analyzing historical resource allocation data of high-frequency usage devices (common devices), the highest demands of each device for resources such as bandwidth, CPU, and memory in the past time are determined.

In this embodiment, the historical resource allocation data is analyzed according to the low-frequency usage device data, and the minimum resource requirement of the low-frequency usage device (streaming device) in the historical time period is calculated. For example, by analyzing historical resource allocation data for low frequency usage devices, the minimum demands of each device for bandwidth, CPU, memory, etc. resources over time are determined.

In this embodiment, the historical resource allocation data is combined with the resource data of the high-frequency and low-frequency usage devices to obtain comprehensive optimized resource allocation data. This provides a basis for a reasonable allocation of network resources to meet the different demands of the device.

；

Optionally, step S2 specifically includes:

step S21: acquiring real-time signal intensity data;

the real-time signal strength data is acquired by a network device or sensor in this embodiment, which may involve Wi-Fi, bluetooth, or other communication protocols. These data reflect the current communication signal strength between the devices. For example, using a specialized signal strength monitoring device, such as a Wi-Fi scanner or sensor, real-time signal strength data between devices is periodically collected and stored in a central database for later analysis.

in this embodiment, the antenna orientation of the router is measured and recorded periodically using an antenna orientation detection tool or device. These antenna bias data are combined with the geographical location information of the router to obtain more comprehensive information.

in this embodiment, the bias effect is calculated by combining the real-time signal strength data and the antenna bias data of the router, and the influence of the signal propagation direction between the devices is considered by the bias effect. The bias effect is calculated by taking into account the antenna orientation and the real-time signal strength between the devices. This may take a method similar to weighted averaging, making the contribution to the signal strength towards similar devices larger.

in this embodiment, the path loss analysis is performed using bias influence, device resource allocation data, and real-time signal strength data, and detailed information including optimization and historical path loss data is obtained. For example, path loss is calculated by taking into account bias impact, device resource requirements, and real-time signal strength. This may use a conventional path loss model, taking into account the distance the signal propagates and the effect of the obstruction.

in this embodiment, a signal propagation model is constructed using the path loss data and the real-time signal strength data. This model can help understand the propagation law of the signal in the network. For example, a signal propagation model is constructed by performing regression analysis or machine learning on the path loss data. This model can predict the propagation effect of the signal under different environmental conditions.

In this embodiment, a path loss matrix is constructed by using a signal propagation model and a network topology, where the path loss matrix reflects the signal propagation loss between devices in the network. For example, a path loss matrix is constructed based on a signal propagation model in combination with a network topology. This can be achieved by mathematical modeling or graph theory methods, ensuring that the elements in the matrix reflect the path loss conditions between the devices.

Optionally, step S23 specifically includes:

in this embodiment, the signal processing technology is used to perform spectrum analysis on the antenna deflection data, and extract frequency domain features, such as dominant frequencies of signals. These frequency domain features reflect the angular information of the signal transmission. Meanwhile, antenna pointing data is extracted through signal processing and statistical methods, for example, the antenna pointing is described using Direction Cosine. In this way, antenna signal transmission angle data and antenna pointing data are obtained.

in this embodiment, spatial feature extraction is performed on the real-time signal strength data. This includes the spatial relationship of the device generating the signal strength in real time to the antenna pointing direction. And calculating signal characteristics in different directions by adopting a signal processing technology and a spatial analysis algorithm to form real-time signal spatial characteristic data. This lays a foundation for subsequent signal excursions analysis.

In this embodiment, the real-time signal offset analysis is performed by using the antenna signal transmission angle data and the real-time signal spatial feature data. This involves a comprehensive analysis of the direction of transmission of the signal and of the spatial features in the environment to identify the excursions of the real-time signal. By means of a suitable algorithm, real-time signal offset data can be obtained, revealing the directional changes of the signal transmission.

in this embodiment, offset signal fluctuation simulation is performed on real-time signal strength data based on real-time signal offset data. And obtaining offset fluctuation simulation data by simulating offset change of the actual signal. This helps to understand the fluctuations of the signal in the actual environment and provides simulation data for the evaluation of system performance.

In this embodiment, the bias influence degree calculation is performed on the bias fluctuation simulation data and the antenna bias data by using a bias influence degree formula. This calculation allows a quantitative assessment of the extent of influence of antenna bias on signal offset, providing important information about the signal transmission direction and antenna pointing relationship. These bias effect values may provide guidance in optimizing antenna layout or improving signal transmission schemes.

Optionally, the bias influence formula in step S233 is specifically:

；

wherein,for biasing influence, let us->Is the base of natural logarithm, +.>For the fluctuation degree coefficient>For the frequency of the wave-motion,for periods of fluctuation，/>For the directional deflection coefficient of the antenna,>for signal propagation direction coefficient, +.>Is an antenna environment parameter.

The invention constructs a deflection influence formula for calculating the deflection influence degree of the passive fluctuation simulation data and the antenna deflection data. The formula fully considers the influence deviation influence degreeBase of natural logarithm of>Fluctuation degree coefficient->Frequency of fluctuation->Period of fluctuation->Direction deviation coefficient of antenna->Signal propagation direction coefficient->Antenna environmental parameter->A functional relationship is formed: />

；

Wherein,part of->The square of (2) plus 1 takes the logarithm of the base 2 to represent a logarithmic transformation for processing the signal strength. />Is to->An exponential transformation, representing the relationship between signal strength and frequency of fluctuation. />For->And->And (3) performing complex operation, and taking a cube root to represent the complex influence of the antenna direction deflection on the signal. />For->And carrying out logarithmic operation and taking the reciprocal thereof. This represents the influence of the period of the fluctuation on the signal. Representing the effect of the antenna environment parameters on the signal. The formula comprehensively considers the influence of a plurality of parameters in the signal strength by carrying out complex mathematical operation on a plurality of parameters of the passive fluctuation simulation data and the antenna deflection data, thereby calculating a comprehensive deflection influence degree. This comprehensive analysis can be used to more fully understand the characteristics of the signal under different conditions. In the art, the bias influence degree is generally calculated by adopting technical means such as ray tracing, machine learning and the like. By adopting the bias provided by the inventionThe bias influence degree can be obtained more accurately to the influence formula.

Optionally, step S3 specifically includes:

by processing the real-time signal strength data in this embodiment, key signal features are extracted to better understand and describe the nature of the signal. For example, features such as spectrum, amplitude, waveform shape, etc. are extracted from the real-time signal strength data using signal processing algorithms such as fourier transforms, wavelet transforms, filters, etc. These features can reflect the frequency distribution, intensity variations and fluctuations of the signal.

in this embodiment, signal processing techniques, such as correlation analysis or feature extraction algorithms, are used to extract interference features, such as interference strength, delay, phase, etc., from the path loss matrix. Meanwhile, directivity characteristics such as the propagation direction of signals, multipath effects and the like are extracted through a spatial analysis method.

in this embodiment, the router manages the cloud platform to obtain the historical signal strength data, and performs timestamp matching on the two data sets of the historical signal strength data and the historical path loss data to ensure that they correspond to the same time period. This may be done through a timestamp field in each dataset. They are combined into a comprehensive dataset. The data set is ensured to contain various environments and conditions to improve the generalization ability of the model.

In this embodiment, an interference intensity prediction model is constructed by selecting an appropriate machine learning or statistical modeling method, and modeling data and extracted feature data are combined. For example, modeling data and extracted interference, directionality, signal feature data are used for training using deep learning models such as neural networks, or traditional machine learning models such as regression models. The model should be able to capture the effects of the complexity and interference of signal propagation.

In this embodiment, the constructed interference intensity prediction model is used to predict the real-time signal intensity data and the path loss matrix, so as to obtain the interference intensity in the current environment. For example, the real-time signal strength data and the path loss matrix are input into a trained model, and the corresponding interference strength data is obtained through the prediction output of the model. This may provide important information for network optimization, spectrum management, etc.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatically adjusting a router signal, comprising the steps of:

step S2: acquiring real-time signal intensity data, and performing deflection path loss analysis according to equipment resource allocation data and the real-time signal intensity data to obtain a path loss matrix, wherein the step S2 specifically comprises:

Step S21: acquiring real-time signal intensity data;

step S23: and calculating deviation influence according to the antenna deviation data and the real-time signal strength data to obtain deviation influence, wherein the step S23 specifically comprises the following steps:

step S234: performing offset signal fluctuation simulation on the real-time signal strength data based on the real-time signal offset data, thereby obtaining offset fluctuation simulation data, wherein a bias influence degree formula in step S234 specifically comprises:

；

wherein,for biasing influence, let us->Is the base of natural logarithm, +.>For the fluctuation degree coefficient>For the frequency of fluctuation, +.>For periods of fluctuation >For the directional deflection coefficient of the antenna,>for signal propagation direction coefficient, +.>Is an antenna environment parameter;

step S235: calculating the deviation influence degree of the deviation fluctuation simulation data and the antenna deviation data through a deviation influence degree formula, so as to obtain deviation influence degree;

step S26: constructing a path loss matrix based on the signal propagation model and the network topology;

2. The method according to claim 1, wherein step S1 is specifically:

step S11: acquiring router log data;

3. The method according to claim 2, wherein step S13 is specifically:

4. A method according to claim 3, wherein step S14 is specifically:

5. The method according to claim 4, wherein the joint break coefficient calculation formula in step S141 is specifically:

；

wherein,for the break coefficient, ++>For observing time, < >>For input port power, +.>For output port power, +.>For the link impedance +.>For inputting data quantity->For outputting data quantity->For packet loss rate, < > for>Is the base of natural logarithms.

6. The method according to claim 1, wherein step S3 is specifically:

7. An automatic router signal conditioning system for performing a method of automatic router signal conditioning according to claim 1, the automatic router signal conditioning system comprising: