Ad hoc network routing method based on wireless link perception

Abstract

The invention relates to the technical field of ad-hoc network routing, in particular to an ad-hoc network routing method based on wireless link perception, which comprises the following steps of: calculating a link quality score of a node by monitoring and analyzing a wireless communication signal and spectrum environment data of the node, recording signal quality, interference condition and bandwidth use state of the node, and calculating a link quality score of the node; and generating link quality score data of the nodes. According to the method, the wireless link state of the node is accurately monitored and analyzed, so that the signal strength can be reflected in real time, the routing decision can be dynamically adjusted, in the aspect of spectrum resource allocation, the spectrum use efficiency of the network is optimized, optimization is performed based on a dynamic spectrum adjustment scheme and real-time feedback, and through a task migration mechanism, the resource allocation efficiency of the network is improved. The task can be automatically migrated to the optimal node, through the link state, the transmission delay is reduced, the network reliability is improved, the calculation efficiency is improved through the intelligent migration of the task load, and the calculation bottleneck problem in the network is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of ad hoc network routing, and in particular to an ad hoc network routing method based on wireless link perception. Background Art

[0002] The field of ad hoc routing technology relates to a network structure in which network devices are able to automatically organize and maintain network connections without the support of fixed infrastructure. This type of network is often used in dynamic environments, such as disaster recovery, military operations, or temporary meeting places, where traditional network infrastructure may not be available or economical. Ad hoc networks rely on direct communication between nodes, with each node being both a transmitter and a receiver of data. Routing protocols are particularly important in such networks because they must be able to adapt to frequent changes in network topology while ensuring that data is efficiently transmitted from source to destination.

[0003] Among them, the wireless link-aware self-organizing network routing method involves a specific routing method that focuses on using real-time wireless link status information to dynamically adjust routing decisions, with the goal of improving routing efficiency and network performance, especially in environments where wireless link conditions may change frequently. By sensing changes in link quality, the method can more accurately select the best data transmission path, thereby optimizing the speed and reliability of data transmission and reducing packet loss and transmission delay. This method is suitable for application scenarios that require high network reliability and efficiency, such as emergency rescue operations, battlefield communications, etc.

[0004] Existing ad hoc network routing methods generally rely on static link quality evaluation indicators, which cannot reflect the dynamic impact of environmental changes on link quality in real time. In the case of drastic changes in wireless communication signals or spectrum environments, traditional methods usually fail to provide timely network adjustment mechanisms, resulting in network performance being difficult to adapt to rapidly changing conditions. Fixed link quality evaluation criteria, such as signal strength and signal-to-noise ratio, cannot fully consider multidimensional factors such as interference and bandwidth usage, which may lead to inaccurate transmission path selection, increased latency, data packet loss and other problems, affecting the stability and efficiency of data transmission. The allocation of spectrum resources is often static, which makes the utilization efficiency of spectrum resources low, especially in high-density network environments, and cannot be dynamically optimized according to demand. In addition, the existing scheme lacks an effective task scheduling mechanism and cannot adjust task allocation in time according to node load conditions, resulting in excessive load on some nodes, causing processing delays or computing bottlenecks. In emergency communication scenarios such as disaster relief, the network environment is complex and changes frequently. Traditional methods cannot effectively cope with these changes, often resulting in link interruption or failure to select the optimal data transmission path. Fixed spectrum allocation schemes cannot solve the problem of uneven spectrum resource allocation, which may result in some nodes being unable to obtain sufficient bandwidth, affecting the performance of the entire network. These deficiencies severely limit the applicability and efficiency of existing technologies in dynamic environments. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a self-organizing network routing method based on wireless link perception.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a self-organizing network routing method based on wireless link perception, comprising the following steps:

[0007] S1: Calculate the link quality score of the node by monitoring and analyzing the wireless communication signal and spectrum environment data of the node, record the signal quality, interference and bandwidth usage status of the node, and generate the link quality score data of the node;

[0008] S2: Based on the link quality score data, calculate the delay, packet loss rate and bandwidth utilization, obtain the delay data and record it in time series, collect the packet loss data and associate it with the link load, analyze the bandwidth utilization and calculate the maximum link bandwidth, perform weighted calculation based on the three data, and generate a link stability index;

[0009] S3: According to the link stability index, combined with network demand and current spectrum idle bandwidth data, the availability of spectrum resources is identified, the link stability of the node is matched with the current available spectrum bandwidth, and spectrum resources are allocated according to the matching result combined with the distribution of spectrum resources to generate a spectrum resource allocation plan;

[0010] S4: applying the spectrum resource allocation scheme, monitoring and observing the use of spectrum resources, recording the frequency band occupancy, analyzing the change of node link quality, and comparing it with the current allocation scheme, identifying potential optimization space, reconfiguring spectrum resources according to the analysis results, and generating a dynamic spectrum adjustment scheme;

[0011] S5: According to the dynamic spectrum adjustment scheme, select the link edge computing node load change, analyze the signal stability, bandwidth utilization, delay and node load of the edge node link, receive the computing task, record the node information of the task assignment and the migration time window, and formulate the task migration plan;

[0012] S6: Apply the task migration scheme to track the performance of the migrated nodes, monitor the node load, task execution delay and bandwidth utilization, combine real-time data with preset execution standards, analyze the expected performance indicators of task migration, and calculate the stability improvement value of the link.

[0013] As a further solution of the present invention, the link quality scoring data includes signal strength, interference level and bandwidth occupancy rate, the link stability index includes delay value, packet loss rate and bandwidth utilization index, the spectrum resource allocation scheme includes bandwidth matching degree, spectrum resource distribution and spectrum allocation priority, the dynamic spectrum adjustment scheme includes frequency band occupancy, link quality fluctuation range and adjusted spectrum resource allocation plan, the task migration scheme includes node load changes, task allocation time window and load information of migration nodes, and the link stability improvement value includes node load monitoring, task execution delay, bandwidth utilization analysis and performance optimization suggestions.

[0014] As a further solution of the present invention, by monitoring and analyzing the wireless communication signals and spectrum environment data of the nodes, calculating the link quality scores of the nodes, recording the signal quality, interference conditions and bandwidth usage status of the nodes, the specific steps of generating the link quality score data of the nodes are as follows:

[0015] S101: Collect wireless communication signal strength, measure signal-to-noise ratio, record bandwidth usage status, analyze spectrum environment conditions, and obtain node link quality data;

[0016] S102: Based on the node link quality data, measure the delay time of the node, filter the data by actual bandwidth utilization, and generate a link quality data filtering result;

[0017] S103: Based on the link quality data screening result, perform weighted comprehensive processing on the overall link quality of the node, calculate the link quality score of the node, and generate link quality score data of the node.

[0018] As a further solution of the present invention, the formula for calculating the link quality score is specifically:

[0019]

[0020] Among them, Q n Represents the calculated link quality score, L1 represents latency, L2 represents bandwidth utilization, L3 represents packet loss rate, L4 represents signal strength, L5 represents network jitter, and L6 represents network load.

[0021] As a further solution of the present invention, based on the link quality score data, the delay, packet loss rate and bandwidth utilization are counted, the delay data is obtained and recorded in time series, the packet loss data is collected and associated with the link load, the bandwidth utilization is analyzed and the maximum link bandwidth is calculated, and the specific steps of generating the link stability index by weighted calculation based on the three data are as follows:

[0022] S201: extracting delay information at a time point based on the link quality score data, and entering the information into a database, performing time point comparison and data tracking, and generating a delay time series data table;

[0023] S202: using the delay time series data table, synchronously collecting packet loss data and link load data at a time point, performing correlation analysis between packet loss and link load data, evaluating link quality to provide basic data, and generating packet loss load relationship data;

[0024] S203: Analyze the bandwidth utilization of the link by using the packet loss load relationship data, and calculate the bandwidth peak of the link in combination with the data flow information of the link to generate a link stability index.

[0025] As a further solution of the present invention, the calculation formula of the bandwidth peak of the link is specifically:

[0026]

[0027] Where U represents the peak bandwidth of the link, u i represents the bandwidth utilization of the data point, l i represents the link load of the data point, d i Represents the data flow of a data point.

[0028] As a further solution of the present invention, according to the link stability index, combined with network demand and current spectrum idle bandwidth data, the availability of spectrum resources is identified, the link stability of the node is matched with the current available spectrum bandwidth, and spectrum resources are allocated according to the matching result combined with the distribution of spectrum resources. The specific steps of generating a spectrum resource allocation scheme are as follows:

[0029] S301: extracting the stability index of the node according to the link stability evaluation index, recording the currently available spectrum bandwidth of each node, and performing data comparison, processing the stability index of each node corresponding to the spectrum bandwidth, and generating a node spectrum matching table;

[0030] S302: using the node spectrum matching table, analyzing and recording the spectrum resource distribution around each node, adjusting the priority and amount of spectrum allocation, and generating a spectrum allocation strategy draft;

[0031] S303: Analyze spectrum utilization according to the spectrum allocation strategy draft, identify the spectrum requirements of nodes, dynamically adjust the spectrum allocation of each node in combination with the spectrum occupancy, and generate a spectrum resource allocation plan.

[0032] As a further solution of the present invention, the spectrum resource allocation scheme is applied to monitor and observe the use of spectrum resources, record the frequency band occupancy, analyze the change of node link quality, and compare it with the current allocation scheme, identify potential optimization space, reconfigure spectrum resources according to the analysis results, and generate a dynamic spectrum adjustment scheme in the following specific steps:

[0033] S401: After deploying the spectrum resource allocation scheme, monitor and track the spectrum usage status of the nodes, record specific occupancy data of each frequency band, and generate spectrum usage monitoring data;

[0034] S402: Analyze the link quality data changes between nodes based on the spectrum usage monitoring data, compare the spectrum allocation plan with the link quality record, and generate a link quality comparison analysis report;

[0035] S403: According to the link quality comparison and analysis report, adjust the spectrum resources of the node, analyze the link quality improvement and real-time demand changes, and generate a dynamic spectrum adjustment plan.

[0036] As a further solution of the present invention, according to the dynamic spectrum adjustment scheme, the link edge is selected to calculate the node load change, the signal stability, bandwidth utilization, delay and node load of the link of the edge node are analyzed, and the computing task is received, and the node information of the task assignment and the migration time window are recorded. The specific steps of formulating the task migration plan are as follows:

[0037] S501: According to the dynamic spectrum adjustment scheme, monitor and track the load changes of edge computing nodes, record the signal stability, bandwidth utilization and delay of each node, evaluate the performance status of the node, and generate a node performance evaluation report;

[0038] S502: Analyze the data in the node performance evaluation report, examine the load of each node, evaluate the processing capacity of the computing task, analyze and record relevant node information and performance data, and generate computing task allocation prediction data;

[0039] S503: Utilizing the computing task allocation prediction data, formulating the task migration time window and migration sequence between nodes, calculating the task migration load and migration efficiency, and generating a task migration plan.

[0040] As a further solution of the present invention, the task migration solution is applied to track the performance of the migrated nodes, monitor the node load, task execution delay and bandwidth utilization, combine real-time data with preset execution standards, analyze the expected performance indicators of task migration, and calculate the stability improvement value of the link in the following specific steps:

[0041] S601: After implementing the task migration solution, track the load changes of the nodes after the system migration, record and organize the task execution delay and bandwidth utilization of each node, and generate preliminary monitoring data;

[0042] S602: Based on the preliminary monitoring data, compare the current performance of the node with the performance before migration, analyze the signal stability and bandwidth utilization of the link, record the nodes with improved performance and the nodes that have not met expectations, and generate a performance comparison analysis report;

[0043] S603: Calculate the link stability improvement value of each node according to the performance comparison analysis report, analyze the nodes that do not meet the expected performance, formulate adjustment measures, and generate a link stability improvement report.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by accurately monitoring and analyzing the wireless link status of the node, not only can the signal strength be reflected in real time, but also the routing decision can be dynamically adjusted. In terms of spectrum resource allocation, the spectrum utilization efficiency of the network is optimized. Based on the dynamic spectrum adjustment scheme, real-time feedback is used for optimization. Through the task migration mechanism, the task can be automatically migrated to the best node. Through the link status, the transmission delay is reduced, the network reliability is improved, and the intelligent migration of task loads improves the computing efficiency, which effectively solves the computing bottleneck problem in the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the steps of the present invention;

[0048] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0049] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0050] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0051] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0052] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0053] Figure 7 This is a flow chart of the steps of S6 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 , a self-organizing network routing method based on wireless link perception, comprising the following steps:

[0060] S1: Calculate the link quality score of the node by monitoring and analyzing the wireless communication signal and spectrum environment data of the node, record the signal quality, interference and bandwidth usage status of the node, and generate the link quality score data of the node;

[0061] S2: Based on the link quality score data, the latency, packet loss rate and bandwidth utilization are counted. The latency data is obtained and recorded in time series. The packet loss data is collected and associated with the link load. The bandwidth utilization is analyzed and the maximum link bandwidth is calculated. The link stability index is generated by performing weighted calculation based on the three data.

[0062] S3: According to the link stability index, the link stability of the node is matched with the currently available spectrum bandwidth, and spectrum resources are allocated according to the matching result and the distribution of spectrum resources to generate a spectrum resource allocation plan;

[0063] S4: Apply the spectrum resource allocation plan, monitor and observe the use of spectrum resources, record the frequency band occupancy, analyze the changes in node link quality, and compare them with the current allocation plan, identify potential optimization space, reconfigure spectrum resources based on the analysis results, and generate a dynamic spectrum adjustment plan;

[0064] S5: According to the dynamic spectrum adjustment plan, select the link edge computing node load change, analyze the signal stability, bandwidth utilization, delay and node load of the edge node link, receive computing tasks, record the node information of the task assignment and the migration time window, and formulate a task migration plan;

[0065] S6: Apply the task migration solution to track the performance of the migrated nodes, monitor the node load, task execution latency, and bandwidth utilization, combine real-time data with preset execution standards, analyze the expected performance indicators of task migration, and calculate the stability improvement value of the link.

[0066] Link quality scoring data includes signal strength, interference level and bandwidth occupancy rate. Link stability index includes delay value, packet loss rate and bandwidth utilization index. Spectrum resource allocation plan includes bandwidth matching degree, spectrum resource distribution and spectrum allocation priority. Dynamic spectrum adjustment plan includes frequency band occupancy, link quality fluctuation range and adjusted spectrum resource allocation plan. Task migration plan includes node load change, task allocation time window and load information of migration node. Link stability improvement value includes node load monitoring, task execution delay, bandwidth utilization analysis and performance optimization suggestions.

[0067] See also Figure 2 , the specific steps of S1 are:

[0068] S101: Collect wireless communication signal strength, measure signal-to-noise ratio, record bandwidth usage status, analyze spectrum environment conditions, and obtain node link quality data;

[0069] In order to obtain basic performance information of node links, the collected signal strength is measured by signal data received in real time by wireless communication devices. The signal-to-noise ratio is calculated by the power ratio of signal to noise. The bandwidth usage status is monitored by spectrum analysis tools to monitor the occupancy of the wireless spectrum. The analysis of the spectrum environment relies on spectrum analyzers or related software. The signal strength and noise background of different frequency bands are identified through the collected spectrum images. The collection of link quality data is based on network protocol analysis tools, which can obtain link parameters such as packet loss rate, delay, and throughput in real time. The data in the recording process can provide a basis for subsequent analysis. Ultimately, these data form a basic judgment on network quality and provide original information for subsequent link quality scores.

[0070] S102: Based on the node link quality data, measure the delay time of the node, filter the data by the actual utilization rate of the bandwidth, and generate a link quality data filtering result;

[0071] Extract key information from link quality data, measure the signal transmission delay of each node through delay measurement tools, and evaluate the response speed of the node through statistical analysis of the recorded delay time data. The bandwidth utilization screening data is screened out by comparing the actual bandwidth occupancy with the maximum bandwidth capacity, and the nodes with bottlenecks or insufficient resource utilization in the data are screened out. Based on this information, the screening results are generated to provide basic data for subsequent link quality scores. The screened data can more accurately reflect the problem areas in the network and perform targeted optimization.

[0072] S103: Based on the link quality data screening result, perform weighted comprehensive processing on the overall link quality of the node, calculate the link quality score of the node, and generate link quality score data of the node;

[0073] The specific formula for calculating link quality score is:

[0074]

[0075] Among them, Q n Represents the calculated link quality score, L1 represents latency, L2 represents bandwidth utilization, L3 represents packet loss rate, L4 represents signal strength, L5 represents network jitter, and L6 represents network load.

[0076] Parameter acquisition process and quantification

[0077] L1 (latency): Latency refers to the time required for data to be transmitted from one node to another, and is usually detected through network monitoring tools (such as the Ping command). In actual networks, the latency value is usually between 0 and 500 milliseconds, and the specific value depends on the network load and transmission distance. The latency value of the current node n is obtained through real-time monitoring, assuming that the current latency value is L1 = 120ms.

[0078] L2 (Bandwidth Utilization): Bandwidth utilization refers to the ratio of the actual bandwidth used to the maximum available bandwidth, which is usually calculated by network monitoring equipment. Bandwidth utilization is generally between 0 and 1. A bandwidth utilization of 0.75 means that node n currently uses 75% of the maximum available bandwidth. Therefore, L2 = 0.75.

[0079] L3 (Packet Loss Rate): The packet loss rate indicates the percentage of data packets lost during data transmission. The packet loss rate is generally obtained through the packet transmission statistics provided by the network device. Assume that the packet loss rate obtained through monitoring is L3 = 0.02, that is, 2% of the data packets are lost.

[0080] L4 (Signal Strength): Signal strength indicates the strength of the wireless signal, usually in dBm (decibel milliwatts). Its value reflects the stability of the signal and the quality of the network. The signal strength obtained through wireless device detection is assumed to be L4 = 50dBm. The higher the value, the stronger the signal.

[0081] L5 (network jitter): Network jitter refers to the fluctuation of the arrival time of data packets, which reflects the stability of the link. The jitter value is usually calculated by measuring the variance of the delay time of multiple data packets. In actual networks, the standard range of jitter values ​​is usually between 0 and 50ms. Assume that the measurement result is L5 = 10ms.

[0082] L6 (network load): Network load usually refers to the usage of network resources, usually expressed as the occupancy rate of network bandwidth. The higher the value, the heavier the network load, which may affect the link quality. Assume that the current network load of node n is 0.85, that is, L6 = 0.85.

[0083] Substituting the above measured parameters, the calculation process of the formula is as follows:

[0084]

[0085] Step by step calculation

[0086] Calculation of absolute value |L1|: Since L1=120, |L1|=120.

[0087] Square root Calculation: L2 = 0.75, so

[0088] Multiplication Part Calculation: 120·0.866·0.02=2.0784.

[0089] Square root Calculation: L5 = 10, so

[0090] Denominator Calculation: 50+3.162=53.162.

[0091] Calculation of the inverse proportion 1 / |L6|: |L6|=0.85, therefore 1 / |L6|=1 / 0.85≈1.176.

[0092] Comprehensive calculation

[0093] The final link quality score Q n for:

[0094]

[0095] See also Figure 3 , the specific steps of S2 are:

[0096] S201: extracting delay information at a time point based on link quality score data, and entering the information into a database, performing time point comparison and data tracking, and generating a delay time series data table;

[0097] First, it is necessary to extract the delay information related to the time point and effectively record it through the database. This processing process includes identifying specific time-related identifiers from massive data, such as timestamps, and then associating these data with delay information. After that, the data is written to a specific table in the database. In order to compare and track data changes, it is also necessary to design a set of algorithms to process these data to ensure that data integration and updates can be carried out in real time. This real-time processing mechanism will be based on database triggers or scheduled tasks to ensure the accuracy and timeliness of the data. Subsequently, through a series of data processing and analysis, a time series data table containing all necessary time points and corresponding delay information is generated. This table not only contains the original data, but also includes processed statistical information, such as average delay and maximum delay.

[0098] S202: using a delayed time series data table, synchronously collecting packet loss data and link load data at a time point, performing correlation analysis between packet loss and link load data, evaluating link quality to provide basic data, and generating packet loss load relationship data;

[0099] Using the delayed time series data table, the packet loss data and link load data at the time point are collected synchronously, and the packet loss and link load data are correlated and analyzed. According to the formula

[0100]

[0101] , calculate the link quality.

[0102] Where, L d Represents link load data, P d Represents packet loss data, N d Represents the total number of data points. d is the link load data obtained from the network monitoring tool, P d is the packet loss data extracted from the network log, N d is the total number of data points in the analysis period. For example, if we collect data every minute for an hour, then N d is 60. Assuming that the link load in a certain minute is 100 Mbps and the number of packet losses is 10, then the L d P d The value is 1000. d P d The accumulated values ​​are divided by 60, and the result R represents the average quality index of the link within this hour.

[0103] S203: Analyze the bandwidth utilization of the link by using the packet loss load relationship data, and calculate the bandwidth peak of the link in combination with the data flow information of the link to generate a link stability index;

[0104] The calculation formula for the link bandwidth peak is:

[0105]

[0106] Where U represents the peak bandwidth of the link, u i represents the bandwidth utilization of the data point, l i represents the link load of the data point, d i Represents the data flow of a data point.

[0107] u i Represents the bandwidth utilization of the data point. This is measured by the network monitoring tool to measure the percentage of bandwidth used by each node in a specific time window. For example, if the total bandwidth of a node is 100Mbps in 10 minutes, and an average of 40Mbps is used during this period, then u i is 40%.

[0108] l iRepresents the link load of the data point. It indicates the load size of the node in the same time window, which is usually recorded by the traffic counter provided by the network device. Assuming that the maximum carrying capacity of the link is 200Mbps and the current traffic is 50Mbps, then l i is 50Mbps.

[0109] d i Represents the data traffic of the data point, that is, the total amount of data passing through the node. This is usually recorded by a packet counter, assuming that the total amount of data passing through the node in 10 minutes is 500MB.

[0110] Now, suppose we have three data points with the following parameter values:

[0111] u1=50%, l1=100Mbps, d1=800MB

[0112] u2=75%, l2=150Mbps, d2=1200MB

[0113] u3=60%, l3=120Mbps, d3=1000MB

[0114] Calculation process: First, calculate the

[0115] For the first data point:

[0116]

[0117] For the second data point:

[0118]

[0119] For the third data point:

[0120]

[0121] Then find the average of these values:

[0122]

[0123] The results show that after considering the comprehensive effects of bandwidth utilization, link load and data traffic, the average bandwidth peak utility of the link is 15.76, which can be used to evaluate the overall performance and stability of the link. This analysis method can provide more accurate bandwidth management decision support, thereby optimizing network resource allocation.

[0124] See also Figure 4 , the specific steps of S3 are:

[0125] S301: extracting the stability index of the node according to the link stability evaluation index, recording the currently available spectrum bandwidth of each node, and performing data comparison, matching the stability index of each node with the spectrum bandwidth, and generating a node spectrum matching table;

[0126] This involves analyzing and extracting the stability data of each node, including the node's operation history and real-time performance indicators. These data are used to evaluate the performance and reliability of each node in the network. At the same time, the current available spectrum bandwidth of each node is recorded for comparison and further analysis. This requires collecting real-time data on spectrum utilization and identifying patterns or problems in spectrum usage through comparative analysis, thereby establishing a relationship between the stability index and spectrum bandwidth of each node. The resulting node spectrum matching table not only lists the stability score of each node, but also displays the corresponding spectrum bandwidth data in detail. This matching table has become an important tool for optimizing network spectrum resource allocation.

[0127] S302: using the node spectrum matching table, analyzing and recording the spectrum resource distribution around each node, adjusting the priority and amount of spectrum allocation, and generating a spectrum allocation strategy draft;

[0128] Use the node spectrum matching table to analyze and record the spectrum resource distribution around each node.

[0129]

[0130] , calculate the spectrum allocation strategy after priority adjustment.

[0131] In the formula, f i,s represents the spectrum resource of node i, w i,s represents the allocation weight, ∑w i,s Represents the total allocation weight.

[0132] In the spectrum resource distribution analysis, the spectrum resource f of each node i,s is collected in real time by the spectrum monitoring system, and the weight w i,s The weight w is adjusted dynamically according to the node’s geographical location and communication needs. For example, if communication conflicts frequently occur in the surrounding environment of node A, its weight w i,s It may be set to 2, while the weight of other nodes is 1. When calculating, if the spectrum resource of node A is 20 MHz, and the spectrum resource allocation scheme of node B is 30 MHz, and the weights are 2 and 1 respectively, then P s The calculation of is as follows:

[0133]

[0134] Deploy spectrum resource allocation scheme MHz.

[0135] S303: Analyze spectrum utilization according to the spectrum allocation strategy draft, identify the spectrum requirements of the nodes, dynamically adjust the spectrum allocation of each node based on the spectrum occupancy, and generate a spectrum resource allocation plan;

[0136] This is usually achieved through historical data analysis and real-time monitoring. Through these data, we can understand the spectrum usage of each node in different time periods. Combined with detailed records of spectrum occupancy, the spectrum allocation of each node can be dynamically adjusted. During this adjustment process, the spectrum management system will automatically calculate and reallocate spectrum resources to ensure that each node can obtain sufficient spectrum when it is needed. The final generated spectrum resource allocation plan lists in detail the spectrum allocation of each node in the future. The formulation of this plan takes into account the business importance of the node and the actual availability of spectrum resources.

[0137] See also Figure 5 , the specific steps of S4 are:

[0138] S401: After the spectrum resource allocation scheme is deployed, the spectrum usage status of the nodes is monitored and tracked, the specific occupancy data of each frequency band is recorded, and spectrum usage monitoring data is generated;

[0139] It involves recording the specific occupancy data of each frequency band, and obtaining data from network equipment and spectrum analysis tools through a real-time data acquisition system. The monitoring data includes but is not limited to the utilization rate of the frequency band, signal strength and interference. These data are integrated and analyzed to monitor the actual use of the spectrum, detect the efficiency of the spectrum and possible congested areas. This monitoring helps network administrators understand the distribution and use of spectrum resources. The spectrum usage monitoring data finally generated will show the occupancy of each frequency band in detail, providing a basis for further policy adjustments.

[0140] S402: Analyze the link quality data changes between nodes based on the spectrum usage monitoring data, compare the spectrum allocation plan with the link quality records, and generate a link quality comparison analysis report;

[0141] Based on the spectrum usage monitoring data, the link quality data changes between nodes are analyzed according to the formula

[0142]

[0143] , calculate the overall score of link quality.

[0144] In the formula, λ i,s represents the weight factor of link i, q i,s represents the quality score of link i, and n represents the total number of links.

[0145] In practical applications, the weight factor λ of each link i,s The link quality score q is determined according to its business importance and traffic volume. i,s It is obtained by analyzing spectrum usage data and communication success rate. For example, for three links, their weights are set to 0.5, 1.0, and 1.5, respectively, and the corresponding quality scores are 95, 90, and 85. The overall link quality score Q is calculated according to the formula s as follows:

[0146]

[0147] S403: adjusting the spectrum resources of the node according to the link quality comparison and analysis report, analyzing the link quality improvement and real-time demand changes, and generating a dynamic spectrum adjustment plan;

[0148] This includes analyzing link quality improvements and real-time demand changes, and using data analysis tools and algorithms to determine which nodes require more spectrum resources and which can be reduced. This usually involves the construction and operation of algorithm models, which analyze multiple factors such as data transmission volume, error rate, and response time to dynamically adjust spectrum allocation. The final dynamic spectrum adjustment plan lists in detail the new spectrum allocation settings for each node, which will be optimized based on actual usage and link quality data to ensure efficient network operation and a good user experience.

[0149] See also Figure 6 , the specific steps of S5 are:

[0150] S501: monitor and track the load changes of edge computing nodes according to the dynamic spectrum adjustment scheme, record the signal stability, bandwidth utilization and delay of each node, evaluate the performance status of the node, and generate a node performance evaluation report;

[0151] It involves real-time recording of the signal stability, bandwidth utilization and delay of the node. Data is collected from each node through dedicated monitoring software. The data will be analyzed to evaluate the performance status of the node. For example, signal stability is usually evaluated by signal strength and signal-to-noise ratio, bandwidth utilization is calculated by analyzing the ratio of transmitted data volume to bandwidth capacity, and delay data is obtained by recording the timestamp during the data packet transmission process. The node performance evaluation report generated by summarizing all this information will provide decision support for network management. The report will describe in detail the performance of each node within a specific evaluation period, providing a basis for subsequent network optimization and resource allocation.

[0152] S502: Analyze the data in the node performance evaluation report, examine the load of each node, and evaluate the processing capacity of the computing task, analyze and record relevant node information and performance data, and generate computing task allocation prediction data;

[0153] Analyze the data in the node performance evaluation report and follow the formula

[0154]

[0155] , calculate the average node load.

[0156] In the formula, u i,s represents the utilization rate of node i, c i,s represents the computing power of node i, and n represents the total number of nodes.

[0157] For example, if there are three nodes with utilization rates of 50%, 75%, and 60%, respectively, and their respective computing capacities are 100, 150, 120, and 150 units, then the average load L s The calculation of is as follows:

[0158]

[0159] S503: using the calculation task allocation prediction data, formulating the task migration time window and migration sequence between nodes, calculating the task migration load and migration efficiency, and generating a task migration plan;

[0160] First, the task processing capacity and future load of each node are determined by analyzing the predicted data. Then, based on this information, the load and efficiency of task migration are calculated. This usually involves considering the size of the task, the communication delay between nodes, and data dependencies. The task completion time and resource consumption are minimized through optimization algorithms. The final generated task migration plan will specify which tasks should be migrated from which node to another node and when, ensuring balanced operation of the entire system and efficient energy utilization.

[0161] See also Figure 7 , the specific steps of S6 are:

[0162] S601: After implementing the task migration plan, track the load changes of the nodes after the system migration, record and organize the task execution delay and bandwidth utilization of each node, and generate preliminary monitoring data;

[0163] For example, accurate monitoring of task execution latency and bandwidth utilization requires the use of high-performance monitoring tools to capture the performance of each node under different operating conditions. These data are organized into preliminary monitoring data through real-time analysis and historical data comparison. During this process, system administrators need to configure data recording parameters, such as sampling frequency and data accuracy, to ensure data reliability and validity. Through these monitoring data, abnormal node load or performance degradation can be quickly identified, so as to adjust resource allocation or optimize system configuration in a timely manner.

[0164] S602: Based on the preliminary monitoring data, compare the current performance of the node with the performance before migration, analyze the signal stability and bandwidth utilization of the link, record the nodes with improved performance and the nodes that have not met expectations, and generate a performance comparison analysis report;

[0165] Based on the preliminary monitoring data, compare the current performance of the node with the performance before migration, according to the formula

[0166]

[0167] , computing performance improved.

[0168] Where P current,i,s represents the performance index of node i after migration, P before,i,s represents the performance index of node i before migration, and n represents the total number of nodes.

[0169] Assume that the performance indicators of three nodes before and after migration are P before =[80,90,85] and P current =[85,95,90], the performance is improved by ΔP s The calculation of is as follows:

[0170]

[0171] , indicating that the average performance of each node is improved by 5 units.

[0172] S603: Calculate the link stability improvement value of each node according to the performance comparison analysis report, analyze the nodes that do not meet the expected performance, formulate adjustment measures, and generate a link stability improvement report;

[0173] The task of calculating the link stability improvement value of each node involves an in-depth analysis of the performance that does not meet expectations for each node. This requires a detailed examination of the operating environment and load conditions of each node. The analysis usually includes multiple performance indicators such as signal stability, response time and error rate. Based on this data, administrators can formulate specific adjustment measures, such as increasing bandwidth, adjusting spectrum allocation or optimizing routing strategies. These measures are aimed at improving the stability and response efficiency of the entire network. The resulting link stability improvement report will record in detail the expected effects and implementation schedule of each measure, providing a reference for future network maintenance and upgrades.

[0174] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A self-organizing network routing method based on wireless link perception, characterized in that: The following steps are involved: By monitoring and analyzing the wireless communication signals and spectrum environment data of the nodes, the link quality scores of the nodes are calculated, the signal quality, interference and bandwidth usage status of the nodes are recorded, and the link quality score data of the nodes is generated; Based on the link quality score data, the delay, packet loss rate and bandwidth utilization are counted, the delay data is obtained and recorded in time series, the packet loss data is collected and associated with the link load, the bandwidth utilization is analyzed and the maximum link bandwidth is calculated, and a weighted calculation is performed based on the three data to generate a link stability index; According to the link stability index, combined with network demand and current spectrum idle bandwidth data, the availability of spectrum resources is identified, the link stability of the node is matched with the current available spectrum bandwidth, and spectrum resources are allocated according to the matching result combined with the distribution of spectrum resources to generate a spectrum resource allocation plan; Apply the spectrum resource allocation scheme, monitor and observe the use of spectrum resources, record frequency band occupancy, analyze changes in node link quality, and compare them with the current allocation scheme, identify potential optimization space, reconfigure spectrum resources according to the analysis results, and generate a dynamic spectrum adjustment scheme; According to the dynamic spectrum adjustment scheme, select the link edge computing node load change, analyze the signal stability, bandwidth utilization, delay and node load of the edge node link, receive computing tasks, record the node information of the task assignment and the migration time window, and formulate a task migration plan; The task migration scheme is applied to track the performance of the migrated nodes, monitor the node load, task execution delay and bandwidth utilization, combine real-time data with preset execution standards, analyze the expected performance indicators of task migration, and calculate the stability improvement value of the link.

2. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: The link quality scoring data includes signal strength, interference level and bandwidth occupancy rate; the link stability index includes delay value, packet loss rate and bandwidth utilization index; the spectrum resource allocation plan includes bandwidth matching degree, spectrum resource distribution and spectrum allocation priority; the dynamic spectrum adjustment plan includes frequency band occupancy, link quality fluctuation range and adjusted spectrum resource allocation plan; the task migration plan includes node load changes, task allocation time window and load information of migration nodes; the link stability improvement value includes node load monitoring, task execution delay, bandwidth utilization analysis and performance optimization suggestions.

3. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: By monitoring and analyzing the wireless communication signals and spectrum environment data of the nodes, calculating the link quality scores of the nodes, recording the signal quality, interference and bandwidth usage status of the nodes, the specific steps of generating the link quality score data of the nodes are as follows: Collect wireless communication signal strength, measure signal-to-noise ratio, record bandwidth usage status, analyze spectrum environment conditions, and obtain node link quality data; Based on the node link quality data, measure the delay time of the node, filter the data by actual bandwidth utilization, and generate a link quality data filtering result; Based on the link quality data screening result, the overall link quality of the node is weighted and comprehensively processed, the link quality score of the node is calculated, and the link quality score data of the node is generated.

4. The self-organizing network routing method based on wireless link perception according to claim 3 is characterized in that: The formula for calculating the link quality score is specifically: Among them, Q n Represents the calculated link quality score, L1 represents latency, L2 represents bandwidth utilization, L3 represents packet loss rate, L4 represents signal strength, L5 represents network jitter, and L6 represents network load.

5. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: Based on the link quality score data, the delay, packet loss rate and bandwidth utilization are counted, the delay data is obtained and recorded in time series, the packet loss data is collected and associated with the link load, the bandwidth utilization is analyzed and the maximum link bandwidth is calculated, and a weighted calculation is performed based on the three data to generate the link stability index. The specific steps are: Based on the link quality score data, extract the delay information at the time point, enter it into the database, perform time point comparison and data tracking, and generate a delay time series data table; The delay time series data table is used to synchronously collect packet loss data and link load data at a time point, perform correlation analysis between packet loss and link load data, evaluate link quality to provide basic data, and generate packet loss load relationship data; The packet loss load relationship data is used to analyze the bandwidth utilization of the link, and combined with the data flow information of the link, the bandwidth peak of the link is calculated to generate a link stability index.

6. The self-organizing network routing method based on wireless link perception according to claim 5, characterized in that: The calculation formula of the bandwidth peak of the link is specifically: Where U represents the peak bandwidth of the link, u i represents the bandwidth utilization of the data point, l i represents the link load of the data point, d i Represents the data flow of a data point.

7. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: According to the link stability index, combined with network demand and current spectrum idle bandwidth data, the availability of spectrum resources is identified, the link stability of the node is matched with the current available spectrum bandwidth, and spectrum resources are allocated according to the matching result combined with the distribution of spectrum resources. The specific steps of generating a spectrum resource allocation plan are as follows: According to the link stability evaluation index, the stability index of the node is extracted, and the currently available spectrum bandwidth of each node is recorded, and data comparison is performed, and the stability index of each node is processed correspondingly with the spectrum bandwidth to generate a node spectrum matching table; The node spectrum matching table is used to analyze and record the spectrum resource distribution around each node, adjust the priority and amount of spectrum allocation, and generate a spectrum allocation strategy draft; According to the draft spectrum allocation strategy, the spectrum utilization is analyzed, and the spectrum requirements of the nodes are identified. In combination with the spectrum occupancy, the spectrum allocation of each node is dynamically adjusted to generate a spectrum resource allocation plan.

8. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: The specific steps of applying the spectrum resource allocation scheme, monitoring and observing the use of spectrum resources, recording frequency band occupancy, analyzing changes in node link quality, and comparing them with the current allocation scheme, identifying potential optimization space, and reconfiguring spectrum resources according to the analysis results to generate a dynamic spectrum adjustment scheme are as follows: After deploying the spectrum resource allocation scheme, monitor and track the spectrum usage of the nodes, record the specific occupancy data of each frequency band, and generate spectrum usage monitoring data; Based on the spectrum usage monitoring data, analyzing the link quality data changes between nodes, comparing the spectrum allocation plan with the link quality records, and generating a link quality comparison analysis report; According to the link quality comparison and analysis report, the spectrum resources of the node are adjusted, and the link quality improvement and real-time demand changes are analyzed to generate a dynamic spectrum adjustment plan.

9. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: According to the dynamic spectrum adjustment scheme, the link edge is selected to calculate the node load change, the signal stability, bandwidth utilization, delay and node load of the edge node link are analyzed, and the computing task is received, and the node information of the task assignment and the migration time window are recorded. The specific steps of formulating the task migration scheme are as follows: According to the dynamic spectrum adjustment scheme, monitor and track the load changes of edge computing nodes, record the signal stability, bandwidth utilization and delay of each node, evaluate the performance status of the node, and generate a node performance evaluation report; Analyze the data in the node performance evaluation report, examine the load of each node, and evaluate the processing capacity of the computing task, analyze and record relevant node information and performance data, and generate computing task allocation prediction data; The computing task allocation prediction data is used to formulate the task migration time window and migration sequence between nodes, calculate the task migration load and migration efficiency, and generate a task migration plan.

10. The self-organizing network routing method based on wireless link perception according to claim 1, characterized in that: The task migration scheme is applied to track the performance of the migrated nodes, monitor the node load, task execution delay and bandwidth utilization, and analyze the expected performance indicators of task migration by combining real-time data with preset execution standards. The specific steps for calculating the stability improvement value of the link are as follows: After implementing the task migration scheme, track the load changes of the nodes after the system migration, record and organize the task execution delay and bandwidth utilization of each node, and generate preliminary monitoring data; Based on the preliminary monitoring data, compare the current performance of the node with the performance before migration, analyze the signal stability and bandwidth utilization of the link, record the nodes with improved performance and the nodes that did not meet expectations, and generate a performance comparison analysis report; Based on the performance comparison and analysis report, the link stability improvement value of each node is calculated, the nodes that do not meet the expected performance are analyzed, and adjustment measures are formulated to generate a link stability improvement report.

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