Orthogonal frequency hopping rapid synchronization method based on large-scale networking

Abstract

The invention relates to the technical field of orthogonal frequency hopping, in particular to an orthogonal frequency hopping rapid synchronization method based on large-scale networking, which comprises the following steps of: acquiring frequency use conditions of network nodes, encoding geographical location data of the nodes, combining communication requirements with location information, analyzing node communication requirement modes, and establishing a network communication mode; and generating a node frequency distribution table. According to the method, more accurate frequency distribution is realized by integrating the frequency use condition and the geographic position data of the network nodes, a communication demand mode is revealed by utilizing clustering analysis, the node frequency distribution table is generated, the spatial feature extraction and the time sequence data processing improve the prediction accuracy, and the network stability is enhanced; through the combination of the real-time conflict early warning information and the dynamic frequency adjustment, and the precise analysis of the synchronization signal and the application of the adaptive adjustment logic, the efficiency of the synchronization process and the signal quality are greatly improved, and the operation efficiency and the data transmission quality of the network are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of orthogonal frequency hopping, and in particular to an orthogonal frequency hopping fast synchronization method based on large-scale networking. Background Art

[0002] Orthogonal frequency hopping technology is a wireless communication technology, which belongs to a type of spread spectrum communication technology. By hopping between a series of frequencies to transmit signals, data transmission is made more secure and highly resistant to interference. Each frequency hop of orthogonal frequency hopping is based on a predetermined pseudo-random sequence, ensuring the randomness and unpredictability of the frequency hopping. The "orthogonal" feature of this technology refers to the mutual orthogonality between the frequency points used, that is, the frequency points are theoretically independent of each other, which helps to reduce interference between channels and improve the signal-to-noise ratio of the signal. Orthogonal frequency hopping is widely used in military and civilian fields, especially in environments that require high security and low signal interference, such as wireless communication networks, radar systems, and secure communications.

[0003] This topic involves a fast synchronization method based on orthogonal frequency hopping in large-scale networking. Its main purpose is to achieve fast synchronization between devices in a large-scale network environment through orthogonal frequency hopping technology. This synchronization method is particularly suitable for applications that need to quickly establish stable communication links in complex or interfering environments, such as large wireless self-organizing networks, unmanned intelligent communication equipment and other types of distributed systems. Through effective synchronization, this technology can optimize the overall performance of the network and improve the reliability and efficiency of data transmission. In large-scale networks, this synchronization technology is particularly important, which can ensure the coordination and consistency of a large number of devices in the network, thereby improving the smoothness and stability of network operations.

[0004] Existing technologies often face the problems of slow synchronization process and frequency conflicts in large-scale network environments. Due to the lack of effective frequency usage monitoring and real-time adjustment mechanisms, these technologies cannot respond quickly to changes in the network environment. In high-interference or complex network environments, this deficiency may lead to communication failures or serious data loss. If frequency conflicts are not effectively identified and resolved in dense network nodes, frequent communication interruptions will occur, affecting the operational efficiency and stability of the entire network. Existing technologies usually fail to make full use of geographic location data, resulting in unreasonable frequency allocation and increasing the risk of resource waste. These problems are particularly prominent in applications that require fast and stable communication links, such as wireless sensor networks and IoT devices, which have an urgent need to optimize synchronization speed and frequency configuration. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an orthogonal frequency hopping fast synchronization method based on large-scale networking.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for fast synchronization of orthogonal frequency hopping based on large-scale networking, comprising the following steps:

[0007] S1: Collect the frequency usage of network nodes, encode the node geographic location data, combine the communication demand with the location information, analyze the node communication demand pattern, and generate a node frequency distribution table;

[0008] S2: Through the node frequency distribution table, the original data is formatted and converted, spatial features are extracted, time series processing is performed on the data, key features of frequency conflicts are identified, an input database with clear time tags, spatial indexes and frequency utilization is formed, and frequency conflict analysis results are output;

[0009] S3: using the frequency conflict analysis results, monitoring frequency changes in the network, counting the frequency segments, time lengths and number of nodes overlapped by nodes, calculating conflict intensity indicators, and generating real-time conflict warning information;

[0010] S4: Integrate frequency usage data of network nodes according to the real-time conflict warning information, adjust frequency allocation, and establish optimized frequency configuration;

[0011] S5: Based on the optimized frequency configuration, collect synchronization signals, analyze signal waveforms, adjust synchronization parameters, adjust the logic to optimize the synchronization process, and repeatedly iterate and correct to achieve an optimized state of the synchronization signal;

[0012] S6: Utilize the synchronization signal optimization state, capture network operation data, perform quantitative analysis on the synchronization signal stability, adjust the synchronization parameters to shorten the frequency hopping interval, optimize the geographical distribution path, and form a network synchronization stability report.

[0013] As a further solution of the present invention, the node frequency distribution table includes node position coding, communication demand pattern and frequency usage statistics, the frequency conflict analysis results include key conflict characteristics, time series analysis results and spatial feature extraction, the real-time conflict warning information includes conflict probability assessment, monitoring data and warning signals, the optimized frequency configuration includes frequency allocation algorithm, frequency data integration and frequency allocation strategy, the synchronization signal optimization status includes signal waveform analysis, parameter adjustment results and logic optimization feedback, and the network synchronization stability report includes stability quantification analysis, parameter fine-tuning data and network operation capture results.

[0014] As a further solution of the present invention, the specific steps of collecting the frequency usage of network nodes, encoding the node geographic location data, combining the communication demand with the location information, analyzing the node communication demand pattern, and generating the node frequency distribution table are as follows:

[0015] S101: Collect network node frequency data through network monitoring, record node frequency utilization and timestamp, archive data according to timestamp, and generate node frequency usage data;

[0016] S102: Collecting geographic information and reading the longitude and latitude of the node frequency usage data, converting the longitude and latitude into a unified geographic coding format, updating the node information, and obtaining a node geographic coding table;

[0017] S103: Extract node location and communication demand data from the node geocoding table, use data association analysis to analyze node location and frequency utilization, data transmission volume, and communication within a time period, combine spatiotemporal pattern recognition, cluster analysis, and regression analysis, analyze the results to construct a node communication pattern, and generate a node frequency distribution table.

[0018] As a further solution of the present invention, the node frequency distribution table is used to normalize the format and convert the encoding of the original data, extract the spatial features, perform time series processing on the data, identify the key features of the frequency conflict, form an input database with clear time tags, spatial indexes and frequency utilization, and output the specific steps of the frequency conflict analysis results:

[0019] S201: Based on the node frequency distribution table, adjust the structure of input data, unify the timestamp and data type of data records, perform field mapping analysis, and generate standardized input data;

[0020] S202: using the standardized input data, extracting the geographic coordinates of the nodes and the corresponding time tags, constructing a comprehensive spatial and temporal feature vector, analyzing the spatial and temporal relationship of the feature vector nodes, and generating spatial and temporal feature data;

[0021] S203: Analyze the frequency usage pattern in the space-time feature data, identify the time point and location of the frequency, systematically mark the frequency conflict, and output the frequency conflict analysis result.

[0022] As a further solution of the present invention, the specific steps of using the frequency conflict analysis results to monitor frequency changes in the network, counting the frequency segments, time lengths and number of nodes overlapped by nodes, calculating the conflict intensity index, and generating real-time conflict warning information are as follows:

[0023] S301: Based on the frequency conflict analysis result, real-time data monitoring is performed to continuously capture and update frequency changes in the network and generate real-time frequency data records;

[0024] S302: using the real-time frequency data record, mining the potential correlation between the frequency usage pattern and the frequency fluctuation, identifying the abnormal fluctuation, mutation trend and potential conflict points in the frequency, analyzing the abnormal fluctuation and potential conflict points in the frequency, and generating frequency change analysis data;

[0025] S303: extract key change points from the frequency change analysis data, set trigger conditions, detect that the frequency exceeds a set threshold and issue a warning, and output real-time conflict warning information.

[0026] As a further solution of the present invention, the specific steps of integrating the frequency usage data of network nodes, adjusting the frequency allocation, and establishing the optimized frequency configuration according to the real-time conflict warning information are as follows:

[0027] S401: calling the real-time conflict warning information, and performing data collection at the network node to capture frequency usage data, calculate the frequency utilization rate of the node, and generate an integrated frequency usage record;

[0028] S402: Analyze the frequency usage data of the node by integrating the frequency usage records, identify the frequency usage conflict area and its corresponding time period, and generate a frequency adjustment demand analysis result;

[0029] S403: According to the frequency adjustment demand analysis result, adjust the frequency allocation, adjust the frequency allocation strategy of each node in the network, identify the high-load areas and time periods of frequency use, and establish an optimized frequency configuration.

[0030] As a further solution of the present invention, the calculation formula of the frequency utilization rate is specifically:

[0031]

[0032] Among them, F represents frequency utilization, f1 and f2 represent the frequency collection values ​​of two types of data transmission respectively, w1 and w2 are weights determined based on network traffic and data importance, t represents the collection time interval, and c is a constant introduced for stable calculation.

[0033] As a further solution of the present invention, based on the optimized frequency configuration, the synchronization signal is collected, the signal waveform is analyzed, the synchronization parameters are adjusted, the logic is adjusted to optimize the synchronization process, and the specific steps of repeatedly iterating and correcting to achieve the optimized state of the synchronization signal are:

[0034] S501: Based on the optimized frequency configuration, synchronous signal acquisition is performed, the synchronous signal and its waveform data sent by the network node are recorded in real time, the frequency and amplitude of the signal are analyzed, and a synchronous signal data record is generated;

[0035] S502: extracting the peak value, baseline noise and time delay of the signal from the synchronization signal data record, analyzing the stability of the signal, calculating the synchronization accuracy, and generating a signal waveform analysis report;

[0036] S503: According to the signal waveform analysis report, adjust the processing parameters of the synchronization signal, analyze the key characteristics of signal amplitude, frequency and delay, and adjust the filter, gain control and delay compensation of the synchronization signal in combination with the signal processing algorithm to obtain the synchronization signal optimization status record.

[0037] As a further solution of the present invention, the calculation formula of the synchronization accuracy is specifically:

[0038]

[0039] Among them, S represents synchronization accuracy, P represents signal peak value, N represents baseline noise, D represents time delay, and B represents the number of time windows for signal sampling.

[0040] As a further solution of the present invention, the specific steps of utilizing the synchronization signal optimization state, capturing network operation data, quantitatively analyzing the stability of the synchronization signal, adjusting the synchronization parameters to shorten the frequency hopping interval, optimizing the geographical distribution path, and forming a network synchronization stability report are as follows:

[0041] S601: Based on the synchronization signal optimization state record, capture network operation data, continuously record the time series and waveform changes of the synchronization signal, measure and analyze the signal delay and fluctuation between nodes, and generate network operation data records;

[0042] S602: Using the network operation data record, calculate the standard deviation and variance of the signal, quantitatively analyze the stability of the synchronization signal, analyze the abnormal fluctuations, and generate a signal stability report;

[0043] S603: According to the signal stability report, adjust the amplitude and frequency of the signal, perform time synchronization correction, and form a network synchronization stability report.

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

[0045] In the present invention, more accurate frequency allocation is achieved by integrating the frequency usage and geographic location data of network nodes, cluster analysis is used to reveal the communication demand pattern, and a node frequency distribution table is generated. Spatial feature extraction and time series data processing improve prediction accuracy and enhance network stability. The combination of real-time conflict warning information and dynamic frequency adjustment, as well as the precise analysis of synchronization signals and the application of adaptive adjustment logic, greatly improve the efficiency of the synchronization process and signal quality, and significantly improve the network's operating efficiency and data transmission quality. 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 fast synchronization method of orthogonal frequency hopping based on large-scale networking, comprising the following steps:

[0060] S1: Collect the frequency usage of network nodes, encode the node geographic location data, combine the communication demand with the location information, analyze the node communication demand pattern, and generate a node frequency distribution table;

[0061] S2: Through the node frequency distribution table, the original data is formatted and converted, spatial features are extracted, time series processing is performed on the data, key features of frequency conflicts are identified, an input database with clear time tags, spatial indexes and frequency utilization is formed, and frequency conflict analysis results are output;

[0062] S3: Use the frequency conflict analysis results to monitor frequency changes in the network, count the frequency segments, time lengths, and number of nodes overlapped by nodes, calculate conflict intensity indicators, and generate real-time conflict warning information;

[0063] S4: Based on the real-time conflict warning information, integrate the frequency usage data of network nodes, adjust the frequency allocation, and establish the optimized frequency configuration;

[0064] S5: Based on the optimized frequency configuration, collect synchronization signals, analyze signal waveforms, adjust synchronization parameters, adjust the logic to optimize the synchronization process, and repeatedly iterate and correct to achieve the optimized state of synchronization signals;

[0065] S6: Utilize the synchronization signal optimization status, capture network operation data, conduct quantitative analysis on the synchronization signal stability, adjust the synchronization parameters to shorten the frequency hopping interval, optimize the geographical distribution path, and form a network synchronization stability report.

[0066] The node frequency distribution table includes node position coding, communication demand pattern and frequency usage statistics. The frequency conflict analysis results include key conflict characteristics, time series analysis results and spatial feature extraction. The real-time conflict warning information includes conflict probability assessment, monitoring data and warning signals. The optimized frequency configuration includes frequency allocation algorithm, frequency data integration and frequency allocation strategy. The synchronization signal optimization status includes signal waveform analysis, parameter adjustment results and logic optimization feedback. The network synchronization stability report includes stability quantitative analysis, parameter fine-tuning data and network operation capture results.

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

[0068] S101: Collect network node frequency data through network monitoring, record node frequency utilization and timestamp, archive data according to timestamp, and generate node frequency usage data;

[0069] Network monitoring involves the accuracy of data collection, which requires continuous monitoring of the frequency utilization of nodes and recording of frequency utilization data at each time point. The collection system needs to be configured with high-precision time synchronization technology to ensure the accuracy of timestamps. These data will be classified and stored according to timestamps. The storage system needs to have high-speed reading and writing capabilities to handle real-time writing of large amounts of data. Data archiving needs to rely on an efficient database management system that can support fast data query and processing. The archived data will support the generation of detailed node frequency usage reports. These reports provide necessary historical data for subsequent analysis, and the recorded data is archived together with the timestamp.

[0070] S102: Collecting geographic information and reading the longitude and latitude of the node frequency usage data, converting the longitude and latitude into a unified geographic coding format, updating the node information, and obtaining a node geographic coding table;

[0071] Geographic information collection requires the use of high-precision global positioning system equipment to ensure the accuracy of longitude and latitude data. The node frequency usage data read needs to be extracted from the previously archived database. The longitude and latitude data are converted into a unified geocoding format through a series of algorithms, including coordinate conversion algorithms and geocoding standardization. The converted data needs to be updated to the node information database. This database system must not only support the rapid insertion of new data, but also support the update of old data. The generation of geocoding tables is part of the database operation to ensure that the geographic information of each node is accurately recorded and updated.

[0072] S103: extracting node location and communication demand data from the node geocoding table, using data association to analyze node location and frequency utilization, data transmission volume, and communication within a time period, combining spatiotemporal pattern recognition, cluster analysis, and regression analysis, analyzing the results to construct a node communication pattern, and generating a node frequency distribution table;

[0073] The extraction process depends on the query efficiency of the database and requires an efficient query algorithm to ensure the instant extraction of data. The association analysis between node location data and communication needs requires the use of data association analysis technology. This analysis usually includes multi-dimensional data comparison and the application of association rules. Through these analyses, the communication mode of the node can be constructed. This mode will reflect the changes in the communication needs of the node in different locations. The node frequency distribution table generated according to these modes can guide the allocation and adjustment of frequency to optimize the communication efficiency of the entire network. The constructed communication mode provides a scientific basis for network management, and the generation of the frequency distribution table is a specific application of the analysis results.

[0074] See also Figure 3 , the specific steps of S2 are:

[0075] S201: Based on the node frequency distribution table, adjust the structure of the input data, unify the timestamp and data type of the data records, perform field mapping analysis, and generate standardized input data;

[0076] Involves data preprocessing, such as time format conversion and data type standardization. Field mapping analysis includes matching the original data fields with the standardized model fields. This process requires data mapping tools and algorithms to ensure that the data fields are correctly matched. Generating standardized input data involves reorganizing and storing the mapped data to meet the needs of subsequent processing steps. Standardized data can improve data processing efficiency and accuracy, and ensure that the data can be correctly understood and used in subsequent steps.

[0077] S202: using standardized input data, extracting the geographic coordinates of the nodes and the corresponding time tags, constructing a comprehensive spatial and temporal feature vector, analyzing the spatial and temporal relationship of the feature vector nodes, and generating spatial and temporal feature data;

[0078] It is necessary to consider the comprehensive processing of geographic and temporal data, which includes the vectorization of geographic coordinates and the serialization of time tags. The spatial and temporal relationship of feature vector nodes is analyzed through data analysis models, such as cluster analysis or spatiotemporal analysis. These analyses help identify the intrinsic connection between geographic and temporal data. After generating spatial and temporal feature data, it can be used for further geographic information system processing or time series analysis. This process emphasizes the comprehensive characteristics of data and multi-dimensional analysis capabilities.

[0079] S203: Analyze the frequency usage pattern in the spatial and temporal feature data, identify the time point and location of the frequency, systematically mark the frequency conflict, and output the frequency conflict analysis result;

[0080] Identify the time points and places where the frequency is highly concentrated, according to the formula Calculate frequency conflict. Where, f usage(t,l) represents the total amount of frequency usage at time t and location l, u i (t,l) represents the frequency usage of the i-th node at time t and position l. Consider a simple example, assuming that at position l1 and time t1, we have three nodes with frequency usage of 10, 15, and 5 respectively. Then the total frequency usage f usage (t1,l1) is:

[0081] f usage (t1,l1)=10+15+5=30

[0082] This indicates that the frequency usage at position l1 and time point t1 is very concentrated, and if this exceeds the allowed threshold, it is marked as a conflict.

[0083] See also Figure 4 , the specific steps of S3 are:

[0084] S301: Based on the frequency conflict analysis results, real-time data monitoring is performed to continuously capture and update frequency changes in the network and generate real-time frequency data records;

[0085] A high-precision monitoring system needs to be set up to capture frequency changes in the network, which includes continuous tracking and real-time updating of network frequency. Generating real-time frequency data records involves the establishment of data collection, storage and real-time update mechanisms. The monitoring system must be able to efficiently process and record a large amount of data changes, ensure the integrity and real-time nature of the data, and provide support for subsequent analysis and application. Accurate capture and recording of real-time data is the key to improving network response speed and processing capabilities.

[0086] S302: using real-time frequency data records, mining the potential correlation between frequency usage patterns and frequency fluctuations, identifying abnormal fluctuations, mutation trends, and potential conflict points in the frequency, analyzing the abnormal fluctuations and potential conflict points in the frequency, and generating frequency change analysis data;

[0087] This involves a detailed comparison of frequency usage data and frequency changes. This comparative analysis requires the use of advanced data processing techniques, such as time series analysis and anomaly detection algorithms. The process of analyzing abnormal fluctuations and potential conflict points in frequency is achieved through algorithm models. These algorithms can identify and distinguish normal fluctuations from abnormal changes. The generated frequency change analysis data will be used for further risk assessment and the formulation of preventive measures, which is an important step to ensure the stable operation of the network.

[0088] S303: extract key change points from the frequency change analysis data, set trigger conditions, detect that the frequency exceeds the set threshold and issue a warning, and output real-time conflict warning information;

[0089] The detection frequency exceeds the set threshold and issues an early warning, according to the formula Calculate real-time conflict warning. Where P(t) represents the total frequency usage at time t, x i (t) represents the frequency usage of the i-th node at time t, and T is the set threshold. Suppose at a specific time t1, we have five nodes with frequency usage of 20, 30, 15, 25, and 10 respectively. Set the threshold T to 100, and the total frequency usage P(t1) is:

[0090] P(t1)=20+30+15+25+10=100

[0091] This is exactly equal to the threshold, indicating that there is a potential risk of frequency conflict at this moment and the system should issue an early warning. This approach ensures that timely response is made when frequency usage exceeds the threshold, protecting the network from frequency conflicts.

[0092] See also Figure 5 , the specific steps of S4 are:

[0093] S401: calling real-time conflict warning information, and performing data collection at network nodes to capture frequency usage data, calculate the frequency utilization rate of the nodes, and generate integrated frequency usage records;

[0094] The calculation formula of frequency utilization is as follows:

[0095]

[0096] Among them, F represents frequency utilization, f1 and f2 represent the frequency collection values ​​of two types of data transmission respectively, w1 and w2 are weights determined based on network traffic and data importance, t represents the collection time interval, and c is a constant introduced for stable calculation.

[0097] Parameter acquisition:

[0098] f1 and f2 are obtained by real-time data monitoring of network nodes. For example, f1 may be 5 times / minute, representing the regular data transmission frequency; f2 may be 3 times / minute, involving the transmission frequency of backup data.

[0099] w1 and w2 are dynamically adjusted based on the real-time network load and data priority. For example, when network traffic is high, w1 may be adjusted to 0.6 and w2 to 0.4 to ensure that more important data is processed first.

[0100] t is determined through historical data analysis, assuming a collection cycle of every 2 minutes.

[0101] c is set to a small value to avoid the denominator being zero, and is taken to be 1.

[0102] Calculation process:

[0103] Calculate f1·w1 and f2·w2: 5×0.6=3 and 3×0.4=1.2

[0104] Add the above results to get: 3+1.2=4.2

[0105] Take the absolute value of the result: |4.2|=4.2

[0106] Calculate the denominator:

[0107] Calculate the final frequency utilization:

[0108] The result shows that the frequency utilization of the network node is 2.424, which reflects the data transmission efficiency of the node under given monitoring time and network conditions. The higher this value, the more efficient the node is in utilizing its transmission frequency. This result is based on real-time data analysis and ensures that important data is processed first when the network is congested through weight adjustment, thereby improving the data processing capacity of the node.

[0109] S402: Analyze the frequency usage data of the node by integrating the frequency usage records, identify the frequency usage conflict area and its corresponding time period, and generate a frequency adjustment demand analysis result;

[0110] Advanced data analysis tools are needed to identify frequency conflict areas and their corresponding time periods. This includes the application of statistical analysis and pattern recognition technology. The generation of frequency adjustment demand analysis results is based on in-depth analysis of the data to determine which areas and time periods are most likely to have frequency conflicts, providing a basis for adjustment strategies. These analysis results will directly affect the formulation and implementation of frequency management strategies and ensure the rational allocation and optimization of network resources.

[0111] S403: Adjust frequency allocation according to the frequency adjustment demand analysis result, adjust the frequency allocation strategy of each node in the network, identify the high-load areas and time periods of frequency use, and establish an optimized frequency configuration;

[0112] Implement frequency allocation adjustment according to formula F adj (n) = F orig (n)+ΔF(n) calculates the optimal frequency configuration. In the formula, F adj (n) represents the adjusted frequency of node n, F orig (n) represents the original frequency of node n, and ΔF(n) represents the adjustment value of the frequency of node n based on the demand analysis results. Formula explanation and formula calculation derivation process: Assume that the original frequency of node n is 500MHz. According to the demand analysis, it is determined that the node needs to increase 50MHz during high traffic periods to reduce conflicts. The adjusted frequency F adj(n) is:

[0113] F adj (n) = 500MHz + 50MHz = 550MHz

[0114] This shows that by adjusting the frequency configuration, we can effectively cope with the peak of frequency usage during specific periods, reduce conflicts and improve network efficiency.

[0115] See also Figure 6 , the specific steps of S5 are:

[0116] S501: Based on the optimized frequency configuration, synchronous signal acquisition is performed, the synchronous signal and its waveform data sent by the network node are recorded in real time, the frequency and amplitude of the signal are analyzed, and a synchronous signal data record is generated;

[0117] It is necessary to record the synchronization signals and their waveform data emitted by network nodes in real time. This process includes monitoring and recording the frequency and amplitude of the signal. The signal acquisition equipment must have high precision and high stability to ensure the accuracy of the data. The analysis of the frequency and amplitude of the signal is completed through advanced signal processing algorithms. These algorithms can effectively extract the necessary information from the signal and convert it into a usable data format. The generated synchronization signal data records are stored in a specific data warehouse for subsequent access and analysis. These records are essential for understanding and optimizing network performance.

[0118] S502: extracting the peak value, baseline noise and time delay of the signal from the synchronization signal data record, analyzing the stability of the signal, calculating the synchronization accuracy, and generating a signal waveform analysis report;

[0119] The calculation formula for synchronization accuracy is as follows:

[0120]

[0121] Among them, S represents synchronization accuracy, P represents signal peak value, N represents baseline noise, D represents time delay, and B represents the number of time windows for signal sampling.

[0122] Each parameter in the formula is obtained through actual monitoring and data analysis:

[0123] P (signal peak) is obtained by measuring the highest point in the signal waveform. Assume that in actual monitoring, the signal peak is measured to be 8.0mV.

[0124] N (baseline noise) is calculated by taking the average level of the signal during periods of no signal activity. From the actual data, the baseline noise was measured to be 0.5mV.

[0125] D (time delay) is the time difference from the signal being sent to the signal being received. By comparing the timestamps, the time delay was measured to be 0.3 seconds.

[0126] B (the number of time windows for signal sampling) is the sampling window count set for the data acquisition period. If a measurement covers 5 windows, B is set to 5.

[0127] The calculation process is as follows:

[0128] Calculate the difference between peak and baseline noise: PN = 8.0 - 0.5 = 7.5 mV.

[0129] To calculate the expression in the denominator, first find B+1=5+1=6, then take the square root:

[0130] Calculate the denominator:

[0131] Calculate the final formula result:

[0132] The result shows that the stability index of the signal is about 10.2. This value expresses the ratio of the difference between the signal peak and the baseline noise after adjusting the time delay and the number of sampling windows, reflecting the synchronization accuracy and overall stability of the signal. A higher result value means that the signal has better synchronization accuracy and low noise interference, which is suitable for further analysis and report generation.

[0133] S503: According to the signal waveform analysis report, adjust the processing parameters of the synchronization signal, analyze the key characteristics of the signal amplitude, frequency and delay, and adjust the filter, gain control and delay compensation of the synchronization signal in combination with the signal processing algorithm to obtain the synchronization signal optimization state record;

[0134] The original signal processing logic needs to be optimized, which includes adjusting parameters such as filter settings, gain control and noise suppression. The purpose of optimization is to reduce the error rate and improve signal quality. Analog and digital signal processing technologies are used in the optimization process to achieve the best configuration of parameters. The synchronized signal optimization status record is to record the optimized parameters in the system. These records are used to monitor and adjust the effects of network operations to ensure continuous optimization of signal processing and improvement of network performance. The optimized signal status is crucial to maintaining network reliability and efficiency.

[0135] See also Figure 7 , the specific steps of S6 are:

[0136] S601: based on the synchronization signal optimization state record, capture network operation data, continuously record the time series and waveform changes of the synchronization signal, measure and analyze the signal delay and fluctuation between nodes, and generate network operation data records;

[0137] High-precision signal acquisition equipment and advanced data recording technology are required. The measurement and analysis of signal delays and fluctuations between nodes are performed through specialized analysis software to ensure that the signal characteristics of each node can be accurately captured. Generating network operation data records is to systematically integrate and store these data so that they can be used for further network performance evaluation and fault diagnosis, ensuring continuous monitoring and optimization of network operations.

[0138] S602: Using the network operation data record, calculate the standard deviation and variance of the signal, quantitatively analyze the stability of the synchronization signal, analyze the abnormal fluctuations, and generate a signal stability report;

[0139] Calculate the standard deviation and variance of the signal according to the formula and Calculate the stability of the synchronization signal. In the formula, jσ 2 represents variance, j represents standard deviation, x i represents each measurement value, μ represents the average value, and N represents the number of data points. Assuming the signal measurement values ​​are 10, 12, 14, 16, and 18, the average value μ is calculated as:

[0140]

[0141] Variance j 2 Calculated as:

[0142]

[0143] The standard deviation σ is:

[0144]

[0145] This indicates that the signal has a volatility of 2.83, which is an important measure of the stability of the synchronization signal.

[0146] S603: According to the signal stability report, adjust the amplitude and frequency of the signal, perform time synchronization correction, and form a network synchronization stability report;

[0147] It uses precise signal processing methods and time correction technology. These adjustments are made to optimize the efficiency and accuracy of network synchronization. During the time synchronization correction process, advanced algorithms are used to fine-tune signal parameters to ensure that the synchronization accuracy between nodes is maximized. The resulting network synchronization stability report provides detailed analysis and optimization results after signal adjustment. These results are crucial information for network administrators, helping them monitor and maintain the stable operation of the network.

[0148] 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 fast synchronization method of orthogonal frequency hopping based on large-scale networking, characterized in that: The following steps are involved: Collect the frequency usage of network nodes, encode the node geographic location data, combine the communication demand with the location information, analyze the node communication demand pattern, and generate a node frequency distribution table; Through the node frequency distribution table, the original data is formatted and converted, spatial features are extracted, time series processing is performed on the data, key features of frequency conflicts are identified, an input database with clear time tags, spatial indexes and frequency utilization is formed, and frequency conflict analysis results are output; Using the frequency conflict analysis results, monitoring frequency changes in the network, counting the frequency segments, time lengths and number of nodes overlapped by nodes, calculating conflict intensity indicators, and generating real-time conflict warning information; Integrate frequency usage data of network nodes, adjust frequency allocation, and establish optimized frequency configuration based on the real-time conflict warning information; Based on the optimized frequency configuration, the synchronization signal is collected, the signal waveform is analyzed, the synchronization parameters are adjusted, the logic is adjusted to optimize the synchronization process, and the correction is repeated to achieve the optimized state of the synchronization signal; The synchronization signal optimization state is utilized to capture network operation data, perform quantitative analysis on the synchronization signal stability, adjust the synchronization parameters to shorten the frequency hopping interval, optimize the geographical distribution path, and form a network synchronization stability report.

2. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: The node frequency distribution table includes node position coding, communication demand pattern and frequency usage statistics; the frequency conflict analysis results include key conflict characteristics, time series analysis results and spatial feature extraction; the real-time conflict warning information includes conflict probability assessment, monitoring data and warning signals; the optimized frequency configuration includes frequency allocation algorithm, frequency data integration and frequency allocation strategy; the synchronization signal optimization status includes signal waveform analysis, parameter adjustment results and logic optimization feedback; the network synchronization stability report includes stability quantification analysis, parameter fine-tuning data and network operation capture results.

3. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: The specific steps of collecting the frequency usage of network nodes, encoding the node geographic location data, combining the communication demand with the location information, analyzing the node communication demand pattern, and generating the node frequency distribution table are as follows: Collect network node frequency data through network monitoring, record node frequency utilization and timestamp, archive data according to timestamp, and generate node frequency usage data; Collecting geographic information and reading the longitude and latitude of the node frequency usage data, converting the longitude and latitude into a unified geographic coding format, updating the node information, and obtaining a node geographic coding table; Node location and communication demand data are extracted from the node geocoding table, and data association is used to analyze the node location and frequency utilization, data transmission volume, and communication within a time period. Combined with spatiotemporal pattern recognition, cluster analysis, and regression analysis, the analysis results construct a node communication pattern and generate a node frequency distribution table.

4. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: Through the node frequency distribution table, the original data is formatted and converted, spatial features are extracted, time series processing is performed on the data, key features of frequency conflicts are identified, and an input database with clear time tags, spatial indexes and frequency utilization is formed. The specific steps of outputting the frequency conflict analysis results are as follows: Based on the node frequency distribution table, the structure of the input data is adjusted, the timestamps and data types of the data records are unified, field mapping analysis is performed, and standardized input data is generated; Using the standardized input data, extracting the geographic coordinates and time tags of the nodes, constructing spatial and temporal feature vectors, analyzing the spatial and temporal relationship between the feature vector nodes, and generating spatial and temporal feature data; Analyze the frequency usage pattern in the space-time feature data, identify the time point and location of the frequency, systematically mark the frequency conflict, and output the frequency conflict analysis result.

5. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: The specific steps of analyzing the frequency conflict results, monitoring the frequency changes in the network, counting the frequency segments, time lengths and number of nodes overlapped by nodes, calculating the conflict intensity index, and generating real-time conflict warning information are as follows: Based on the frequency conflict analysis results, real-time data monitoring is performed to continuously capture and update frequency changes in the network and generate real-time frequency data records; Using the real-time frequency data records, mining the potential correlation between frequency usage patterns and frequency fluctuations, identifying abnormal fluctuations, mutation trends, and potential conflict points in the frequency, analyzing the abnormal fluctuations and potential conflict points in the frequency, and generating frequency change analysis data; Extract key change points from the frequency change analysis data, set trigger conditions, detect when the frequency exceeds a set threshold and issue a warning, and output real-time conflict warning information.

6. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: The specific steps of integrating the frequency usage data of network nodes, adjusting frequency allocation, and establishing an optimized frequency configuration according to the real-time conflict warning information are as follows: Calling the real-time conflict warning information, and performing data collection at the network node to capture frequency usage data, calculate the frequency utilization rate of the node, and generate an integrated frequency usage record; By integrating the frequency usage records, analyzing the frequency usage data of the nodes, identifying the conflicting areas and time periods of frequency usage, and generating frequency adjustment demand analysis results; According to the frequency adjustment demand analysis results, frequency allocation is adjusted, the frequency allocation strategy of each node in the network is adjusted, high-load areas and time periods of frequency use are identified, and an optimized frequency configuration is established.

7. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 6, characterized in that: The calculation formula of the frequency utilization rate is specifically: Among them, F represents frequency utilization, f1 and f2 represent the frequency collection values ​​of two types of data transmission respectively, w1 and w2 are weights determined based on network traffic and data importance, t represents the collection time interval, and c is a constant introduced for stable calculation.

8. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: Based on the optimized frequency configuration, the specific steps of collecting synchronization signals, analyzing signal waveforms, adjusting synchronization parameters, adjusting the logic to optimize the synchronization process, and repeatedly iterating and correcting to achieve the optimized state of synchronization signals are as follows: Based on the optimized frequency configuration, synchronous signal acquisition is performed, the synchronous signal and its waveform data emitted by the network node are recorded in real time, the frequency and amplitude of the signal are analyzed, and a synchronous signal data record is generated; Extracting the peak value, baseline noise and time delay of the signal from the synchronization signal data record, analyzing the stability of the signal, calculating the synchronization accuracy, and generating a signal waveform analysis report; According to the signal waveform analysis report, the processing parameters of the synchronization signal are adjusted, the key characteristics of the signal amplitude, frequency and delay are analyzed, and the filter, gain control and delay compensation of the synchronization signal are adjusted in combination with the signal processing algorithm to obtain the synchronization signal optimization status record.

9. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 8, characterized in that: The calculation formula of the synchronization accuracy is specifically: Among them, S represents synchronization accuracy, P represents signal peak value, N represents baseline noise, D represents time delay, and B represents the number of time windows for signal sampling.

10. The orthogonal frequency hopping fast synchronization method based on large-scale networking according to claim 1, characterized in that: The specific steps of utilizing the synchronization signal optimization state, capturing network operation data, quantitatively analyzing the synchronization signal stability, adjusting the synchronization parameters to shorten the frequency hopping interval, optimizing the geographical distribution path, and forming a network synchronization stability report are as follows: Based on the synchronization signal optimization state record, capture network operation data, continuously record the time series and waveform changes of the synchronization signal, measure and analyze the signal delay and fluctuation between nodes, and generate network operation data records; Using the network operation data records, calculate the standard deviation and variance of the signal, quantitatively analyze the stability of the synchronization signal, analyze abnormal fluctuations, and generate a signal stability report; According to the signal stability report, the amplitude and frequency of the signal are adjusted, time synchronization correction is performed, and a network synchronization stability report is formed.

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