A communication control method based on power line network dual-mode communication

Abstract

The present invention relates to the field of wireless communication technology, and in particular to a communication control method based on dual-mode communication of a power line network. The method comprises the following steps: obtaining the topological structure and device connection status of the power line network in real time through network sniffing technology, thereby generating a dynamic network topology diagram; modeling the complex multipath power line channel based on fractal theory, thereby obtaining a fractal channel response matrix describing the time-varying multipath effect; splitting the single codec engine into multiple microservices according to function, thereby obtaining codec microservice data; containerizing and independently deploying the codec microservice data, and adaptively and dynamically adjusting the code stream data transmitted between the codec microservices according to the fractal channel response matrix, thereby generating adaptive code stream data. The present invention accurately models the complex multipath channel through fractal theory, thereby improving the accuracy of delay spread estimation.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a communication control method based on power line network dual-mode communication. Background Art

[0002] With the rapid growth of IoT devices, communication technology is required to cover a wide area and work reliably in various environments. HRF can provide flexible communication in wireless environments, but the communication quality may be affected in cases of signal blocking or interference. Smart grids require a reliable communication method that can cover a wide area to support data transmission and control between power devices. HPLC provides a reliable communication path through existing power lines, but may have limitations in some cases (such as environments with severe power line interference). HPLC may experience communication interruptions when power line interference is severe, while HRF may also be unstable when signal blocking or interference is large; a single communication method cannot meet the needs of all application scenarios. The dual-mode communication module based on HPLC+HRF was born in the context of meeting the diverse communication needs of smart grids and the Internet of Things. It overcomes the limitations of a single communication method in terms of reliability, anti-interference ability and coverage, and provides an efficient, reliable, flexible and secure communication solution that adapts to different application scenarios and significantly improves the overall performance and user experience of the system.

[0003] However, traditional communication control methods based on power line network dual-mode communication often have some problems: first, when switching between HPLC and HRF modes, data loss or interruption due to switching is prone to occur; in addition, the power line network environment is extremely complex, and there are many factors such as line loops, impedance mismatch, high-frequency crosstalk, etc. that affect the transmission quality of HPLC. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a communication control method based on power line network dual-mode communication to solve at least one of the above technical problems.

[0005] To achieve the above object, a communication control method based on power line network dual-mode communication includes the following steps:

[0006] Step S1: obtaining the topology structure and device connection status of the power line network in real time through network sniffing technology, thereby generating a dynamic network topology map;

[0007] Step S2: Modeling the complex multipath power line channel based on fractal theory, thereby obtaining a fractal channel response matrix describing the time-varying multipath effect;

[0008] Step S3: splitting the single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data; performing containerized independent deployment according to the codec microservice data, and adaptively and dynamically adjusting the bitstream data transmitted between the codec microservices according to the fractal channel response matrix, thereby generating adaptive bitstream data;

[0009] Step S4: construct a virtual overlay network based on the dynamic network topology and adaptive bitstream data through SDN and NFV technologies; decompose the pre-acquired node state set into a serialized object model based on the virtual overlay network, and perform incremental snapshots to obtain incremental state snapshot data; store the incremental state snapshot data based on a distributed hash table, and perform state visualization. Figure 1 Consistency processing and hot backup are performed to obtain seamless mirror data;

[0010] Step S5: Based on the blockchain technology, consensus switching transmission is performed in the blockchain network according to the seamless state mirror data, and information verification is performed through hash fingerprints to obtain hash comparison result data.

[0011] The present invention obtains the topological structure and device connection status of the power line network in real time and can provide a comprehensive understanding of the power line network. The generated dynamic network topology map can be used for network management and optimization, helping to identify bottlenecks, faults and abnormal conditions in the network, and improving network performance and reliability. Modeling complex multipath power line channels based on fractal theory can more accurately describe the time-varying multipath effect of power line channels. The fractal channel response matrix can provide information about channel attenuation, delay spread and frequency selectivity, which is helpful to optimize signal transmission and communication system design. Splitting the single codec engine into multiple microservices can improve the scalability and flexibility of the system. Through containerized independent deployment, each microservice can be managed and operated independently. Adaptive dynamic adjustment of code stream data according to the fractal channel response matrix can optimize the coding and decoding process, improve the transmission performance of the communication system in complex power line channels, and simplify the complex power line network environment. Building a virtual overlay network through SDN (software defined network) and NFV (network function virtualization) technology can provide a more flexible and manageable network architecture. Decomposing and incrementally snapshotting the node state set based on the virtual overlay network can reduce the overhead of data transmission and storage. Using distributed hash tables for storage and state visualization Figure 1Consistency processing can improve the reliability and availability of data. The generation of seamless state mirror data helps to realize functions such as real-time status monitoring, fault detection and network recovery. Consensus switching transmission of seamless state mirror data based on blockchain technology can ensure the security and immutability of data. Information verification through hash fingerprint can verify the integrity and consistency of data. This can help ensure the credibility of network status data and provide audit capabilities for network operations. In addition, blockchain technology can also achieve decentralized management and control, and improve the security and reliability of the network. In summary, through the implementation of the above steps, it is possible to achieve real-time acquisition of power line network topology and device connection status, optimize signal transmission and communication system design, improve the transmission performance of communication systems in complex power line channels, build a manageable virtual overlay network, realize real-time status monitoring and fault detection, ensure data security and integrity, and other effects. These effects will help improve the performance, reliability and management efficiency of power line networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0013] Figure 1 It is a schematic flow chart of the steps of the communication control method based on power line network dual-mode communication of the present invention;

[0014] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0015] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a communication control method based on power line network dual-mode communication, the method comprising the following steps:

[0020] Step S1: obtaining the topology structure and device connection status of the power line network in real time through network sniffing technology, thereby generating a dynamic network topology map;

[0021] Step S2: Modeling the complex multipath power line channel based on fractal theory, thereby obtaining a fractal channel response matrix describing the time-varying multipath effect;

[0022] Step S3: splitting the single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data; performing containerized independent deployment according to the codec microservice data, and adaptively and dynamically adjusting the bitstream data transmitted between the codec microservices according to the fractal channel response matrix, thereby generating adaptive bitstream data;

[0023] Step S4: construct a virtual overlay network based on the dynamic network topology and adaptive bitstream data through SDN and NFV technologies; decompose the pre-acquired node state set into a serialized object model based on the virtual overlay network, and perform incremental snapshots to obtain incremental state snapshot data; store the incremental state snapshot data based on a distributed hash table, and perform state visualization. Figure 1 Consistency processing and hot backup are performed to obtain seamless mirror data;

[0024] Step S5: Based on the blockchain technology, consensus switching transmission is performed in the blockchain network according to the seamless state mirror data, and information verification is performed through hash fingerprints to obtain hash comparison result data.

[0025] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a communication control method based on power line network dual-mode communication of the present invention. In this example, the communication control method based on power line network dual-mode communication includes the following steps:

[0026] Step S1: obtaining the topology structure and device connection status of the power line network in real time through network sniffing technology, thereby generating a dynamic network topology map;

[0027] The embodiment of the present invention uses a network sniffing tool, such as Wireshark or tcpdump, to capture data packets on the power line network. By analyzing the source address, destination address and protocol information in the data packet, the connection relationship and topology between devices can be identified, and a dynamic network topology map can be generated.

[0028] Step S2: Modeling the complex multipath power line channel based on fractal theory, thereby obtaining a fractal channel response matrix describing the time-varying multipath effect;

[0029] The embodiment of the present invention uses fractal theory to model complex multipath power line channels. This may involve a fractal noise generator, a fractal interpolation method or other fractal modeling techniques. By performing fractal modeling on the power line channel, a fractal channel response matrix describing the time-varying multipath effect can be obtained.

[0030] Step S3: splitting the single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data; performing containerized independent deployment according to the codec microservice data, and adaptively and dynamically adjusting the bitstream data transmitted between the codec microservices according to the fractal channel response matrix, thereby generating adaptive bitstream data;

[0031] The embodiment of the present invention uses appropriate programming languages ​​and development frameworks, such as Python and Docker, to split the single codec engine into multiple microservices. Each microservice is responsible for a specific function. These microservices are containerized and independently deployed, and Docker or a similar containerization platform can be used. According to the fractal channel response matrix, the bitstream data transmitted between the codec microservices is adaptively and dynamically adjusted to generate adaptive bitstream data.

[0032] Step S4: construct a virtual overlay network based on the dynamic network topology and adaptive bitstream data through SDN and NFV technologies; decompose the pre-acquired node state set into a serialized object model based on the virtual overlay network, and perform incremental snapshots to obtain incremental state snapshot data; store the incremental state snapshot data based on a distributed hash table, and perform state visualization. Figure 1 Consistency processing and hot backup are performed to obtain seamless mirror data;

[0033] The embodiment of the present invention uses software defined network (SDN) and network function virtualization (NFV) technologies to build a virtual overlay network. SDN technology can dynamically manage and configure network traffic through a controller. NFV technology can deploy network functions on ordinary servers in a virtualized manner. According to the dynamic network topology map and adaptive code stream data, a virtual overlay network is built using SDN and NFV technologies to map physical network resources into a virtual network. Based on the virtual overlay network, the pre-acquired node state set is decomposed into a serialized object model and an incremental snapshot is taken. This can be done by running an agent on the node to collect node state data, serialize it into an object model, and then generate incremental state snapshot data as needed. A distributed hash table is used to store incremental state snapshot data. A distributed hash table is a distributed storage structure that can store data in a dispersed manner on multiple nodes to achieve high availability and load balancing. By sharding and distributing the incremental state snapshot data in a distributed hash table, the reliability and scalability of the data can be ensured. In a distributed system, the state views between nodes may be inconsistent due to network delays and node failures. In order to maintain the consistency of the state view, a consistency protocol such as Paxos or Raft can be used to reach a consensus between nodes. These protocols ensure that all nodes have a consistent understanding of the state view, thereby ensuring the accuracy and reliability of seamless state mirroring data. In order to improve the reliability and fault tolerance of the system, hot backup is a common practice in a distributed environment. By replicating data and services to multiple nodes and updating them synchronously in real time, you can quickly switch to the backup node when the primary node fails, thereby achieving high availability and continuous service.

[0034] Step S5: Based on the blockchain technology, consensus switching transmission is performed in the blockchain network according to the seamless state mirror data, and information verification is performed through hash fingerprints to obtain hash comparison result data.

[0035] The embodiment of the present invention uses blockchain technology to achieve consensus switching transmission of seamless state mirror data. Blockchain is a distributed, tamper-proof database that can ensure the security and consistency of data. The seamless state mirror data is stored in the blockchain network, and a consensus algorithm, such as Proof of Work or Proof of Stake, is used to ensure the credibility and consistency of the data. By calculating the hash fingerprint of the seamless state mirror data, the integrity and authenticity of the data can be verified. The hash fingerprint is a unique identifier, and any modification to the data will result in a change in the hash fingerprint. By comparing the calculated hash fingerprint with the expected hash fingerprint, it can be verified whether the data has been tampered with or damaged.

[0036] The present invention obtains the topological structure and device connection status of the power line network in real time and can provide a comprehensive understanding of the power line network. The generated dynamic network topology map can be used for network management and optimization, helping to identify bottlenecks, faults and abnormal conditions in the network, and improving network performance and reliability. Modeling complex multipath power line channels based on fractal theory can more accurately describe the time-varying multipath effect of power line channels. The fractal channel response matrix can provide information about channel attenuation, delay spread and frequency selectivity, which is helpful to optimize signal transmission and communication system design. Splitting the single codec engine into multiple microservices can improve the scalability and flexibility of the system. Through containerized independent deployment, each microservice can be managed and operated independently. Adaptive dynamic adjustment of code stream data according to the fractal channel response matrix can optimize the coding and decoding process, improve the transmission performance of the communication system in complex power line channels, and simplify the complex power line network environment. Building a virtual overlay network through SDN (software defined network) and NFV (network function virtualization) technology can provide a more flexible and manageable network architecture. Decomposing and incrementally snapshotting the node state set based on the virtual overlay network can reduce the overhead of data transmission and storage. Using distributed hash tables for storage and state visualization Figure 1Consistency processing can improve the reliability and availability of data. The generation of seamless state mirror data helps to realize functions such as real-time status monitoring, fault detection and network recovery. Consensus switching transmission of seamless state mirror data based on blockchain technology can ensure the security and immutability of data. Information verification through hash fingerprint can verify the integrity and consistency of data. This can help ensure the credibility of network status data and provide audit capabilities for network operations. In addition, blockchain technology can also achieve decentralized management and control, and improve the security and reliability of the network. In summary, through the implementation of the above steps, it is possible to achieve real-time acquisition of power line network topology and device connection status, optimize signal transmission and communication system design, improve the transmission performance of communication systems in complex power line channels, build a manageable virtual overlay network, realize real-time status monitoring and fault detection, ensure data security and integrity, and other effects. These effects will help improve the performance, reliability and management efficiency of power line networks.

[0037] Preferably, step S1 comprises the following steps:

[0038] Step S11: deploying a network sniffing device on the existing power line network to obtain a sniffing perception network, wherein the network sniffing device includes an active detection probe and a passive detection probe;

[0039] Step S12: Initiate a customized detection data packet according to a preset cycle time by sniffing an active detection probe in the sensing network, thereby obtaining detection indicator data;

[0040] Step S13: Analyze the link status and device connectivity of the detection indicator data to obtain active detection result data;

[0041] Step S14: continuously capture network data packets by sniffing the passive detection probe in the sensing network, and perform node behavior detection by parsing MAC / IP layer information, thereby obtaining passive capture result data;

[0042] Step S15: performing event causal relationship analysis based on timestamps on the passive capture result data, thereby obtaining event causal relationship data;

[0043] Step S16: construct a dynamic network topology map based on the event causal relationship data and the active detection result data.

[0044] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0045] Step S11: deploying a network sniffing device on the existing power line network to obtain a sniffing perception network, wherein the network sniffing device includes an active detection probe and a passive detection probe;

[0046] The embodiment of the present invention uses a network sniffing device to monitor data flow and communication activities on the power line network. The network sniffing device generally includes a network probe, a sensor or a specific hardware device. An active detection probe can actively initiate a detection request and obtain a response, while a passive detection probe passively captures passing data packets.

[0047] Step S12: Initiate a customized detection data packet according to a preset cycle time by sniffing an active detection probe in the sensing network, thereby obtaining detection indicator data;

[0048] The active detection probe of the embodiment of the present invention can use a network detection tool, such as Ping, Traceroute or a custom detection tool, to send a customized detection data packet according to a preset cycle time. These detection data packets can contain specific requests or instructions for measuring detection indicators such as link delay, packet loss rate, and bandwidth utilization.

[0049] Step S13: Analyze the link status and device connectivity of the detection indicator data to obtain active detection result data;

[0050] The embodiments of the present invention use network analysis tools and algorithms, such as path analysis algorithms, link state algorithms, or topology discovery algorithms, to analyze the detection index data. These tools and algorithms can parse the information in the detection data packet and calculate the link state, device connectivity, and path information to generate active detection result data.

[0051] Step S14: continuously capture network data packets by sniffing the passive detection probe in the sensing network, and perform node behavior detection by parsing MAC / IP layer information, thereby obtaining passive capture result data;

[0052] The passive detection probe of the embodiment of the present invention can use a network sniffing tool, such as Wireshark or tcpdump, to continuously capture the network data packets passing through. By parsing the MAC layer and IP layer information in the data packet, the behavior, communication mode and data interaction of the node can be identified, thereby generating passive capture result data.

[0053] Step S15: performing event causal relationship analysis based on timestamps on the passive capture result data, thereby obtaining event causal relationship data;

[0054] The embodiment of the present invention uses a log analysis tool or a custom analysis script to perform timestamp analysis on the passive capture result data. By comparing and sorting the event occurrence times, the causal relationship between events can be determined and event causal relationship data can be generated.

[0055] Step S16: construct a dynamic network topology map based on the event causal relationship data and the active detection result data.

[0056] The embodiments of the present invention use network topology discovery tools, graph theory algorithms or customized topology construction methods to construct a dynamic network topology graph based on event causal relationship data and active detection result data. This may include representing nodes as nodes in the graph, representing links as edges in the graph, and determining the relationship between nodes and links based on events and detection results.

[0057] The present invention can monitor the communication interaction in the power line network in real time through the deployment of network sniffing devices. The active detection probe can actively send customized detection data packets, while the passive detection probe can capture network data packets. Such a sniffing perception network can provide comprehensive perception of power line network activities, including information such as data flow, device connection and communication behavior. The active detection probe can periodically send customized detection data packets to measure and evaluate the performance and availability of the power line network. By collecting detection indicator data, key performance indicators such as network delay, packet loss rate, bandwidth utilization, etc. can be obtained. These indicator data are crucial for network management and troubleshooting. By analyzing the link status and device connectivity of the detection indicator data, the stability and reliability of each link in the power line network can be evaluated. The active detection result data provides information about link quality, device health status and network topology, which is helpful for network fault diagnosis, performance optimization and resource planning. The passive detection probe can continuously capture network data packets in the power line network, and parse and analyze them. By parsing MAC / IP layer information, the behavior of the node can be detected, such as identifying the device, monitoring the communication mode and detecting abnormal behavior. The passive capture result data provides insights into real-time behavior in the network, which is helpful for network security monitoring and intrusion detection. By performing timestamp-based event causality analysis on passively captured result data, the causal relationship between events in the network can be determined. This helps to understand the order of events, their correlations and impacts. Event causality data provides an understanding of the evolution and interaction of events in the network, providing important clues for network troubleshooting and performance optimization. Based on event causality data and active detection result data, a dynamic network topology diagram can be constructed to show the connection relationship and communication traffic between devices in the power line network. The dynamic network topology diagram can provide a visual representation of the network structure and evolution, helping network administrators and operation and maintenance personnel to better understand changes in network topology and perform network planning, troubleshooting, capacity management and other tasks.

[0058] Preferably, step S16 comprises the following steps:

[0059] Step S161: performing format unification processing on the event causal relationship data, thereby obtaining purified event data;

[0060] The embodiment of the present invention uses data preprocessing tools and techniques, such as a data processing library (e.g., pandas) in the Python programming language, to perform format unification processing on event causal relationship data. This may include processing missing data, removing duplicate data, performing data type conversion, and other operations to ensure the consistency and availability of event data.

[0061] Step S162: performing outlier detection and mean filling on the active detection result data to obtain purified detection data;

[0062] The embodiment of the present invention uses an anomaly detection algorithm, such as a box plot or an outlier detection algorithm, to perform outlier detection on the active detection result data. For the detected outliers, corresponding processing strategies can be adopted according to business needs, such as deleting outliers or performing mean padding to obtain purified detection data.

[0063] Step S163: Identify potential network nodes and links on the cleansed event data, thereby obtaining a topology element set;

[0064] The embodiment of the present invention uses network topology analysis tools and algorithms, such as graph theory algorithms or network analysis libraries (such as NetworkX), to identify nodes and links in the cleansed event data. This can determine network nodes and links based on information such as source addresses, destination addresses, and communication modes in the event data, and construct a topology element set.

[0065] Step S164: verifying the topology element set based on link status and device connectivity according to the cleansing detection data, thereby obtaining a topology available element set;

[0066] The embodiment of the present invention uses network detection tools and algorithms, such as Ping, Traceroute or link state algorithms, to verify the topology element set based on the purified detection data. By detecting the sending and receiving of data packets, the connectivity of the link can be verified, the link delay, packet loss rate and other indicators can be measured, and the available topology elements can be screened out from the topology element set to form a topology available element set.

[0067] Step S165: mining the potential causal relationship between node behavior and network state change based on the cleansed detection data and the cleansed event data, and constructing a causal model based on time series that describes the dynamic relationship between events and topology changes;

[0068] The embodiments of the present invention use data mining and machine learning techniques, such as association rule mining, time series analysis or causal reasoning algorithms, to analyze the purified detection data and event data. By mining the association between node behavior and network state changes, a dynamic causal model describing events and topology changes can be constructed.

[0069] Step S166: Integrate the elements and structures of the topology available element set and the causal model to obtain a dynamic network topology diagram.

[0070] The embodiment of the present invention uses a topology integration tool, a data visualization library or a customized topology construction method to integrate the topology available element set and the causal model. This may involve representing nodes, links and event relationships as nodes and edges in a graph, and describing node behaviors and topology changes based on the time series information in the causal model, thereby generating a dynamic network topology graph.

[0071] The format unification processing of the present invention can standardize the representation form of event causal relationship data so that it conforms to a unified data structure and standard. The acquisition of purified event data helps to improve the consistency and availability of data and provide a better data basis for subsequent analysis and mining. Outlier detection can identify outliers in active detection result data, that is, data points that are significantly deviated from normal conditions. Mean filling can fill in the missing data at the location of the outliers, making the data set more complete and reliable. The acquisition of purified detection data helps to eliminate the interference of outliers on subsequent analysis and improve the accuracy and credibility of the data. By identifying network nodes and links in the purified event data, the nodes and links existing in the power line network can be determined, and an initial set of topological elements can be constructed. The topological element set provides a basis for subsequent topological verification and association analysis. By verifying the topological element set using purified detection data, the validity and availability of the topological elements can be evaluated. Verification based on link status and device connectivity helps to identify problems in the topological element set, such as disconnected links or unreachable devices. After obtaining the topologically available element set, it can be ensured that the constructed dynamic network topology map is more accurate and reliable. By mining the potential causal associations between cleansed detection data and cleansed event data, we can understand the relationship between node behavior and network state changes. Building a causal model based on time series helps describe the dynamic relationship between events and topology changes, providing an understanding and prediction capability of the evolution of network behavior. By integrating the elements and structures of the topologically available element set and the causal model, reliable topological information and dynamic relationship models can be combined to obtain a dynamic network topology map. The dynamic network topology map can display the nodes, links, and their evolution process in the power line network, providing a more accurate and comprehensive view for network management and troubleshooting. Taken together, these steps include improving the consistency and availability of data, improving the accuracy and reliability of topology, revealing the relationship between node behavior and network status, and providing an accurate and comprehensive view of the dynamic network topology, thereby helping tasks such as network management and troubleshooting.

[0072] Preferably, step S2 comprises the following steps:

[0073] Step S21: sampling the power line signal at different time and frequency resolutions through programmable logic to obtain multi-scale time domain data and multi-scale frequency domain data;

[0074] Step S22: performing self-similarity analysis on the multi-scale time domain data and the multi-scale frequency domain data, and performing long-range dependency feature detection, thereby obtaining self-similarity statistical feature data;

[0075] Step S23: performing fractal dimension estimation of the multipath power line channel on the self-similarity statistical feature data by box counting, thereby obtaining fractal dimension estimation data;

[0076] Step S24: performing channel multipath response analysis according to the fractal dimension estimation data and constructing a random fractal process model;

[0077] Step S25: coefficientizing the random fractal process model to obtain a fractal channel response matrix describing the time-varying multipath effect.

[0078] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0079] Step S21: sampling the power line signal at different time and frequency resolutions through programmable logic to obtain multi-scale time domain data and multi-scale frequency domain data;

[0080] The embodiment of the present invention uses a programmable logic device (such as FPGA) to sample the power line signal. By adjusting the sampling rate and sampling duration, time domain data with different time resolutions can be obtained. At the same time, frequency domain analysis methods such as fast Fourier transform (FFT) or wavelet transform can be used to perform frequency decomposition on the sampled signal to obtain multi-scale frequency domain data.

[0081] Step S22: performing self-similarity analysis on the multi-scale time domain data and the multi-scale frequency domain data, and performing long-range dependency feature detection, thereby obtaining self-similarity statistical feature data;

[0082] The embodiment of the present invention uses a self-similarity analysis method, such as an overlapping sliding window method or a fractal block analysis method, to perform self-similarity analysis on multi-scale time domain data. By calculating a self-similarity index, such as an autocorrelation function or a fractal dimension, the long-range dependency characteristics of the signal are detected, and self-similarity statistical feature data is obtained.

[0083] Step S23: performing fractal dimension estimation of the multipath power line channel on the self-similarity statistical feature data by box counting, thereby obtaining fractal dimension estimation data;

[0084] The embodiment of the present invention uses the box counting method to estimate the fractal dimension of the self-similarity statistical feature data. The box counting method divides the data into boxes of different scales and calculates the number of boxes required at each scale. By fitting the relationship between the number of boxes and the scale, the fractal dimension of the channel can be estimated, thereby obtaining fractal dimension estimation data.

[0085] Step S24: performing channel multipath response analysis according to the fractal dimension estimation data and constructing a random fractal process model;

[0086] The embodiment of the present invention uses a channel multipath response analysis method, such as time-frequency analysis or correlation analysis, to analyze the multipath response of the channel based on fractal dimension estimation data. According to the analysis results, a random fractal process model suitable for describing the multipath response can be constructed, such as a fractal Gaussian process or a fractal Brownian motion model.

[0087] Step S25: coefficientizing the random fractal process model to obtain a fractal channel response matrix describing the time-varying multipath effect.

[0088] The embodiment of the present invention converts the random fractal process model into a fractal channel response matrix describing the event multipath response through coefficientization operation. The specific coefficientization method depends on the selected fractal process model, and related technologies and algorithms, such as least squares method or maximum likelihood estimation, can be used to estimate the parameters and coefficients of the model.

[0089] The present invention can obtain data at different time and frequency resolutions by performing multi-scale sampling on power line signals. Multi-scale time domain data and multi-scale frequency domain data contain detailed information of power line signals, which are helpful for subsequent analysis and processing, such as self-similarity analysis and spectrum analysis. Self-similarity analysis can reveal the long-range dependency characteristics existing in the signal, that is, the signal has similar statistical properties at different time scales. By detecting the self-similarity statistical characteristics, the long-range dependency characteristics of the signal can be extracted, providing a basis for subsequent channel analysis. The box counting method is a technology for estimating fractal dimension. By performing box counting on self-similarity statistical feature data, the fractal dimension of a multipath power line channel can be estimated. Fractal dimension estimation data provides information about the multipath characteristics of the channel, which can help understand the propagation and attenuation of the signal in the power line channel. By analyzing the channel multipath response based on the fractal dimension estimation data, the propagation characteristics and multipath effects of the signal in the power line channel can be understood. By constructing a random fractal process model, the multipath propagation process in the power line channel can be simulated, providing a basis for subsequent signal processing and communication system design. By coefficientizing the random fractal process model, a fractal channel response matrix describing the event multipath response can be obtained. This matrix provides detailed information about the multipath effect and can be used for the design and performance evaluation of signal processing algorithms, such as equalization, demodulation, and signal recovery. The combined effect of these steps is that through multi-scale sampling, self-similarity analysis, fractal dimension estimation, and random fractal process model construction, detailed characteristics and descriptions of the power line channel can be obtained. These characteristics and descriptions can provide important references for the design of power line communication systems, the optimization of signal processing algorithms, and performance evaluation, thereby improving the reliability and efficiency of power line communication.

[0090] Preferably, step S22 includes the following steps:

[0091] Step S221: performing wavelet decomposition on the multi-scale time domain data to decompose the signal into approximate components and detail components of different time scales, thereby obtaining time domain wavelet decomposition coefficients;

[0092] The embodiment of the present invention collects or generates time domain data to be analyzed, such as time series or signals. According to the required resolution and properties, an appropriate wavelet basis function is selected. Common choices include Daubechies wavelet, Haar wavelet, Symlet wavelet, etc. Use wavelet transform tools, such as discrete wavelet transform (DWT) or continuous wavelet transform (CWT), to perform wavelet decomposition on the time domain data. Different decomposition levels can be selected, and each level of decomposition produces an approximate component and a detail component.

[0093] Step S222: performing wavelet packet decomposition on the multi-scale frequency domain data to decompose the signal into different frequency sub-bands, thereby obtaining frequency domain wavelet packet decomposition coefficients;

[0094] The embodiment of the present invention can obtain a frequency domain representation by performing Fourier transform or other frequency analysis methods on the time domain data. According to the required frequency resolution and properties, an appropriate wavelet packet basis function is selected. Common choices include Daubechies wavelet packets, Haar wavelet packets, Symlet wavelet packets, etc. Use wavelet packet transform tools, such as discrete wavelet packet transform (DWPT) or continuous wavelet packet transform (CWT), to perform wavelet packet decomposition on the frequency domain data. Different decomposition layers and nodes can be selected, and each layer of decomposition produces multiple sub-bands.

[0095] Step S223: Calculate the statistics of standard deviation, peak factor, skewness and kurtosis according to the frequency domain wavelet packet decomposition coefficients and the time domain wavelet decomposition coefficients, so as to obtain statistical set data;

[0096] The embodiment of the present invention extracts the coefficient value of each subband according to the frequency domain wavelet packet decomposition coefficient obtained in step S222. The coefficient value of each approximate component and detail component is extracted according to the time domain wavelet decomposition coefficient obtained in step S221. The extracted coefficient values ​​are statistically calculated, and common statistics include standard deviation, peak factor, skewness and kurtosis. These statistics can be calculated by formula or statistical function. The calculated statistics are integrated into a statistical set data, and each statistic corresponds to a feature.

[0097] Step S224: performing a self-similarity test based on a change trend on the statistical quantity set data by using a variogram technique, thereby obtaining self-similarity test result data;

[0098] The embodiment of the present invention calculates the variogram of the statistical quantity set data. The variogram is a function that measures the trend of data changes, and describes the degree of data change by calculating the difference between data points. The calculated variogram is further processed to calculate its rate of change. The rate of change can be obtained by calculating adjacent differences or slopes. According to the rate of change of the variogram, the data is judged for self-similarity. Common methods include R / S analysis, periodogram method and wavelet transform domain technology. These methods can perform statistical tests based on the self-similarity structure of the data to determine whether the data has self-similarity. The results of the self-similarity test are integrated into self-similarity test result data. Usually, the result data will contain indicators, thresholds or probability values ​​related to self-similarity.

[0099] Step S225: performing long-range dependency parameter estimation through FBM based on the self-similarity test result data, thereby obtaining a long-range dependency parameter set;

[0100] The embodiment of the present invention establishes a fractional Brownian motion model based on the self-similarity test result data. FBM is a random process model with a long-range dependency structure, which is characterized by self-similarity. By estimating the parameters of the FBM model, parameters describing the long-range dependency are obtained, and common parameters include the Hurst exponent or the fractal dimension. These parameters can reflect the long-range dependency properties of the signal. The estimated long-range dependency parameters are integrated into a long-range dependency parameter set, and each parameter corresponds to a feature.

[0101] Step S226: performing feature selection on the long-range dependency parameter set and the statistical quantity set data, and performing weight assignment, so as to obtain self-similarity statistical feature data.

[0102] The embodiment of the present invention performs feature selection on the long-range dependent parameter set and the statistical quantity set data according to the specific application requirements. Statistical methods (such as correlation analysis, chi-square test) or machine learning methods (such as principal component analysis (PCA), recursive feature elimination (RFE)) can be used to determine the most representative and important features. According to the importance of the features, weights are assigned to the selected features. The weights can be set according to experience or based on the importance of the features. The features that have been feature selected and weighted are integrated into self-similarity statistical feature data, which will be used to describe the self-similarity characteristics of the signal.

[0103] The present invention can extract the features of the signal at different time scales by decomposing the signal into approximate components and detail components of different scales. By performing wavelet decomposition on multi-scale time domain data, wavelet decomposition coefficients with different time resolutions can be obtained for subsequent feature extraction and analysis. Wavelet packet decomposition is a signal decomposition technology performed in the frequency domain. By decomposing the signal into different frequency sub-bands, the features of the signal at different frequencies can be extracted. By performing wavelet packet decomposition on multi-scale frequency domain data, wavelet packet decomposition coefficients with different frequency resolutions can be obtained for subsequent feature extraction and analysis. By calculating the statistics of the wavelet packet decomposition coefficients and the time domain wavelet decomposition coefficients, such as standard deviation, peak factor, skewness and kurtosis, the statistical features of the signal can be obtained. The statistical quantity set data provides the statistical properties of the signal at different time and frequency scales, which is used for subsequent self-similarity analysis and feature selection. The variogram technology is a method for testing the self-similarity of the signal. By analyzing the change trend in the statistical quantity set data, it can be tested whether the signal has self-similarity. The self-similarity test result data provides information about the self-similarity of the signal, which provides a basis for subsequent parameter estimation and feature selection. Fractional Brownian motion (FBM) is a method for modeling the long-range dependence of signals. By estimating parameters based on the self-similarity test result data, the long-range dependence parameter set of the signal can be obtained. The long-range dependence parameter set provides information about the long-range correlation of the signal, which is of great significance for understanding the long-term changes of the signal and establishing the corresponding model. By performing feature selection on the long-range dependence parameter set and the statistical set data, the most representative features can be screened out and weighted. The self-similarity statistical feature data provides a comprehensive description of the self-similarity of the signal, which contains important feature information. These features can be used for further analysis, modeling or classification tasks to help understand the self-similarity characteristics of the signal and extract relevant information. In summary, the combination of the above steps can extract the multi-scale features, statistical features and self-similarity features of the signal, providing a basis for further analysis, modeling and classification. These steps have broad application potential in signal processing, pattern recognition, time series analysis and other fields.

[0104] Preferably, step S23 includes the following steps:

[0105] Step S231: performing zero mean conversion on the self-similarity statistical feature data to obtain normalized feature data;

[0106] The embodiment of the present invention sums each feature and divides it by the number of features to obtain the mean of the feature data. The corresponding mean is subtracted from each feature data, that is, the feature data is subtracted from the mean calculated in step 2, so as to convert the feature data into zero mean.

[0107] Step S232: Divide the numerical space range of the normalized feature data into boxes of different scales, and count the number of normalized feature data falling into each box at each scale, so as to obtain box point distribution data;

[0108] According to the application requirements, the embodiment of the present invention determines to divide the numerical space into boxes of different scales, and the range of each box is predefined. The range of the box can be determined according to the minimum and maximum values, and the entire range can be equally divided according to the number of scales. For each box, the number of normalized feature data that falls into the range of the box is counted. The statistics can be performed using methods such as counters or histograms; repeat until all boxes are counted. The statistical data of the number of points in the box at each scale is integrated into the box point distribution data, and each scale corresponds to a feature.

[0109] Step S233: performing least square fitting on the relationship between the scale and the number of non-empty boxes according to the box point number distribution data, thereby obtaining fitting curve parameters and fitting curve residual data;

[0110] The embodiment of the present invention uses the scale in the box point distribution data as the horizontal coordinate and the corresponding number of non-empty boxes as the vertical coordinate to draw a scatter plot. According to the scatter plot, the least squares method is used for fitting, and a curve is fitted to describe the relationship between the scale and the number of non-empty boxes. Common fitting methods include linear regression, polynomial fitting, etc. According to the fitting results, the parameters of the fitting curve, such as the slope, intercept, etc., are calculated. The difference between the fitting curve and the actual data is called the residual, and the residual value of each data point is calculated.

[0111] Step S234: performing a fractal dimension value estimation based on the curve slope according to the fitting curve parameters, thereby obtaining a fractal dimension estimation value;

[0112] The embodiment of the present invention calculates the estimated value of the fractal dimension based on the slope of the fitting curve using a specific calculation formula or algorithm. The specific calculation method depends on the fractal dimension estimation algorithm used, such as the box counting method, the information dimension method, etc.

[0113] Step S235: Calculate the confidence interval of the fractal dimension for the fractal dimension estimation value according to the residual distribution in the residual data of the fitting curve, so as to obtain the fractal dimension estimation data.

[0114] The embodiment of the present invention performs statistical analysis on the residual data, such as calculating the mean, variance, standard deviation, etc. of the residual. According to the characteristics of the residual distribution, an appropriate statistical method is used to calculate the confidence interval of the fractal dimension estimate. Commonly used methods include the Bootstrap method of the confidence interval, the confidence interval of the parameter estimation, etc. According to the calculation result of the confidence interval, the estimated value of the fractal dimension and the corresponding confidence interval range are obtained to describe the credibility of the fractal feature.

[0115] The present invention can adjust the mean of the data to zero by performing zero mean on the self-similarity statistical feature data, so that the data is more comparable in value. Normalized feature data helps to eliminate the scale difference between feature data and provide a more accurate feature representation. By dividing the normalized feature data into boxes of different scales and counting the number of data points in each box, the distribution of data at different scales can be obtained. The box point distribution data provides the distribution characteristics of the data at different scales, which is helpful for subsequent fractal dimension estimation and confidence interval calculation. By performing least squares fitting on the box point distribution data, a relationship curve between the scale and the number of non-empty boxes can be fitted. The fitting curve parameters provide a description of the data distribution and can be used for further fractal dimension estimation. The fitting curve residual data provides an evaluation of the fitting degree of the fitting curve, which is helpful for subsequent fractal dimension confidence interval calculation. The fractal dimension of the data can be estimated by the slope of the fitting curve. The fractal dimension is an indicator to measure the self-similarity of the data, which describes the roughness and complexity of the data at different scales. The fractal dimension estimate provides a quantitative description of the self-similarity of the data, which helps to understand the intrinsic structure and characteristics of the data. By analyzing the residual distribution in the residual data of the fitting curve, the confidence interval of the fractal dimension estimate can be calculated. The confidence interval of the fractal dimension provides a measure of the uncertainty of the fractal dimension estimate, which helps to evaluate the reliability of the estimation results. The fractal dimension estimate data provides statistical characteristics of the self-similarity of the data, which is helpful for further analysis and comparison. Combining these steps, the fractal dimension estimate and confidence interval of the self-similarity statistical characteristic data can be obtained, which provides a quantitative description of the intrinsic structure and characteristics of the data, which is helpful for data analysis, comparison and further application.

[0116] Preferably, step S3 comprises the following steps:

[0117] Step S31: splitting a single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data, wherein the microservices include source coding service, channel coding service, modulation and demodulation service, channel decoding service and source decoding service;

[0118] The embodiment of the present invention performs functional decomposition on the original single codec engine, and divides different codec functions into independent microservices. According to the specific codec algorithm and functional requirements, it is divided into microservices such as source coding service, channel coding service, modulation and demodulation service, channel decoding service and source decoding service. Determine the input and output interfaces of each microservice, and clearly define the data interaction method and format between them; these interfaces include input data format, output data format and service call method, etc. For each microservice, implement the corresponding encoding or decoding algorithm according to its functional requirements and interface definition. Use appropriate programming languages ​​and tools to develop according to the independence of microservices.

[0119] Step S32: each microservice is independently packaged and deployed in a containerized form according to the codec microservice data, thereby obtaining decoupled and horizontally expanded container image metadata;

[0120] The embodiment of the present invention packages each microservice into an independent container image; uses containerization technology, such as Docker, to package each microservice and its operating environment, dependencies, etc. into a container image; writes a container configuration file for each microservice to define the container's operating environment, dependencies, port mapping, etc.; the configuration file can be written in Dockerfile or a similar format. According to the container configuration file, a containerization tool is used to build and generate a container image for each microservice; these images contain the code, operating environment, and dependencies of the microservice; upload the generated container image to the image repository, and record relevant metadata, such as version number, label, etc. These metadata can be used for subsequent deployment and management.

[0121] Step S33: Kubernetes automatically orchestrates the deployment, expansion, and failover of the container according to the container image metadata, thereby obtaining the running status data of the microservice instance;

[0122] The embodiment of the present invention builds and configures a Kubernetes cluster, including the settings of the Master node and the Worker node. Ensure the availability and scalability of the cluster; use Kubernetes resource definition files (such as Deployment, Service, etc.) to define the deployment configuration of each microservice; the configuration file specifies information such as container images, number of replicas, resource requirements, etc. Use Kubernetes commands or management tools (such as kubectl) to apply the defined deployment configuration file to the Kubernetes cluster to trigger the deployment of microservices; Kubernetes will automatically create and manage microservice instances according to the configuration; Kubernetes can automatically perform expansion and contraction operations according to the actual load conditions, and increase or decrease microservice instances according to preset rules. At the same time, Kubernetes also supports failover and automatic recovery to ensure the high availability of microservices; Kubernetes provides monitoring and log collection tools that can monitor the operating status of microservice instances in real time, including indicators such as CPU usage and memory usage. These data can be obtained and analyzed through the Kubernetes API or an integrated monitoring system.

[0123] Step S34: transferring code streams between microservices according to the running status data of the microservice instances through the message queue, and obtaining the intermediate code stream data generated or required by each microservice;

[0124] The embodiment of the present invention selects a suitable message queue system, such as Apache Kafka, RabbitMQ, etc. The message queue system is used to implement asynchronous communication and data transfer between microservices. Determine the format and content of the message, including the data structure and field definition for communication between microservices. Common data exchange formats such as JSON or Protocol Buffers can be used. A client library of the message queue is integrated in the microservice, and the sending and receiving of messages is implemented through the library. Microservices can send and receive specific types of messages as needed, including microservice instance running status data and intermediate code stream data. After receiving the message, the microservice can perform corresponding processing according to the content of the message, such as performing encoding, decoding, flow control and other operations, and send the processing results to downstream microservices.

[0125] Step S35: according to the microservice load status in the intermediate bitstream data, the bitstream generation rate is limited by a flow control strategy, thereby obtaining the policy-limited bitstream data, wherein the flow control strategy includes limiting the bitstream generation rate. During the encoding or decoding process, the intermediate bitstream data is processed according to the flow control strategy to limit the bitstream generation rate;

[0126] The embodiment of the present invention can obtain the load status of the microservice by monitoring the running status data of the microservice (such as CPU and memory usage). The monitoring tools provided by Kubernetes or a customized monitoring system can be used for collection and analysis. According to the load status and performance requirements of the microservice, an appropriate flow control strategy is formulated. These strategies may include limiting the bitstream generation rate, adjusting the bitstream priority, etc. During the encoding or decoding process, the intermediate bitstream data is processed according to the flow control strategy to limit the bitstream generation rate. The bitstream generation rate can be dynamically adjusted using algorithms or rules to adapt to the current microservice load situation.

[0127] Step S36: Adaptively adjust the coding parameters of the policy-constrained code stream data according to the fractal channel response matrix, thereby generating adaptive code stream data.

[0128] The embodiments of the present invention understand the transmission characteristics and influencing factors of the channel by analyzing the characteristics and response matrix of the channel. This may include the frequency response and time-varying properties of the channel. According to the analyzed channel response matrix, a strategy for adaptive coding parameters is formulated. These strategies may include adjusting the coding method, code rate, modulation method, etc. According to the strategy, the coding parameters of the policy-restricted code stream data are adaptively adjusted. According to the channel response matrix and the current communication conditions, the coding parameters are dynamically adjusted to optimize the transmission effect and reliability of the code stream.

[0129] The present invention can realize functional decoupling and independent development, testing and deployment by splitting a single codec engine into multiple microservices; each microservice focuses on a specific functional module, thereby improving the maintainability and scalability of the system. Different microservices can be developed and deployed in parallel, thereby improving development efficiency; by independently packaging and deploying each microservice in a containerized form, the decoupling and independent deployment of microservices can be realized; each microservice is packaged as a container image, has its own operating environment and dependencies, and can be independently run on different hosts; the containerized deployment method has good portability and scalability, and can be easily expanded horizontally. By using Kubernetes for automated orchestration, automatic management of container deployment, expansion and contraction, and failover can be realized. Kubernetes can automatically schedule and manage the operating status of microservice instances according to the rules and policies defined by the container image metadata; this can improve the reliability and availability of the system and reduce the workload of manual management. By using message queues for communication between microservices, decoupling and asynchronous communication can be realized. Microservices can transmit microservice instance running status data and intermediate code stream data through message queues, avoiding direct point-to-point communication; this can improve the scalability and flexibility of the system and allow decoupling and independent evolution between microservices; by limiting the flow control strategy according to the microservice load status in the intermediate code stream data, performance problems caused by excessive microservice load can be avoided. By limiting the code stream generation rate, the load of the system can be controlled to prevent excessive resource occupation and performance degradation; this can improve the stability and reliability of the system. By adaptively adjusting the encoding parameters of the policy-limited code stream data according to the fractal channel response matrix, the transmission performance and adaptability of the code stream can be optimized. The fractal channel response matrix provides information about channel characteristics and can be used to dynamically adjust the encoding parameters so that the code stream can adapt to the transmission requirements under different channel conditions. This can improve the transmission efficiency and reliability of the system under different channel environments.

[0130] Preferably, step S36 includes the following steps:

[0131] Step S361: performing characteristic analysis on the fractal channel response matrix based on multipath effects and fading parameters, thereby obtaining a channel characteristic parameter set;

[0132] The embodiment of the present invention obtains the response matrix of the fractal channel through actual measurement or simulation; the matrix describes the transmission characteristics of the channel, including multipath effect, fading, etc. The channel response matrix is ​​subjected to feature analysis to extract parameters related to multipath effect and fading; commonly used feature parameters include path delay, path gain, phase, etc.; according to the result of feature analysis, a channel feature parameter set is generated. The parameter set is used to describe the characteristics of the channel as the basis for subsequent adaptive adjustment strategies.

[0133] Step S362: matching the channel characteristic parameter set with the preset adaptive strategy to obtain an adaptive adjustment strategy;

[0134] The embodiment of the present invention presets a set of adaptive adjustment strategies according to the requirements of the system and the characteristics of the channel. These strategies describe how to adjust the modulation and coding parameters according to the characteristic parameters of the channel to optimize the transmission performance of the system. The channel characteristic parameter set is matched with the preset adaptive strategy. According to a specific matching rule or algorithm, the adaptive adjustment strategy that best matches the channel characteristics is selected. According to the matching result, the adaptive adjustment strategy that best matches the current channel characteristics is obtained. These strategies will be used for subsequent code stream parameter adjustment.

[0135] Step S363: dynamically adjusting the modulation and coding parameters of the policy-restricted bitstream data according to the adaptive adjustment strategy, thereby obtaining an adjusted bitstream parameter set;

[0136] The embodiment of the present invention extracts the corresponding modulation and coding parameters from the policy-restricted code stream data obtained in the previous step; these parameters describe the modulation mode, code rate, etc. of the original code stream. According to the preset adaptive adjustment strategy, the modulation and coding parameters of the policy-restricted code stream data are dynamically adjusted; according to the channel characteristics and policy requirements, the parameter values ​​are adjusted to adapt to the current channel conditions; the adjusted modulation and coding parameters are combined into a code stream parameter set. These parameters will be used for subsequent code stream reconstruction.

[0137] Step S364: reconstructing the policy-restricted bitstream data using the bitstream parameter set, thereby obtaining adaptive bitstream data.

[0138] The embodiment of the present invention reconstructs the policy-restricted code stream data according to the code stream parameter set. According to the modulation and coding parameters, the code stream is decoded and modulated to restore the code stream representation of the original data. After the code stream is reconstructed, adaptive code stream data is obtained. These data have been optimized according to the channel characteristics and the adaptive adjustment strategy to improve the transmission performance of the system.

[0139] The present invention can obtain detailed information about channel characteristics by performing feature analysis on the fractal channel response matrix; in particular, the time-varying properties and fading conditions of the channel can be described more accurately when considering the multipath effect and fading parameters. By extracting the channel characteristic parameter set, effective basic data can be provided for the subsequent adaptive adjustment strategy; by matching the channel characteristic parameter set with the preset adaptive strategy, a suitable adaptive adjustment strategy can be selected according to the actual channel characteristics. The adaptive strategy can dynamically adjust the coding parameters according to the time-varying properties and fading conditions of the channel to optimize the transmission performance and adapt to different channel conditions. By matching the adaptive strategy, the system can be made more intelligent and adaptable, and the reliability and efficiency of the transmission can be improved. According to the adaptive adjustment strategy, the modulation and coding parameters of the policy-restricted code stream data are dynamically adjusted, and can be optimized according to the real-time channel characteristics. By dynamically adjusting the code stream parameters, the code stream can be made more adaptable to the current channel conditions, and the transmission efficiency and reliability of the code stream can be improved; thus, the transmission error and packet loss rate can be reduced, and the performance and user experience of the system can be improved. By using the adjusted code stream parameter set to reconstruct the code stream of the policy-restricted code stream data, adaptive code stream data that adapts to the current channel conditions can be generated. Adaptive bitstream data has optimized coding parameters and modulation methods, and can better adapt to the time-varying nature and fading of the channel. This can improve the transmission quality and reliability of the signal, reduce the transmission error rate, and thus improve the system performance and user experience. In short, the implementation of the above steps can achieve optimization and adaptive adjustment of policy-restricted bitstream data through channel feature analysis, adaptive adjustment strategy, dynamic adjustment of bitstream parameters and bitstream reconstruction, thereby improving transmission performance, reliability and user experience, and making the system more intelligent and adaptable.

[0140] Preferably, step S4 comprises the following steps:

[0141] Step S41: The SDN controller interacts with network devices according to the dynamic network topology map, thereby obtaining network global view data;

[0142] The embodiment of the present invention uses an SDN controller to interact with network devices, and sends commands and obtains network device status information through the controller. Based on device interaction and status information, a dynamic network topology map is constructed; the map reflects the connection relationship, topology structure, and link status of devices in the network. According to the dynamic network topology map, the interaction data of network devices is sorted and processed to obtain network global view data. These data describe the overall status and topology information of the network.

[0143] Step S42: virtualize the routing and firewall network functions in the dynamic network topology diagram into VNF components, and manage the VNF component life cycle based on the MANO architecture to obtain VNF instance operation status data;

[0144] The embodiment of the present invention virtualizes network functions such as routing and firewall in the dynamic network topology diagram into VNF components. These components have corresponding network functions and run in a virtualized environment. The MANO (Management and Orchestration) architecture is used to manage VNF components; this includes life cycle management such as creation, deployment, configuration, monitoring and maintenance of VNFs. The MANO architecture is used to monitor and manage VNF components and obtain the operating status data of their instances. These data include the operating status, performance indicators, resource utilization, etc. of the VNF.

[0145] Step S43: construct a logical overlay network according to the network global view data and the VNF instance running status data, thereby obtaining virtual overlay network view data;

[0146] The embodiment of the present invention constructs a logical overlay network based on the network global view data and the VNF instance running status data. The logical overlay network is an abstract network model that describes the network connection and functional relationship in the virtualized environment. According to the logical overlay network, virtual overlay network view data is generated. These data describe the network topology, connection relationship, and deployment location of VNF components in the virtualized environment.

[0147] Step S44: deploying the adaptive code stream data to the path of the virtual overlay network view data, and configuring corresponding forwarding and service demand strategies, thereby obtaining the virtual overlay network data;

[0148] The embodiment of the present invention deploys the adaptive bitstream data to the path of the virtual overlay network view data according to the adaptive bitstream data. This involves selecting appropriate paths and nodes to achieve data transmission and processing. According to the requirements of the adaptive bitstream data, the corresponding forwarding strategy and service requirements are configured. This includes setting forwarding rules for data packets, adjusting parameters of network devices, and specifying quality of service requirements. The deployment and configuration information of the adaptive bitstream data is combined with the virtual overlay network view data to obtain virtual overlay network data. These data describe the path, configuration, and service requirements of the adaptive bitstream in the virtualized environment.

[0149] Step S45: periodically acquire node status of the virtual overlay network data to obtain a node status set; serialize the node status set into an object model, compare it with the previous status, extract the incremental part, and perform an incremental snapshot to obtain incremental status snapshot data;

[0150] The embodiment of the present invention periodically obtains the status data of each node in the virtual overlay network. This includes the running status, connection status, resource utilization, etc. of the node. The obtained node status data is serialized into an object model for further processing and comparison. The current node status set is compared with the previous state. Through the comparison, the change of the node status can be determined, that is, which nodes have changed or updated. According to the comparison results, the incremental part of the node status set is extracted. These incremental parts represent the changes in the current state relative to the previous state. Based on the incremental part, incremental state snapshot data is generated. The incremental snapshot only contains the changed part of the state, which can reduce the transmission and storage overhead of data.

[0151] Step S46: Using a distributed hash table to store incremental state snapshot data in multiple nodes of the virtual overlay network data, and Figure 1 Coordination of consistency to obtain visual Figure 1 Incremental data; Figure 1 The incremental data is periodically mirrored to generate full data images, which are persistently stored on the hot backup node, thus obtaining seamless state mirror data.

[0152] The embodiment of the present invention uses a distributed hash table to store incremental state snapshot data on multiple nodes of the virtual overlay network data; this can achieve redundant storage and high availability of data. Figure 1 Coordinate the consistency of data on each node to avoid data inconsistency. Figure 1 Coordination and coordination Figure 1 Consistent incremental data; these data contain incremental state snapshot data on each node and maintain data consistency. Figure 1 The full data image is generated periodically for incremental data; the full data image contains complete network status information for backup and recovery. The full data image is stored persistently on the hot backup node; the hot backup node can provide fast data recovery and ensure data persistence. The full data image after persistent storage is seamless state image data; this data can be used for applications such as fault recovery, backup recovery, and visualization of network status.

[0153] The present invention interacts with network devices through an SDN controller, and obtains network global view data according to a dynamic network topology map, so as to realize comprehensive management and control of the entire network. The network global view data provides a global perspective on the network topology, device connection relationship and network status, and provides basic data for subsequent network management and decision-making. By virtualizing network functions such as routing and firewall in the dynamic network topology map into VNF components and managing them using the MANO architecture, flexible deployment and management of network functions can be realized. The VNF instance running status data provides information about the running status, resource utilization and performance indicators of the VNF components, and provides a basis for subsequent network optimization and fault handling. Based on the network global view data and the VNF instance running status data, constructing a logical overlay network can better describe the logical connection and service path in the network. The virtual overlay network view data provides information about the topology, connection relationship and service path of the virtual network, and provides a basis for subsequent traffic scheduling and service management. Deploying adaptive code stream data on the path of the virtual overlay network view data can realize flexible routing and service scheduling of the code stream. By configuring forwarding and service demand strategies, the code stream can be directed to the optimal path and service node according to the real-time network status and service requirements, thereby improving the transmission efficiency and service quality of the network. By periodically acquiring node status, the status information of each node in the virtual overlay network can be monitored in real time. By serializing the node status set into an object model and comparing it with the previous state, the incremental part of the node status can be extracted to achieve incremental snapshots. The incremental state snapshot data provides information about changes in the state of network nodes, providing a basis for subsequent state management and fault recovery. Using distributed hash tables to store incremental state snapshot data in multiple nodes of the virtual overlay network data can achieve distributed backup and redundant storage of data, improving data reliability and availability. Figure 1 Consistency coordination can ensure that the incremental state snapshot data on multiple nodes is consistent, avoiding data inconsistency issues. Periodically generating full data images and persistently storing them on hot backup nodes can achieve long-term data preservation and rapid recovery, ensuring seamless switching and continuity of network status.

[0154] Preferably, step S5 comprises the following steps:

[0155] Step S51: performing hash calculation on the seamless state mirror data to obtain state hash fingerprint data;

[0156] The embodiment of the present invention selects a suitable hash algorithm tool, such as SHA-256 (Secure Hash Algorithm), etc., for hash calculation of seamless state mirror data; according to the input requirements of the hash algorithm, the seamless state mirror data is divided into data blocks of appropriate size; larger data blocks may lead to low hash calculation efficiency, and smaller data blocks may increase the probability of hash collision; hash calculation tools are used to perform hash calculation on each data block to generate a corresponding hash value. The hash values ​​of all data blocks are combined into state hash fingerprint data; a simple method is to connect the hash values ​​in sequence to form a long hash string.

[0157] Step S52: redundantly encode the state hash fingerprint data using an error correction coding algorithm, and perform sharding to obtain redundant sharding data;

[0158] The embodiment of the present invention selects a suitable error correction coding algorithm, such as Reed-Solomon coding, etc.; error correction coding can make data fault-tolerant by adding redundant information, and can be restored even if part of the data is damaged. According to the requirements of error correction coding, the size and number of shards are determined; the shard size should be reasonably set according to the requirements of the redundant coding algorithm and the amount of data. The state hash fingerprint data is redundantly encoded to generate redundant coded data; redundant coding combines the original data with redundant information to provide fault tolerance. The redundant coded data is divided into multiple shards, and each shard is identified and numbered. A shard data set containing redundant information is obtained, and each shard contains part of the original data and redundant information.

[0159] Step S53: broadcast and diffuse the redundant shard data based on blockchain technology, form a consensus among participating nodes, and execute the switching process to obtain the switching consensus result data, wherein the switching process involves the backup and recovery of the state and the switching operation of the node, and the consensus process ensures that all participating nodes reach an agreement on the switching operation;

[0160] The embodiment of the present invention establishes a blockchain network, including participating nodes and a consensus algorithm; broadcasts and diffuses redundant shard data through the blockchain network; each participating node can receive the broadcasted shard data. The participating nodes use a consensus algorithm, such as Byzantine Fault Tolerance or Proof of Stake, to form a consensus in the blockchain network; the consensus process ensures that all participating nodes reach an agreement on the switching operation. According to the result of the consensus formation, the switching process is executed; this involves state backup and recovery, node switching operations, etc. The result data of the switching consensus is obtained, which represents the consensus reached by the participating nodes and indicates the execution status and result of the switching process.

[0161] Step S54: performing error correction decoding and integrity verification on the received redundant shard data according to the switching consensus result data, thereby obtaining a verified state hash value;

[0162] In the embodiment of the present invention, each participating node receives the broadcasted fragmented data; according to the redundant fragmented data and the error correction coding algorithm, the received fragmented data is error-corrected and decoded; the error correction decoding can restore partially damaged data and improve the integrity of the data. The decoded data is integrity verified to ensure that the data has not been tampered with or damaged; the decoded data can be hashed using a hash algorithm and compared with the received checksum. After error correction decoding and integrity verification, a verified state hash value is obtained, indicating the correctness and integrity of the received data.

[0163] Step S55: Compare the verified state hash value with the state hash fingerprint data to obtain hash comparison result data.

[0164] The embodiment of the present invention compares the verified state hash value with the previously calculated state hash fingerprint data; based on the result of the comparison operation, it is determined whether the state hash value matches the state hash fingerprint data; if they match, it indicates that the integrity of the data is verified; if they do not match, there is data tampering or damage. Based on the comparison result, hash comparison result data is generated to indicate the result of the data integrity verification; the comparison result can be recorded on the blockchain for subsequent audit or verification.

[0165] The present invention can generate state hash fingerprint data for unique identification and integrity verification of data by performing hash calculation on seamless state mirror data. The state hash fingerprint data can be used to verify the integrity of data in subsequent data transmission and processing to ensure that the data has not been tampered with or damaged. By redundantly encoding the state hash fingerprint data using an error correction coding algorithm, the redundancy of the data can be increased, and the reliability and fault tolerance of the data can be improved. The redundantly encoded data can be fragmented to further increase the transmission efficiency and parallel processing capability of the data. Based on the blockchain technology, the redundant fragmented data is broadcast and diffused to achieve distributed propagation and sharing of data. Participating nodes form a consensus to ensure the consistency and credibility of the data. The switching process can be performed to achieve seamless switching and state migration of the network and improve the availability and robustness of the network. According to the switching consensus result data, the received fragmented data is subjected to error correction decoding and integrity verification to restore the integrity and accuracy of the original data. The verified state hash value can be used to verify the correctness of the data and serve as a reference for subsequent steps. The verified state hash value is compared with the state hash fingerprint data to verify the integrity and consistency of the data. The hash comparison result data can be used to determine whether the data has been tampered with or damaged, and take corresponding processing measures to ensure the accuracy and security of the network status.

[0166] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0167] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A communication control method based on power line network dual-mode communication, characterized in that: The following steps are involved: Step S1: obtaining the topology structure of the power line network and the device connection status in real time through network sniffing technology, thereby generating a dynamic network topology map; Step S2: Modeling the complex multipath power line channel based on fractal theory, thereby obtaining a fractal channel response matrix describing the time-varying multipath effect; Step S3: splitting the single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data; Independent container deployment is performed based on the codec microservice data, and adaptive dynamic adjustment is performed on the bitstream data transmitted between the codec microservices based on the fractal channel response matrix, thereby generating adaptive bitstream data; Step S4: construct a virtual overlay network based on the dynamic network topology map and adaptive bitstream data through SDN and NFV technologies; Based on the virtual overlay network, the pre-acquired node state set is decomposed into a serialized object model, and incremental snapshots are performed to obtain incremental state snapshot data; the incremental state snapshot data is stored based on a distributed hash table, and the state view consistency processing is performed, and hot backup is performed to obtain seamless state mirror data; Step S5 is specifically: Step S51: performing hash calculation on the seamless state mirror data to obtain state hash fingerprint data; Step S52: redundantly encode the state hash fingerprint data using an error correction coding algorithm, and perform sharding to obtain redundant sharding data; Step S53: broadcast and diffuse the redundant shard data based on blockchain technology, form a consensus among participating nodes, and execute the switching process to obtain the switching consensus result data, wherein the switching process involves the backup and recovery of the state and the switching operation of the node, and the consensus process ensures that all participating nodes reach an agreement on the switching operation; Step S54: performing error correction decoding and integrity verification on the received redundant shard data according to the switching consensus result data, thereby obtaining a verified state hash value; Step S55: Compare the verified state hash value with the state hash fingerprint data to obtain hash comparison result data.

2. The communication control method based on power line network dual-mode communication according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: deploying a network sniffing device on the existing power line network to obtain a sniffing perception network, wherein the network sniffing device includes an active detection probe and a passive detection probe; Step S12: Initiate a customized detection data packet according to a preset cycle time by sniffing an active detection probe in the sensing network, thereby obtaining detection indicator data; Step S13: Analyze the link status and device connectivity of the detection indicator data to obtain active detection result data; Step S14: continuously capture network data packets by sniffing the passive detection probe in the sensing network, and perform node behavior detection by parsing MAC / IP layer information, thereby obtaining passive capture result data; Step S15: performing event causal relationship analysis based on timestamps on the passive capture result data, thereby obtaining event causal relationship data; Step S16: construct a dynamic network topology map based on the event causal relationship data and the active detection result data.

3. The communication control method based on power line network dual-mode communication according to claim 2, characterized in that: Step S16 includes the following steps: Step S161: performing format unification processing on the event causal relationship data, thereby obtaining purified event data; Step S162: performing outlier detection and mean filling on the active detection result data to obtain purified detection data; Step S163: Identify potential network nodes and links on the cleansed event data, thereby obtaining a topology element set; Step S164: verifying the topology element set based on link status and device connectivity according to the cleansing detection data, thereby obtaining a topology available element set; Step S165: mining the potential causal relationship between node behavior and network state change based on the cleansed detection data and the cleansed event data, and constructing a causal model based on time series that describes the dynamic relationship between events and topology changes; Step S166: Integrate the elements and structures of the topology available element set and the causal model to obtain a dynamic network topology diagram.

4. The communication control method based on power line network dual-mode communication according to claim 3, characterized in that: Step S2 includes the following steps: Step S21: sampling the power line signal at different time and frequency resolutions through programmable logic to obtain multi-scale time domain data and multi-scale frequency domain data; Step S22: performing self-similarity analysis on the multi-scale time domain data and the multi-scale frequency domain data, and performing long-range dependency feature detection, thereby obtaining self-similarity statistical feature data; Step S23: performing fractal dimension estimation of the multipath power line channel on the self-similarity statistical feature data by box counting, thereby obtaining fractal dimension estimation data; Step S24: performing channel multipath response analysis according to the fractal dimension estimation data and constructing a random fractal process model; Step S25: coefficientizing the random fractal process model to obtain a fractal channel response matrix describing the time-varying multipath effect.

5. The communication control method based on power line network dual-mode communication according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: performing wavelet decomposition on the multi-scale time domain data to decompose the signal into approximate components and detail components of different time scales, thereby obtaining time domain wavelet decomposition coefficients; Step S222: performing wavelet packet decomposition on the multi-scale frequency domain data to decompose the signal into different frequency sub-bands, thereby obtaining frequency domain wavelet packet decomposition coefficients; Step S223: Calculate the statistics of standard deviation, peak factor, skewness and kurtosis according to the frequency domain wavelet packet decomposition coefficients and the time domain wavelet decomposition coefficients, so as to obtain statistical set data; Step S224: performing a self-similarity test based on a change trend on the statistical quantity set data by using a variogram technique, thereby obtaining self-similarity test result data; Step S225: performing long-range dependency parameter estimation through FBM based on the self-similarity test result data, thereby obtaining a long-range dependency parameter set; Step S226: performing feature selection on the long-range dependency parameter set and the statistical quantity set data, and performing weight assignment, so as to obtain self-similarity statistical feature data.

6. The communication control method based on power line network dual-mode communication according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: performing zero mean conversion on the self-similarity statistical feature data to obtain normalized feature data; Step S232: Divide the numerical space range of the normalized feature data into boxes of different scales, and count the number of normalized feature data falling into each box at each scale, so as to obtain box point distribution data; Step S233: performing least square fitting on the relationship between the scale and the number of non-empty boxes according to the box point number distribution data, thereby obtaining fitting curve parameters and fitting curve residual data; Step S234: performing a fractal dimension value estimation based on the curve slope according to the fitting curve parameters, thereby obtaining a fractal dimension estimation value; Step S235: Calculate the confidence interval of the fractal dimension for the fractal dimension estimation value according to the residual distribution in the residual data of the fitting curve, so as to obtain the fractal dimension estimation data.

7. The communication control method based on power line network dual-mode communication according to claim 6, characterized in that: Step S3 includes the following steps: Step S31: splitting the single codec engine into multiple microservices according to functions, thereby obtaining codec microservice data, wherein the microservices include source coding service, channel coding service, modulation and demodulation service, channel decoding service and source decoding service; Step S32: each microservice is independently packaged and deployed in a containerized form according to the codec microservice data, thereby obtaining decoupled and horizontally expanded container image metadata; Step S33: Kubernetes automatically orchestrates the deployment, expansion, and failover of the container according to the container image metadata, thereby obtaining the running status data of the microservice instance; Step S34: transferring code streams between microservices according to the running status data of the microservice instances through the message queue, and obtaining the intermediate code stream data generated or required by each microservice; Step S35: according to the microservice load status in the intermediate bitstream data, the bitstream generation rate is limited by a flow control strategy, thereby obtaining the policy-limited bitstream data, wherein the flow control strategy includes limiting the bitstream generation rate. During the encoding or decoding process, the intermediate bitstream data is processed according to the flow control strategy to limit the bitstream generation rate; Step S36: Adaptively adjust the coding parameters of the policy-constrained code stream data according to the fractal channel response matrix, thereby generating adaptive code stream data.

8. The communication control method based on power line network dual-mode communication according to claim 7, characterized in that: Step S36 includes the following steps: Step S361: performing characteristic analysis on the fractal channel response matrix based on multipath effects and fading parameters, thereby obtaining a channel characteristic parameter set; Step S362: matching the channel characteristic parameter set with the preset adaptive strategy to obtain an adaptive adjustment strategy; Step S363: dynamically adjusting the modulation and coding parameters of the policy-restricted bitstream data according to the adaptive adjustment strategy, thereby obtaining an adjusted bitstream parameter set; Step S364: reconstructing the policy-restricted bitstream data using the bitstream parameter set, thereby obtaining adaptive bitstream data.

9. The communication control method based on power line network dual-mode communication according to claim 8, characterized in that: Step S4 includes the following steps: Step S41: The SDN controller interacts with network devices according to the dynamic network topology map, thereby obtaining network global view data; Step S42: virtualize the routing and firewall network functions in the dynamic network topology diagram into VNF components, and manage the VNF component life cycle based on the MANO architecture to obtain VNF instance operation status data; Step S43: construct a logical overlay network according to the network global view data and the VNF instance running status data, thereby obtaining virtual overlay network view data; Step S44: deploying the adaptive code stream data to the path of the virtual overlay network view data, and configuring corresponding forwarding and service demand strategies, thereby obtaining the virtual overlay network data; Step S45: periodically acquire node status of the virtual overlay network data to obtain a node status set; serialize the node status set into an object model, compare it with the previous status, extract the incremental part, and perform an incremental snapshot to obtain incremental status snapshot data; Step S46: Use a distributed hash table to store incremental state snapshot data in multiple nodes of the virtual overlay network data, and perform view consistency coordination to obtain view consistent incremental data; periodically generate a full data mirror based on the view consistent incremental data, and store it persistently in the hot backup node to obtain seamless state mirror data.

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