A high-speed power line communication method and system based on dual-mode communication

Abstract

The present invention relates to the technical field of data processing, and discloses a high-speed power line communication method and system based on dual-mode communication, which is used to improve the efficiency of high-speed power line carrier communication based on dual-mode communication. The method includes: real-time monitoring and noise analysis and detection processing of power grid parameters to obtain power grid communication quality event reports; collecting and phase comparison analysis processing of voltage waveform samples to obtain phase identification results and load balancing optimization strategies; encrypting communication data and transmitting communication strategies to obtain target communication links and update system firmware information; channel screening and strategy generation through target communication links and update system firmware information to obtain target communication channels and target communication strategies, and automatically registering and hierarchical data collection processing of smart meters to obtain meter registration information and hierarchically managed electricity consumption data and perform high-speed power line carrier communication control.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a high-speed power line communication method and system based on dual-mode communication. Background Art

[0002] Existing power line communication systems usually adopt a single communication mode, such as narrowband power line carrier communication (PLC) or high-speed power line carrier communication (HPLC). These systems can realize the transmission of power information under specific environments, but they face many challenges. The PLC system has strong anti-attenuation ability and a long communication distance, but weak anti-interference ability and low transmission rate; the HPLC system has the advantage of high-speed transmission, but is easily interfered in complex power grid environments and has limited communication distance.

[0003] However, with the rapid development of smart grids, the requirements for communication performance in power systems are increasing. Systems with a single communication mode are difficult to adapt to the complex and changing power grid environment and cannot simultaneously meet the requirements of high-speed transmission, long-distance communication, and strong anti-interference capabilities. In addition, the existing systems have limited capabilities in network topology optimization, abnormal event detection, phase identification, load balancing, etc., and are difficult to support the advanced application requirements of smart grids. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a high-speed power line communication method and system based on dual-mode communication, which are used to improve the efficiency of high-speed power line carrier communication based on dual-mode communication.

[0005] The present invention provides a high-speed power line communication method based on dual-mode communication, comprising: initializing and performing area identification processing on a dual-mode communication unit to obtain area affiliation information of the dual-mode communication unit; based on the area affiliation information, performing intelligent networking and adaptive selection of communication modes on a communication network to obtain an optimized network topology structure and communication mode configuration information; through the optimized network topology structure and the communication mode configuration information, performing real-time monitoring and noise analysis and detection processing on power grid parameters to obtain a power grid communication quality event report; according to the power grid communication quality event report, collecting and performing phase comparison and analysis processing on voltage waveform samples to obtain a phase identification Results and load balancing optimization strategy; based on the phase identification results and load balancing optimization strategy, the communication data is encrypted and the communication strategy is transmitted to obtain the target communication link and update system firmware information; channel screening and strategy generation are performed through the target communication link and the update system firmware information to obtain the target communication channel and the target communication strategy, and the smart meter is automatically registered and data is collected and processed in a hierarchical manner through the target communication channel and the target communication strategy to obtain the meter registration information and the hierarchically managed electricity usage data, and high-speed power line carrier communication control is performed through the meter registration information and the hierarchically managed electricity usage data.

[0006] In the present invention, the step of initializing and processing the dual-mode communication unit for area identification to obtain the area affiliation information of the dual-mode communication unit includes: performing self-check processing on the dual-mode communication unit to obtain the status information of the high-speed power line carrier communication module and the micro-power wireless communication module; based on the status information of the high-speed power line carrier communication module and the micro-power communication module, performing parallel activation processing on the high-speed power line carrier communication module and the micro-power communication module to obtain the dual-mode monitoring state; collecting and processing the communication signal on the power line through the dual-mode monitoring state to obtain the original area identification signal; performing noise reduction processing on the original area identification signal through an adaptive filtering algorithm to obtain an optimized area identification signal; The optimized substation identification signal is decoded to obtain the substation code and communication parameters; the signal strength and quality of the high-speed power line carrier communication module and the micro-power communication module are analyzed and processed by a signal quality assessment algorithm to obtain a target communication mode; based on the target communication mode, the unique identifier of the dual-mode communication unit and the substation code are encapsulated to obtain response information; based on the communication parameters, the response information is sent to obtain concentrator confirmation information; the concentrator confirmation information is verified by a secure hash algorithm to obtain a verification result; based on the verification result, the substation affiliation information of the dual-mode communication unit is updated and stored to obtain the substation affiliation information.

[0007] In the present invention, the step of performing intelligent networking and adaptive selection of communication modes on the communication network based on the substation affiliation information to obtain an optimized network topology structure and communication mode configuration information includes: parsing the substation affiliation information to obtain an initial network node list; performing topology structure generation processing on the initial network node list through a graph theory optimization algorithm to obtain a candidate network topology structure; performing high-speed power line carrier communication mode test processing on the nodes in the candidate network topology structure to obtain high-speed carrier communication quality data; performing micro-power wireless communication mode test processing on the nodes in the candidate network topology structure to obtain wireless communication quality data; and optimizing the high-speed carrier communication quality data through a multi-objective optimization algorithm. The invention relates to a method for preparing a node communication mode allocation scheme, comprising: performing a comprehensive evaluation process on the wireless communication quality data and the wireless communication quality data to obtain a node communication mode allocation scheme; performing conflict detection process on the node communication mode allocation scheme to obtain a conflict node list; performing communication mode reallocation process on the conflict node list through a genetic algorithm to obtain an optimized communication mode allocation scheme; based on the optimized communication mode allocation scheme, adjusting the candidate network topology structure to obtain an optimized network topology structure; performing dynamic routing strategy generation process on the optimized network topology structure through a deep reinforcement learning algorithm to obtain an adaptive routing table; integrating the optimized network topology structure and the adaptive routing table to obtain the optimized network topology structure and the communication mode configuration information.

[0008] In the present invention, the step of performing real-time monitoring and noise analysis detection processing on power grid parameters through the optimized network topology structure and the communication mode configuration information to obtain a power grid communication quality event report includes: performing node grouping processing on the optimized network topology structure to obtain a monitoring node group; performing data acquisition strategy allocation processing on the monitoring node group based on the communication mode configuration information to obtain a distributed acquisition scheme; performing parallel sampling processing on the power grid voltage, current and power factor through the distributed acquisition scheme to obtain original power grid parameter data; performing time synchronization processing on the original power grid parameter data to obtain synchronous power grid parameter data; performing noise elimination processing on the synchronous power grid parameter data through a wavelet transform algorithm to obtain optimized power grid parameter data; performing statistical analysis processing on the optimized power grid parameter data to obtain power grid parameter statistical characteristics; performing abnormal pattern recognition processing on the power grid parameter statistical characteristics through a support vector machine algorithm to obtain a preliminary abnormal event list; performing priority sorting processing on the preliminary abnormal event list to obtain a priority abnormal event list; performing cause analysis processing on the priority abnormal event list through a deep learning algorithm to obtain abnormal event cause analysis results; formatting the abnormal event cause analysis results to obtain the power grid communication quality event report.

[0009] In the present invention, the steps of collecting and performing phase comparison and analysis on voltage waveform samples according to the power grid communication quality event report to obtain phase identification results and load balancing optimization strategy include: parsing the power grid communication quality event report to obtain an abnormal node list; based on the abnormal node list, performing high-frequency sampling on the voltage waveform to obtain original voltage waveform data; performing instantaneous phase extraction on the original voltage waveform data by the Hilbert-Huang transform algorithm to obtain an instantaneous phase sequence; performing statistical analysis on the instantaneous phase sequence to obtain a phase feature vector; classifying the phase feature vector by a clustering algorithm to obtain a preliminary phase grouping result; cross-validating the preliminary phase grouping result to obtain an optimized phase grouping result; performing phase identification on the optimized phase grouping result by a neural network algorithm to obtain the phase identification result; performing load analysis on the phase identification result to obtain the load distribution of each phase; performing balanced optimization on the load distribution of each phase by a genetic algorithm to obtain a load adjustment plan; performing feasibility assessment on the load adjustment plan to obtain the load balancing optimization strategy.

[0010] In the present invention, the steps of encrypting the communication data and transmitting the communication strategy based on the phase identification result and the load balancing optimization strategy to obtain the target communication link and update the system firmware information include: analyzing and processing the phase identification result to obtain the phase communication characteristic parameters; grouping the communication data based on the phase communication characteristic parameters to obtain a phase-divided data packet; encrypting the phase-divided data packet by an elliptic curve encryption algorithm to obtain an encrypted data packet; digitally signing the encrypted data packet to obtain a signed encrypted data packet; based on the load balancing optimization strategy, performing transmission path planning on the signed encrypted data packet to obtain an optimized transmission path; performing channel allocation on the optimized transmission path by a dynamic time division multiplexing algorithm to obtain the target communication link; performing version verification on the preset communication strategy package to obtain a strategy package integrity verification result; compressing the communication strategy package by a differential compression algorithm to obtain a compressed strategy package; sharding the communication strategy package to obtain a strategy package shard sequence; performing redundant encoding on the strategy package shard sequence by an erasure code algorithm to obtain the updated system firmware information.

[0011] In the present invention, the target communication link and the update system firmware information are used to perform channel screening and strategy generation to obtain a target communication channel and a target communication strategy, and the smart meter is automatically registered and data is collected and processed in a hierarchical manner through the target communication channel and the target communication strategy to obtain meter registration information and hierarchically managed electricity consumption data, and a high-speed power line carrier communication control step is performed through the meter registration information and the hierarchically managed electricity consumption data, including: link quality evaluation processing is performed on the target communication link to obtain a communication link performance indicator; channel screening is performed based on the communication link performance indicator to obtain the target communication channel, and strategy extraction is performed on the update system firmware information to obtain the target communication strategy, and list information extraction processing is performed on the smart meter through the target communication channel and the target communication strategy to obtain a list of meters to be registered; a zero-knowledge proof algorithm is used to perform Perform identity authentication on the list of meters to be registered to obtain a list of meters that have passed authentication; perform station affiliation verification on the list of meters that have passed authentication to obtain the meter registration information; perform importance rating on the meter data based on the meter registration information to obtain a data classification scheme; perform hierarchical collection and processing on the meter data through an adaptive sampling algorithm to obtain original hierarchical electricity consumption data; perform data cleaning on the original hierarchical electricity consumption data to obtain cleaned hierarchical electricity consumption data; perform compression on the cleaned hierarchical electricity consumption data through a time series compression algorithm to obtain the hierarchically managed electricity consumption data; perform fusion analysis on the meter registration information and the hierarchically managed electricity consumption data to obtain a communication control strategy; optimize the communication control strategy through a reinforcement learning algorithm to obtain an optimized communication control strategy, and control high-speed power line carrier communication based on the optimized communication control strategy.

[0012] The present invention also provides a high-speed power line communication system based on dual-mode communication, comprising:

[0013] A processing module, used for initializing and performing area identification processing on the dual-mode communication unit to obtain area belonging information of the dual-mode communication unit;

[0014] A selection module is used to perform intelligent networking and adaptive selection of communication modes on the communication network based on the station area ownership information, so as to obtain optimized network topology and communication mode configuration information;

[0015] A detection module, used to perform real-time monitoring and noise analysis detection processing on power grid parameters through the optimized network topology and the communication mode configuration information, and obtain a power grid communication quality event report;

[0016] A comparison module is used to collect and perform phase comparison analysis on voltage waveform samples according to the power grid communication quality event report to obtain phase identification results and load balancing optimization strategies;

[0017] A transmission module, used to perform encryption processing on the communication data and transmission processing on the communication strategy based on the phase identification result and the load balancing optimization strategy, so as to obtain the target communication link and update the system firmware information;

[0018] A control module is used to perform channel screening and strategy generation through the target communication link and the updated system firmware information to obtain a target communication channel and a target communication strategy, automatically register and perform data hierarchical collection and processing on smart meters through the target communication channel and the target communication strategy to obtain meter registration information and hierarchically managed electricity usage data, and perform high-speed power line carrier communication control through the meter registration information and the hierarchically managed electricity usage data.

[0019] In the technical solution provided by the present invention, by combining the advantages of high-speed power line carrier communication (HPLC) and micropower wireless communication (HRF), adaptive selection of communication modes is achieved, which greatly improves the adaptability and communication reliability of the system in complex power grid environments; through intelligent networking and dynamic routing strategies, the network topology is optimized, and the communication efficiency and network stability are improved; real-time monitoring and noise analysis and detection mechanisms enhance the system's rapid response capability to power grid failures, providing a strong guarantee for the safe operation of the power grid; accurate phase identification and load balancing optimization strategies effectively improve the load distribution of the power grid, improve the power supply quality and energy utilization efficiency; advanced encryption algorithms and security authentication mechanisms ensure the security and integrity of communication data, and effectively prevent potential network attacks and data leakage risks; the remote upgrade function realized through communication strategies simplifies the system maintenance process and improves operation and maintenance efficiency; the automatic The dynamic registration and data hierarchical collection and processing mechanism optimizes the data management process and improves the efficiency and accuracy of data collection; the communication control strategy based on machine learning continuously optimizes the system performance so that it can adapt to the ever-changing network environment; in addition, the method also has good scalability and compatibility, and can be seamlessly integrated with the existing power system, laying a technical foundation for the further development of smart grids; through the comprehensive use of multiple advanced algorithms, such as graph optimization, deep reinforcement learning, support vector machines, neural networks, etc., the method has made significant breakthroughs in key links such as network optimization, anomaly detection, and phase recognition, greatly improving the overall performance and intelligence level of the system; at the same time, the modular design of the method enables the functional modules to be flexibly combined and expanded according to actual needs to meet the application needs in different scenarios; the method proposed in the present invention comprehensively improves the performance, reliability, security and intelligence level of the power line communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is a flow chart of a high-speed power line communication method based on dual-mode communication in an embodiment of the present invention;

[0022] Figure 2 Schematic diagram of a high-speed power line communication system based on dual-mode communication in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0025] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0026] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , Figure 1 is a flow chart of a high-speed power line communication method based on dual-mode communication according to an embodiment of the present invention, such as Figure 1 As shown, the following steps are included:

[0027] S101, initializing and performing area identification processing on the dual-mode communication unit to obtain area affiliation information of the dual-mode communication unit;

[0028] S102, based on the station area ownership information, intelligent networking and adaptive selection of communication modes are performed on the communication network to obtain optimized network topology and communication mode configuration information;

[0029] S103, by optimizing the network topology and communication mode configuration information, real-time monitoring of power grid parameters and noise analysis and detection processing are performed to obtain a power grid communication quality event report;

[0030] S104, according to the power grid communication quality event report, collect voltage waveform samples and perform phase comparison analysis and processing to obtain phase identification results and load balancing optimization strategies;

[0031] S105, based on the phase identification result and the load balancing optimization strategy, encrypt the communication data and perform transmission processing on the communication strategy to obtain the target communication link and update system firmware information;

[0032] S106. Channel screening and strategy generation are performed through the target communication link and the updated system firmware information to obtain the target communication channel and the target communication strategy. The smart meter is automatically registered and data is collected and processed in a hierarchical manner through the target communication channel and the target communication strategy to obtain the meter registration information and the hierarchically managed electricity usage data. High-speed power line carrier communication control is performed through the meter registration information and the hierarchically managed electricity usage data.

[0033] It should be noted that the dual-mode communication unit is initialized and the area identification process is performed. This process includes self-checking the high-speed power line carrier communication (HPLC) module and the micropower wireless communication (HRF) module to ensure that the modules function normally. Subsequently, by activating the two modules in parallel, the dual-mode communication unit enters the dual-mode listening state and collects the communication signal on the power line at the same time. The collected original area identification signal is subjected to noise reduction processing by the adaptive filtering algorithm to obtain the optimized area identification signal. After decoding this signal, the area code and communication parameters are obtained. The signal strength and quality of the HPLC and HRF modules are analyzed by the signal quality evaluation algorithm to determine the most suitable communication mode. Based on the selected communication mode, the unique identifier and area code of the dual-mode communication unit are encapsulated into a response message and sent to the concentrator. After the confirmation information returned by the concentrator is verified by the secure hash algorithm, the area attribution information of the dual-mode communication unit is updated and stored.

[0034] After obtaining the substation ownership information, intelligent networking and adaptive selection of communication modes are performed. First, the substation ownership information is parsed to obtain the initial network node list. The candidate network topology is generated using a graph theory optimization algorithm, and then the communication quality of each node is tested in HPLC and HRF modes. The multi-objective optimization algorithm comprehensively evaluates the quality data of the two communication modes and generates a node communication mode allocation scheme. Conflict detection is performed on the scheme to find out the conflicting nodes, and the communication modes of these nodes are reallocated through a genetic algorithm. Based on the optimized communication mode allocation scheme, the candidate network topology is adjusted. The deep reinforcement learning algorithm is used to generate a dynamic routing strategy and form an adaptive routing table. Finally, the optimized network topology and the adaptive routing table are integrated to obtain the optimized network topology and communication mode configuration information.

[0035] Based on the optimized network structure and configuration information, real-time monitoring and noise analysis and detection of power grid parameters are carried out. First, the network nodes are grouped, and data acquisition strategies are assigned to each group according to the communication mode configuration information. Through the distributed acquisition scheme, the voltage, current and power factor of the power grid are sampled in parallel to obtain the original power grid parameter data. These data are time-synchronized to ensure the time consistency of the data. The wavelet transform algorithm is used to eliminate the noise in the synchronized data and obtain the optimized power grid parameter data. The optimized data is statistically analyzed to extract the statistical features of the power grid parameters. The support vector machine algorithm is used to identify abnormal patterns and generate a preliminary abnormal event list. The events in the list are prioritized, and the causes of high-priority events are analyzed through a deep learning algorithm. Finally, the analysis results are formatted to form a power grid communication quality event report.

[0036] According to the power grid communication quality event report, voltage waveform sample collection and phase comparison analysis are performed. First, the report is parsed to determine the abnormal nodes. The voltage waveforms of these nodes are sampled at high frequency to obtain the original voltage waveform data. The instantaneous phase sequence is extracted from the original data through the Hilbert-Huang transform algorithm. The instantaneous phase sequence is statistically analyzed to obtain the phase feature vector. The clustering algorithm is used to perform preliminary classification of the phase feature vector. After cross-validation and optimization of the classification results, the neural network algorithm is used to phase identify the optimized grouping results to obtain the phase identification results. Load analysis is performed based on the identification results to obtain the load distribution of each phase. The genetic algorithm is used to optimize load balancing and generate a load adjustment plan. The feasibility of the plan is evaluated to form the final load balancing optimization strategy.

[0037] Based on the phase identification results and load balancing optimization strategy, the communication data is encrypted and the communication strategy transmission is processed. First, the phase identification results are analyzed to obtain the phase communication characteristic parameters. According to these parameters, the communication data is grouped into data packets of different phases. The phase-divided data packets are encrypted using the elliptic curve encryption algorithm and digital signatures are added. Based on the load balancing optimization strategy, the transmission path is planned for the signature encrypted data packet. The dynamic time division multiplexing algorithm is used to allocate channels for the optimized transmission path to form the target communication link. At the same time, the communication strategy is version checked to ensure integrity. The communication strategy is compressed using the differential compression algorithm and fragmented. Finally, the communication strategy fragmentation sequence is redundantly encoded using the erasure code algorithm to obtain the updated system firmware information.

[0038] The automatic registration and hierarchical data collection of smart meters are realized through the target communication link and the updated system firmware information. First, the quality of the communication link is evaluated to obtain the performance indicators. Based on these indicators, the channel screening and strategy generation are performed on the target communication link and the updated system firmware information to obtain the target communication channel and the target communication strategy. The list of meters to be registered is extracted through the target communication channel and the target communication strategy. The zero-knowledge proof algorithm is used to authenticate the identity of the meters to be registered. The meters that pass the authentication are also required to verify the area affiliation, and finally the meter registration information is obtained. According to the registration information, the importance of the meter data is rated and a data classification scheme is formulated. The adaptive sampling algorithm is used to collect meter data according to the classification scheme. The collected raw data is cleaned and processed by the time series compression algorithm to obtain the hierarchically managed electricity consumption data. The meter registration information and the hierarchically managed electricity consumption data are fused and analyzed to generate a communication control strategy. The reinforcement learning algorithm is used to optimize the strategy, and finally the optimized communication control strategy is obtained for controlling high-speed power line carrier communication.

[0039] For example, 100 dual-mode communication units were installed in a certain area. Through initialization and area identification processing, it was determined that these units belonged to 5 different areas. In the process of intelligent networking, the graph optimization algorithm generated 10 candidate network topologies. Through communication quality testing and multi-objective optimization, a network topology was finally selected, in which 60% of the nodes used HPLC mode and 40% used HRF mode. In abnormal event detection, the support vector machine algorithm identified 3 abnormal events, and the highest priority was a voltage drop event. The phase identification results showed that 35 of the 100 units belonged to phase A, 33 to phase B, and 32 to phase C. The load balancing optimization strategy recommended transferring 2 loads from phase B to phase C. In the data encryption process, the key length of the elliptic curve encryption algorithm was 256 bits, which ensured communication security. The communication policy package size was 10MB, which was reduced to 7MB after differential compression. In the process of automatic registration of smart meters, 98 of the 100 meters passed the identity authentication of zero-knowledge proof. Data classification collection divides data into three levels: high, medium and low, with sampling frequencies every 5 minutes, every 15 minutes and once an hour respectively.

[0040] By executing the above steps, by combining the advantages of high-speed power line carrier communication (HPLC) and micropower wireless communication (HRF), adaptive selection of communication mode is achieved, which greatly improves the system's adaptability and communication reliability in complex power grid environments; through intelligent networking and dynamic routing strategies, the network topology is optimized, and the communication efficiency and network stability are improved; real-time monitoring and noise analysis and detection mechanisms enhance the system's rapid response capability to power grid faults, providing a strong guarantee for the safe operation of the power grid; accurate phase identification and load balancing optimization strategies effectively improve the load distribution of the power grid, improve the power supply quality and energy utilization efficiency; advanced encryption algorithms and security authentication mechanisms ensure the security and integrity of communication data, and effectively prevent potential network attacks and data leakage risks; the remote upgrade function realized through communication strategies simplifies the system maintenance process and improves operation and maintenance efficiency; the automatic The registration and data hierarchical collection and processing mechanism optimizes the data management process and improves the efficiency and accuracy of data collection; the communication control strategy based on machine learning continuously optimizes the system performance so that it can adapt to the ever-changing network environment; in addition, the method also has good scalability and compatibility, and can be seamlessly integrated with the existing power system, laying a technical foundation for the further development of smart grids; through the comprehensive use of multiple advanced algorithms, such as graph optimization, deep reinforcement learning, support vector machines, neural networks, etc., the method has made significant breakthroughs in key links such as network optimization, anomaly detection, and phase recognition, greatly improving the overall performance and intelligence level of the system; at the same time, the modular design of the method enables the functional modules to be flexibly combined and expanded according to actual needs to meet the application needs in different scenarios; the method proposed in the present invention comprehensively improves the performance, reliability, security and intelligence level of the power line communication system.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Perform self-check processing on the dual-mode communication unit to obtain status information of the high-speed power line carrier communication module and the micro-power wireless communication module;

[0043] (2) Based on the status information of the high-speed power line carrier communication module and the micro-power wireless communication module, the high-speed power line carrier communication module and the micro-power wireless communication module are activated in parallel to obtain a dual-mode monitoring state;

[0044] (3) The communication signals and micro-power wireless signals on the power lines are collected and processed through the dual-mode monitoring state to obtain the original station area identification signal;

[0045] (4) The original station area identification signal is subjected to noise reduction processing through an adaptive filtering algorithm to obtain an optimized station area identification signal;

[0046] (5) Decoding the optimized area identification signal to obtain the area coding and communication parameters;

[0047] (6) Analyze and process the signal strength and quality of the high-speed power line carrier communication module and the micro-power wireless communication module through the signal quality evaluation algorithm to obtain the target communication mode;

[0048] (7) Based on the target communication mode, encapsulate the unique identifier and station area code of the dual-mode communication unit to obtain response information;

[0049] (8) Based on the communication parameters, the response information is sent and processed to obtain the concentrator confirmation information;

[0050] (9) Verify the concentrator confirmation information through a secure hash algorithm to obtain a verification result;

[0051] (10) Based on the verification result, the area belonging information of the dual-mode communication unit is updated and stored to obtain the area belonging information.

[0052] Specifically, the dual-mode communication unit is self-checked. This process includes hardware self-checking and software initialization of the high-speed power line carrier communication (HPLC) module and the low-power wireless communication (HRF) module, checking the key parameters of each module such as the operating voltage, clock frequency, and memory status, and performing basic communication tests. The self-check results form status information, including the module's operating status (normal / abnormal), communication capability evaluation, etc.

[0053] Based on the status information obtained, the HPLC and HRF modules are activated in parallel. Parallel activation means starting the two modules at the same time so that they can work in parallel. This process includes configuring the working parameters of the modules, such as communication frequency, transmission power, receiving sensitivity, etc., so that both modules enter the waiting state. After parallel activation, the dual-mode communication unit enters the dual-mode listening state and can listen to both HPLC and HRF signals at the same time.

[0054] In the dual-mode monitoring state, the communication signals and micro-power wireless signals on the power line are collected and processed. This process uses an analog-to-digital converter (ADC) to sample the analog signals on the power line and convert them into digital signals. The sampling frequency needs to meet the Nyquist sampling theorem and is usually more than twice the highest frequency of the signal. The collected digital signal is the original station area identification signal.

[0055] Next, the original area identification signal is subjected to noise reduction processing through an adaptive filtering algorithm. The adaptive filtering algorithm can dynamically adjust the filter parameters according to the characteristics of the signal and effectively remove the noise in the signal. Commonly used adaptive filtering algorithms include the least mean square error (LMS) algorithm and the recursive least squares (RLS) algorithm. This process greatly reduces the noise in the original signal and obtains an optimized area identification signal.

[0056] The optimized area identification signal is decoded to extract the area code and communication parameters. The decoding process usually includes steps such as signal synchronization, bit extraction, frame synchronization and data analysis. The area code is the unique identifier for identifying the area to which the power equipment belongs, and the communication parameters include information such as communication frequency, modulation mode, and data rate.

[0057] Then, the signal strength and quality of the HPLC and HRF modules are analyzed and processed by the signal quality evaluation algorithm. This algorithm usually considers parameters such as signal-to-noise ratio (SNR), bit error rate (BER), and received signal strength indication (RSSI). By comprehensively analyzing these parameters, it is determined which communication mode is more suitable for the current network environment, thereby obtaining the target communication mode.

[0058] Based on the determined target communication mode, the unique identifier (such as MAC address) of the dual-mode communication unit and the previously obtained station area code are encapsulated to form a response message. The encapsulation process follows a specific communication protocol and organizes the data into a specified format, which usually includes fields such as frame header, data payload, and checksum.

[0059] The encapsulated response information is sent using the communication parameters decoded previously. The sending process includes steps such as modulation, power amplification, and coupling to the power line. After sending, wait for the confirmation information from the concentrator. After receiving the response information, the concentrator will return a confirmation information, which includes the recognition of the communication unit and the necessary configuration information.

[0060] After receiving the confirmation information from the concentrator, it is verified through a secure hash algorithm. A secure hash algorithm (such as SHA-256) can generate a fixed-length summary to verify the integrity and authenticity of the information. The verification process includes calculating the hash value of the received confirmation information and comparing it with the expected hash value.

[0061] Finally, based on the verification result, the area belonging information of the dual-mode communication unit is updated and stored. If the verification is successful, the area information confirmed by the concentrator is written into the non-volatile memory of the communication unit to complete the area identification process.

[0062] For example, during the self-test process, a dual-mode communication unit found that both the HPLC module and the HRF module were working properly, with the transmission power of the HPLC module being 20dBm and the transmission power of the HRF module being 30dBm. After parallel activation, the communication unit collected power line signals at a sampling rate of 200kHz and obtained the original substation identification signal containing noise. Through the LMS adaptive filtering algorithm, the signal-to-noise ratio was increased from the initial 5dB to 15dB. After decoding, the substation code "TA001" and communication parameters (HPLC: 2-30MHz, OFDM modulation; HRF: 3-500kHz, FSK modulation) were obtained. Signal quality evaluation shows that the SNR of the HPLC mode is 25dB and the BER is , while the SNR of HRF mode is 35dB and the BER is , so HRF is selected as the target communication mode. The MAC address "00:1A:2B:3C:4D:5E" and the area code "TA001" are encapsulated as response information and sent using FSK modulation. The confirmation information returned by the concentrator is verified by the SHA-256 algorithm, and the generated hash value matches the expected value. Finally, the area affiliation information "TA001" is stored in the EEPROM of the communication unit, completing the entire area identification process.

[0063] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0064] (1) Analyze the area ownership information to obtain the initial network node list;

[0065] (2) Using a graph optimization algorithm to generate a topological structure for the initial network node list, a candidate network topology is obtained;

[0066] (3) Performing high-speed power line carrier communication mode test processing on the nodes in the candidate network topology structure to obtain high-speed carrier communication quality data;

[0067] (4) Performing micro-power wireless communication mode test processing on the nodes in the candidate network topology structure to obtain wireless communication quality data;

[0068] (5) Comprehensively evaluate and process high-speed carrier communication quality data and wireless communication quality data through a multi-objective optimization algorithm to obtain a node communication mode allocation plan;

[0069] (6) Perform conflict detection on the node communication mode allocation scheme to obtain a list of conflicting nodes;

[0070] (7) Using genetic algorithm to reallocate the communication mode of the conflicting node list, and obtain an optimized communication mode allocation scheme;

[0071] (8) Based on the optimized communication mode allocation scheme, the candidate network topology structure is adjusted to obtain the optimized network topology structure;

[0072] (9) Using deep reinforcement learning algorithms to generate dynamic routing strategies for the optimized network topology structure to obtain an adaptive routing table;

[0073] (10) Integrate the optimized network topology structure and the adaptive routing table to obtain the optimized network topology structure and communication mode configuration information.

[0074] It should be noted that the area ownership information is parsed to obtain the initial network node list. This process includes parsing the area code, physical location, communication capability and other information of each communication unit to form a data structure containing detailed information of all nodes. Then, the initial network node list is processed for topology generation through a graph optimization algorithm to obtain a candidate network topology. Graph optimization algorithms, such as the minimum spanning tree algorithm or the Delaunay triangulation algorithm, are used to construct the connection relationship between nodes, taking into account factors such as physical distance and communication quality, and generate multiple possible network topologies.

[0075] The nodes in the candidate network topology are tested in high-speed power line carrier communication (HPLC) mode to obtain high-speed carrier communication quality data. This process includes testing the HPLC link quality between nodes, such as signal-to-noise ratio, bit error rate, throughput and other parameters. Similarly, the nodes are tested in micropower wireless communication (HRF) mode to obtain wireless communication quality data. These two steps of testing provide important basis for the subsequent communication mode selection.

[0076] The high-speed carrier communication quality data and wireless communication quality data are comprehensively evaluated and processed through a multi-objective optimization algorithm to obtain a node communication mode allocation scheme. Multi-objective optimization algorithms, such as NSGA-II (non-dominated sorting genetic algorithm II), consider multiple objectives such as communication quality, energy consumption, and network load, weigh the pros and cons of HPLC and HRF, and allocate the most suitable communication mode to each node.

[0077] The node communication mode allocation scheme is processed for conflict detection to obtain a list of conflicting nodes. Conflict detection includes checking whether adjacent nodes use the same frequency band to cause interference, or whether the communication mode of some nodes does not match their hardware capabilities. The communication mode is reallocated on the conflicting node list through a genetic algorithm to obtain an optimized communication mode allocation scheme. The genetic algorithm simulates the biological evolution process and iteratively optimizes the communication mode allocation of conflicting nodes through operations such as selection, crossover, and mutation until the preset optimization goal is met.

[0078] Based on the optimized communication mode allocation scheme, the candidate network topology is adjusted to obtain the optimized network topology. This process includes adjusting the connection relationship between nodes according to the allocated communication mode, and may require adding or deleting some links to adapt to the new communication mode allocation.

[0079] The deep reinforcement learning algorithm is used to generate dynamic routing strategies for the optimized network topology to obtain an adaptive routing table. Deep reinforcement learning algorithms, such as the Deep Q Network (DQN), learn the optimal routing strategy by simulating various network states and actions. It considers factors such as network load, link quality, and latency, and generates a routing table for each node that can adapt to dynamic changes in the network.

[0080] Finally, the optimized network topology and adaptive routing table are integrated to obtain the final optimized network topology and communication mode configuration information. This integration process combines network topology information, node communication mode and routing strategy into a complete network configuration solution.

[0081] For example, a certain area contains 100 dual-mode communication units, and the initial network node list contains detailed information of these 100 nodes. The Kruskal minimum spanning tree algorithm is used to generate candidate network topologies, and 3 possible topologies are obtained. The HPLC mode test shows that in the 2-30MHz frequency band, the average signal-to-noise ratio is 20dB and the bit error rate is 10^-5. The HRF mode test is carried out in the 3-500kHz frequency band, with an average signal-to-noise ratio of 30dB and a bit error rate of 10^-6. The NSGA-II algorithm is used for multi-objective optimization, considering communication quality (weight 0.5), energy consumption (weight 0.3) and network load (weight 0.2), and a preliminary communication mode allocation scheme is obtained: 60 nodes use HPLC and 40 nodes use HRF. Conflict detection found that there was frequency band interference in 5 pairs of adjacent nodes. The genetic algorithm was used for 100 generations of iterative optimization, with a population size of 50 per generation, a crossover rate of 0.8, and a mutation rate of 0.1, and all conflicts were finally resolved. The optimized network topology contains 98 directly connected links. The routing strategy was trained using the DQN algorithm, with the discount factor γ set to 0.9. After 10,000 training iterations, an adaptive routing table that can adapt to changes in network load was obtained. The final network configuration solution included the optimized network topology, the allocation of 58 HPLC nodes, 42 HRF nodes, and an adaptive routing table for each node.

[0082] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0083] (1) Node grouping is performed on the optimized network topology structure to obtain a monitoring node group;

[0084] (2) Based on the communication mode configuration information, the data collection strategy is allocated to the monitoring node group to obtain a distributed collection solution;

[0085] (3) Through the distributed data acquisition scheme, the grid voltage, current and power factor are sampled and processed in parallel to obtain the original grid parameter data;

[0086] (4) Performing time synchronization processing on the original grid parameter data to obtain synchronized grid parameter data;

[0087] (5) Using the wavelet transform algorithm to eliminate noise from the synchronous power grid parameter data, and obtain optimized power grid parameter data;

[0088] (6) Perform statistical analysis on the optimized power grid parameter data to obtain the statistical characteristics of the power grid parameters;

[0089] (7) Use the support vector machine algorithm to perform abnormal pattern recognition on the statistical characteristics of power grid parameters and obtain a preliminary abnormal event list;

[0090] (8) Prioritizing the preliminary abnormal event list to obtain a priority abnormal event list;

[0091] (9) Perform cause analysis on the priority abnormal event list through deep learning algorithms to obtain the cause analysis results of the abnormal events;

[0092] (10) Format the abnormal event cause analysis results to obtain a power grid communication quality event report.

[0093] Specifically, the optimized network topology is processed by node grouping to obtain monitoring node groups. This process groups nodes according to their geographical location, electrical connection relationship, and communication capability to ensure that the nodes in each group can effectively cover a certain range of power grid areas. Next, based on the communication mode configuration information, the monitoring node group is assigned a data collection strategy to obtain a distributed collection solution. This step takes into account the communication mode of each node (high-speed power line carrier communication HPLC or micro-power wireless communication HRF), and allocates appropriate sampling frequencies and data transmission strategies to different nodes to balance the comprehensiveness of data collection and communication load.

[0094] Through the distributed acquisition solution, the grid voltage, current and power factor are sampled and processed in parallel to obtain the original grid parameter data. Parallel sampling means that multiple nodes collect data at the same time, and each node is responsible for collecting the grid parameters at its location. The sampling process uses an analog-to-digital converter (ADC) to convert the analog signal into a digital signal. The sampling frequency is usually an integer multiple of the grid base frequency. For example, a 50Hz grid may use a sampling frequency of 6400Hz.

[0095] The original power grid parameter data is time synchronized to obtain synchronized power grid parameter data. Time synchronization is to ensure that data from different nodes have a consistent time reference, usually using GPS clock or Network Time Protocol (NTP). The synchronized data provides time consistency guarantee for subsequent analysis.

[0096] The synchronous power grid parameter data is de-noised by wavelet transform algorithm to obtain optimized power grid parameter data. Wavelet transform algorithm can analyze signals in both time domain and frequency domain, and is particularly suitable for processing non-stationary signals. The processing process includes selecting appropriate wavelet basis functions (such as Daubechies wavelet), decomposing the signal at multiple scales, removing high-frequency noise components, and then reconstructing the signal to obtain optimized data after de-noising.

[0097] The optimized grid parameter data is statistically analyzed and processed to obtain the statistical characteristics of the grid parameters. The statistical analysis includes calculating the mean, standard deviation, peak factor, harmonic content and other statistical quantities, which can reflect various aspects of the grid operation status.

[0098] The support vector machine (SVM) algorithm is used to identify abnormal patterns in the statistical characteristics of power grid parameters and obtain a preliminary list of abnormal events. SVM is an effective classification algorithm that distinguishes normal and abnormal patterns by constructing a hyperplane in a high-dimensional feature space. The training process of SVM includes selecting a suitable kernel function (such as radial basis function RBF), optimizing the classification hyperplane parameters, and finally obtaining a classifier that can identify abnormal patterns.

[0099] The preliminary abnormal event list is prioritized to obtain a priority abnormal event list. The sorting criteria usually include factors such as the severity of the event, the scope of impact, and the duration, to ensure that the most important abnormal events can be handled in a timely manner.

[0100] The cause analysis of the priority abnormal event list is processed through deep learning algorithms to obtain the cause analysis results of the abnormal events. Deep learning algorithms, such as long short-term memory networks (LSTM) or convolutional neural networks (CNN), can learn complex patterns and relationships from large amounts of historical data. This step infers possible fault causes by analyzing the characteristics of abnormal events and their contextual information.

[0101] Finally, the cause analysis results of the abnormal event are formatted to obtain a power grid communication quality event report. The formatting process converts the analysis results into a standardized report format, including the type of abnormal event, occurrence time, impact range, possible causes, and recommended treatment measures.

[0102] For example, a power system contains 100 dual-mode communication units, which are grouped into 10 monitoring node groups, each with 10 nodes. According to the communication mode configuration, 60 HPLC nodes use a sampling frequency of once per minute, and 40 HRF nodes use a sampling frequency of once per five minutes. During the parallel sampling process, each node uses a 16-bit ADC to collect voltage, current, and power factor data at a frequency of 6400 Hz. GPS timing is used for time synchronization to control the time error of all nodes within 100 microseconds. The db4 wavelet is used to decompose the data into 5 layers, and the high-frequency coefficients of the first two layers are removed and reconstructed, which effectively reduces 50% of the noise. Statistical analysis calculates 20 features such as the mean and standard deviation within a 10-minute window. The SVM uses the RBF kernel function and is trained on 10,000 historical samples with an accuracy of 95%. The priority of abnormal events is calculated based on factors such as the number of affected users and the voltage deviation amplitude. The deep learning model uses a 3-layer LSTM network and is trained on 50,000 historical events, which can identify 10 common fault causes. The resulting power grid communication quality event report contains information such as event description, impact scope, possible causes, and processing suggestions, providing powerful decision-making support for operation and maintenance personnel.

[0103] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0104] (1) Analyze and process the power grid communication quality event report to obtain a list of abnormal nodes;

[0105] (2) Based on the abnormal node list, the voltage waveform is sampled at high frequency to obtain the original voltage waveform data;

[0106] (3) The instantaneous phase sequence is obtained by extracting the instantaneous phase of the original voltage waveform data using the Hilbert-Huang transform algorithm;

[0107] (4) Perform statistical analysis on the instantaneous phase sequence to obtain the phase feature vector;

[0108] (5) Classify the phase feature vectors through clustering algorithm to obtain preliminary phase grouping results;

[0109] (6) Cross-validate the preliminary phase grouping results to obtain the optimized phase grouping results;

[0110] (7) Perform phase identification processing on the optimized phase grouping results through a neural network algorithm to obtain a phase recognition result;

[0111] (8) Perform load analysis on the phase identification results to obtain the load distribution of each phase;

[0112] (9) Using genetic algorithms to balance and optimize the load distribution of each phase, a load adjustment plan is obtained;

[0113] (10) Perform feasibility evaluation on the load adjustment scheme to obtain the load balancing optimization strategy.

[0114] Specifically, the power grid communication quality event report is parsed and processed to obtain a list of abnormal nodes. This process includes extracting the location information, timestamp and abnormal type of the abnormal event from the report, and correlating this information with the network topology to determine the nodes that need to be analyzed in detail.

[0115] Based on the abnormal node list, the voltage waveform is sampled at high frequency to obtain the original voltage waveform data. High-frequency sampling usually uses a high-precision analog-to-digital converter (ADC), and the sampling frequency can reach tens or even hundreds of kilohertz to capture subtle changes in the voltage waveform. The sampled data includes the amplitude and the corresponding timestamp.

[0116] Next, the Hilbert-Huang transform algorithm is used to extract the instantaneous phase of the original voltage waveform data to obtain the instantaneous phase sequence. The Hilbert-Huang transform is a method suitable for nonlinear and non-stationary signal analysis. It first decomposes the signal into a series of intrinsic mode functions (IMFs), then applies the Hilbert transform to each IMF, and finally obtains the instantaneous phase and instantaneous frequency of the signal. This process can effectively extract the phase information of the voltage waveform even in the presence of harmonics and transient interference.

[0117] The instantaneous phase sequence is statistically analyzed to obtain the phase feature vector. The statistical analysis includes calculating the mean, standard deviation, skewness, kurtosis and other statistics of the phase sequence, as well as performing spectrum analysis to extract the amplitude and phase angle of the main frequency components. These features together constitute a high-dimensional vector that describes the phase characteristics.

[0118] The phase feature vectors are classified by clustering algorithms to obtain preliminary phase grouping results. Commonly used clustering algorithms include K-means, DBSCAN or hierarchical clustering. The clustering process groups phase vectors with similar characteristics into a group and preliminarily divides different phase categories.

[0119] The preliminary phase grouping results are cross-validated to obtain the optimized phase grouping results. Cross-validation evaluates the stability and accuracy of the grouping results by splitting the data set into a training set and a validation set, and repeating the clustering and validation process multiple times. This step can optimize the clustering parameters and improve the reliability of the grouping results.

[0120] The optimized phase grouping results are processed by phase identification through a neural network algorithm to obtain phase recognition results. Neural networks, such as multi-layer perceptron (MLP) or convolutional neural networks (CNN), establish a mapping relationship between phase features and specific phases (A, B, C phases) by learning a large number of samples with known phase labels. The trained neural network can accurately identify the phase to which each node belongs.

[0121] The phase identification results are subjected to load analysis to obtain the load distribution of each phase. Load analysis includes calculating the average load, peak load, load change trend and other indicators of each phase to comprehensively evaluate the balance of the three-phase load.

[0122] The load distribution of each phase is balanced and optimized through genetic algorithm to obtain the load adjustment plan. Genetic algorithm simulates the biological evolution process and iteratively optimizes the load distribution plan through selection, crossover and mutation operations. The optimization goal includes minimizing the three-phase load difference while considering the economy and feasibility of the adjustment.

[0123] Finally, the feasibility of the load adjustment plan is evaluated to obtain the load balancing optimization strategy. The feasibility assessment takes into account multiple factors such as technical implementation difficulty, economic cost, and impact on users, and comprehensively scores the adjustment plan to select the optimal load balancing strategy.

[0124] For example, a distribution network contains 100 dual-mode communication nodes, of which 5 nodes are identified as abnormal. The 5 nodes are sampled at a high frequency of 100kHz, and each node collects 10 seconds of voltage waveform data. The voltage waveform of each node is decomposed into 5 IMFs through the Hilbert-Huang transform algorithm, and the instantaneous phase sequence is extracted. Statistical analysis calculates 20 features, including phase mean, standard deviation, main frequency amplitude, etc. K-means algorithm is used for clustering, and the nodes are initially divided into 3 groups. 5-fold cross validation optimizes the clustering results, and the accuracy is increased from 85% to 92%. A 3-layer MLP neural network is trained, with 20 neurons in the input layer, 50 neurons in the hidden layer, and 3 neurons in the output layer (corresponding to phases A, B, and C). The recognition accuracy is 98% when trained on 1,000 known samples. Load analysis shows that the average load of phase A is 40kW, phase B is 35kW, and phase C is 45kW. The genetic algorithm sets the population size to 100, the evolution to 500 generations, the crossover rate to 0.8, and the mutation rate to 0.1. The load adjustment scheme obtained recommends transferring 2kW load from phase C to phase B. The feasibility assessment takes 10 factors into consideration, such as adjustment cost and power outage time, and finally determines the specific load transfer strategy. It is expected that the three-phase load difference will be reduced from 10kW to 6kW after implementation.

[0125] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0126] (1) Analyze and process the phase identification results to obtain phase communication characteristic parameters;

[0127] (2) Based on the phase communication characteristic parameters, the communication data is grouped and processed to obtain phase-divided data packets;

[0128] (3) Encrypting the phase-separated data packet using an elliptic curve encryption algorithm to obtain an encrypted data packet;

[0129] (4) Perform digital signature processing on the encrypted data packet to obtain a signed encrypted data packet;

[0130] (5) Based on the load balancing optimization strategy, the transmission path planning process is performed on the signed encrypted data packet to obtain the optimized transmission path;

[0131] (6) Channel allocation is performed on the optimized transmission path through a dynamic time division multiplexing algorithm to obtain the target communication link;

[0132] (7) Perform version verification on the preset communication strategy package to obtain the strategy package integrity verification result;

[0133] (8) compressing the communication strategy packet by using a differential compression algorithm to obtain a compressed strategy packet;

[0134] (9) Slice the compressed policy package to obtain a policy package slice sequence;

[0135] (10) Redundant encoding is performed on the policy package fragment sequence through an erasure coding algorithm to obtain updated system firmware information.

[0136] Specifically, the phase identification results are analyzed and processed to obtain phase communication characteristic parameters. This process includes analyzing the signal propagation characteristics, noise level, channel capacity, etc. of each phase to form a set of parameters that describe the communication performance of each phase. Based on these phase communication characteristic parameters, the communication data is grouped and processed to obtain phase-divided data packets. Grouping processing divides the original communication data according to the phase to which the target node belongs, ensuring that the data packet can be transmitted through the most suitable communication path.

[0137] Next, the phased data packet is encrypted using the elliptic curve cryptography (ECC) algorithm to obtain an encrypted data packet. ECC is a public key encryption algorithm based on the discrete logarithm problem on the elliptic curve, providing high security. The encryption process includes selecting appropriate elliptic curve parameters, generating a public-private key pair, and encrypting the data using the receiver's public key. The encrypted data packet is digitally signed to obtain a signed encrypted data packet. The digital signature uses the sender's private key to encrypt the data packet summary to ensure data integrity and non-repudiation.

[0138] Based on the load balancing optimization strategy, the transmission path planning of the signed encrypted data packet is processed to obtain the optimized transmission path. The path planning takes into account factors such as network topology, the load of each node, and the link quality, and uses the Dijkstra algorithm or A* algorithm to calculate the optimal path. The channel allocation processing of the optimized transmission path is performed through the dynamic time division multiplexing (DTDMA) algorithm to obtain the target communication link. DTDMA dynamically allocates time slots according to the priority of the data packet and the link status to improve the efficiency of spectrum utilization.

[0139] The communication policy package is processed for version verification to obtain the policy package integrity verification result. Version verification includes comparing the policy package version number, calculating the checksum, etc., to ensure the correctness and applicability of the policy package. The communication policy package is compressed using a differential compression algorithm to obtain a compressed policy package. The differential compression algorithm compares the differences between the new and old versions of the firmware and only transmits the changed parts, greatly reducing the amount of data.

[0140] The compressed policy package is fragmented to obtain a policy package fragment sequence. The fragmentation process divides the large policy package into small data packets suitable for transmission, and each fragment contains a sequence number and content. The policy package fragment sequence is redundantly encoded using an erasure code algorithm to obtain updated system firmware information. Erasure codes, such as Reed-Solomon codes, increase data redundancy and improve transmission reliability. Even if some data packets are lost, the complete information can be restored.

[0141] For example, a power distribution network contains 100 dual-mode communication nodes distributed in three phases A, B, and C. The phase identification results show that there are 40 nodes in phase A, 35 nodes in phase B, and 25 nodes in phase C. The analysis of the phase communication characteristic parameters shows that the average signal-to-noise ratio of phase A is 20dB, 18dB in phase B, and 22dB in phase C. According to these parameters, 1MB of communication data is grouped into 40 A-phase data packets, 35 B-phase data packets, and 25 C-phase data packets. The data packets are encrypted using the ECC algorithm of the NIST P-256 curve with a key length of 256 bits. The digital signature uses the ECDSA algorithm to ensure data integrity. The load balancing optimization strategy takes into account the load conditions of each phase, with an average load of 40kW in phase A, 35kW in phase B, and 45kW in phase C. The improved Dijkstra algorithm is used for path planning, taking into account link quality and load factors. The DTDMA algorithm allocates more time slots for high-priority data packets, such as an average of 4 time slots for A-phase data packets, 3 for B-phase data packets, and 5 for C-phase data packets. The communication policy package size is 10MB, the version number is v2.1.0, and the checksum is calculated by the SHA-256 algorithm for integrity verification. The differential compression algorithm compresses the policy package to 3MB, with a compression rate of 70%. The compressed policy package is divided into 3000 1KB data slices. RS (255, 223) encoding is used for redundancy processing, and 32 redundant slices are generated for every 223 original slices, which improves the fault tolerance by 13%. In this way, the safe and efficient transmission and reliable installation of the policy package can be ensured even in complex power line communication environments.

[0142] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0143] (1) Perform link quality assessment on the target communication link to obtain the communication link performance index;

[0144] (2) Perform channel screening based on the communication link performance index to obtain the target communication channel, extract the policy of updating the system firmware information to obtain the target communication policy, extract the list information of the smart meter through the target communication channel and the target communication policy to obtain the list of meters to be registered;

[0145] (3) Perform identity authentication on the list of registered electric meters through a zero-knowledge proof algorithm to obtain a list of electric meters that have passed authentication;

[0146] (4) Performing area affiliation verification on the list of authenticated electric meters to obtain the electric meter registration information;

[0147] (5) Based on the meter registration information, the meter data is graded according to its importance to obtain a data classification scheme;

[0148] (6) The meter data is collected and processed in a hierarchical manner through an adaptive sampling algorithm to obtain the original hierarchical electricity consumption data;

[0149] (7) Perform data cleaning on the original graded electricity consumption data to obtain cleaned graded electricity consumption data;

[0150] (8) Compress the cleaned and graded electricity consumption data through a time series compression algorithm to obtain graded electricity consumption data;

[0151] (9) Fusion analysis and processing of meter registration information and hierarchically managed electricity consumption data to obtain communication control strategies;

[0152] (10) The communication control strategy is optimized by a reinforcement learning algorithm to obtain an optimized communication control strategy, and the high-speed power line carrier communication is controlled based on the optimized communication control strategy.

[0153] It should be noted that the link quality assessment process is performed on the target communication link to obtain the communication link performance indicators. This process includes measuring parameters such as signal-to-noise ratio (SNR), bit error rate (BER), and throughput to comprehensively evaluate the stability and transmission capacity of the link. Based on these communication link performance indicators, channel screening is performed to obtain the target communication channel, and the policy extraction of the update system firmware information is performed to obtain the target communication policy. Then, the list information of the smart meter is extracted through the target communication channel and the target communication policy to obtain the list of meters to be registered. The information extraction includes key information such as the unique identifier, communication address, and hardware version of the meter.

[0154] Next, the list of meters to be registered is authenticated using a zero-knowledge proof algorithm to obtain a list of meters that have passed authentication. Zero-knowledge proof is a cryptographic technique that allows a prover to prove to a verifier that a statement is correct without revealing any information other than the fact that the statement is correct. Here, the meter proves its identity without revealing key information. The list of meters that have passed authentication is authenticated by the area, and the meter registration information is obtained. Area authentication ensures that the meter belongs to the correct distribution transformer service area.

[0155] Based on the meter registration information, the meter data is graded and processed to obtain a data classification scheme. The rating criteria may include meter type (such as residential, industrial), power consumption level, abnormal frequency of historical data, etc. The meter data is graded and collected through an adaptive sampling algorithm to obtain the original graded power consumption data. The adaptive sampling algorithm dynamically adjusts the sampling frequency according to the importance and change rate of the data to optimize the data collection efficiency.

[0156] The original hierarchical electricity consumption data is cleaned to obtain cleaned hierarchical electricity consumption data. Data cleaning includes operations such as removing outliers, filling missing values, and correcting timestamps to improve data quality. The cleaned hierarchical electricity consumption data is compressed using a time series compression algorithm to obtain hierarchically managed electricity consumption data. Time series compression algorithms such as piecewise linear representation (PLA) or discrete wavelet transform (DWT) can effectively reduce the amount of data storage and transmission.

[0157] The communication control strategy is obtained by integrating and analyzing the meter registration information and the hierarchically managed electricity consumption data. The integrated analysis takes into account the meter characteristics, electricity consumption mode and network conditions to formulate a targeted communication strategy. The communication control strategy is optimized through the reinforcement learning algorithm to obtain the optimized communication control strategy, and the high-speed power line carrier communication is controlled based on the optimized communication control strategy. Reinforcement learning algorithms such as Q-learning or deep Q network (DQN) can continuously optimize the decision-making process by interacting with the environment.

[0158] For example, a power distribution network contains 1,000 smart meters using dual-mode communication (HPLC and HRF). Link quality evaluation shows that the average HPLC SNR is 25dB and the BER is , throughput 100Mbps; HRF average SNR is 35dB, BER is , throughput 1Mbps. Based on these indicators, the information of 980 meters to be registered was extracted. Zero-knowledge proof uses the Schnorr protocol, and the authentication pass rate reaches 98%, with a total of 961 meters passing the authentication. The area attribution verification confirms that these meters are distributed in 10 areas.

[0159] The importance rating divides the meters into three levels: high, medium, and low, with a ratio of 20%, 30%, and 50%. The adaptive sampling algorithm samples high-level meters every 5 minutes, medium-level meters every 15 minutes, and low-level meters every hour. The raw data volume reaches 100GB / day. Data cleaning removes 2% of outliers and fills 1% of missing values. The PLA algorithm is used for time series compression, with an average compression ratio of 10:1, and the final data volume is reduced to 8GB / day.

[0160] The communication control strategies generated by the fusion analysis include: high-level meters give priority to HPLC, low-level meters give priority to HRF, and the middle level is dynamically selected according to the network load. Reinforcement learning uses the DQN algorithm, and the reward function is set to consider the communication success rate, delay and energy consumption. After 10,000 rounds of training, the optimized strategy increased the average communication success rate from 92% to 98%, reduced the average delay from 200ms to 100ms, and reduced energy consumption by 15%. Ultimately, this method achieves efficient and reliable data acquisition and communication control of smart meters, greatly improving the management efficiency of the distribution network.

[0161] The embodiment of the present invention also provides a high-speed power line communication system based on dual-mode communication, such as Figure 2 As shown, the high-speed power line communication system based on dual-mode communication specifically includes:

[0162] The processing module 201 is used to initialize and identify the dual-mode communication unit, and obtain the area belonging information of the dual-mode communication unit;

[0163] A selection module 202 is used to perform intelligent networking and adaptive selection of communication modes on the communication network based on the station area ownership information to obtain optimized network topology and communication mode configuration information;

[0164] The detection module 203 is used to perform real-time monitoring and noise analysis detection processing on power grid parameters through the optimized network topology structure and the communication mode configuration information to obtain a power grid communication quality event report;

[0165] A comparison module 204 is used to collect and perform phase comparison analysis on voltage waveform samples according to the power grid communication quality event report to obtain a phase identification result and a load balancing optimization strategy;

[0166] Transmission module 205, used to encrypt communication data and transmit communication strategy based on the phase identification result and load balancing optimization strategy, to obtain target communication link and update system firmware information;

[0167] The control module 206 is used to perform channel screening and strategy generation through the target communication link and the updated system firmware information, obtain the target communication channel and the target communication strategy, automatically register the smart meter and perform data hierarchical collection and processing through the target communication channel and the target communication strategy, obtain the meter registration information and the hierarchically managed electricity usage data, and perform high-speed power line carrier communication control through the meter registration information and the hierarchically managed electricity usage data.

[0168] Through the coordinated work of the above modules, by combining the advantages of high-speed power line carrier communication (HPLC) and low-power wireless communication (HRF), the adaptive selection of communication mode is realized, which greatly improves the system's adaptability and communication reliability in complex power grid environments; through intelligent networking and dynamic routing strategies, the network topology is optimized, and the communication efficiency and network stability are improved; real-time monitoring and noise analysis and detection mechanisms enhance the system's rapid response capability to power grid faults, providing a strong guarantee for the safe operation of the power grid; accurate phase identification and load balancing optimization strategies effectively improve the load distribution of the power grid, improve the power supply quality and energy utilization efficiency; advanced encryption algorithms and security authentication mechanisms ensure the security and integrity of communication data, and effectively prevent potential network attacks and data leakage risks; the remote upgrade function realized through communication strategies simplifies the system maintenance process and improves operation and maintenance efficiency; the smart meter The automatic registration and data hierarchical collection and processing mechanism optimizes the data management process and improves the efficiency and accuracy of data collection; the communication control strategy based on machine learning continuously optimizes the system performance so that it can adapt to the ever-changing network environment; in addition, the method also has good scalability and compatibility, and can be seamlessly integrated with the existing power system, laying a technical foundation for the further development of smart grids; through the comprehensive use of multiple advanced algorithms, such as graph optimization, deep reinforcement learning, support vector machines, neural networks, etc., the method has made significant breakthroughs in key links such as network optimization, anomaly detection, and phase recognition, greatly improving the overall performance and intelligence level of the system; at the same time, the modular design of the method enables the functional modules to be flexibly combined and expanded according to actual needs to meet the application needs in different scenarios; the method proposed in the present invention comprehensively improves the performance, reliability, security and intelligence level of the power line communication system.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A high-speed power line carrier communication method based on dual-mode communication, characterized in that: include: Initializing and performing area identification processing on the dual-mode communication unit to obtain area belonging information of the dual-mode communication unit; Based on the substation area ownership information, the communication network is intelligently networked and the communication mode is adaptively selected to obtain an optimized network topology structure and communication mode configuration information, including: parsing the substation area ownership information to obtain an initial network node list; performing topology structure generation processing on the initial network node list through a graph theory optimization algorithm to obtain a candidate network topology structure; performing high-speed power line carrier communication mode test processing on the nodes in the candidate network topology structure to obtain high-speed carrier communication quality data; performing micro-power wireless communication mode test processing on the nodes in the candidate network topology structure to obtain wireless communication quality data; and performing multi-objective optimization algorithm on the high-speed carrier communication quality data and the wireless The network communication quality data is comprehensively evaluated to obtain a node communication mode allocation scheme; the node communication mode allocation scheme is subjected to conflict detection processing to obtain a conflict node list; the conflict node list is subjected to communication mode reallocation processing by a genetic algorithm to obtain an optimized communication mode allocation scheme; based on the optimized communication mode allocation scheme, the candidate network topology structure is adjusted to obtain an optimized network topology structure; the optimized network topology structure is subjected to dynamic routing strategy generation processing by a deep reinforcement learning algorithm to obtain an adaptive routing table; the optimized network topology structure and the adaptive routing table are integrated to obtain the optimized network topology structure and the communication mode configuration information; By optimizing the network topology and the communication mode configuration information, real-time monitoring and noise analysis and detection processing are performed on power grid parameters to obtain a power grid communication quality event report; According to the power grid communication quality event report, voltage waveform samples are collected and phase comparison and analysis are performed to obtain phase identification results and load balancing optimization strategies; Based on the phase identification result and the load balancing optimization strategy, the communication data is encrypted and the communication strategy is transmitted to obtain the target communication link and update system firmware information; Channel screening and strategy generation are performed through the target communication link and the updated system firmware information to obtain a target communication channel and a target communication strategy. Automatic registration and hierarchical data collection and processing are performed on smart meters through the target communication channel and the target communication strategy to obtain meter registration information and hierarchically managed electricity usage data. High-speed power line carrier communication control is performed through the meter registration information and the hierarchically managed electricity usage data.

2. The high-speed power line carrier communication method based on dual-mode communication according to claim 1, characterized in that: The step of initializing and performing area identification processing on the dual-mode communication unit to obtain the area belonging information of the dual-mode communication unit includes: Performing a self-check process on the dual-mode communication unit to obtain status information of the high-speed power line carrier communication module and the micro-power wireless communication module; Based on the status information of the high-speed power line carrier communication module and the micro-power wireless communication module, the high-speed power line carrier communication module and the micro-power wireless communication module are activated in parallel to obtain a dual-mode listening state; The communication signal and the micro-power wireless signal on the power line are collected and processed through the dual-mode monitoring state to obtain the original station area identification signal; Performing noise reduction processing on the original station area identification signal by using an adaptive filtering algorithm to obtain an optimized station area identification signal; Decoding the optimized station area identification signal to obtain station area code and communication parameters; The signal strength and quality of the high-speed power line carrier communication module and the micro-power wireless communication module are analyzed and processed by a signal quality evaluation algorithm to obtain a target communication mode; Based on the target communication mode, encapsulate the unique identifier of the dual-mode communication unit and the station area code to obtain response information; Based on the communication parameters, the response information is sent and processed to obtain concentrator confirmation information; Verify the concentrator confirmation information using a secure hash algorithm to obtain a verification result; Based on the verification result, the station area belonging information of the dual-mode communication unit is updated and stored to obtain the station area belonging information.

3. The high-speed power line carrier communication method based on dual-mode communication according to claim 1, characterized in that: The step of performing real-time monitoring and noise analysis detection processing on power grid parameters through the optimization of the network topology structure and the communication mode configuration information to obtain a power grid communication quality event report comprises: Performing node grouping processing on the optimized network topology structure to obtain a monitoring node group; Based on the communication mode configuration information, data collection strategy allocation processing is performed on the monitoring node group to obtain a distributed collection solution; Through the distributed acquisition scheme, the grid voltage, current and power factor are sampled and processed in parallel to obtain the original grid parameter data; Performing time synchronization processing on the original power grid parameter data to obtain synchronized power grid parameter data; Performing noise elimination processing on the synchronous power grid parameter data by using a wavelet transform algorithm to obtain optimized power grid parameter data; Performing statistical analysis on the optimized power grid parameter data to obtain statistical characteristics of the power grid parameters; Performing abnormal pattern recognition processing on the statistical characteristics of the power grid parameters by using a support vector machine algorithm to obtain a preliminary abnormal event list; Prioritizing the preliminary abnormal event list to obtain a priority abnormal event list; Performing cause analysis on the priority abnormal event list through a deep learning algorithm to obtain abnormal event cause analysis results; The abnormal event cause analysis result is formatted to obtain the power grid communication quality event report.

4. The high-speed power line carrier communication method based on dual-mode communication according to claim 1, characterized in that: The step of collecting and performing phase comparison analysis on voltage waveform samples according to the power grid communication quality event report to obtain phase identification results and load balancing optimization strategy comprises: Parsing the power grid communication quality event report to obtain an abnormal node list; Based on the abnormal node list, high-frequency sampling processing is performed on the voltage waveform to obtain original voltage waveform data; Performing instantaneous phase extraction processing on the original voltage waveform data by using a Hilbert-Huang transform algorithm to obtain an instantaneous phase sequence; Performing statistical analysis on the instantaneous phase sequence to obtain a phase feature vector; Classifying the phase feature vectors by using a clustering algorithm to obtain a preliminary phase grouping result; Performing cross-validation processing on the preliminary phase grouping results to obtain optimized phase grouping results; Performing phase identification processing on the optimized phase grouping result by a neural network algorithm to obtain the phase identification result; Performing load analysis on the phase identification results to obtain load distribution of each phase; The load distribution of each phase is balanced and optimized by a genetic algorithm to obtain a load adjustment plan; A feasibility assessment is performed on the load adjustment scheme to obtain the load balancing optimization strategy.

5. The high-speed power line carrier communication method based on dual-mode communication according to claim 1, characterized in that: The step of encrypting the communication data and transmitting the communication strategy based on the phase identification result and the load balancing optimization strategy to obtain the target communication link and update the system firmware information includes: Analyzing and processing the phase identification result to obtain phase communication characteristic parameters; Based on the phase communication characteristic parameters, the communication data is grouped to obtain phase-divided data packets; Encrypting the phase-separated data packet by using an elliptic curve encryption algorithm to obtain an encrypted data packet; Performing digital signature processing on the encrypted data packet to obtain a signed encrypted data packet; Based on the load balancing optimization strategy, a transmission path planning process is performed on the signed encrypted data packet to obtain an optimized transmission path; Performing channel allocation processing on the optimized transmission path by a dynamic time division multiplexing algorithm to obtain the target communication link; Perform version verification on the preset communication policy package to obtain the policy package integrity verification result; The communication strategy packet is compressed by a differential compression algorithm to obtain a compressed strategy packet; Slice the communication policy packet to obtain a policy packet slice sequence; The policy package fragment sequence is subjected to redundant encoding processing by using an erasure coding algorithm to obtain the update system firmware information.

6. The high-speed power line carrier communication method based on dual-mode communication according to claim 5, characterized in that: The step of performing channel screening and strategy generation through the target communication link and the update system firmware information to obtain a target communication channel and a target communication strategy, automatically registering and hierarchical data collection and processing the smart meter through the target communication channel and the target communication strategy to obtain meter registration information and hierarchically managed electricity consumption data, and performing high-speed power line carrier communication control through the meter registration information and the hierarchically managed electricity consumption data includes: Performing link quality assessment processing on the target communication link to obtain a communication link performance indicator; Perform channel screening based on the communication link performance indicator to obtain the target communication channel, perform policy extraction on the update system firmware information to obtain the target communication policy, perform list information extraction processing on the smart meter through the target communication channel and the target communication policy to obtain a list of meters to be registered; Performing identity authentication on the list of electric meters to be registered by using a zero-knowledge proof algorithm to obtain a list of electric meters that have passed authentication; Performing a station area affiliation verification process on the list of electric meters that have passed the authentication to obtain the registration information of the electric meters; Based on the meter registration information, importance rating is performed on the meter data to obtain a data classification scheme; The meter data is collected and processed in a hierarchical manner through an adaptive sampling algorithm to obtain original hierarchical electricity consumption data; Performing data cleaning on the original graded electricity consumption data to obtain cleaned graded electricity consumption data; Compressing the cleaning hierarchical electricity consumption data by a time series compression algorithm to obtain the hierarchically managed electricity consumption data; Performing fusion analysis and processing on the meter registration information and the hierarchically managed electricity consumption data to obtain a communication control strategy; The communication control strategy is optimized by a reinforcement learning algorithm to obtain an optimized communication control strategy, and high-speed power line carrier communication is controlled based on the optimized communication control strategy.

7. A high-speed power line carrier communication system based on dual-mode communication, used to execute the high-speed power line carrier communication method based on dual-mode communication according to any one of claims 1 to 6, characterized in that: include: A processing module, used for initializing and performing area identification processing on the dual-mode communication unit to obtain area belonging information of the dual-mode communication unit; The selection module is used to perform intelligent networking and adaptive selection of communication modes on the communication network based on the substation affiliation information to obtain an optimized network topology structure and communication mode configuration information, including: parsing the substation affiliation information to obtain an initial network node list; performing topology structure generation processing on the initial network node list through a graph theory optimization algorithm to obtain a candidate network topology structure; performing high-speed power line carrier communication mode test processing on the nodes in the candidate network topology structure to obtain high-speed carrier communication quality data; performing micro-power wireless communication mode test processing on the nodes in the candidate network topology structure to obtain wireless communication quality data; and optimizing the high-speed carrier communication quality data through a multi-objective optimization algorithm. and the wireless communication quality data to obtain a node communication mode allocation scheme; perform conflict detection processing on the node communication mode allocation scheme to obtain a conflict node list; perform communication mode reallocation processing on the conflict node list through a genetic algorithm to obtain an optimized communication mode allocation scheme; based on the optimized communication mode allocation scheme, adjust the candidate network topology structure to obtain an optimized network topology structure; perform dynamic routing strategy generation processing on the optimized network topology structure through a deep reinforcement learning algorithm to obtain an adaptive routing table; integrate the optimized network topology structure and the adaptive routing table to obtain the optimized network topology structure and the communication mode configuration information; A detection module, used to perform real-time monitoring and noise analysis detection processing on power grid parameters through the optimized network topology and the communication mode configuration information, and obtain a power grid communication quality event report; A comparison module is used to collect and perform phase comparison analysis on voltage waveform samples according to the power grid communication quality event report to obtain phase identification results and load balancing optimization strategies; A transmission module, used to encrypt the communication data and transmit the communication strategy based on the phase identification result and the load balancing optimization strategy, so as to obtain the target communication link and update the system firmware information; A control module is used to perform channel screening and strategy generation through the target communication link and the updated system firmware information to obtain a target communication channel and a target communication strategy, automatically register and perform data hierarchical collection and processing on smart meters through the target communication channel and the target communication strategy to obtain meter registration information and hierarchically managed electricity usage data, and perform high-speed power line carrier communication control through the meter registration information and the hierarchically managed electricity usage data.

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