A communication power supply and distribution monitoring system

Abstract

The present application relates to the field of communication power supply and distribution management, and discloses a communication power supply and distribution monitoring system, including an edge sensing system, a carrier communication network, and a master station monitoring platform, which greatly improves performance and reliability and ensures the stability of communication services. The edge sensing system is deployed in branch distribution cabinets, terminal cabinets, and equipment loads, and can accurately collect power parameters such as voltage, current, and frequency in real time, and refine the monitoring granularity to a single device. The carrier communication network uses power lines as data transmission media and applies carrier communication technology. It not only uses existing facilities to reduce construction costs, but also transmits load-side data to the master station stably and timely through dynamic signal adjustment. The master station monitoring platform integrates multiple functions and can centrally manage the system operation status. The three work together to achieve full-link panoramic monitoring from branch nodes to single devices, making up for the shortcomings of existing technologies and comprehensively improving the performance of communication power supply and distribution monitoring systems.

Description

Technical Field

[0001] The present application relates to the field of communication power supply and distribution management, and more specifically, to a communication power supply and distribution monitoring system. Background Art

[0002] Amidst the rapid growth of the communications industry, power supply and distribution monitoring systems have consistently played a critical role in ensuring the stable operation of communications networks. Early systems were limited to simple on / off monitoring of the power supply. With the growth of communications traffic and the increasing complexity of equipment, these systems have evolved to include monitoring of the main power supply and power conversion system.

[0003] In power communication environments, such as large data centers and communication base stations, existing AC / DC power supply and distribution monitoring systems play a crucial role. While these systems can provide basic monitoring of the total power input and power conversion system, ensuring the power supply security of core data center equipment to a certain extent, they suffer from significant shortcomings in load-side device management. They can only monitor the total power supply of AC / DC head units and are unable to obtain detailed power data for the equipment loads in branch distribution cabinets and terminal cabinets. This makes it difficult to achieve comprehensive and transparent monitoring of communication power information nodes. This extensive load-side device monitoring method results in the loss of critical data from branch nodes to individual devices throughout the power supply and distribution system, making comprehensive and detailed monitoring impossible. This makes it difficult to meet the growing demand for efficient and stable operation of communication power supply and distribution systems. Therefore, improvements to communication power supply and distribution monitoring systems are essential.

[0004] Based on this, this application proposes a communication power supply and distribution monitoring system to achieve full-link and all-round monitoring of the power supply and distribution system. Summary of the Invention

[0005] The present application provides a communication power supply and distribution monitoring system, which solves the defects of the existing technology that it cannot provide panoramic and transparent monitoring and the insufficient control of load-side equipment, significantly improves the performance and reliability of the communication power supply and distribution monitoring system, and effectively ensures the stable operation of communication services.

[0006] A communication power supply and distribution monitoring system, comprising an edge sensing system, a carrier communication network, and a master station monitoring platform;

[0007] The edge sensing system is deployed in the load-side branch distribution cabinet, terminal cabinet and equipment load to collect power parameters in real time;

[0008] The carrier communication network uses power lines as data transmission media, adopts carrier communication technology to achieve data transmission of each node device in the power supply and distribution system, and supports dynamic signal strength detection and amplification adjustment;

[0009] The master station monitoring platform is used to centrally manage the operating status of the power supply and distribution system, including data collection, risk warning, remote diagnosis and load planning functions;

[0010] Among them, the edge perception system, the carrier communication network and the main station monitoring platform work together to achieve full-link panoramic monitoring from branch nodes to single devices.

[0011] Optionally, the edge perception system includes a sensor acquisition module, a signal conversion module, and a data storage module;

[0012] The sensor acquisition module is used to collect analog signals of voltage, current, frequency and load data in real time;

[0013] The signal conversion module is connected to the sensor acquisition module and is used to filter and amplify the analog signal and convert it into a digital signal;

[0014] The data storage module is used to perform noise reduction processing and feature extraction on the digital signal and the load data to form power parameters, and temporarily store them in a local cache so as to be uploaded to the master station monitoring platform via the carrier communication network.

[0015] Optionally, the carrier communication network is composed of a transmitting end module group deployed at the load-side branch distribution cabinet, the terminal cabinet and the equipment load, and a receiving end module group deployed in the master station monitoring platform;

[0016] The transmitting end module group includes a parameter acquisition module, a signal modulation module, a power line interface module and an amplification and relay module, and the receiving end module group includes the signal demodulation module;

[0017] The parameter acquisition module is used to receive the power parameters of the edge sensing system;

[0018] The signal modulation module uses orthogonal frequency division multiplexing technology to modulate the power parameter into the carrier signal compatible with the power line;

[0019] The power line interface module is equipped with a filter and an isolation device for coupling the carrier signal to the power line for data transmission and suppressing power frequency interference;

[0020] The amplification and relay module is used to dynamically adjust the signal gain based on the real-time detected power line channel signal strength to support long-distance data transmission;

[0021] The signal demodulation module is used to receive and demodulate the carrier signal to restore the power parameters, and perform data cleaning and normalization processing on the power parameters.

[0022] Optionally, the master station monitoring platform includes a risk warning module, a remote diagnosis module, a load planning module and a visual display module;

[0023] The risk warning module analyzes the power parameters processed by the signal demodulation module in real time based on preset dynamic thresholds and LSTM deep learning models, detects overcurrent risks and overvoltage risks, and triggers graded alarms;

[0024] The remote diagnosis module, in combination with the fault feature database, performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module, generates a fault analysis result and issues a corresponding fault handling instruction;

[0025] The load planning module, based on the load data contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform, uses a time series prediction model to predict the load change after the new equipment is connected, and generates an access optimization plan for the new equipment;

[0026] The visual display module displays the real-time operating status of the power supply and distribution system in a graphical interface.

[0027] Optionally, the carrier communication network further includes an anti-interference and error correction module, a network security module, and a network supervision module, wherein the anti-interference and error correction module and the network security module are integrated into the transmitting end module group and are arranged between the signal modulation module and the power line interface module, and the network supervision module is connected across the transmitting end module group and the receiving end module group;

[0028] The anti-interference error correction module combines forward error correction algorithm and adaptive filtering technology to eliminate the impact of power line electromagnetic noise on data transmission;

[0029] The network security module uses encryption algorithms and identity authentication mechanisms to ensure data transmission security;

[0030] The network monitoring module is used to monitor the status of the carrier communication network in real time and provide fault diagnosis.

[0031] Optionally, the anti-interference error correction module includes a channel equalization unit and a redundancy check unit;

[0032] The channel equalization unit uses a minimum mean square error algorithm to dynamically compensate for power line channel distortion, and cooperates with the adaptive filtering technology to monitor and eliminate interference of power line electromagnetic noise on the signal transmission path in real time;

[0033] The redundancy check unit is used to perform a cyclic redundancy check on the data output by the channel equalization unit, and correct the erroneous data obtained by the check in combination with the forward error correction algorithm.

[0034] Optionally, the risk warning module includes a threshold dynamic configuration unit, a multi-dimensional feature extraction unit and a warning push unit;

[0035] The threshold dynamic configuration unit determines the safety threshold range according to the load device type and the ambient temperature. The dynamic threshold serves as an input parameter of the LSTM deep learning model.

[0036] The multi-dimensional feature extraction unit is used to extract risk features from voltage harmonic distortion rate, current phase offset and frequency fluctuation, and analyze the risk features in real time through the LSTM deep learning model to detect overcurrent risk and overvoltage risk;

[0037] The early warning push unit triggers a graded alarm based on the risk level corresponding to the risk analysis result of the dimensional feature extraction unit, and pushes the alarm information to a designated operation and maintenance terminal in a targeted manner.

[0038] Optionally, the remote diagnosis module includes a fault signal tracing unit, a knowledge graph matching unit, and a fault reset adjustment unit;

[0039] The fault signal tracing unit locates the fault node based on signal propagation delay and impedance spectrum analysis;

[0040] The knowledge graph matching unit performs similarity matching between the current fault feature and the fault feature database, and generates a fault analysis result in combination with the root cause analysis. The current fault feature is the fault feature corresponding to the overcurrent risk or the overvoltage risk detected by the risk warning module;

[0041] The fault reset adjustment unit determines an optimal processing solution according to the fault analysis result, and sends a reset instruction or a parameter adjustment instruction corresponding to the optimal processing solution to the fault node through the carrier communication network.

[0042] Optionally, the load planning module includes a load balancing analysis unit, a digital twin simulation unit, and an optimization strategy generation unit;

[0043] The load balancing analysis unit uses the time series prediction model to predict the load change after the new device is connected based on the load data contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform;

[0044] The digital twin simulation unit constructs a virtual model of the power supply and distribution system, simulates system stability based on the predicted load changes, and generates simulation results;

[0045] The optimization strategy generating unit generates the access optimization solution including device deployment location, power allocation and timing control strategy according to the simulation result.

[0046] Optionally, the amplification relay module detects the power line channel signal strength in real time through a closed-loop control algorithm, compares it with a preset reference value, calculates an error, and optimizes and adjusts the gain parameter based on the error value.

[0047] It can be seen from the above technical solutions that the communication power supply and distribution monitoring system provided by the embodiment of the present application comprehensively solves the defects of the existing technology that it is unable to provide panoramic and transparent monitoring and insufficient control of load-side equipment, significantly improves the performance and reliability of the communication power supply and distribution monitoring system, and effectively guarantees the stable operation of communication services.

[0048] First, the edge sensing system is deployed on the load side at branch distribution cabinets, terminal cabinets, and equipment loads. It collects power parameters such as voltage, current, and frequency in real time, reducing monitoring granularity down to branch nodes and even individual devices. Second, the carrier communication network utilizes power lines as the data transmission medium and employs carrier communication technology to transmit data between devices at each node in the power supply and distribution system. It also supports dynamic signal strength detection and amplification factor adjustment. This data transmission method fully utilizes existing power line infrastructure, eliminating the need for laying dedicated data transmission cables and reducing system construction costs. Furthermore, dynamic signal strength detection and amplification factor adjustment enable flexible signal optimization based on the actual transmission environment, effectively preventing data transmission bottlenecks caused by communication bottlenecks. This ensures that data collected by each node on the load side is transmitted stably and promptly to the master monitoring platform, eliminating monitoring blind spots. Finally, the master monitoring platform integrates data collection, risk warning, remote diagnostics, and load planning functions to centrally manage the operating status of the power supply and distribution system.

[0049] The edge sensing system, carrier communication network, and master station monitoring platform work together to achieve comprehensive monitoring of the entire link, from branch nodes to individual devices. This collaborative working model comprehensively addresses the shortcomings of existing technologies, such as the lack of comprehensive and transparent monitoring and insufficient control of load-side equipment. It significantly improves the performance and reliability of the communication power supply and distribution monitoring system, effectively ensuring the stable operation of communication services. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0051] Figure 1 A schematic diagram of a communication power supply and distribution monitoring system disclosed in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of an edge sensing system disclosed in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of a carrier communication network disclosed in an embodiment of the present application;

[0054] Figure 4 This is a schematic diagram of a master station monitoring platform disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] Next, we will introduce the application scheme. This application proposes the following technical scheme, please see below for details.

[0057] Figure 1 This is a schematic diagram of a communication power supply and distribution monitoring system disclosed in an embodiment of the present application.

[0058] like Figure 1 As shown, the system may include:

[0059] Edge sensing system 1, carrier communication network 2, and master station monitoring platform 3;

[0060] The edge sensing system 1 is deployed in the load-side branch distribution cabinet, terminal cabinet and equipment load to collect power parameters in real time;

[0061] The carrier communication network 2 uses power lines as data transmission media, adopts carrier communication technology to achieve data transmission of each node device in the power supply and distribution system, and supports dynamic signal strength detection and amplification factor adjustment;

[0062] The master station monitoring platform 3 is used to centrally manage the operating status of the power supply and distribution system, including data collection, risk warning, remote diagnosis and load planning functions;

[0063] Among them, the edge perception system 1, the carrier communication network 2 and the main station monitoring platform 3 work together to achieve full-link panoramic monitoring from branch nodes to single devices.

[0064] Specifically, the edge sensing system 1 is deployed in the branch distribution cabinets, terminal cabinets and equipment loads on the load side, and can directly obtain the most accurate power parameter information. In actual operation, the branch distribution cabinet is a key node in power distribution. By monitoring it, we can understand the distribution of power in the branch lines. The terminal cabinet is the direct hub for connecting the equipment load, and monitoring its power parameters helps to detect abnormalities in the operation of the equipment in a timely manner. The equipment load directly reflects the power consumption of the equipment. The system can collect power parameters such as voltage, current, frequency and load data of load equipment in real time. These data and information play a vital role in evaluating the operating stability of the power supply and distribution system, the power efficiency of the equipment, and whether there are abnormal conditions. For example, when the current fluctuates abnormally, it may mean that the equipment is at risk of failure or overload.

[0065] Carrier Communication Network 2 innovatively utilizes power lines as a data transmission medium. The advantage of this design is that it eliminates the need for laying dedicated data transmission lines, significantly reducing system construction costs and difficulty. It utilizes carrier communication technology, which modulates data signals onto the AC signals of power lines for transmission, enabling data transmission between devices at each node in the power supply and distribution system. It also supports dynamic signal strength detection and amplification factor adjustment. In actual power environments, signal strength is affected by a variety of factors, such as line length and load variations. The dynamic signal strength detection function monitors signal strength in real time and automatically adjusts the amplification factor when signal strength is weak, ensuring accurate and stable data transmission. This function effectively improves the reliability and stability of data transmission and ensures the normal operation of the system.

[0066] The master station monitoring platform 3 is the core of the entire communication power supply and distribution monitoring system, used to centrally manage the operating status of the power supply and distribution system. It has many powerful functions:

[0067] Data collection: Collect various power parameter data transmitted from the edge sensing system 1 through the carrier communication network 2, organize and store them, and provide a basis for subsequent analysis and decision-making.

[0068] Risk Warning: Based on collected data, pre-set algorithms and models are used to predict and warn of potential risks in the power supply and distribution system. For example, if voltage exceeds the normal range or equipment load is continuously too high, an alarm will be issued to prompt staff to take appropriate measures to avoid accidents.

[0069] Remote Diagnosis: When a system anomaly occurs, staff can remotely diagnose the equipment through the main station monitoring platform 3, quickly locating the fault point and cause, reducing troubleshooting time and costs. For example, by analyzing changes in parameters such as current and voltage, it can be determined whether the equipment has a short circuit, open circuit, or other fault.

[0070] Load planning: Based on historical data and real-time monitoring data, the load of the power supply and distribution system is rationally planned. By optimizing load distribution, the utilization efficiency of power resources is improved, energy consumption is reduced, and the service life of equipment is extended.

[0071] The edge perception system 1, carrier communication network 2, and master monitoring platform 3 work together to achieve comprehensive monitoring of the entire link, from branch nodes to individual devices. The edge perception system 1 is responsible for data collection, the carrier communication network 2 handles data transmission, and the master monitoring platform 3 processes and manages the data, making decisions based on the analysis results. This collaborative working model enables comprehensive, real-time monitoring of the operating status of the entire power supply and distribution system, improving the system's intelligent management and operational reliability.

[0072] It can be seen from the above technical solutions that the communication power supply and distribution monitoring system provided by the embodiment of the present application comprehensively solves the defects of the existing technology that it is unable to provide panoramic and transparent monitoring and insufficient control of load-side equipment, significantly improves the performance and reliability of the communication power supply and distribution monitoring system, and effectively guarantees the stable operation of communication services.

[0073] First, the edge sensing system is deployed on the load side at branch distribution cabinets, terminal cabinets, and equipment loads. It collects power parameters such as voltage, current, and frequency in real time, reducing monitoring granularity down to branch nodes and even individual devices. Second, the carrier communication network utilizes power lines as the data transmission medium and employs carrier communication technology to transmit data between devices at each node in the power supply and distribution system. It also supports dynamic signal strength detection and amplification factor adjustment. This data transmission method fully utilizes existing power line infrastructure, eliminating the need for laying dedicated data transmission cables and reducing system construction costs. Furthermore, dynamic signal strength detection and amplification factor adjustment enable flexible signal optimization based on the actual transmission environment, effectively preventing data transmission bottlenecks caused by communication bottlenecks. This ensures that data collected by each node on the load side is transmitted stably and promptly to the master monitoring platform, eliminating monitoring blind spots. Finally, the master monitoring platform integrates data collection, risk warning, remote diagnostics, and load planning functions to centrally manage the operating status of the power supply and distribution system.

[0074] The edge sensing system, carrier communication network, and master station monitoring platform work together to achieve comprehensive monitoring of the entire link, from branch nodes to individual devices. This collaborative working model comprehensively addresses the shortcomings of existing technologies, such as the lack of comprehensive and transparent monitoring and insufficient control of load-side equipment. It significantly improves the performance and reliability of the communication power supply and distribution monitoring system, effectively ensuring the stable operation of communication services.

[0075] In some embodiments of the present application, each component of the communication power supply and distribution monitoring system is introduced respectively:

[0076] Edge Perception System 1:

[0077] Figure 2 A schematic diagram of an edge sensing system disclosed in an embodiment of the present application.

[0078] like Figure 2 As shown, the edge perception system 1 may include:

[0079] Sensor acquisition module 11, signal conversion module 12 and data storage module 13;

[0080] The sensor acquisition module 11 is used to collect analog signals of voltage, current, frequency and load data in real time;

[0081] The signal conversion module 12 is connected to the sensor acquisition module and is used to filter and amplify the analog signal and convert it into a digital signal;

[0082] The data storage module 13 is used to perform noise reduction processing and feature extraction on the digital signal and the load data to form power parameters, and temporarily store them in a local cache so as to be uploaded to the master station monitoring platform via the carrier communication network.

[0083] Specifically, the sensor acquisition module 11 is deployed in key locations such as branch distribution cabinets, terminal cabinets, and equipment loads. Branch distribution cabinets are important nodes for power distribution, and terminal cabinets are connected to specific equipment loads, which directly reflect the power consumption of the equipment. This module collects analog signals of voltage, current, and frequency, as well as load data in real time. These analog signals are basic data reflecting the operating status of the power system. The load data covers information such as the real-time power of the load, load type, load working status, and load current waveform characteristics. For example, by collecting voltage analog signals, the stability of the power supply can be understood, current analog signals can reflect the actual power load of the equipment, and frequency analog signals are crucial for evaluating the operating status of the power system. The real-time power data in the load data can intuitively show the power consumption intensity of the equipment, the load type data can help analyze the power loss characteristics, the working status data can promptly detect equipment abnormalities, and the current waveform characteristics can assist in judging the power quality.

[0084] The signal conversion module 12 is connected to the sensor acquisition module 11. Because collected analog signals are susceptible to interference and difficult to process, the signal conversion module 12 filters and amplifies the analog signals. Filtering removes noise and improves signal purity, while amplification enhances signal strength to facilitate subsequent conversion operations. After processing, the analog signals are converted to digital signals, which offer advantages such as strong interference resistance and ease of storage and processing, laying the foundation for subsequent data processing and analysis.

[0085] The data storage module 13 is responsible for performing noise reduction and feature extraction on the digital signals and load data to generate power parameters. Noise reduction further improves data quality by removing any residual noise. Feature extraction extracts key power parameters, such as effective voltage and peak current, from the large amount of digital signal and load data. These power parameters are temporarily stored in a local cache, awaiting transmission to the master monitoring platform via the carrier communication network. This local cache ensures data integrity, preventing data loss in the event of a temporary network outage.

[0086] Carrier Communication Network 2:

[0087] Figure 3 A schematic diagram of a carrier communication network disclosed in an embodiment of the present application.

[0088] like Figure 3 As shown, the carrier communication network 2 can be composed of a transmitting end module group deployed at the load-side branch distribution cabinet, the terminal cabinet and the equipment load, and a receiving end module group deployed in the master station monitoring platform;

[0089] The transmitting end module group includes a parameter acquisition module 21, a signal modulation module 22, a power line interface module 23 and an amplification and relay module 24, and the receiving end module group includes the signal demodulation module 25;

[0090] The parameter acquisition module is used to receive the power parameters of the edge sensing system;

[0091] The signal modulation module uses orthogonal frequency division multiplexing technology to modulate the power parameter into the carrier signal compatible with the power line;

[0092] The power line interface module is equipped with a filter and an isolation device for coupling the carrier signal to the power line for data transmission and suppressing power frequency interference;

[0093] The amplification and relay module is used to dynamically adjust the signal gain based on the real-time detected power line channel signal strength to support long-distance data transmission;

[0094] The signal demodulation module is used to receive and demodulate the carrier signal to restore the power parameters, and perform data cleaning and normalization processing on the power parameters.

[0095] The amplification and relay module 24 detects the power line channel signal strength in real time through a closed-loop control algorithm, compares it with a preset reference value, calculates an error, and optimizes and adjusts the gain parameter based on the error value.

[0096] Specifically, carrier communication network 2 consists of a transmitter module group deployed on the load side (including branch distribution cabinets, terminal cabinets, and equipment loads), and a receiver module group deployed within the master station monitoring platform. This distributed approach enables efficient transmission of load-side data to the master station monitoring platform, enabling centralized monitoring and management of information such as power parameters.

[0097] Transmitter module group:

[0098] The parameter acquisition module 21 primarily receives power parameters from the edge sensing system. The edge sensing system acquires and processes power parameters through its sensor acquisition module, signal conversion module, and data storage module. The carrier communication module serves as the interface for receiving these parameters, laying the foundation for subsequent data transmission.

[0099] Signal modulation module 22 uses Orthogonal Frequency Division Multiplexing (OFDM) technology. This technology modulates power parameters into a power line-compatible carrier signal, enabling data transmission over the power line. OFDM technology offers advantages such as high spectrum utilization and strong resistance to multipath fading, ensuring stable and reliable data transmission in complex power line environments.

[0100] The power line interface module 23 is equipped with a filter and an isolation device. The filter couples the carrier signal to the power line, allowing the carrier signal to use the power line as a data transmission medium for data transmission while suppressing power frequency interference. Power frequency interference is a common source of interference in power systems and can affect the transmission quality of the carrier signal. The filter effectively reduces the impact of power frequency interference on the carrier signal. The isolation device further ensures electrical isolation between the carrier communication network and the power system, improving system safety.

[0101] The amplification relay module 24 dynamically adjusts the signal gain based on the real-time detection of the power line channel signal strength to support long-distance data transmission. This function is achieved through a closed-loop control algorithm. Specifically, the power line channel signal strength is detected in real time, compared with a preset reference value, and the error is calculated. The gain parameters are then optimized and adjusted based on this error value. Regarding the relationship between the error value and the gain parameter adjustment, the gain adjustment amount is determined by considering three factors. First, it is directly related to the real-time error value. A preset proportional coefficient is used to multiply the error value by this proportional coefficient to obtain a portion of the adjustment. Second, it considers the integration of the error value over a period of time. A preset integral coefficient is used to determine a portion of the adjustment based on the integral of the error value. Third, it considers the rate of change of the error value. A preset differential coefficient is used to calculate a portion of the adjustment based on the rate of change of the error value. Adding these three adjustments together yields the total gain adjustment.

[0102] Signal strength is determined by continuously monitoring the signal-to-noise ratio (SNR) of the power line channel. If the monitored SNR falls below a pre-set SNR threshold, the signal strength is considered weak. The gain adjustment mechanism uses a PID control algorithm to dynamically calculate the appropriate gain adjustment based on the difference between the current SNR and the SNR threshold.

[0103] The gain adjustment amount is linearly related to the absolute value of the difference between the signal-to-noise ratio threshold and the SNR. That is, the larger the absolute value of the difference, the greater the gain adjustment. The gain adjustment amount is calculated by multiplying the absolute value of the difference by a proportionality factor determined experimentally. Once the monitored SNR rises above the signal-to-noise ratio threshold, no further gain adjustment is performed.

[0104] Receiver module group:

[0105] The signal demodulation module 25 is used to receive and demodulate the carrier signal at the receiving module group on the master station monitoring platform side, restoring it to the original power parameters. During the transmission process, the carrier signal may be affected by various interferences and changes. The demodulation process is to correct these changes and restore the power parameters. In addition, this module also performs data cleaning and normalization processing on the power parameters. Data cleaning is to remove noise, erroneous data, etc. from the data to improve data quality; normalization processing is to convert power parameters of different ranges and units into a unified format to facilitate subsequent data analysis and processing. For example, it can be used to predict load changes after the addition of new equipment based on load data.

[0106] Main station monitoring platform 3:

[0107] Figure 4 This is a schematic diagram of a master station monitoring platform disclosed in an embodiment of the present application.

[0108] like Figure 4As shown, the master station monitoring platform 3 may include:

[0109] Risk warning module 31, remote diagnosis module 32, load planning module 33 and visual display module 34;

[0110] The risk warning module analyzes the power parameters processed by the signal demodulation module in real time based on preset dynamic thresholds and LSTM deep learning models, detects overcurrent risks and overvoltage risks, and triggers graded alarms;

[0111] The remote diagnosis module, in combination with the fault feature database, performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module, generates a fault analysis result and issues a corresponding fault handling instruction;

[0112] The load planning module, based on the load data contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform, uses a time series prediction model to predict the load change after the new equipment is connected, and generates an access optimization plan for the new equipment;

[0113] The visual display module displays the real-time operating status of the power supply and distribution system in a graphical interface.

[0114] Specifically, the risk warning module 31 analyzes power parameters in real time based on preset dynamic thresholds and an LSTM (Long Short-Term Memory) deep learning model. Dynamic thresholds are set dynamically based on different power parameters and system operating status, more accurately reflecting the actual system conditions. The LSTM deep learning model has powerful sequential data processing capabilities and can learn the changing patterns of power parameters. Through analysis, it detects abnormal conditions such as overcurrent and overvoltage risks, generates fault signatures corresponding to these risks, and triggers graded alarms. These graded alarms are divided into different levels based on the severity of the risk, allowing personnel to take appropriate measures based on the alarm level and promptly address potential risks.

[0115] The LSTM model is used in the risk warning module. During training, it mainly uses historical power parameter data processed by the signal demodulation module, covering time series data such as voltage, current, and frequency. The training process is as follows:

[0116] Data processing: Clean and normalize the raw data, and extract features such as voltage fluctuation rate.

[0117] Build the architecture: Construct a network structure with an input layer, two LSTM hidden layers, and a fully connected output layer. Use the ReLU activation function, cross entropy loss function, and Adam optimizer.

[0118] Training and validation: Split the training and validation sets into a 7:3 ratio, set a training cycle of 100 and a batch size of 32, and dynamically adjust the learning rate. Evaluate performance on the validation set, and add a Dropout layer if overfitting occurs.

[0119] Model deployment: Load the trained model to the main station monitoring platform to process power parameters in real time for risk warning.

[0120] The remote diagnostic module 32 uses a fault signature database to perform fault tracing and root cause analysis. The database stores characteristic information about various faults. When the system detects an anomaly, the remote diagnostic module 32 compares the actual detected fault signatures for overcurrent or overvoltage risks with those in the database. This allows the module to trace the source of the overcurrent or overvoltage risk, determining the location and cause of the fault. It then generates fault analysis results and issues corresponding fault handling instructions to guide personnel in troubleshooting, improving the efficiency and accuracy of troubleshooting.

[0121] The load planning module 33 primarily utilizes the load data (such as real-time power, load type, and operating status) contained in the power parameters processed by the signal demodulation module, as well as historical load data stored locally on the master station monitoring platform. Using a time series prediction model, this module can predict load changes after the addition of new equipment. This model can predict future load trends based on the changing trends and patterns of historical load data, combined with current load conditions. Based on these predictions, an optimized plan for the addition of new equipment is generated, such as determining the optimal location and time of insertion, as well as appropriate power allocation, to ensure stable operation of the power supply and distribution system and the rational use of resources.

[0122] The time series prediction model is used in the load planning module. Training relies on the real-time load data from the signal demodulation module and the historical load data stored in the master station platform. The training steps are:

[0123] Data preparation: Integrate at least one year's worth of data and construct a time series dataset with hourly granularity.

[0124] Feature engineering: Use sliding window technology to generate input sequences and add auxiliary features such as holidays and seasonal cycles.

[0125] Model training: Choose the Prophet or Transformer model, use the mean square error as the loss function, and use early stopping to prevent overfitting.

[0126] Prediction optimization: Input the power parameters of newly added equipment to predict load changes, verify with digital twin simulation, and retrain if the deviation exceeds 5%.

[0127] The visualization module 34 uses a graphical interface to display the real-time operating status of the power supply and distribution system. It presents complex power parameters and system operating information to personnel through intuitive graphics and charts. For example, it displays real-time voltage and current curves, as well as equipment operating status. This visualization allows personnel to quickly understand the system's operating status, identify anomalies promptly, and take appropriate measures, thereby improving system management efficiency.

[0128] In addition, the master station monitoring platform 3 may also include a data storage management module, which is responsible for storing and managing all collected historical data, supporting data query, backup and recovery functions, ensuring the security and integrity of the data, and providing support for long-term analysis and optimization.

[0129] In some embodiments of the present application, based on the above content, the carrier communication network 2 may further include:

[0130] Anti-interference error correction module 26, network security module 27 and network supervision module 28;

[0131] The anti-interference error correction module and the network security module are integrated into the transmitter module group and arranged between the signal modulation module and the power line interface module, and the network supervision module is connected across the transmitter module group and the receiver module group;

[0132] The anti-interference error correction module 26 combines the forward error correction algorithm and the adaptive filtering technology to eliminate the influence of the power line electromagnetic noise on the data transmission;

[0133] The network security module 27 uses encryption algorithms and identity authentication mechanisms to ensure data transmission security;

[0134] The network monitoring module 28 is used to monitor the status of the carrier communication network in real time and provide fault diagnosis.

[0135] Wherein, the anti-interference error correction module 26 includes a channel equalization unit and a redundancy check unit;

[0136] The channel equalization unit uses a minimum mean square error algorithm to dynamically compensate for power line channel distortion, and cooperates with the adaptive filtering technology to monitor and eliminate interference of power line electromagnetic noise on the signal transmission path in real time;

[0137] The redundancy check unit is used to perform a cyclic redundancy check on the data output by the channel equalization unit, and correct the erroneous data obtained by the check in combination with the forward error correction algorithm.

[0138] Specifically, the anti-interference error correction module 26 is integrated into the transmitter module group and is located between the signal modulation module and the power line interface module. After the signal modulation module modulates the power parameters into a carrier signal, the anti-interference error correction module takes effect. Due to the complex power line environment, various noise sources such as electromagnetic interference may affect the carrier signal. The anti-interference error correction module combines forward error correction algorithms and adaptive filtering technology to eliminate the impact of power line electromagnetic noise on data transmission, ensuring that data can be accurately transmitted on the power line. This module is further subdivided into a channel equalization unit and a redundancy check unit.

[0139] Channel equalization unit: Due to the inherent characteristics of power line channels, signal distortion can occur during data transmission, seriously affecting data accuracy. The channel equalization unit uses the Minimum Mean Square Error (MMSE) algorithm to dynamically compensate for power line channel distortion. This algorithm continuously adjusts compensation parameters based on actual channel conditions, restoring the signal to its original state as closely as possible during transmission. Simultaneously, this unit collaborates with adaptive filtering technology to monitor in real time the interference of power line electromagnetic noise on the signal transmission path. Adaptive filtering automatically adjusts filter parameters based on noise fluctuations, providing targeted noise reduction. For example, when large electrical equipment is started near a power line, generating strong electromagnetic noise, adaptive filtering can quickly adjust to effectively suppress noise interference and ensure stable signal transmission.

[0140] Redundancy Check Unit: After the channel equalization unit processes the signal, the redundancy check unit performs a cyclic redundancy check (CRC) on the output data. CRC is a commonly used error detection method. It performs specific calculations on the data to generate a checksum and transmits it along with the data. Upon receiving the data, the receiver performs the same calculation again and compares the calculated checksum with the received checksum to determine whether any errors occurred during data transmission. If errors are detected, the redundancy check unit, combined with the forward error correction (FEC) algorithm, corrects the erroneous data. The forward error correction algorithm adds redundant information to the transmitted data. This redundant information allows the receiver to automatically correct the erroneous data without requesting a retransmission, improving data transmission efficiency and reliability.

[0141] The network security module 27 is also integrated into the transmitter module group and is located between the signal modulation module and the power line interface module. The network security module uses encryption algorithms and identity authentication mechanisms to ensure data transmission security. During the data transmission process, data security is of paramount importance. Once the data is stolen or tampered with, serious consequences may result. The encryption algorithm encrypts the transmitted data and converts the plaintext data into ciphertext data. Only the recipient with the correct key can restore the ciphertext to plaintext. Common encryption algorithms such as symmetric encryption algorithms and asymmetric encryption algorithms can effectively protect the privacy of data. At the same time, the identity authentication mechanism ensures that only legitimate devices or users can access the network and transmit data. By verifying the identity information of the device or user, such as user name, password, digital certificate, etc., the intrusion of illegal devices or users is prevented and the security of the network is guaranteed.

[0142] The network monitoring module 28 bridges the transmitter module group and the receiver module group, and is used to monitor the status of the carrier communication network in real time and provide fault diagnosis. During operation, the carrier communication network may experience various faults, such as line faults and equipment failures. The network monitoring module monitors various network parameters in real time, such as signal strength, data transmission rate, and bit error rate. When an abnormal network status is detected, the module analyzes these parameters to quickly locate the fault point and provide a detailed fault diagnosis report. For example, if a sudden drop in data transmission rate is detected, the network monitoring module can determine whether it is a line or equipment problem by analyzing parameters such as signal strength and bit error rate, and provide a corresponding solution to help maintenance personnel promptly troubleshoot the problem and ensure the normal operation of the network.

[0143] By adding the anti-interference and error correction module 26, the network security module 27 and the network supervision module 28, the performance of the carrier communication network 2 has been significantly improved, which can better adapt to the complex power line environment, ensure the safe and accurate transmission of data, and provide solid support for the stable operation of the entire communication power supply and distribution monitoring system.

[0144] In some embodiments of the present application, the risk warning module 31, the remote diagnosis module 32, and the load planning module 33 are introduced respectively:

[0145] Risk Warning Module 31:

[0146] The risk warning module includes a threshold dynamic configuration unit, a multi-dimensional feature extraction unit and a warning push unit;

[0147] The threshold dynamic configuration unit determines the safety threshold range according to the load device type and the ambient temperature. The dynamic threshold serves as an input parameter of the LSTM deep learning model.

[0148] The multi-dimensional feature extraction unit is used to extract risk features from voltage harmonic distortion rate, current phase offset and frequency fluctuation, and analyze the risk features in real time through the LSTM deep learning model to detect overcurrent risk and overvoltage risk;

[0149] The early warning push unit triggers a graded alarm based on the risk level corresponding to the risk analysis result of the dimensional feature extraction unit, and pushes the alarm information to a designated operation and maintenance terminal in a targeted manner.

[0150] Specifically, the risk warning module 31 consists of a threshold dynamic configuration unit, a multi-dimensional feature extraction unit and a warning push unit. Its main function is to timely detect overcurrent risks and overvoltage risks in the power supply and distribution system, and to perform graded alarms and information push.

[0151] Dynamic Threshold Configuration Unit: Different types of load devices have different power parameter requirements, and ambient temperature can also affect the normal operation of the devices and the safe range of power parameters. The dynamic threshold configuration unit determines the safe threshold range based on the load device type and ambient temperature. For example, for precision electronic equipment with high voltage stability requirements, the safe voltage threshold range is relatively narrow; for general lighting equipment, the safe voltage threshold range can be wider. As the ambient temperature rises, the heat dissipation capacity of the device may decrease, resulting in a decrease in its current tolerance. In this case, the safe current threshold also needs to be adjusted accordingly. These dynamically determined thresholds serve as input parameters for the LSTM deep learning model, enabling the model to perform more accurate risk analysis based on actual conditions.

[0152] Multi-dimensional feature extraction unit: The operating status of a power system can be reflected through features across multiple dimensions. The multi-dimensional feature extraction unit extracts risk features from aspects such as voltage harmonic distortion, current phase shift, and frequency fluctuation. Voltage harmonic distortion reflects the degree to which the voltage waveform deviates from an ideal sine wave. Excessively high harmonic distortion can cause equipment overheating and damage. Current phase shift reflects the phase relationship between current and voltage. Abnormal phase shift may indicate a power factor problem or equipment failure. Frequency fluctuation directly impacts power system stability. Using the LSTM deep learning model to analyze these risk features in real time, the unit effectively detects overcurrent and overvoltage risks. The LSTM model learns how these features change over time, enabling early detection of potential risks.

[0153] Early Warning Push Unit: Based on the risk analysis results from the Multi-Dimensional Feature Extraction Unit, the Early Warning Push Unit determines the risk level based on the severity of the risk and triggers a graded alarm. This graded alarm allows operations personnel to more intuitively understand the severity of the risk and take appropriate measures. For example, for lower risk levels, a prompt alarm can be issued to remind operations personnel to pay attention to the system status; for higher risk levels, an emergency alarm can be issued, requiring immediate action by operations personnel. Alarm information is also pushed to designated operations terminals to ensure that relevant personnel receive and process the information promptly.

[0154] Remote diagnosis module 32:

[0155] The fault signal tracing unit locates the fault node based on signal propagation delay and impedance spectrum analysis;

[0156] The knowledge graph matching unit performs similarity matching between the current fault feature and the fault feature database, and generates a fault analysis result in combination with the root cause analysis. The current fault feature is the fault feature corresponding to the overcurrent risk or the overvoltage risk detected by the risk warning module;

[0157] The fault reset adjustment unit determines an optimal processing solution according to the fault analysis result, and sends a reset instruction or a parameter adjustment instruction corresponding to the optimal processing solution to the fault node through the carrier communication network.

[0158] Specifically, the remote diagnosis module 32 is composed of a fault signal tracing unit, a knowledge graph matching unit and a fault reset adjustment unit, and is mainly used to quickly locate the fault node, analyze the cause of the fault and perform fault processing.

[0159] Fault Signal Tracing Unit: When a fault occurs in the power supply and distribution system, the fault signal tracing unit locates the fault node based on signal propagation delay and impedance spectrum analysis. When a signal propagates through a power line, there will be a certain propagation delay due to factors such as line length and impedance. By measuring the propagation delay of the fault signal, the approximate location of the fault node can be determined. At the same time, impedance spectrum analysis can further determine the specific condition of the fault node, such as whether there is a short circuit, open circuit, or other fault. For example, if an abnormal signal propagation delay is detected and the impedance spectrum shows low impedance, it may indicate a short circuit fault.

[0160] Knowledge Graph Matching Unit: This unit compares the current fault signature with the fault signature database. The fault signature database contains a large amount of known fault signature information. This matching process allows rapid identification of historical fault cases similar to the current fault. Combined with root cause analysis, this unit further analyzes the cause of the fault and generates detailed fault analysis results. For example, if the current fault signature closely resembles a short-circuit fault case in the database, root cause analysis can determine that the short-circuit fault is caused by line aging.

[0161] Fault reset and adjustment unit: Based on the fault analysis results generated by the knowledge graph matching unit, the fault reset and adjustment unit determines the optimal solution. For simple faults, such as equipment malfunction, a reset command can be sent to restore normal operation of the equipment. For faults that require parameter adjustment, such as improper equipment parameter settings, parameter adjustment commands can be sent to resolve the problem. Reset commands or parameter adjustment commands corresponding to the optimal solution are sent to the faulty node via the carrier communication network. The carrier communication network serves as a channel for data transmission, ensuring that commands are accurately transmitted to the faulty node. After receiving the command, the faulty node performs the corresponding operations, such as equipment reset and parameter adjustment, to achieve fault processing and repair, so that the power system can resume normal operation as soon as possible.

[0162] Load planning module 33:

[0163] The load planning module includes a load balancing analysis unit, a digital twin simulation unit and an optimization strategy generation unit;

[0164] The load balancing analysis unit uses the time series prediction model to predict the load change after the new device is connected based on the load data contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform;

[0165] The digital twin simulation unit constructs a virtual model of the power supply and distribution system, simulates system stability based on predicted load changes, and generates simulation results;

[0166] The optimization strategy generating unit generates an access optimization solution including device deployment location, power allocation and timing control strategy according to the simulation result.

[0167] Specifically, the load planning module 33 consists of a load balancing analysis unit, a digital twin simulation unit and an optimization strategy generation unit, aiming to rationally plan the load of the power supply and distribution system and improve the operating efficiency and stability of the system.

[0168] Load Balancing Analysis Unit: Based on current and historical load data, the load balancing analysis unit uses a time series prediction model to predict load changes after the addition of new devices. This model combines historical load data trends with current load conditions to predict future loads. For example, by analyzing load data for different time periods over a period of time, it can predict load changes at different times after the addition of new devices, providing a basis for subsequent load planning.

[0169] Digital Twin Simulation Unit: This unit constructs a virtual model of the power supply and distribution system that accurately simulates its actual operation. Combined with the load changes predicted by the load balancing analysis unit, it simulates the system's stability under varying load conditions. This simulation can proactively identify potential system issues such as overloads and voltage instability when loads fluctuate. For example, the virtual model simulates system operation after adding new equipment, observing changes in system parameters such as voltage and current to assess system stability.

[0170] Optimization Strategy Generation Unit: Based on the simulation results generated by the digital twin simulation unit, the optimization strategy generation unit generates an access optimization plan that includes device deployment location, power allocation, and timing control strategies. By rationally planning device deployment locations, line losses can be reduced and power transmission efficiency improved. Optimizing power allocation ensures that each device receives the appropriate power supply, avoiding overloads and underloads. The timing control strategy can rationally schedule device start and stop times based on the power requirements of different devices, further improving system operational efficiency and stability.

[0171] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, system, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, system, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, system, article, or device comprising the element.

[0172] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0173] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A communication power supply and distribution monitoring system, characterized in that: Includes edge sensing system, carrier communication network and master station monitoring platform; The edge sensing system is deployed in the load-side branch distribution cabinet, terminal cabinet and equipment load, and is used to collect power parameters in real time. It includes a sensor acquisition module, a signal conversion module and a data storage module. The carrier communication network uses power lines as data transmission media, adopts carrier communication technology to achieve data transmission of each node device in the power supply and distribution system, and supports dynamic signal strength detection and amplification adjustment; The carrier communication network is composed of a transmitting end module group deployed at the load-side branch distribution cabinet, the terminal cabinet and the equipment load, and a receiving end module group deployed in the master station monitoring platform; The transmitting end module group includes a parameter acquisition module, a signal modulation module, a power line interface module and an amplification and relay module, and the receiving end module group includes a signal demodulation module; The amplification relay module is used to detect the power line channel signal strength in real time through a closed-loop control algorithm, compare it with a preset reference value to calculate the error, and optimize and adjust the gain parameter based on the error value; The master station monitoring platform is used to centrally manage the operating status of the power supply and distribution system, including data collection, risk warning, remote diagnosis and load planning functions, including a risk warning module, a remote diagnosis module, a load planning module and a visual display module; The risk warning module includes a threshold dynamic configuration unit, a multi-dimensional feature extraction unit and a warning push unit; The remote diagnosis module includes a fault signal tracing unit, a knowledge graph matching unit and a fault reset adjustment unit; The load planning module includes a load balancing analysis unit, a digital twin simulation unit and an optimization strategy generation unit; Among them, the edge perception system, the carrier communication network and the main station monitoring platform work together to achieve full-link panoramic monitoring from branch nodes to single devices.

2. The system according to claim 1, wherein: The sensor acquisition module is used to collect analog signals of voltage, current, frequency and load data in real time; The signal conversion module is connected to the sensor acquisition module and is used to filter and amplify the analog signal and convert it into a digital signal; The data storage module is used to perform noise reduction processing and feature extraction on the digital signal and the load data to form power parameters, and temporarily store them in a local cache so as to be uploaded to the master station monitoring platform via the carrier communication network.

3. The system according to claim 1, wherein: The parameter acquisition module is used to receive the power parameters of the edge sensing system; The signal modulation module uses orthogonal frequency division multiplexing technology to modulate the power parameters into a power line compatible carrier signal; The power line interface module is equipped with a filter and an isolation device for coupling the carrier signal to the power line for data transmission and suppressing power frequency interference; The signal demodulation module is used to receive and demodulate the carrier signal to restore the power parameters, and perform data cleaning and normalization processing on the power parameters.

4. The system according to claim 3, characterized in that The risk warning module analyzes the power parameters processed by the signal demodulation module in real time based on a preset dynamic threshold and LSTM deep learning model, detects overcurrent risk and overvoltage risk, and triggers graded alarms; The remote diagnosis module, in combination with the fault feature database, performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module, generates a fault analysis result and issues a corresponding fault handling instruction; The load planning module, based on the load data contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform, uses a time series prediction model to predict the load change after the new equipment is connected, and generates an access optimization plan for the new equipment; The visual display module displays the real-time operating status of the power supply and distribution system in a graphical interface.

5. The system according to claim 3, wherein: The carrier communication network further includes an anti-interference and error correction module, a network security module, and a network supervision module, wherein the anti-interference and error correction module and the network security module are integrated into the transmitting end module group and are arranged between the signal modulation module and the power line interface module, and the network supervision module is connected across the transmitting end module group and the receiving end module group; The anti-interference error correction module combines forward error correction algorithm and adaptive filtering technology to eliminate the impact of power line electromagnetic noise on data transmission; The network security module uses encryption algorithms and identity authentication mechanisms to ensure data transmission security; The network monitoring module is used to monitor the status of the carrier communication network in real time and provide fault diagnosis.

6. The system according to claim 5, characterized in that The anti-interference error correction module includes a channel equalization unit and a redundancy check unit; The channel equalization unit uses a minimum mean square error algorithm to dynamically compensate for power line channel distortion, and cooperates with the adaptive filtering technology to monitor and eliminate interference of power line electromagnetic noise on the signal transmission path in real time; The redundancy check unit is used to perform a cyclic redundancy check on the data output by the channel equalization unit, and correct the erroneous data obtained by the check in combination with the forward error correction algorithm.

7. The system according to claim 4, wherein: The threshold dynamic configuration unit determines the safety threshold range according to the load device type and the ambient temperature. The dynamic threshold serves as an input parameter of the LSTM deep learning model. The multi-dimensional feature extraction unit is used to extract risk features from voltage harmonic distortion rate, current phase offset and frequency fluctuation, and analyze the risk features in real time through the LSTM deep learning model to detect overcurrent risk and overvoltage risk; The early warning push unit triggers a graded alarm based on the risk level corresponding to the risk analysis result of the dimensional feature extraction unit, and pushes the alarm information to a designated operation and maintenance terminal in a targeted manner.

8. The system according to claim 4, wherein: The fault signal tracing unit locates the fault node based on signal propagation delay and impedance spectrum analysis; The knowledge graph matching unit performs similarity matching between the current fault feature and the fault feature database, and generates a fault analysis result in combination with the root cause analysis. The current fault feature is the fault feature corresponding to the overcurrent risk or the overvoltage risk detected by the risk warning module; The fault reset adjustment unit determines an optimal processing solution according to the fault analysis result, and sends a reset instruction or a parameter adjustment instruction corresponding to the optimal processing solution to the fault node through the carrier communication network.

9. The system according to claim 4, wherein: The load balancing analysis unit predicts the load change after the new device is connected using the time series prediction model based on the load data included in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform; The digital twin simulation unit constructs a virtual model of the power supply and distribution system, simulates system stability based on the predicted load changes, and generates simulation results; The optimization strategy generating unit generates the access optimization solution including device deployment location, power allocation and timing control strategy according to the simulation result.

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