Communication power supply and distribution monitoring system

Abstract

The invention relates to the field of communication power supply and distribution management, and discloses a communication power supply and distribution monitoring system, which comprises an edge sensing system, a carrier communication network and a master station monitoring platform, greatly improves the performance and reliability, and guarantees the stability of communication services. The edge sensing system is deployed at a branch power distribution cabinet, a tail end cabinet and an equipment load, can accurately collect voltage, current, frequency and other electric power parameters in real time, and refines the monitoring granularity to a single device. The carrier communication network uses a power line as a data transmission medium and uses a carrier communication technology, so that the construction cost is reduced by using existing facilities, and load side data can be stably and timely transmitted to a master station through dynamic signal adjustment. The master station monitoring platform integrates multiple functions and can centrally manage the operation state of the system. The three devices work cooperatively to achieve full-link panoramic monitoring from the branch nodes to the single device, thereby making up for the defects of the prior art and improving the efficiency of the communication power supply and distribution monitoring system in all directions.

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] In the process of rapid development of the communications industry, the communications power supply and distribution monitoring system has always played a key role in ensuring the stable operation of the communications network. The early communications power supply and distribution monitoring system could only realize simple on-off monitoring of the power supply. With the growth of communications business volume and the increase of equipment complexity, the monitoring system has gradually developed to be able to monitor the total power supply and power conversion system.

[0003] In power communication sites, such as large data centers and communication base stations, the current AC / DC power supply and distribution monitoring system plays an important role. Although the current AC / DC power supply and distribution monitoring system can implement basic monitoring of the total power input and power conversion system, and to a certain extent ensure the power supply security of the core equipment in the data center, it has significant shortcomings in the control of load-side equipment. It can only monitor the total power supply of the AC / DC head cabinet, and cannot obtain detailed power data of the equipment load in the branch distribution cabinet and the terminal cabinet, which makes it difficult for the communication power supply information node to achieve panoramic and transparent monitoring. This extensive monitoring method of load-side equipment leads to the lack of key data from branch nodes to individual devices in the entire power supply and distribution system, and cannot achieve comprehensive and detailed monitoring. It is difficult to meet the growing demand for efficient and stable operation of the communication power supply and distribution system. Therefore, it is necessary to improve the communication power supply and distribution monitoring system.

[0004] Based on this, the present 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 prior art that the system cannot provide panoramic and transparent monitoring and the load-side equipment has insufficient control, significantly improves the performance and reliability of the communication power supply and distribution monitoring system, and effectively ensures the stable operation of the communication business.

[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 at the load-side 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 sensing 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 through 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] Wherein, 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 the preset dynamic threshold and LSTM deep learning model, detects overcurrent risk and overvoltage risk and triggers graded alarms;

[0024] The remote diagnosis module performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module in combination with the fault feature database, 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 error correction module, a network security module and a network supervision module, wherein the anti-interference error correction module and the network security module are integrated in the transmitting end module group and 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 the forward error correction algorithm and the adaptive filtering technology to eliminate the influence of the power line electromagnetic noise on the 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 state 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 to 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, and the dynamic threshold is used 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 shift and frequency fluctuation, and analyze the risk features in real time through an 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, wherein the current fault feature is a 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 predicts the load change after the new equipment is connected by using the time series prediction model 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 in combination with the predicted load change, 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 and 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 in the embodiment of the present application comprehensively solves the defects of the prior art 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 ensures the stable operation of communication services.

[0048] First, the edge sensing system is deployed at the branch distribution cabinet, terminal cabinet and equipment load on the load side, which can collect power parameters such as voltage, current, frequency and so on in real time, and refine the monitoring granularity to branch nodes and even individual devices. Secondly, the carrier communication network uses power lines as data transmission media, and adopts carrier communication technology to realize data transmission of each node device in the power supply and distribution system, while supporting dynamic signal strength detection and amplification adjustment. This data transmission method makes full use of the existing power line infrastructure, without the need to lay special data transmission cables, and reduces the system construction cost. In addition, through dynamic signal strength detection and amplification adjustment, the signal can be flexibly optimized according to the actual transmission environment, effectively avoiding poor data transmission caused by communication bottlenecks, ensuring that the data collected by each node device on the load side can be stably and timely transmitted to the main station monitoring platform, eliminating the monitoring blind spot. Finally, the main station monitoring platform integrates functions such as data collection, risk warning, remote diagnosis and load planning, and centrally manages the operation status of the power supply and distribution system.

[0049] The edge sensing system, carrier communication network and main station monitoring platform work together to achieve full-link panoramic monitoring from branch nodes to single devices. This collaborative working mode comprehensively solves the defects of existing technologies that cannot 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. 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying 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 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] Next, the present application scheme is introduced. The present application proposes the following technical scheme, please see below for details.

[0057] Figure 1 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 at the load-side 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 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 cabinet, terminal cabinet and equipment load 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 line. The terminal cabinet is the direct hub for connecting the equipment load, and the monitoring of 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 are crucial to 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] The carrier communication network 2 innovatively uses power lines as a data transmission medium. The advantage of this design is that there is no need to lay additional special data transmission lines, which greatly reduces the construction cost and construction difficulty of the system. It uses carrier communication technology, which can modulate data signals onto the AC signals of power lines for transmission, and realize data transmission of each node device in the power supply and distribution system. At the same time, it also supports dynamic signal strength detection and amplification adjustment functions. In the actual power environment, the signal strength will be affected by many factors, such as line length, load changes, etc. The dynamic signal strength detection function can monitor the strength of the signal in real time. When the signal strength is weak, the amplification factor is automatically adjusted to ensure that the data can be transmitted accurately and stably. 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 part of the entire communication power supply and distribution monitoring system, which is 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 the collected data, the system uses preset algorithms and models to predict and warn of possible risks in the power supply and distribution system. For example, when the voltage exceeds the normal range or the equipment load is continuously too high, an alarm is issued in time to remind the staff to take appropriate measures to avoid accidents.

[0069] Remote diagnosis: When the system is abnormal, the staff can remotely diagnose the equipment through the main station monitoring platform 3, quickly locate the fault point and cause of the fault, and reduce the time and cost of troubleshooting. For example, by analyzing the changes in parameters such as current and voltage, it can be determined whether the equipment has short circuit, open circuit and other faults.

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

[0071] 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. The edge perception system 1 is responsible for data collection, the carrier communication network 2 is responsible for data transmission, and the main station monitoring platform 3 processes and manages the data and makes corresponding decisions based on the analysis results. This collaborative working mode enables the operating status of the entire power supply and distribution system to be fully and real-time mastered, improving the system's intelligent management level and operational reliability.

[0072] It can be seen from the above technical solutions that the communication power supply and distribution monitoring system provided in the embodiment of the present application comprehensively solves the defects of the prior art 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 ensures the stable operation of communication services.

[0073] First, the edge sensing system is deployed at the branch distribution cabinet, terminal cabinet and equipment load on the load side, which can collect power parameters such as voltage, current, frequency and so on in real time, and refine the monitoring granularity to branch nodes and even individual devices. Secondly, the carrier communication network uses power lines as data transmission media, and adopts carrier communication technology to realize data transmission of each node device in the power supply and distribution system, while supporting dynamic signal strength detection and amplification adjustment. This data transmission method makes full use of the existing power line infrastructure, without the need to lay special data transmission cables, and reduces the system construction cost. In addition, through dynamic signal strength detection and amplification adjustment, the signal can be flexibly optimized according to the actual transmission environment, effectively avoiding poor data transmission caused by communication bottlenecks, ensuring that the data collected by each node device on the load side can be stably and timely transmitted to the main station monitoring platform, eliminating the monitoring blind spot. Finally, the main station monitoring platform integrates functions such as data collection, risk warning, remote diagnosis and load planning, and centrally manages the operation status of the power supply and distribution system.

[0074] The edge sensing system, carrier communication network and main station monitoring platform work together to achieve full-link panoramic monitoring from branch nodes to single devices. This collaborative working mode comprehensively solves the defects of existing technologies that cannot 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.

[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 sensing 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 through the carrier communication network.

[0083] Specifically, the sensor acquisition module 11 is deployed at key locations such as branch distribution cabinets, terminal cabinets and equipment loads. Branch distribution cabinets are important nodes for power distribution. Terminal cabinets are connected to specific equipment loads, and equipment loads directly reflect the power consumption of the equipment. This module collects analog signals of voltage, current, and frequency and 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, the current analog signal can reflect the actual power load of the equipment, and the frequency analog signal is crucial to 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 helps to 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. Since the collected analog signal is easily interfered and inconvenient for subsequent processing, the signal conversion module 12 performs filtering and amplification processing on the analog signal. Filtering is to remove noise in the signal and improve the purity of the signal; amplification is to enhance the strength of the signal to facilitate subsequent conversion operations. After processing, the analog signal is converted into a digital signal, which has the advantages of strong anti-interference ability, easy storage and processing, etc., laying the foundation for subsequent data processing and analysis.

[0085] The data storage module 13 is responsible for performing noise reduction processing and feature extraction on the digital signal and load data to form power parameters. Noise reduction processing further improves the quality of the data and removes possible residual noise. Feature extraction is to extract key power parameters, such as effective voltage value, current peak value, etc., from a large amount of digital signals and load data. These power parameters are temporarily stored in the local cache, waiting to be transmitted to the main station monitoring platform through the carrier communication network. The setting of the local cache can ensure that the data will not be lost and the integrity of the data in the event of a temporary network interruption.

[0086] Carrier Communications 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 may 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 amplifying and relaying module 24 detects the signal strength of the power line channel in real time through a closed-loop control algorithm, compares it with a preset reference value, calculates the error, and optimizes and adjusts the gain parameter based on the error value.

[0096] Specifically, the carrier communication network 2 is composed 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 in the master station monitoring platform. This distribution method enables the data on the load side to be effectively transmitted to the master station monitoring platform, realizing centralized monitoring and management of information such as power parameters.

[0097] Transmitter module group:

[0098] The main function of the parameter acquisition module 21 is to receive power parameters from the edge sensing system. The edge sensing system obtains and processes power parameters through its sensor acquisition module, signal conversion module and data storage module, and the carrier communication module serves as an interface for receiving these parameters, laying the foundation for subsequent data transmission.

[0099] The signal modulation module 22 adopts Orthogonal Frequency Division Multiplexing (OFDM) technology. This technology modulates the power parameters into a carrier signal compatible with the power line, so that data can be transmitted on the power line. OFDM technology has the advantages of high spectrum utilization and strong anti-multipath fading ability, and can ensure the stability and reliability of data transmission in a complex power line environment.

[0100] The power line interface module 23 is equipped with a filter and an isolation device. The function of the filter is to couple the carrier signal to the power line so that the carrier signal can use the power line as a data transmission medium for data transmission, while suppressing power frequency interference. Power frequency interference is a common interference source in the power system, which will affect the transmission quality of the carrier signal. Through the filter, the impact of power frequency interference on the carrier signal can be effectively reduced. The isolation device further ensures the electrical isolation between the carrier communication network and the power system, and improves the safety of the system.

[0101] The amplification relay module 24 dynamically adjusts the signal gain based on the real-time detected power line channel signal strength to support long-distance data transmission. It achieves this function through a closed-loop control algorithm. Specifically, the power line channel signal strength is detected in real time, and the error is calculated by comparing it with the preset reference value, and then the gain parameter is optimized and adjusted based on the error value. Regarding the adjustment relationship between the error value and the gain parameter: the determination of the gain adjustment amount will comprehensively consider three aspects. First, it is directly related to the real-time error value. Through the preset proportional coefficient, the error value is multiplied by this proportional coefficient to obtain a part of the adjustment amount; second, considering the integration of the error value over a period of time, through the preset integral coefficient, a part of the adjustment amount is determined according to the integral of the error value; third, pay attention to the rate of change of the error value, and through the preset differential coefficient, a part of the adjustment amount is calculated according to the rate of change of the error value. Adding these three parts of the adjustment amount together, the total gain adjustment amount is obtained.

[0102] The signal strength is determined by continuously monitoring the signal-to-noise ratio (SNR) of the power line channel. If the monitored SNR is lower than the preset SNR threshold, the current signal strength is determined to be weak. The adjustment mechanism uses a PID control algorithm to dynamically calculate how much gain should be adjusted 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. In other words, the larger the absolute value of the difference, the larger the gain adjustment amount. The gain adjustment amount can be calculated by multiplying the proportional coefficient determined by experiment with the absolute value of the difference. Once the SNR is monitored to rise to the signal-to-noise ratio threshold or above, the gain adjustment will no longer be performed.

[0104] Receiver module group:

[0105] The signal demodulation module 25 is used to receive and demodulate the carrier signal in the receiving module group on the main station monitoring platform side, and restore 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, the module also performs data cleaning and normalization processing on the power parameters. Data cleaning is to remove noise, erroneous data, etc. in the data to improve the quality of the data; normalization processing is to convert power parameters of different ranges and units into a unified format to facilitate subsequent data analysis and processing, such as for predicting load changes after the connection of new equipment based on load data.

[0106] Main station monitoring platform 3:

[0107] Figure 4 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 the preset dynamic threshold and LSTM deep learning model, detects overcurrent risk and overvoltage risk and triggers graded alarms;

[0111] The remote diagnosis module performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module in combination with the fault feature database, 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 LSTM (Long Short-Term Memory, LSTM) deep learning models. The dynamic threshold is dynamically set according to different power parameters and system operating status, and can more accurately reflect the actual situation of the system. The LSTM deep learning model has a powerful sequence data processing capability and can learn the changing laws of power parameters. Through analysis, abnormal conditions such as overcurrent risk and overvoltage risk are detected, and fault characteristics corresponding to overcurrent risk or overvoltage risk are generated and graded alarms are triggered. Graded alarms are divided into different levels according to the severity of the risk, so that staff can take corresponding measures according to the alarm level and deal with potential risks in a timely manner.

[0115] The LSTM model is used in the risk warning module. During training, the historical power parameter data processed by the signal demodulation module is mainly used, 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 architecture: Build a network structure including input layer, two LSTM hidden layers, and fully connected output layer, and use ReLU activation function, cross entropy loss function and Adam optimizer.

[0118] Training and validation: Divide the training set and validation set by 7:3, set the training cycle of 100 rounds and the batch size of 32, and dynamically adjust the learning rate. Use the validation set to evaluate the performance, and add a Dropout layer when 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 diagnosis module 32 performs fault tracing and root cause analysis in conjunction with the fault feature database. The fault feature database stores feature information of various faults. When the system detects an abnormality, the remote diagnosis module 32 compares the fault features of the overcurrent risk or overvoltage risk actually detected with the fault features in the database. The module can trace the fault to the overcurrent risk or overvoltage risk, that is, determine the location and cause of the fault. Then, the fault analysis results are generated and the corresponding fault handling instructions are issued to guide the staff to troubleshoot and improve the efficiency and accuracy of fault handling.

[0121] The load planning module 33 mainly uses the load data (such as real-time power, load type, working status, etc.) contained in the power parameters processed by the signal demodulation module and the historical load data stored locally on the master station monitoring platform. Through the time series prediction model, the module can predict the load changes after the new equipment is connected. The time series prediction model can predict the future load development trend based on the change trend and law of historical load data and the current load status. Based on these prediction results, an access optimization plan for the new equipment is generated, such as determining the best access location, access time and reasonable power allocation of the new equipment, so as to ensure the 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. The training relies on the real-time load data of 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: Select the Prophet or Transformer model, use the mean square error as the loss function, and use the early stopping method 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 display module 34 displays the real-time operating status of the power supply and distribution system in a graphical interface. The complex power parameters and system operating information are presented to the staff through intuitive graphics, charts, etc. For example, the real-time curves of voltage and current, the operating status of the equipment, etc. The staff can quickly understand the operating status of the system through the visualization interface, find abnormalities in time and take corresponding measures to improve the management efficiency of the system.

[0128] In addition, the main station monitoring platform 3 can 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 in 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;

[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 carrier communication network status 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 to 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 transmitting end module group and is arranged 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, there are various noise sources such as electromagnetic interference, which may affect the carrier signal. The anti-interference error correction module 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, and ensure that the 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 its own characteristics, the power line channel will experience signal distortion during data transmission, which will seriously affect the accuracy of the data. The channel equalization unit uses the minimum mean square error (MMSE) algorithm to dynamically compensate for the power line channel distortion. The minimum mean square error algorithm can continuously adjust the compensation parameters according to the actual situation of the channel, so that the signal can be restored to its original state as much as possible during the transmission process. At the same time, the unit cooperates with the adaptive filtering technology to monitor the interference of the power line electromagnetic noise on the signal transmission path in real time. The adaptive filtering technology can automatically adjust the parameters of the filter according to the changes in the noise and eliminate the noise in a targeted manner. For example, when large electrical equipment is started near the power line and strong electromagnetic noise is generated, the adaptive filtering technology can quickly adjust and effectively suppress the interference of noise on the signal to ensure the stable transmission of the signal.

[0140] Redundancy check unit: After the channel equalization unit processes the signal, the redundancy check unit performs a cyclic redundancy check (CRC) on the data it outputs. Cyclic redundancy check is a commonly used error detection method that generates a check code by performing specific calculations on the data and transmits the check code together with the data. After receiving the data, the receiving end performs the same calculation again and compares the calculated check code with the received check code to determine whether there is an error in the data transmission process. When erroneous data is detected, the redundancy check unit corrects the erroneous data obtained by the check in conjunction with the forward error correction (FEC) algorithm. The forward error correction algorithm adds certain redundant information when sending data. The receiving end can correct the erroneous data by itself based on this redundant information without requesting retransmission, thereby improving the efficiency and reliability of data transmission.

[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. In the process of data transmission, the security of data is of vital importance. Once the data is stolen or tampered with, serious consequences may occur. 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 supervision module 28 is connected across the transmitting module group and the receiving module group to monitor the status of the carrier communication network in real time and provide fault diagnosis. During the operation of the carrier communication network, various faults may occur, such as line faults, equipment faults, etc. The network supervision module monitors various parameters of the network in real time, such as signal strength, data transmission rate, bit error rate, etc. When an abnormal network status is detected, the fault point is quickly located by analyzing these parameters, and a detailed fault diagnosis report is provided. For example, when it is found that the data transmission rate suddenly drops, the network supervision module can determine whether it is a line problem or an equipment problem by analyzing parameters such as signal strength and bit error rate, and provide corresponding solutions to help maintenance personnel troubleshoot in time and ensure the normal operation of the network.

[0143] By adding the anti-interference 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, and the dynamic threshold is used 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 shift and frequency fluctuation, and analyze the risk features in real time through an 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 is composed 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 perform graded alarms and information push.

[0151] Threshold dynamic configuration unit: Different types of load devices have different requirements for power parameters, and the ambient temperature will also affect the normal operation of the equipment and the safe range of power parameters. The threshold dynamic configuration unit determines the safe threshold range according to the load device type and the ambient temperature. For example, for some precision electronic equipment with high voltage stability requirements, the safe voltage threshold range is relatively narrow; while for some ordinary lighting equipment, the safe voltage threshold range can be relatively wide. When the ambient temperature rises, the heat dissipation capacity of the equipment may decrease, resulting in a decrease in its ability to withstand current. At this time, the safe threshold of the current also needs to be adjusted accordingly. These dynamically determined thresholds are used as input parameters of 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 the power system can be reflected by features in multiple dimensions. The multi-dimensional feature extraction unit extracts risk features from aspects such as voltage harmonic distortion rate, current phase shift, and frequency fluctuation. Voltage harmonic distortion rate reflects the degree to which the voltage waveform deviates from the ideal sine wave. Excessive harmonic distortion rate may cause problems such as heating and damage to the equipment. Current phase shift reflects the phase relationship between current and voltage. Abnormal phase shift may indicate that the system has power factor problems or equipment failures. Frequency fluctuations directly affect the stability of the power system. By analyzing these risk features in real time through the LSTM deep learning model, overcurrent risks and overvoltage risks can be effectively detected. The LSTM model can learn the change patterns of these features over time, thereby discovering potential risks in advance.

[0153] Early warning push unit: Based on the risk analysis results of the multi-dimensional feature extraction unit, the early warning push unit determines the risk level according to the severity of the risk and triggers a graded alarm. The graded alarm allows operation and maintenance personnel to understand the severity of the risk more intuitively so that they can take appropriate measures. For example, for lower risk levels, a prompt alarm can be issued to remind operation and maintenance personnel to pay attention to the system status; for higher risk levels, an emergency alarm is issued to require operation and maintenance personnel to handle it immediately. At the same time, the alarm information will be pushed to the designated operation and maintenance terminal to ensure that relevant personnel can receive the information and handle it in time.

[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, wherein the current fault feature is a 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 the signal propagates in the 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 situation of the fault node, such as whether there are short circuits, open circuits and other faults. For example, when 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: The knowledge graph matching unit matches the current fault feature with the fault feature database for similarity. The fault feature database stores a large amount of feature information of known faults. Through matching, historical fault cases similar to the current fault can be quickly found. Combined with root cause analysis, the cause of the fault is further analyzed in depth to generate detailed fault analysis results. For example, if the current fault feature has a high similarity with the feature of 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 adjustment unit: According to the fault analysis results generated by the knowledge graph matching unit, the fault reset adjustment unit determines the optimal processing solution. For some simple faults, such as equipment malfunction, the normal operation of the equipment can be restored by sending a reset instruction; for some faults that require parameter adjustment, such as improper equipment parameter settings, the problem can be solved by sending parameter adjustment instructions. The reset instruction or parameter adjustment instruction corresponding to the optimal processing solution is sent to the faulty node through the carrier communication network. The carrier communication network serves as a channel for data transmission to ensure that the instructions can be transmitted to the faulty node accurately. After receiving the instruction, the faulty node performs corresponding operations, such as equipment reset, parameter adjustment, etc., so as to realize the processing and repair of the fault and restore the power system to 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 predicts the load change after the new equipment is connected by using the time series prediction model 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 in combination with 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 is composed of a load balancing analysis unit, a digital twin simulation unit and an optimization strategy generation unit, which aims 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: The load balancing analysis unit uses the time series prediction model to predict the load changes after the addition of new equipment based on the current load data and historical load data. The time series prediction model can predict future loads based on the change trend of historical load data and the current load situation. For example, by analyzing the load data of each period in the past, the load changes in different periods after the addition of new equipment can be predicted, thus providing a basis for subsequent load planning.

[0169] Digital twin simulation unit: The digital twin simulation unit constructs a virtual model of the power supply and distribution system, which can accurately simulate the actual operation of the power supply and distribution system. Combined with the load changes predicted by the load balancing analysis unit, the stability of the system under different load conditions is simulated. Through simulation, problems that may occur in the system when the load changes, such as overload and voltage instability, can be discovered in advance. For example, the system operation after the new equipment is connected is simulated in the virtual model, and the changes in the system's voltage, current and other parameters are observed to evaluate the stability of the system.

[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 the device deployment location, power allocation, and timing control strategy. By rationally planning the deployment location of the equipment, line losses can be reduced and power transmission efficiency can be improved; optimizing power allocation can ensure that each device can obtain appropriate power supply to avoid overload or underload; the timing control strategy can reasonably arrange the start and stop time of the equipment according to the power demand of different equipment, further improving the operating efficiency and stability of the system.

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

[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 enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will 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 at the load-side distribution cabinet, terminal cabinet and equipment load to collect power parameters in real time; 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 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; 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, characterized in that The edge sensing system includes a sensor acquisition module, a signal conversion module and a data storage module; 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 through the carrier communication network.

3. The system according to claim 1, characterized in that 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; Wherein, 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; 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 parameter into the carrier signal compatible with the power line; 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 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; 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 master station monitoring platform includes a risk warning module, a remote diagnosis module, a load planning module and a visual display module; The risk warning module analyzes the power parameters processed by the signal demodulation module in real time based on the preset dynamic threshold and LSTM deep learning model, detects overcurrent risk and overvoltage risk and triggers graded alarms; The remote diagnosis module performs fault tracing and root cause analysis on the overcurrent risk or the overvoltage risk detected by the risk warning module in combination with the fault feature database, 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, characterized in that The carrier communication network further includes an anti-interference error correction module, a network security module and a network supervision module, wherein the anti-interference error correction module and the network security module are integrated in the transmitting end module group and 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 the forward error correction algorithm and the adaptive filtering technology to eliminate the influence of the power line electromagnetic noise on the 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 state 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 to correct the erroneous data obtained by the check in combination with the forward error correction algorithm.

7. The system according to claim 4, characterized in that The risk warning module includes a threshold dynamic configuration unit, a multi-dimensional feature extraction unit and a warning push unit; The threshold dynamic configuration unit determines the safety threshold range according to the load device type and the ambient temperature, and the dynamic threshold is used 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 shift and frequency fluctuation, and analyze the risk features in real time through an 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, characterized in that The remote diagnosis module includes a fault signal tracing unit, a knowledge graph matching unit and a fault reset adjustment unit; 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, wherein the current fault feature is a 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, characterized in that The load planning module includes a load balancing analysis unit, a digital twin simulation unit and an optimization strategy generation unit; The load balancing analysis unit predicts the load change after the new equipment is connected by using the time series prediction model 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; The digital twin simulation unit constructs a virtual model of the power supply and distribution system, simulates system stability in combination with the predicted load change, 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.

10. The system according to claim 3, characterized in that The amplification and 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 the error, and optimizes and adjusts the gain parameter based on the error value.

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