Remote meter reading method and system for electric energy meter

Abstract

The invention discloses an electric energy meter remote meter reading method and system. The method comprises the following steps of data acquisition and preprocessing of an intelligent electric energy meter, communication link establishment of the intelligent electric energy meter, data transmission of the intelligent electric energy meter, data processing and storage of an intelligent concentrator and abnormity monitoring and processing of a master station management system. The system comprises an intelligent electric energy meter, an intelligent concentrator and a master station management system. According to the invention, the efficiency and reliability of power data acquisition, transmission and processing are greatly improved. In communication, the hybrid communication module selects an optimal communication mode by using fuzzy logic according to parameters such as signal strength; the master station processing layer processes data through distributed calculation and hierarchical storage and constructs a model; threshold judgment and machine learning are fused in abnormity monitoring, and electric quantity, voltage abnormity, electricity stealing and the like are early warned in real time. The device has the advantages of high efficiency, accuracy, intelligence, compatibility, stability and low consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy meter, in particular to a remote meter reading method and system for electric energy meter. BACKGROUND

[0002] Under the background of digital transformation of the power industry, the importance of remote meter reading technology for electric energy meter is increasingly prominent. The early manual meter reading mode exposes significant defects after the expansion of the user scale: taking a 100,000-household medium-sized city as an example, the manual meter reading cycle is as long as 2-3 months, which is seriously lagging behind the electricity settlement; at the same time, the 3%-5% meter reading error rate not only causes economic losses to the power company, but also damages the public credibility due to user disputes.

[0003] Although the remote meter reading technology has gradually replaced the manual mode, the existing scheme still has multiple bottlenecks:

[0004] In terms of wired communication, power line carrier communication (PLC) is affected by power grid electromagnetic interference, harmonics, etc., and the communication success rate in old communities is less than 60%, and the frequent data retransmission leads to low efficiency; RS-485 communication is limited by bus topology, and signal attenuation occurs when the node exceeds 32, and the communication distance is limited within 1200 meters, and node expansion requires rewiring.

[0005] In terms of wireless communication, although GPRS communication has wide coverage, the monthly communication cost is as high as hundreds of thousands of yuan under the scale of 500,000 electric meters, and the module power consumption is high, and the battery-powered electric meter needs to be replaced frequently, which has the risk of data interruption; the communication rate of NB-IoT is low (tens of Kbps), and network congestion during peak electricity consumption leads to data delay for several hours, which cannot meet the real-time demand of power dispatching.

[0006] In terms of system compatibility and data processing, the difference in electric meter protocols of different manufacturers increases the system development and maintenance cost by more than 30%, and the traditional data processing method can only complete simple statistics, cannot dig the user electricity consumption behavior rules, and is difficult to support the optimization of power resources.

[0007] In summary, the existing technology is difficult to meet the development needs of smart grid in terms of communication stability, data processing efficiency, system compatibility and cost control, and innovative solutions are needed to realize high-precision data acquisition, dynamic communication optimization, intelligent data analysis and multi-device compatibility to promote the upgrading of the power system to high efficiency and intelligence. SUMMARY

[0008] To solve the problems raised in the background art, the purpose of the present application is to provide a remote meter reading method and system for electric energy meter, which has the advantages of high efficiency, precision, intelligence, compatibility and stability.

[0009] To achieve the above-mentioned purpose, the present application provides the following technical solution: a remote meter reading method for electric energy meter, comprising the following steps:

[0010] Step one: data acquisition and preprocessing of smart electric energy meter: the data acquisition module of the smart electric energy meter collects operating parameters according to the adaptive sampling frequency strategy, filters and denoises by using the wavelet transform algorithm in the digital signal processing unit, smoothes by using the Kalman filtering algorithm, and is packaged according to the self-defined data frame format, the data frame containing timestamp, device identification, and data check code information;

[0011] Step two: establishment of communication link of smart electric energy meter: the mixed communication module of the smart electric energy meter is built-in environment perception and communication quality evaluation unit, real-time monitoring communication signal strength, bit error rate, network load parameters, combined with fuzzy logic algorithm to build communication quality evaluation model, according to dynamic priority strategy to select communication mode, priority to power line carrier communication for short distance, small data transmission, when the power line carrier communication quality is lower than the signal strength-80dBm and the bit error rate is higher than 5% threshold or the data volume is larger, switch to GPRS communication, NB-IoT communication is enabled in areas with poor network coverage and low real-time requirements, and LoRa communication networking transmission is used in specific intensive meter reading scenarios;

[0012] Step three: data transmission of smart electric energy meter: after establishing the communication link, the smart electric energy meter transmits the preprocessed data frame to the smart concentrator through the mixed communication module, and the smart concentrator processes and transmits the data to the master station system through the TCP / IP protocol, the sliding window mechanism and the timeout retransmission mechanism;

[0013] Step four: data processing and storage of smart concentrator: the data processing center of the master station system cleans the original data, uses distributed computing technology for parallel processing, constructs user electricity behavior, load prediction, and device health evaluation model, realizes electricity consumption statistics, peak and valley prediction, and device state evaluation function; the storage module adopts hierarchical storage, high-frequency recent data is stored in high-performance solid state disk, and low-frequency historical data is stored in large-capacity mechanical hard disk or cloud storage;

[0014] Step five: abnormality monitoring and processing of master management system: the monitoring and alarm module of the master station system adopts a multi-dimensional abnormality detection mechanism combining threshold judgment and machine learning algorithm, generates alarm information containing abnormal type, time, and device information when detecting sudden increase or decrease of electricity (such as 300% change of electricity within 24 hours), voltage anomaly (exceeding rated voltage ± 20%), electricity stealing behavior, and device fault anomaly, and notifies the management personnel through SMS, email, and APP push, which can be remotely diagnosed and processed, and the system records the process results.

[0015] As preferred in the present application, the mixed communication module can complete seamless switching of communication mode within 200ms when the communication quality changes, ensuring the continuity of data transmission, and the data loss rate during switching is not more than 0.1%.

[0016] As preferred of the present application, the smart concentrator marks the data retransmitted more than 3 times as abnormal data and records error log containing error time, type, and involved power meter identification information.

[0017] As preferred of the present application, the data acquisition module of the smart power meter samples at a frequency of 1Hz-10Hz during a stable power consumption period, and samples at a frequency of 50Hz-100Hz during a power consumption peak period or data mutation.

[0018] As preferred of the present application, the report analysis module of the master station management system generates visual daily power consumption curve report and monthly power consumption peak valley comparison report based on the analysis result of the data processing center.

[0019] As preferred of the present application, the system comprises a smart power meter, a smart concentrator, and a master station management system.

[0020] The smart power meter comprises a data acquisition module, a hybrid communication module, a microprocessor unit, a security encryption module, and a power management module. The data acquisition module is based on high-precision metering chips and sensor arrays, and is used for real-time acquisition of multi-dimensional operating parameters such as power consumption, voltage, current, power factor, and harmonic content. The hybrid communication module integrates power line carrier, GPRS, NB-IoT, LoRa communication chips and corresponding antennas, and is responsible for transmitting preprocessed and encrypted data to the smart concentrator. The microprocessor unit is based on ARM architecture and runs a real-time operating system, coordinates the work of each module, and performs data preprocessing, communication control, and security management tasks. The security encryption module uses the SM4 algorithm to encrypt data. The power management module ensures stable power supply for the power meter.

[0021] The smart concentrator integrates a high-speed processor, a large-capacity memory, and multiple communication interfaces, and is provided with a multi-level cache and data scheduling mechanism of receiving data cache queue, verification processing queue, and forwarding queue. After receiving the data of the smart power meter, CRC verification, data integrity check, and protocol analysis are performed, and then the data is transmitted to the master station system. The smart routing module dynamically optimizes the data transmission path every 5 minutes according to the network topology structure and communication quality. The smart concentrator has a local storage function and can save at least 72 hours of power meter data when communication is interrupted.

[0022] The master station management system constructs a distributed big data processing platform, adopts Hadoop and Spark framework, the master station management system includes a data processing center, a user management module, a device management module, a monitoring and alarm module, a report analysis module and a system configuration module, cleans, analyzes and stores received data, monitors abnormalities in real time and generates alarm information, the data processing center uses a deep learning algorithm to construct a load prediction model with an accuracy of more than 95%, the user management module realizes user file management, permission management and electricity fee settlement, the device management module remotely configures, upgrades and diagnoses faults of electric energy meters and intelligent concentrators, the data processing center of the master station system cleans raw data, uses distributed computing technology for parallel processing, and constructs user electricity consumption behavior, load prediction and device health evaluation models.

[0023] Preferably, the plurality of communication interfaces of the intelligent concentrator include an Ethernet interface, a 4G / 5G communication module interface and a power line carrier communication interface.

[0024] Preferably, the power management module of the intelligent electric energy meter has three power supply modes of commercial power, battery and solar energy, and sends a low power alarm when the power is less than 10%.

[0025] Compared with the prior art, the present application has the following advantages:

[0026] 1. This invention employs multi-dimensional technological innovation, significantly improving the efficiency and reliability of power data acquisition, transmission, and processing. In the data acquisition stage, an adaptive sampling frequency strategy is adopted: during periods of stable power consumption, low-frequency sampling of 1Hz-10Hz is used to reduce energy consumption; during peak periods or when data changes abruptly, sampling is increased to a high-frequency range of 50Hz-100Hz. Combined with wavelet transform and Kalman filtering algorithms, high-precision data preprocessing is achieved, effectively filtering out noise and retaining key power consumption parameters, providing a reliable foundation for subsequent analysis. Regarding communication link establishment, the hybrid communication module dynamically selects the optimal communication method based on fuzzy logic by real-time monitoring of signal strength, bit error rate, and other parameters: short-distance, small-data transmission prioritizes power line carrier; when quality is substandard or the data volume is large, it switches to GPRS; and for remote, low-frequency transmission... NB-IoT is used for real-time scenarios, while LoRa networking is adopted for intensive meter reading scenarios. Seamless switching within 200ms and a data loss rate of ≤0.1% completely solve the problems of easy interruption and high cost of traditional single communication methods. In the data transmission link, multi-level caching of the smart concentrator and TCP / IP protocol optimization of the main station ensure efficient and reliable data transmission. Data that fails to be retransmitted more than 3 times is marked as abnormal and logged, providing accurate basis for fault diagnosis. The main station processing layer adopts distributed computing and hierarchical storage to process massive amounts of data in parallel and build models of electricity consumption behavior, load forecasting, and equipment health, realizing real-time power consumption statistics, peak and valley prediction, and equipment status assessment. High-frequency data is stored on solid-state drives to ensure response speed, while low-frequency data is stored on mechanical hard drives or in the cloud to reduce storage costs. Anomaly monitoring combines threshold judgment and machine learning to provide real-time warnings for sudden increases or decreases in power consumption, abnormal voltage, and electricity theft. Management personnel are notified through multiple channels and remote processing is supported, significantly shortening fault response time. The overall solution achieves breakthroughs in communication stability, data processing efficiency, system compatibility, and cost control through intelligent design throughout the entire process. It provides real-time, accurate, and secure electricity data support for the smart grid, promoting the upgrading of the power system towards higher efficiency and intelligence. The device has the advantages of high efficiency and accuracy, intelligent compatibility, and stable low power consumption.

[0027] 2、The application completes seamless switching within 200ms and the data loss rate is not more than 0.1% when the communication quality changes, which greatly enhances the stability of the system communication. In the actual power network environment, the communication condition is complex and changeable, the power line carrier communication is easily disturbed by the power grid, and the wireless communication is affected by the signal strength and network load. The traditional single communication mode is prone to data transmission interruption or delay when the environment changes. The hybrid communication module of the application has the ability of fast switching, and can quickly switch to the appropriate wireless communication mode when the power line carrier communication quality decreases. For example, in an old community, the aging of the power line leads to the deterioration of the PLC communication quality, and the module can quickly switch to GPRS communication to ensure continuous data transmission. The low data loss rate ensures the data integrity and accuracy, and provides reliable data for electricity settlement and power dispatch. For example, during the peak of electricity consumption, a large number of meters upload data at the same time, and part of the communication link is congested. The module quickly switches the communication mode to ensure that the data is completely uploaded to the master station, avoiding errors in electricity settlement or power dispatch caused by data loss, ensuring the stable operation of the power system and the interests of users. Compared with the traditional meter reading system, the communication reliability is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The system block diagram of the application is shown in the figure;

[0029] Figure 2 The principle block diagram of the data acquisition module in the application is shown in the figure;

[0030] Figure 3 The principle block diagram of the data processing center in the application is shown in the figure;

[0031] Figure 4 The principle block diagram of the monitoring and alarm module in the application is shown in the figure. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0033] As shown in the figure, a remote meter reading method of an electric energy meter comprises the following steps: Figures 1 to 4

[0034] Step one: data acquisition and preprocessing of the smart electric energy meter: the data acquisition module of the smart electric energy meter collects operating parameters according to the adaptive sampling frequency strategy, filters and denoises by using the wavelet transform algorithm, smoothes by using the Kalman filter algorithm, and packs according to the self-defined data frame format. The data frame contains time stamp, device identification, and data check code information.​

[0035] Step 2: Establishing the communication link for the smart meter: The hybrid communication module of the smart meter has a built-in environmental perception and communication quality assessment unit. It monitors the communication signal strength, bit error rate, and network load parameters in real time. It constructs a communication quality assessment model by combining fuzzy logic algorithm and selects the communication mode according to a dynamic priority strategy. Power line carrier communication is preferred for short-distance and small data volume transmission. When the power line carrier communication quality is lower than the signal strength of -80dBm and the bit error rate is higher than the 5% threshold or the data volume is large, it switches to GPRS communication. NB-IoT communication is used in areas with low real-time requirements and poor network coverage. LoRa communication is used for network transmission in specific dense meter reading scenarios.

[0036] Step 3: Data transmission of smart energy meters: After establishing a communication link, the smart energy meter transmits the pre-processed data frame to the smart concentrator via the hybrid communication module. After processing, the smart concentrator transmits the data to the main station system using the TCP / IP protocol, through a sliding window mechanism and a timeout retransmission mechanism.

[0037] Step 4: Data processing and storage of the intelligent concentrator: The data processing center of the main station system cleans the raw data, uses distributed computing technology to process it in parallel, and builds user electricity consumption behavior, load forecasting, and equipment health assessment models to realize electricity consumption statistics, peak and off-peak prediction, and equipment status assessment functions; the storage module adopts hierarchical storage, with high-frequency recent data stored on high-performance solid-state drives, and low-frequency historical data stored on large-capacity mechanical hard drives or cloud storage.

[0038] Step 5: Anomaly Monitoring and Handling in the Main Station Management System: The main station system's monitoring and alarm module employs a multi-dimensional anomaly detection mechanism that combines threshold judgment and machine learning algorithms. When it detects sudden increases or decreases in power consumption (e.g., power consumption changes exceeding the normal range by 300% within 24 hours), voltage anomalies (exceeding the rated voltage by ±20%), electricity theft, or equipment malfunctions, it generates alarm information containing the anomaly type, time, and equipment information. This information is then pushed to administrators via SMS, email, and APP for remote diagnosis and handling. The system records the process results.

[0039] refer to Figure 1 The hybrid communication module can seamlessly switch communication modes within 200ms when communication quality changes, ensuring data transmission continuity, and the data loss rate during switching is no more than 0.1%.

[0040] As a technical optimization of this invention, the hybrid communication module achieves seamless switching within 200ms and a data loss rate of no more than 0.1% when communication quality changes, greatly enhancing the system's communication stability. In actual power network environments, communication conditions are complex and variable. Power line carrier communication is susceptible to grid interference, while wireless communication is affected by signal strength and network load. Traditional single communication methods are prone to data transmission interruptions or delays when the environment changes. This invention's hybrid communication module, with its rapid switching capability and real-time monitoring of communication quality, can quickly switch to a suitable wireless communication method when power line carrier communication quality deteriorates. For example, in older residential areas, aging power lines can cause PLC communication quality to deteriorate; the module can quickly switch to GPRS communication to ensure continuous data transmission. The low data loss rate ensures data integrity and accuracy, providing reliable data for electricity billing and power dispatching. For instance, during peak electricity consumption periods, when many meters upload data simultaneously, some communication links may become congested. The module quickly switches communication methods to ensure complete data upload to the main station, avoiding errors in electricity billing or power dispatching due to data loss, thus ensuring the stable operation of the power system and protecting user rights. Compared to traditional meter reading systems, communication reliability is significantly improved.

[0041] refer to Figure 1 The intelligent concentrator marks data that fails to be retransmitted more than 3 times as abnormal data and records an error log, which includes the error time, type, and relevant electricity meter identification information.

[0042] As a technical optimization of this invention, data that fails to retransmit more than three times is marked as abnormal data by an intelligent concentrator, and detailed error logs are recorded, providing crucial information for system troubleshooting and optimization. In large-scale remote meter reading scenarios, data transmission is affected by various factors, such as communication interference and equipment failure. Traditional meter reading systems handle failed transmission data simply, making it difficult to pinpoint the root cause of the problem. The abnormal data marking and log recording functions of the intelligent concentrator in this invention record detailed information such as error time, type, and involved meter identifiers. When data transmission fails, staff can quickly identify the abnormal meter and faulty link based on the logs, such as determining whether it is a fault in the meter's communication module, a problem with the communication line, or a fault in the intelligent concentrator's reception. By analyzing the error type, targeted measures can be taken, such as replacing the faulty meter, repairing the communication line, or optimizing the intelligent concentrator parameters. For example, if some meters in a certain area frequently fail to retransmit data, after the intelligent concentrator records the logs, staff may find that signal interference in that area is causing unstable wireless communication, and the problem can be solved by enhancing the signal or adjusting the communication method. This function improves the efficiency of system fault handling, reduces manual troubleshooting time and costs, and accumulates data for system optimization and upgrades, thereby improving the overall reliability and stability of the meter reading system and ensuring accurate and timely transmission of power data.

[0043] refer to Figure 1The data acquisition module of the smart energy meter samples at a frequency of 1Hz-10Hz during periods of stable electricity consumption, and at a frequency of 50Hz-100Hz during peak electricity consumption periods or when data changes abruptly.

[0044] As a technical optimization solution of this invention, the smart energy meter data acquisition module adaptively adjusts the sampling frequency during different electricity consumption periods to achieve efficient and accurate data acquisition. During periods of stable electricity consumption, low-frequency sampling of 1Hz-10Hz reduces energy meter power consumption, extends equipment lifespan, and reduces maintenance costs. For battery-powered meters, low-frequency sampling significantly reduces battery consumption frequency, avoiding frequent battery replacements. During peak electricity consumption periods or when data changes abruptly, sampling automatically increases to 50Hz-100Hz to accurately capture instantaneous changes in electricity consumption data. For example, when industrial user equipment experiences sudden current and power fluctuations upon startup, high-frequency sampling ensures that critical data is not lost, providing power companies with detailed data for accurate analysis of user electricity consumption characteristics. Compared to traditional fixed-frequency sampling, adaptive sampling meets the data acquisition needs of different scenarios while also considering energy consumption and cost. Power companies can optimize electricity pricing strategies based on accurate data, setting reasonable prices for peak-consumption users; it is also used for power system load forecasting, using detailed historical peak-period data to more accurately predict future electricity load, allocate power resources in advance, avoid power shortages or waste, improve power system operating efficiency and economy, and achieve optimal allocation of power resources.

[0045] refer to Figure 1 The report analysis module of the main station management system generates visualized daily electricity consumption curve reports and monthly peak-valley comparison reports based on the analysis results of the data processing center.

[0046] As a technical optimization of this invention, the report analysis module of the main station management system generates visualized reports based on the results of the data processing center, providing intuitive and efficient data support for power companies' decision-making. Traditional power data processing presents simple statistical reports, which lack intuitive information expression and make it difficult to uncover the potential value of the data. This invention's visualized reports transform complex electricity consumption data into intuitive charts. For example, the daily electricity consumption curve report clearly shows the trend of electricity consumption changes over 24 hours, helping power companies understand peak and off-peak periods of user electricity consumption, rationally arrange power dispatch, and reduce the pressure on the power grid. The monthly peak-valley comparison report compares the differences in peak and off-peak electricity consumption across different months, analyzing the impact of factors such as seasons, weather, and holidays on electricity consumption, providing a basis for power resource planning. For example, if the report reveals that air conditioning consumption in summer leads to a significant increase in electricity consumption in a certain area, the power company can expand the power grid in that area in advance or adjust the power supply plan. The visualized reports are also easy for personnel from different departments to understand; even non-technical personnel can quickly obtain key information, promoting inter-departmental collaboration and communication. Simultaneously, they can be used to display electricity consumption information to users, enhancing user transparency, guiding users to use electricity rationally, improving user satisfaction, and helping power companies achieve refined management and high-quality service.

[0047] refer to Figure 1 The system includes smart energy meters, smart concentrators, and a master station management system;

[0048] The smart energy meter consists of a data acquisition module, a hybrid communication module, a microprocessor unit, a security encryption module, and a power management module. The data acquisition module is based on a high-precision metering chip and a sensor array, and is used to collect multi-dimensional operating parameters such as energy consumption, voltage, current, power factor, and harmonic content in real time. The hybrid communication module integrates power line carrier, GPRS, NB-IoT, and LoRa communication chips and corresponding antennas, and is responsible for transmitting pre-processed and encrypted data to the smart concentrator. The microprocessor unit is based on an ARM architecture and runs a real-time operating system, coordinating the work of each module and performing data preprocessing, communication control, and security management tasks. The security encryption module uses the national cryptographic SM4 algorithm to encrypt the data. The power management module ensures stable power supply to the energy meter.

[0049] The intelligent concentrator integrates a high-speed processor, large-capacity memory, and multiple communication interfaces. It features a multi-level caching and data scheduling mechanism, including a receiving data buffer queue, a verification processing queue, and a forwarding queue. After receiving data from the smart energy meter, it performs CRC verification, data integrity checks, and protocol parsing before transmitting it to the main station system. The intelligent routing module dynamically optimizes the data transmission path every 5 minutes based on the network topology and communication quality. The intelligent concentrator also has local storage capabilities, allowing it to retain at least 72 hours of energy meter data during communication interruptions.

[0050] The main station management system constructs a distributed big data processing platform using the Hadoop and Spark frameworks. The system includes a data processing center, user management module, equipment management module, monitoring and alarm module, report analysis module, and system configuration module. It cleans, analyzes, and stores received data, monitors for anomalies in real time, and generates alarm information. The data processing center uses deep learning algorithms to build a load forecasting model with an accuracy of over 95%. The user management module manages user profiles, permissions, and electricity bills. The equipment management module remotely configures, upgrades, and diagnoses faults in electricity meters and smart concentrators. The main station system's data processing center cleans raw data and uses distributed computing technology for parallel processing to build models of user electricity consumption behavior, load forecasting, and equipment health assessment.

[0051] As a technical optimization of this invention, a complete closed loop is formed from data acquisition to terminal management through the coordinated operation of smart meters, smart concentrators, and a main station management system. The smart meter has clearly defined modules: the data acquisition module collects multi-dimensional parameters and adjusts the frequency intelligently, accurately capturing changes in electricity consumption data and avoiding the omission of critical information; the hybrid communication module integrates multiple communication technologies, providing diverse path options for data transmission; and the security encryption module ensures data security throughout the entire process, preventing data leakage and tampering. The smart concentrator, as a regional data hub, employs multi-level caching and scheduling mechanisms to ensure orderly data processing, an intelligent routing module to optimize transmission paths and improve data transmission efficiency, and local storage to ensure data security and prevent loss during communication interruptions. The main station management system relies on a big data processing platform to achieve in-depth data analysis and intelligent management, with multiple modules collaboratively completing various management tasks related to users, equipment, and data. This system and method effectively solve the efficiency, accuracy, security, and compatibility issues of traditional meter reading. Through intelligent design across the entire process, it provides real-time, accurate, and secure electricity consumption data for the power system, significantly improving the level of automated management and providing strong support for the efficient operation and decision-making of power companies, thus propelling smart grid construction to new heights. For example, in large industrial parks facing complex electricity consumption environments and massive amounts of meter data, this system can quickly and accurately collect data, efficiently process it through intelligent concentrators, and transmit it to the main station. The main station analyzes the data in a timely manner and generates decision-making basis, helping the park optimize the allocation of power resources and reduce energy costs. This device has the advantages of high efficiency and accuracy, intelligent compatibility, and stable low power consumption.

[0052] refer to Figure 1 The intelligent concentrator has multiple communication interfaces, including Ethernet interface, 4G / 5G communication module interface, and power line carrier communication interface.

[0053] As a technical optimization of this invention, the intelligent concentrator features multiple communication interfaces, including an Ethernet interface, a 4G / 5G communication module interface, and a power line carrier communication interface, significantly improving the system's network adaptability and compatibility. The Ethernet interface is suitable for areas with well-developed network infrastructure, providing a stable, high-speed wired connection to ensure rapid transmission of large amounts of data. In locations such as smart buildings and commercial centers, a stable network connection ensures timely uploading of meter data. The 4G / 5G communication module interface enables wide-area wireless communication, suitable for remote areas or areas with difficult wiring, such as rural areas and mountainous regions, ensuring full coverage of power data collection. The power line carrier communication interface utilizes existing power lines to transmit data, reducing construction costs. In newly built communities or renovated areas, no additional communication lines need to be laid. The combination of multiple interfaces allows the intelligent concentrator to select the optimal communication method according to different scenarios, or switch to a backup method when a single communication method fails, ensuring uninterrupted data transmission. Simultaneously, it is compatible with different communication technologies, facilitating the connection of electricity meters from different manufacturers and with different communication standards, avoiding device access failures due to incompatible communication interfaces, reducing system upgrade and expansion costs, and promoting the diversified and intelligent development of power meter reading systems to adapt to the complex and ever-changing application scenarios of smart grids.

[0054] refer to Figure 1 The power management module of the smart energy meter has three power supply modes: mains power, battery, and solar power, and issues a low power alarm when the power is below 10%.

[0055] As a technical optimization of this invention, the smart energy meter's power management module utilizes multiple power supply modes and a low-battery alarm function to ensure the stability and reliability of the energy meter's power supply. Mains power provides a stable, conventional power source suitable for most routine power usage scenarios; battery power ensures normal meter operation during mains power outages or temporary power needs, preventing data collection interruptions; solar power is environmentally friendly and suitable for remote areas without mains power or locations with high environmental requirements, expanding the meter's application range. The three power supply modes can automatically switch. For example, in the event of a mains power outage, it automatically switches to battery power and switches back to mains power when mains power is restored, ensuring continuous meter operation. The low-battery alarm function issues an alarm when the battery level drops below 10%, reminding staff to replace the battery or check the power supply system. This prevents the meter from stopping and losing data due to battery depletion. For example, in remote mountainous areas where solar power is affected by weather, an alarm may sound when the battery is low, allowing staff to take timely action. This function improves system maintenance efficiency, reduces meter reading failures and data loss due to power supply issues, ensures the continuity and integrity of power data, reduces power company maintenance costs and data management risks, and ensures stable operation of the energy meter in various environments.

[0056] The working principle and usage process of this invention are as follows: The data acquisition and preprocessing principle: The data acquisition module of the smart energy meter is based on a high-precision metering chip and sensor array, operating according to an adaptive sampling frequency adjustment strategy. During periods of stable electricity consumption, sampling is performed at a low frequency of 1Hz-10Hz to reduce unnecessary data redundancy and lower the energy meter's power consumption. When during peak electricity consumption periods or when a sudden data change is detected, the sampling frequency automatically increases to 50Hz-100Hz to ensure accurate capture of instantaneous changes in multi-dimensional operating parameters such as current, voltage, power factor, and harmonic content. The acquired data is processed by a digital signal processing unit, using a wavelet transform algorithm to remove noise interference, then smoothed using a Kalman filter algorithm, and finally packaged according to a custom data frame format, laying the foundation for reliable data transmission.

[0057] Communication link establishment principle: The hybrid communication module has a built-in environmental perception and communication quality assessment unit that monitors key parameters such as communication signal strength, bit error rate, and network load in real time, and constructs a communication quality assessment model using fuzzy logic algorithms. Under normal circumstances, power line carrier communication is prioritized for short-distance, small-data-volume transmission, making full use of existing power line resources. Once the power line carrier communication quality falls below a preset threshold (signal strength below -80dBm and bit error rate above 5%), or when encountering a large data transmission demand, the module will quickly switch to GPRS communication; for areas with low real-time requirements and poor network coverage, NB-IoT communication is automatically enabled; in specific intensive meter reading scenarios, LoRa communication is used for network transmission to ensure the stability and efficiency of data transmission.

[0058] Data transmission principle: After establishing a communication link, the smart energy meter transmits the pre-processed data frames to the smart concentrator through the hybrid communication module. Upon receiving the data, the smart concentrator utilizes a multi-level caching and data scheduling mechanism to sequentially perform CRC checks, data integrity checks, and protocol parsing to ensure data accuracy. Subsequently, based on the TCP / IP protocol, the smart concentrator and the main station management system use a sliding window mechanism and a timeout retransmission mechanism to stably transmit the processed data to the main station system, ensuring that data is not lost or damaged during transmission.

[0059] Data Processing and Storage Principles: The main station management system constructs a distributed big data processing platform, employing the Hadoop and Spark frameworks to perform in-depth processing on the received data. The data processing module first cleans the raw data, removing invalid or erroneous data, and then uses distributed computing technology to process the data in parallel. By constructing user electricity consumption behavior models, load forecasting models, and equipment health assessment models, it achieves functions such as electricity consumption statistics for various regions, prediction of peak and off-peak electricity consumption periods, and assessment of electricity meter operating status. The storage module adopts a hierarchical storage strategy, storing frequently accessed recent data on high-performance solid-state drives for fast retrieval; and storing infrequently accessed historical data on large-capacity hard disk drives or cloud storage, achieving reasonable data storage and efficient management.

[0060] Anomaly Monitoring and Handling Principle: The main station system's monitoring and alarm module employs a multi-dimensional anomaly detection mechanism combining threshold judgment and machine learning algorithms. When anomalies are detected, such as sudden increases or decreases in power consumption (e.g., power changes exceeding 300% of the normal range within 24 hours), voltage anomalies (exceeding rated voltage ±20%), electricity theft identified through a trained abnormal behavior model, or equipment malfunctions, an alarm message is immediately generated containing detailed information including the anomaly type, occurrence time, and involved equipment. This message is then sent to relevant management personnel via SMS, email, and app push notifications. Management personnel can utilize the system's remote diagnostic functions to troubleshoot and handle abnormal equipment. Simultaneously, the system fully records the anomaly handling process and results for subsequent analysis and summarization.

[0061] The usage process is as follows: Equipment installation and initialization: Install smart meters at the power user end, ensuring their data acquisition module, hybrid communication module, microprocessor unit, security encryption module, and power management module are operating normally. Configure the power supply method (mains, battery, or solar) for the power management module according to the site environment. Install smart concentrators at suitable locations within the area, connect multiple communication interfaces, complete the network connection with the smart meters, and configure parameters. Build the main station management system, configure the distributed big data processing platform, initialize various functional modules such as the user management module and equipment management module, and establish communication connections with the smart concentrators.

[0062] Routine Data Collection and Processing: Smart meters collect electricity consumption data in real time according to their established operating principles and perform preprocessing. Then, they transmit the data to the smart concentrator via a hybrid communication module, selecting the appropriate communication method. The smart concentrator verifies and parses the data before transmitting it to the main station management system. Upon receiving the data, the main station management system cleans, analyzes, and stores it. Simultaneously, it utilizes various models to perform in-depth data mining, generating visualized reports such as daily electricity consumption curves and monthly peak-valley comparison reports, providing data support for power companies' operational decisions.

[0063] Equipment Management and Maintenance: The equipment management module of the main station management system allows for remote configuration of electricity meters and smart concentrators, adjusting communication parameters, sampling frequencies, etc., according to actual needs. When the system detects equipment abnormalities, the monitoring and alarm module issues alarm information. Management personnel can then use the remote diagnostic function to troubleshoot the equipment based on the alarm prompts, and arrange on-site repairs if necessary. Simultaneously, the equipment management module can also remotely push upgrade programs to update the software and upgrade the functions of electricity meters and smart concentrators.

[0064] Abnormal Situation Handling: In the event of abnormal power consumption, abnormal voltage, electricity theft, or equipment malfunction, the monitoring and alarm module will promptly issue an alarm. Upon receiving the notification, relevant management personnel will first use the remote diagnostic function of the main station management system to preliminarily determine the cause of the fault. If the problem can be resolved remotely, it will be addressed directly; otherwise, on-site handling will be arranged, and maintenance personnel will be dispatched to the site to repair the fault based on the abnormal information recorded by the system. After the handling is completed, the system records the entire abnormal handling process and results for subsequent analysis and optimization of system operation.

[0065] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for remote meter reading of an electricity meter, characterized in that: Includes the following steps: Step 1: Data Acquisition and Preprocessing of Smart Energy Meters: The data acquisition module of smart energy meters collects operating parameters according to an adaptive sampling frequency strategy. After being filtered and denoised by wavelet transform algorithm and smoothed by Kalman filter algorithm by digital signal processing unit, the data is packaged according to a custom data frame format. The data frame contains timestamp, device identifier, and data check code information. Step 2: Establishing the communication link for the smart meter: The hybrid communication module of the smart meter has a built-in environmental perception and communication quality assessment unit. It monitors the communication signal strength, bit error rate, and network load parameters in real time. It constructs a communication quality assessment model by combining fuzzy logic algorithm and selects the communication mode according to a dynamic priority strategy. Power line carrier communication is preferred for short-distance and small data volume transmission. When the power line carrier communication quality is lower than the signal strength of -80dBm and the bit error rate is higher than the 5% threshold or the data volume is large, it switches to GPRS communication. NB-IoT communication is used in areas with low real-time requirements and poor network coverage. LoRa communication is used for network transmission in specific dense meter reading scenarios. Step 3: Data transmission of smart energy meters: After establishing a communication link, the smart energy meter transmits the pre-processed data frame to the smart concentrator via the hybrid communication module. After processing, the smart concentrator transmits the data to the main station system using the TCP / IP protocol, through a sliding window mechanism and a timeout retransmission mechanism. Step 4: Data processing and storage of the intelligent concentrator: The data processing center of the main station system cleans the raw data, uses distributed computing technology to process it in parallel, and builds user electricity consumption behavior, load forecasting, and equipment health assessment models to realize electricity consumption statistics, peak and off-peak prediction, and equipment status assessment functions; the storage module adopts hierarchical storage, with high-frequency recent data stored on high-performance solid-state drives, and low-frequency historical data stored on large-capacity mechanical hard drives or cloud storage. Step 5: Anomaly Monitoring and Handling in the Main Station Management System: The main station system's monitoring and alarm module employs a multi-dimensional anomaly detection mechanism that combines threshold judgment and machine learning algorithms. When it detects sudden increases or decreases in power consumption (e.g., power consumption changes exceeding the normal range by 300% within 24 hours), voltage anomalies (exceeding the rated voltage by ±20%), electricity theft, or equipment malfunctions, it generates alarm information containing the anomaly type, time, and equipment information. This information is then pushed to administrators via SMS, email, and APP for remote diagnosis and handling. The system records the process results.

2. The method for remote meter reading of an electricity meter according to claim 1, characterized in that: The hybrid communication module can seamlessly switch communication modes within 200ms when communication quality changes, ensuring data transmission continuity, and the data loss rate during switching does not exceed 0.1%.

3. The method for remote meter reading of an electricity meter according to claim 1, characterized in that: The intelligent concentrator marks data that fails to be retransmitted more than 3 times as abnormal data and records an error log, which includes the error time, type, and relevant electricity meter identification information.

4. The method for remote meter reading of an electricity meter according to claim 1, characterized in that: The data acquisition module of the smart energy meter samples at a frequency of 1Hz-10Hz during periods of stable electricity consumption, and at a frequency of 50Hz-100Hz during peak electricity consumption periods or when data changes abruptly.

5. The method for remote meter reading of an electricity meter according to claim 1, characterized in that: The report analysis module of the main station management system generates visualized daily electricity consumption curve reports and monthly peak-valley electricity consumption comparison reports based on the analysis results of the data processing center.

6. The system for a remote meter reading method for an electricity meter according to claim 1, characterized in that: The system includes smart energy meters, smart concentrators, and a master station management system; The smart energy meter consists of a data acquisition module, a hybrid communication module, a microprocessor unit, a security encryption module, and a power management module. The data acquisition module is based on a high-precision metering chip and a sensor array, and is used to collect multi-dimensional operating parameters such as energy consumption, voltage, current, power factor, and harmonic content in real time. The hybrid communication module integrates power line carrier, GPRS, NB-IoT, and LoRa communication chips and corresponding antennas, and is responsible for transmitting pre-processed and encrypted data to the smart concentrator. The microprocessor unit is based on an ARM architecture and runs a real-time operating system, coordinating the work of each module and performing data preprocessing, communication control, and security management tasks. The security encryption module uses the national cryptographic SM4 algorithm to encrypt the data. The power management module ensures stable power supply to the energy meter. The intelligent concentrator integrates a high-speed processor, large-capacity memory, and multiple communication interfaces. It features a multi-level caching and data scheduling mechanism, including a receiving data buffer queue, a verification processing queue, and a forwarding queue. After receiving data from the smart energy meter, it performs CRC verification, data integrity checks, and protocol parsing before transmitting it to the main station management system. The intelligent concentrator is equipped with an intelligent routing module that dynamically optimizes the data transmission path every 5 minutes based on network topology and communication quality. The intelligent concentrator also has local storage capabilities, allowing it to retain at least 72 hours of energy meter data during communication interruptions. The main station management system constructs a distributed big data processing platform using the Hadoop and Spark frameworks. The system includes a data processing center, user management module, device management module, monitoring and alarm module, report analysis module, and system configuration module. The main station management system cleans, analyzes, and stores received data, monitors for anomalies in real time, and generates alarm information. The data processing center uses deep learning algorithms to build a load forecasting model with an accuracy of over 95%. The user management module manages user profiles, permissions, and electricity bills. The device management module remotely configures, upgrades, and diagnoses faults in electricity meters and smart concentrators. The main station system's data processing center cleans raw data and uses distributed computing technology for parallel processing to build models of user electricity consumption behavior, load forecasting, and device health assessment.

7. The system for a remote meter reading method for an electricity meter according to claim 6, characterized in that: The intelligent concentrator has multiple communication interfaces, including an Ethernet interface, a 4G / 5G communication module interface, and a power line carrier communication interface.

8. The system for a remote meter reading method for an electricity meter according to claim 6, characterized in that: The power management module of the smart energy meter has three power supply modes: mains power, battery, and solar power, and issues a low power alarm when the power level is below 10%.

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