Environmental data management system for metal electric meter box

Abstract

The invention provides an environmental data management system for a metal electric meter box, relates to the technical field of power equipment monitoring, and realizes three-dimensional perception of environmental risks through multi-dimensional monitoring and accurate early warning and through combination of a multi-sensor array and a correlation analysis model, thereby improving the early warning accuracy of the system. Through double-link anti-interference transmission, a 5G + LoRa double-link design is adopted, so that the success rate of data transmission of the system is improved, and the communication problem in a complex power environment is solved; through intelligent operation and maintenance decision making and a fault prediction model based on machine learning, the maintenance stability of the system is improved, and the non-planned power failure frequency is reduced; through high-reliability design, self-diagnosis, remote upgrading and a data redundancy mechanism are realized, and the fault rate of the system can be effectively reduced; through flexible expansibility, flexible configuration of sensor types and number is supported by a modular architecture, and the method is suitable for metal electric meter box scenes of different specifications, so that the applicability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to an environmental data management system for a metal electric meter box. Background Art

[0002] Metal meter boxes are important carriers of power system terminal equipment. The stability of their operating environment directly affects the accuracy of power metering and the life of the equipment.

[0003] In the existing technology, metal meter boxes face the following problems:

[0004] Traditional manual inspections are inefficient and unable to detect abnormal internal temperature and humidity, smoke hazards, and other issues in real time;

[0005] The monitoring range of a single sensor is limited, and there is a lack of collaborative analysis of multi-dimensional environmental data;

[0006] Data transmission is not stable enough and is susceptible to interference from complex electromagnetic environments;

[0007] The early warning mechanism lags behind, making it difficult to predict faults in advance;

[0008] To this end, we propose an environmental data management system for metal meter boxes to solve the above problems. Summary of the Invention

[0009] The problems to be solved by the present invention are that the monitoring range of a single sensor is limited and there is a lack of collaborative analysis of multi-dimensional environmental data; the data transmission stability is insufficient and it is susceptible to interference from complex electromagnetic environments.

[0010] In order to solve the above technical problems, the present invention provides an environmental data management system for a metal electric meter box, the management system specifically comprising:

[0011] The data acquisition module collects real-time environmental data inside and outside the meter box through sensors and converts physical signals into electrical or digital signals;

[0012] The data transmission module transmits the collected data to the server or cloud platform via wireless or wired means to ensure stable data transmission;

[0013] The data processing module uses an edge-to-cloud collaborative processing architecture to clean, reduce noise, and pre-process the data to ensure data reliability;

[0014] Data storage module, which stores data in the database and supports historical data query and backup;

[0015] The real-time monitoring module uses ECharts to achieve data visualization, develops web and mobile monitoring interfaces, displays real-time data in curves, displays real-time environmental data through a visual interface, sets thresholds, and issues warnings through SMS and APP push notifications when anomalies occur;

[0016] The real-time early warning module uses a multi-dimensional early warning mechanism to provide early warning of environmental changes in the metal meter box;

[0017] The data analysis and prediction module uses algorithms to analyze historical data, predict environmental change trends, and provide a basis for maintenance strategies;

[0018] The user management and authority module adopts a hierarchical authority control method to manage the authority of system operators, record operation logs, and ensure system security;

[0019] System management and maintenance module, which performs system parameter configuration, software upgrades, and equipment status self-inspection to ensure stable system operation;

[0020] Preferably, the data acquisition module specifically includes temperature and humidity sensors, smoke sensors, vibration sensors, infrared sensors and current and voltage monitoring units, and adopts a multi-sensor array layout, distributing sensor nodes at the four corners of the inner wall of the meter box and the heat dissipation vents to form a three-dimensional monitoring network, which can prevent single-point monitoring and inaccurate data problems.

[0021] Preferably, the data transmission module adopts a dual-link transmission architecture and anti-interference design. At the same time, the transmission protocol layer adopts the TCP / IP+MQTT dual protocol stack, realizes data caching and retransmission through the message queue, and integrates the EMI filtering circuit at the hardware level to reduce the impact of electromagnetic interference of power equipment, wherein:

[0022] Main link component: Based on the 5G communication module, it achieves high-speed data transmission with a peak rate of 1.2Gbps, suitable for real-time video surveillance and large data transmission;

[0023] Backup link component: Using LoRa wireless transmission technology, the communication distance reaches 3km, and it automatically switches in 5G signal blind spots to ensure uninterrupted data.

[0024] Preferably, the data processing module and the data storage module specifically include:

[0025] Edge layer: Deploy edge computing units to perform real-time data preprocessing. Kalman filtering is used to reduce noise in temperature and humidity data, reducing the error to ±0.1°C. Wavelet transform is used to extract features from vibration signals and identify mechanical vibration and external impact.

[0026] Cloud layer: Uses a distributed time-series database to store data, supports second-level queries on millions of data points, and ensures data reliability through data sharding and redundant backup mechanisms.

[0027] Preferably, the real-time monitoring module and the real-time warning module specifically include:

[0028] Multi-dimensional early warning mechanism unit:

[0029] Threshold warning component: set multi-level thresholds, and send out SMS, APP push, and sound and light alarms when triggered;

[0030] Trend warning component: Based on the LSTM neural network model, it performs trend analysis on the temperature data of the past 24 hours and issues an early warning when the temperature rise rate exceeds 5°C / h.

[0031] Correlation warning component: Establishes a correlation analysis model between temperature, humidity, current, and smoke concentration. When the current is abnormal and the temperature and humidity exceed the standard, it automatically determines that there is an overload risk.

[0032] Visual monitoring unit: Develop web and mobile monitoring interfaces, use ECharts to achieve data visualization, support real-time data curve display, historical data backtracking, and device topology display, and intuitively display the operating status of each meter box.

[0033] Preferably, the data analysis and prediction module includes:

[0034] Fault prediction model: Build a multi-feature fusion prediction model based on XGBoost, with input parameters including environmental parameters, operating parameters, and meteorological data;

[0035] Operation and maintenance decision-making unit: Generates operation and maintenance recommendation reports, including maintenance priority sorting, spare parts recommendation, and maintenance cycle optimization.

[0036] Preferably, the user management and authority module specifically includes:

[0037] Hierarchical authority control unit:

[0038] System Administrator: Has full module operation authority, can configure warning rules and manage user accounts;

[0039] Operations and maintenance engineers: can view real-time data, perform equipment control, and generate maintenance reports;

[0040] View user: can only browse the visual interface, no operation permissions;

[0041] Security Audit Unit: records all operation logs, including user login, data modification, and warning confirmation, and uses blockchain technology to hash and store key operation records to ensure that data cannot be tampered with.

[0042] Preferably, the system management and maintenance module specifically includes:

[0043] Self-diagnosis function unit: performs sensor calibration regularly with configurable calibration cycle, monitors the working status of each module in real time, and automatically marks and triggers the switch of backup sensors when a sensor failure is found;

[0044] The remote upgrade unit supports over-the-air download technology to remotely upgrade the edge computing unit firmware. Differential transmission technology is used during the upgrade process to reduce data traffic consumption. If the upgrade fails, it will automatically roll back to the historical version to ensure system availability.

[0045] The technical effects and advantages of the present invention are as follows:

[0046] The present invention realizes three-dimensional perception of environmental risks through multi-dimensional monitoring and precise early warning, and combines a multi-sensor array with a correlation analysis model, thereby improving the early warning accuracy of the system; through dual-link anti-interference transmission, the 5G+LoRa dual-link design is adopted, which is conducive to improving the success rate of data transmission of the system and solving the communication problems in complex power environments; through intelligent operation and maintenance decision-making, the fault prediction model based on machine learning is used to improve the maintenance stability of the system and reduce the number of unplanned power outages; through high-reliability design, self-diagnosis, remote upgrade and data redundancy mechanism are realized, which can effectively reduce the failure rate of the system; through flexible scalability, the modular architecture supports flexible configuration of sensor type and quantity, and is suitable for metal meter box scenarios of different specifications, thereby improving the applicability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0048] The present invention provides an environmental data management system for a metal electric meter box, such as Figure 1 As shown, the management system specifically includes:

[0049] The data acquisition module collects real-time environmental data inside and outside the meter box through sensors and converts physical signals into electrical or digital signals;

[0050] Furthermore, the data acquisition module specifically includes temperature and humidity sensors, smoke sensors, vibration sensors, infrared sensors, and current and voltage monitoring units. It adopts a multi-sensor array layout, distributing sensor nodes at the four corners of the inner wall of the meter box and at the heat dissipation vents to form a three-dimensional monitoring network, which can prevent the problem of single-point monitoring and inaccurate data.

[0051] Furthermore, the temperature and humidity sensor uses an SHT30 chip, collecting temperature and humidity data inside the box at a frequency of 0.5 seconds per time, with an accuracy of ±0.3°C / ±2%RH. The smoke sensor uses an MQ-2 semiconductor sensor to monitor combustible gas and smoke concentrations in real time. The vibration sensor uses a three-axis accelerometer (ADXL345) to detect abnormal vibration of the box. An infrared sensor is used to monitor the door opening status, and the current and voltage monitoring unit is connected to the meter circuit to collect operating parameters.

[0052] By adopting a multi-sensor array layout, sensor nodes are distributed at the four corners of the inner wall of the meter box and at the heat dissipation vents to form a three-dimensional monitoring network that covers multi-dimensional environmental parameters, has strong real-time performance, ensures data synchronization, supports multiple sensor access, adapts to different monitoring scenarios, and is highly flexible.

[0053] The data transmission module transmits the collected data to the server or cloud platform via wireless or wired means to ensure stable data transmission;

[0054] Furthermore, the data transmission module adopts a dual-link transmission architecture and anti-interference design. At the same time, the transmission protocol layer adopts the TCP / IP+MQTT dual protocol stack, and realizes data caching and retransmission through message queues. The hardware level integrates EMI filtering circuits to reduce the impact of electromagnetic interference on power equipment. Among them:

[0055] Main link components: Based on 5G communication modules, such as the ME919-SA, they achieve high-speed data transmission with a peak rate of 1.2 Gbps, suitable for real-time video surveillance and large-scale data transmission;

[0056] Backup link components: Using LoRa wireless transmission technology, such as the SX1278 chip, with a communication distance of up to 3km, automatic switching in 5G signal blind spots to ensure uninterrupted data;

[0057] The dual-link transmission channel automatically switches according to the signal quality. When the packet loss rate of the main link exceeds 10%, it automatically switches to the backup link and switches back seamlessly after the signal is restored. It supports multiple communication protocols, adapts to different network environments, ensures that data is not lost, has high transmission efficiency, can meet real-time monitoring needs, and reduce delays.

[0058] The data processing module uses an edge-to-cloud collaborative processing architecture to clean, reduce noise, and pre-process the data to ensure data reliability;

[0059] Data storage module, which stores data in the database and supports historical data query and backup;

[0060] Specifically include:

[0061] Edge layer: Deploy edge computing units, such as those based on the ARM Cortex-A53 processor, to perform real-time data preprocessing. Kalman filtering is used to reduce noise in temperature and humidity data, reducing the error to ±0.1°C. Wavelet transform is used to extract features from vibration signals and identify mechanical vibration and external impact.

[0062] Cloud layer: Uses a distributed time-series database to store data, supports querying millions of data points in seconds, and ensures data reliability through data sharding and redundant backup mechanisms;

[0063] Improve data availability through data standardization and distributed storage design to ensure data security and traceability, facilitating long-term analysis;

[0064] The real-time monitoring module uses ECharts to achieve data visualization, develops web and mobile monitoring interfaces, displays real-time data in curves, displays real-time environmental data through a visual interface, sets thresholds, and issues warnings through SMS, APP push, and other methods in the event of anomalies.

[0065] The real-time early warning module uses a multi-dimensional early warning mechanism to provide early warning of environmental changes in the metal meter box;

[0066] Specifically include:

[0067] Multi-dimensional early warning mechanism unit:

[0068] Threshold warning component: Set multiple thresholds, such as temperature warning thresholds: Level 1 warning at 50°C, Level 2 warning at 60°C, and Level 3 warning at 70°C. When triggered, the system will send SMS, APP push notifications, and send audio and visual alarms. The audio and visual alarm devices include a buzzer and LED lights inside the box.

[0069] Trend warning component: Based on the LSTM neural network model, it performs trend analysis on the temperature data of the past 24 hours and issues an early warning when the temperature rise rate exceeds 5°C / h.

[0070] Correlation warning component: Establishes a correlation analysis model between temperature, humidity, current and smoke concentration. When the current is abnormal and the temperature and humidity exceed the standard, it is automatically determined to be an overload hazard.

[0071] Visual monitoring unit: Developed web and mobile monitoring interfaces, using ECharts for data visualization, supporting real-time data curve display with a resolution of 1 minute / point, historical data backtracking, supporting data query within 3 months, device topology display, and intuitively displaying the operating status of each meter box;

[0072] The intuitive and easy-to-understand visual interface supports remote monitoring, reduces manual inspection costs, and has a multi-level early warning mechanism that can promptly detect risks such as equipment overheating, moisture, and external damage, thereby improving safety.

[0073] The data analysis and prediction module uses algorithms to analyze historical data, predict environmental change trends, and provide a basis for maintenance strategies;

[0074] Specifically include:

[0075] Fault prediction model: A multi-feature fusion prediction model based on XGBoost was constructed. Input parameters include environmental parameters (temperature and humidity, smoke concentration, vibration amplitude), operating parameters (current, voltage, power factor), and meteorological data (real-time outdoor temperature, humidity, and atmospheric pressure) obtained through an API. The model's prediction accuracy reached 92%, and it can predict potential faults such as equipment overheating and poor contact 48 hours in advance.

[0076] Operation and maintenance decision-making unit: Generates operation and maintenance recommendation reports, including maintenance priority sorting, such as based on failure probability and impact, spare parts recommendations, such as automatic matching of parts based on historical failure patterns, and maintenance cycle optimization, such as determining the optimal inspection interval through reliability analysis;

[0077] By driving the data, we can predict faults, change passive maintenance to active prevention, tap into the value of data, optimize operation and maintenance plans, and reduce operation and maintenance costs.

[0078] The user management and authority module adopts a hierarchical authority control method to manage the authority of system operators, record operation logs, and ensure system security;

[0079] Specifically include:

[0080] Hierarchical authority control unit:

[0081] System Administrator: Has full module operation authority, can configure warning rules and manage user accounts;

[0082] Operations and maintenance engineers: can view real-time data, perform equipment control, and generate maintenance reports;

[0083] View user: can only browse the visual interface, no operation permissions;

[0084] Security Audit Unit: records all operation logs, including user login, data modification, and warning confirmation. It uses blockchain technology to hash key operation records to ensure that data cannot be tampered with.

[0085] Through hierarchical authority control, data misoperation or leakage is prevented, security regulations are met, operation records are traceable, and auditing and responsibility division are facilitated.

[0086] System management and maintenance module, which performs system parameter configuration, software upgrades, and equipment status self-inspection to ensure stable system operation;

[0087] Specifically include:

[0088] Self-diagnosis function unit: performs sensor calibration regularly, such as through built-in standard source comparison, with a configurable calibration cycle, such as the default of 7 days, and monitors the working status of each module in real time. When a sensor failure is found, it is automatically marked and triggers the backup sensor switch;

[0089] Remote upgrade unit, supports over-the-air download technology to remotely upgrade the edge computing unit firmware. Differential transmission technology is used during the upgrade process to reduce data traffic consumption. If the upgrade fails, it will automatically roll back to the previous version to ensure system availability.

[0090] Automated maintenance functions reduce manual intervention, improve system reliability, enable remote configuration and upgrades, reduce maintenance complexity, and adapt to distributed deployment scenarios.

[0091] Example:

[0092] Taking 100 metal meter boxes as an example, after deploying this system:

[0093] The data acquisition module installs three temperature and humidity sensors, one smoke sensor, and one vibration sensor in each meter box;

[0094] The data transmission module adopts 5G+LoRa dual link, with 5G signal coverage of 95%, and LoRa backup link to ensure data transmission in signal blind areas;

[0095] The edge computing unit is deployed in the residential power distribution room to process data in real time, and the cloud server uses Alibaba Cloud ECS instances;

[0096] Operation and maintenance personnel receive early warning information through a mobile phone APP. Within six months of the system's operation, it successfully issued warnings for 23 equipment overheating failures. Compared with traditional inspection methods, faults were discovered 4.2 hours earlier, and operation and maintenance efficiency was improved by 60%.

[0097] The present invention realizes three-dimensional perception of environmental risks through multi-dimensional monitoring and precise early warning, and combines a multi-sensor array with a correlation analysis model, thereby improving the early warning accuracy of the system; through dual-link anti-interference transmission, the 5G+LoRa dual-link design is adopted, which is conducive to improving the success rate of data transmission of the system and solving the communication problems in complex power environments; through intelligent operation and maintenance decision-making, the fault prediction model based on machine learning is used to improve the maintenance stability of the system and reduce the number of unplanned power outages; through high-reliability design, self-diagnosis, remote upgrade and data redundancy mechanism are realized, which can effectively reduce the failure rate of the system; through flexible scalability, the modular architecture supports flexible configuration of sensor type and quantity, and is suitable for metal meter box scenarios of different specifications, thereby improving the applicability of the system.

[0098] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.

Claims

1. An environmental data management system for a metal electric meter box, characterized in that: The management system specifically includes: The data acquisition module collects real-time environmental data inside and outside the meter box through sensors and converts physical signals into electrical or digital signals; The data transmission module transmits the collected data to the server or cloud platform via wireless or wired means to ensure stable data transmission; The data processing module uses an edge-to-cloud collaborative processing architecture to clean, reduce noise, and pre-process the data to ensure data reliability; Data storage module, which stores data in the database and supports historical data query and backup; The real-time monitoring module uses ECharts to achieve data visualization, develops web and mobile monitoring interfaces, displays real-time data in curves, displays real-time environmental data through a visual interface, sets thresholds, and issues warnings through SMS and APP push notifications when anomalies occur; The real-time early warning module uses a multi-dimensional early warning mechanism to provide early warning of environmental changes in the metal meter box; The data analysis and prediction module uses algorithms to analyze historical data, predict environmental change trends, and provide a basis for maintenance strategies; The user management and authority module adopts a hierarchical authority control method to manage the authority of system operators, record operation logs, and ensure system security; The system management and maintenance module provides functions such as system parameter configuration, software upgrade, and equipment status self-check to ensure stable system operation.

2. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The data acquisition module specifically includes temperature and humidity sensors, smoke sensors, vibration sensors, infrared sensors and current and voltage monitoring units, and adopts a multi-sensor array layout, distributing sensor nodes at the four corners of the inner wall of the meter box and the heat dissipation vents to form a three-dimensional monitoring network, which can prevent single-point monitoring and inaccurate data problems.

3. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The data transmission module adopts a dual-link transmission architecture and anti-interference design. At the same time, the transmission protocol layer adopts the TCP / IP+MQTT dual protocol stack, realizes data caching and retransmission through message queues, and integrates EMI filtering circuits at the hardware level to reduce the impact of electromagnetic interference on power equipment. Main link component: Based on the 5G communication module, it achieves high-speed data transmission with a peak rate of 1.2Gbps, suitable for real-time video surveillance and large data transmission; Backup link component: Using LoRa wireless transmission technology, the communication distance reaches 3km, and it automatically switches in 5G signal blind spots to ensure uninterrupted data.

4. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The data processing module and the data storage module specifically include: Edge layer: Deploy edge computing units to perform real-time data preprocessing. Kalman filtering is used to reduce noise in temperature and humidity data, reducing the error to ±0.1°C. Wavelet transform is used to extract features from vibration signals and identify mechanical vibration and external impact. Cloud layer: Uses a distributed time-series database to store data, supports second-level queries on millions of data points, and ensures data reliability through data sharding and redundant backup mechanisms.

5. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The real-time monitoring module and the real-time early warning module specifically include: Multi-dimensional early warning mechanism unit: Threshold warning component: set multi-level thresholds, and send out SMS, APP push, and sound and light alarms when triggered; Trend warning component: Based on the LSTM neural network model, it performs trend analysis on the temperature data of the past 24 hours and issues an early warning when the temperature rise rate exceeds 5°C / h. Correlation warning component: Establishes a correlation analysis model between temperature, humidity, current, and smoke concentration. When the current is abnormal and the temperature and humidity exceed the standard, it automatically determines that there is an overload risk. Visual monitoring unit: Develop web and mobile monitoring interfaces, use ECharts to achieve data visualization, support real-time data curve display, historical data backtracking, and device topology display, and intuitively display the operating status of each meter box.

6. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The data analysis and prediction module includes: Fault prediction model: Build a multi-feature fusion prediction model based on XGBoost, with input parameters including environmental parameters, operating parameters, and meteorological data; Operation and maintenance decision-making unit: Generates operation and maintenance recommendation reports, including maintenance priority sorting, spare parts recommendation, and maintenance cycle optimization.

7. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The user management and authority module specifically includes: Hierarchical authority control unit: System Administrator: Has full module operation authority, can configure warning rules and manage user accounts; Operations and maintenance engineers: can view real-time data, perform equipment control, and generate maintenance reports; View user: can only browse the visual interface, no operation permissions; Security Audit Unit: records all operation logs, including user login, data modification, and warning confirmation, and uses blockchain technology to hash and store key operation records to ensure that data cannot be tampered with.

8. The environmental data management system for a metal electric meter box according to claim 1, characterized in that: The system management and maintenance module specifically includes: Self-diagnosis function unit: performs sensor calibration regularly with configurable calibration cycle, monitors the working status of each module in real time, and automatically marks and triggers the switch of backup sensors when a sensor failure is found; The remote upgrade unit supports over-the-air download technology to remotely upgrade the edge computing unit firmware. Differential transmission technology is used during the upgrade process to reduce data traffic consumption. If the upgrade fails, it will automatically roll back to the historical version to ensure system availability.

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