Power distribution network scene low-voltage electric quantity Internet of Things monitoring device and monitoring method

Abstract

The invention discloses a power distribution network scene low-voltage electric quantity internet-of-things monitoring device and method, and the device comprises a sensing layer, a network layer and an application layer, the sensing layer comprises a plurality of electric quantity collection terminal nodes, and each electric quantity collection terminal node comprises a current transformer, a voltage transformer, a metering chip, a first Bluetooth Mesh communication module and a first power module. And the metering chip converts analog signals acquired by the current transformer and the voltage transformer into digital signals and calculates to obtain voltage, current, power, electric quantity, frequency and other electric quantity parameters. According to the invention, automatic acquisition, wireless transmission, remote monitoring, intelligent analysis and visual display of the electric quantity data of the low-voltage side of the power distribution network can be realized; and meanwhile, an early warning effect can be achieved, early warning can be given out for possible faults of the line, the manual field patrol intensity is reduced, and an Internet of Things solution which is low in cost, high in efficiency, easy to deploy and wide in coverage is provided for low-voltage electric quantity monitoring of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of electric quantity monitoring, and in particular to a low-voltage electric quantity Internet of Things monitoring device and a monitoring method in a distribution network scenario. Background Art

[0002] With the continuous advancement of power grid upgrading and agricultural modernization, the number of electrical equipment in the field is increasing, and the power consumption continues to grow, which puts forward higher requirements for power safety, energy saving and consumption reduction, and power quality. However, there is currently a lack of effective power monitoring methods on the low-voltage side, and the main technical problems are as follows: (1) Traditional power monitoring devices mostly use wired communication methods such as RS485, which require large wiring workload, are difficult to construct, and have high maintenance costs. They cannot adapt to the complexity and variability of the production environment. (2) Conventional power collection equipment has high power consumption, inconvenient power supply, and cannot work continuously for a long time. The real-time and continuity of data cannot be guaranteed; (3) Most electricity monitoring systems lack data analysis and visualization capabilities, are unable to tap the potential value of electricity data, and cannot provide decision support for farmers and power supply departments; (4) There are relatively few IoT applications developed for low-voltage power monitoring, and there is a lack of effective system solutions. There is an urgent need for a low-voltage power IoT monitoring device and monitoring method that is suitable for the scenario.

[0003] Therefore, in order to solve the above technical problems, the present invention proposes a low-voltage electricity Internet of Things monitoring device and a monitoring method for a distribution network scenario. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the problems pointed out by the above background technology and provide a low-voltage electricity Internet of Things monitoring device and monitoring method in a distribution network scenario, thereby solving the above problems.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a low-voltage electricity Internet of Things monitoring device in a distribution network scenario, comprising a perception layer, a network layer and an application layer, the perception layer comprising a plurality of electricity collection terminal nodes, each of which comprises a current transformer, a voltage transformer, a metering chip, a first Bluetooth Mesh communication module and a first power supply module, the current transformer obtains a current signal by measuring the current induction of the device under test, the voltage transformer obtains a voltage signal by measuring the live wire voltage, the metering chip has a built-in ADC converter and a digital signal processing unit, the analog signals collected by the current transformer and the voltage transformer are converted into digital signals and voltage, current, power, electricity and frequency electricity parameters are calculated, the first Bluetooth Mesh communication module has a built-in ARMCortex-M4 core, supports the Bluetooth 5.0 protocol, and is used to send the electricity parameters calculated by the metering chip to a data aggregation node through Bluetooth Mesh networking communication, the first power supply module uses 220V AC power to obtain 5V and 3.3V DC power after capacitor step-down, and supplies power to the metering chip and the first Bluetooth Mesh communication module respectively.

[0006] As a preferred embodiment of the present invention, the power collection terminal node is one or more of a socket type, a wiring type or an open transformer type, and is used in different application scenarios. The socket type collection terminal has a built-in current transformer and a voltage transformer, and the outer shell is made of flame-retardant PVC material. The maximum load-bearing current is 15A and the maximum load-bearing power is 3000W. It is suitable for power monitoring of daily electrical appliances in residents' households. The wiring type collection terminal leads out the current transformer and the voltage transformer, connects them to the power supply line of the device under test through a wire, and is installed in a narrow space of a dark box. It is suitable for distributed power monitoring of equipment in a facility agriculture greenhouse. The open transformer type collection terminal adopts an open and closed current transformer with a maximum diameter of 36mm and a maximum measuring current of 600A. It is mounted on a thick cable and is suitable for power monitoring on the low-voltage outlet side of the transformer.

[0007] As a preferred embodiment of the present invention, the network layer includes at least one data aggregation node, the data aggregation node includes a second Bluetooth Mesh communication module, a WiFi communication module and a second power supply module, the second Bluetooth Mesh communication module adopts the same nRF52832 chip as the power collection terminal node, receives power data from multiple power collection terminal nodes through the Bluetooth Mesh networking protocol, and realizes multi-point to multi-point networking communication; the WiFi communication module uploads the aggregated power data to the cloud platform through the MQTT protocol; the second power supply module is the same as the first power supply module of the power collection terminal node, ensuring reliable power supply of the data aggregation node.

[0008] As a preferred embodiment of the present invention, the application layer includes a cloud platform, and the cloud platform includes an Internet of Things platform, a cloud database, a cloud server and a visualization terminal. The Internet of Things platform adopts the Alibaba Cloud Internet of Things platform, receives and parses the power data from the data aggregation node through the MQTT protocol, and forwards the data to the cloud database and the cloud server through the cloud API interface. The cloud database adopts the Alibaba Cloud relational database RDS, supports multiple database engines such as MySQL, SQLServer, PostgreSQL, etc., stores and manages the power data safely and reliably, and provides real-time backup and disaster recovery functions. The cloud server performs secondary processing on the power data in the cloud database by deploying data cleaning and data analysis algorithms to improve data quality and value. The visualization terminal adopts the Alibaba Cloud DataV data visualization tool, retrieves the cleaned power data from the cloud database through a Web page, and displays it in a variety of charts, real-time monitoring of the operating status of the power-consuming equipment in the scene, energy consumption statistical analysis, and abnormal alarm functions.

[0009] As a preferred embodiment of the present invention, the cloud server uses an improved Chebyshev inequality theorem to remove outliers from the electricity data. The Chebyshev inequality describes the relationship between the probability of a random variable deviating from the mean and its standard deviation. Assuming that X is a random variable, E(X) is the mathematical expectation of X, and D(X) is the variance of X, then for any positive number ε, P{|XE(X)|<ε}≥1-D(X) / ε^2, the electricity data set X is divided into n intervals, and the mean E(Xi) and variance D(Xi) are calculated for each interval, and an abnormal threshold ε is set. If the probability of the data in the interval deviating from the mean exceeds the threshold, it is determined to be an outlier and removed. The cloud server uses the neighboring mean interpolation method to fill the missing values ​​of the electricity data. For the missing value Xi, the m data points before and after it are taken to calculate the mean E(Xi), and the missing value Xi is replaced by the mean E(Xi), which can maintain the continuity and smoothness of the data.

[0010] As a preferred embodiment of the present invention, when the device is applied to the monitoring of household electricity consumption, multiple socket-type electricity collection terminal nodes are arranged in the residents' homes to collect the electricity consumption data of household appliances such as refrigerators, televisions, air conditioners, and washing machines in real time, and upload them to the cloud platform through the data aggregation node. Residents can view the electricity consumption, electricity consumption time, and electricity consumption habit data of each appliance through the visualization terminal, which helps to improve the residents' awareness of electricity safety and energy conservation. When the device is applied to the monitoring of facility agriculture electricity consumption, multiple wiring-type electricity collection terminal nodes are arranged in agricultural greenhouses and livestock and poultry houses to collect the lighting, ventilation, heating, feeding, etc. in real time. The electricity consumption data of the equipment is collected and uploaded to the cloud platform through the data aggregation node. Farmers can remotely monitor the equipment operation status through the visualization terminal, and timely discover equipment failures and energy consumption anomalies, which is helpful to improve agricultural production efficiency and economic benefits. When the device is used for power monitoring on the low-voltage side of the transformer, multiple open transformer-type power collection terminal nodes are arranged in the low-voltage integrated distribution cabinet to collect the power data of each low-voltage outgoing line in real time, and upload it to the cloud platform through the data aggregation node. The power supply company can use the visualization terminal to finely manage the power load of the substation and timely grasp the fault information, which is helpful to improve the power supply reliability and intelligence level of the power grid.

[0011] A monitoring method for a low-voltage power Internet of Things, comprising the following steps: S1: The power collection terminal node is deployed on the low-voltage side, and the voltage and current analog signals of the equipment under test are collected through the current transformer and the voltage transformer. The analog signals are converted into digital signals through the ADC converter and the digital signal processing unit of the metering chip, and the voltage, current, power, power, frequency and power parameters are calculated; S2: The power collection terminal node packages the power parameter data through the first Bluetooth Mesh communication module and transmits it to the data aggregation node through multiple hops using the flooding routing mechanism. The data aggregation node receives the data packets from multiple power collection terminal nodes through the second Bluetooth Mesh communication module and aggregates them. S3: The data aggregation node packages the aggregated power data through the WiFi communication module and uploads it to the IoT platform of the cloud platform through the Internet using the MQTT protocol; S4: The IoT platform of the cloud platform parses the received MQTT data packets, extracts the power data, and forwards the power data to the cloud database for storage through the cloud API interface, while triggering the cloud server to start data cleaning and data analysis tasks; S5: The cloud server reads the original power data from the cloud database, uses the improved Chebyshev inequality theorem to remove outliers from the power data in each interval, and uses the neighboring mean interpolation method to fill in the missing values ​​to obtain the cleaned power data; S6: The cloud server performs energy consumption statistics, power load analysis, and abnormal alarm data mining and analysis on the power data after cleaning according to application requirements to obtain power data analysis results; S7: The visualization terminal retrieves the power data after cleaning and the analysis results generated by the cloud server from the cloud database through WebAPI, and displays them through charts, curves, and indicator card visualization components, providing users with an intuitive and friendly human-computer interaction interface.

[0012] As a preferred embodiment of the present invention, in step S1, according to the electricity consumption characteristics of different scenarios on the low-voltage side, a suitable type of power collection terminal node is selected: in the residential life scenario, a socket-type power collection terminal node is selected, the measuring power range is 0~3000W, and it is directly connected to household appliances; in the facility agriculture scenario, a wiring-type power collection terminal node is selected, the measuring power range is 0~10000W, and it is connected to the equipment power supply line through a wire; in the transformer low-voltage outlet scenario, an open transformer-type power collection terminal node is selected, the measuring current range is 0~600A, and it is mounted on a thick cable; the measurement accuracy of the power collection terminal nodes reaches level 0.5, which can meet the needs of low-voltage side power monitoring.

[0013] As a preferred embodiment of the present invention, in step S2, Bluetooth Mesh networking communication technology is used to realize many-to-many two-way communication between multiple scattered power collection terminal nodes and data aggregation nodes in the scene: the power collection terminal nodes and the data aggregation nodes are both equipped with built-in Bluetooth Mesh protocol stacks and can serve as nodes of the Bluetooth Mesh network; the Bluetooth Mesh network adopts a flooding routing mechanism, and each node can relay and forward data, so that the network coverage can reach hundreds of meters; the Bluetooth Mesh network has self-organization and self-repairing characteristics, and nodes can dynamically join or exit the network; the Bluetooth Mesh network is based on the BLE5.0 standard, realizing low-power, high-reliability, and low-cost wireless networking communication.

[0014] As a preferred embodiment of the present invention, in steps S4-S6, the cloud platform's massive storage, high-performance computing and data analysis capabilities are fully utilized to perform full life cycle management of the scene's massive power monitoring data: the Internet of Things platform can access millions of data aggregation nodes, providing device management, identity authentication, and data analysis functions; the cloud database adopts a distributed architecture, supports PB-level structured and unstructured data storage, and provides data backup, disaster recovery, and audit data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1) Bluetooth Mesh networking communication technology is adopted to self-organize a Bluetooth Mesh network through multiple micro-power consumption power collection terminal nodes and data aggregation nodes. It has the advantages of wide coverage, flexible networking, reliable transmission, low power consumption, etc. It can adapt to the complex terrain and harsh conditions of the environment. The power collection terminal node integrates high-precision current transformers, voltage transformers and metering chips, which can collect voltage, current, power, electricity, frequency and other power parameters in real time, and transmit data through the Bluetooth Mesh communication module, realizing the automatic collection, wireless transmission and remote monitoring of power data on the low-voltage side of the distribution network. The data aggregation node uploads the power data to the cloud platform through the WiFi communication module, and uses the cloud platform's massive storage and powerful computing power to clean, store, analyze and visualize the power data, providing users with value-added services such as power consumption monitoring, energy consumption statistics, and abnormal alarms, thereby tapping the application value of power data.

[0016] 2) The power collection terminal node can be flexibly packaged into socket type, wiring type and open transformer type according to different application scenarios. It is suitable for power monitoring of residential electricity consumption, facility agriculture electricity consumption and transformer low-voltage outlet lines. It has the characteristics of strong versatility and wide application range. It adopts the improved Chebyshev inequality theorem and the neighboring mean interpolation method to eliminate outliers and fill missing values ​​in the power data, ensuring the authenticity, reliability and integrity of the power data, laying the foundation for subsequent data analysis and application. Through the visualization terminal of the cloud platform, users can check the operating status and energy consumption of electrical equipment anytime and anywhere, understand the patterns and abnormalities of electricity consumption, which is helpful to improve the level of electricity management and promote energy saving and efficiency improvement.

[0017] 3) The present invention realizes the automatic collection, wireless transmission, remote monitoring, intelligent analysis and visual display of the power data on the low-voltage side of the distribution network. It can also play an early warning role, issue early warnings for possible line failures, reduce the intensity of manual on-site inspections, and provide a low-cost, high-efficiency, easy-to-deploy, and wide-coverage Internet of Things solution for low-voltage power monitoring in distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a low-voltage electricity IoT monitoring device and a monitoring method for a distribution network scenario according to the present invention; In the figure: 1. Power collection terminal node; 2. Data aggregation node; 3. Cloud platform; 101. Current transformer; 102. Voltage transformer; 103. Metering chip; 104. First Bluetooth Mesh communication module; 105. First power module; 201. Second Bluetooth Mesh communication module; 202. WiFi communication module; 203. Second power module; 301. Internet of Things platform; 302. Cloud database; 303. Cloud server; 304. Visualization terminal. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 The present invention provides a technical solution: a low-voltage electricity Internet of Things monitoring device for a distribution network scenario, including a perception layer, a network layer and an application layer. The perception layer includes a plurality of electricity collection terminal nodes 1, each of which includes a current transformer 101, a voltage transformer 102, a metering chip 103, a first Bluetooth Mesh communication module 104 and a first power module 105. The current transformer 101 obtains a current signal by measuring the current induction of a device under test, and the voltage transformer 102 obtains a voltage signal by measuring the live line voltage. The metering chip 103 has a built-in ADC converter and a digital signal processing unit. The analog signals collected by the current transformer 101 and the voltage transformer 102 are converted into digital signals and the power parameters such as voltage, current, power, power, frequency, etc. are calculated. The first Bluetooth Mesh communication module 104 has a built-in ARMCortex-M4 core and supports the Bluetooth 5.0 protocol. It is used to send the power parameters calculated by the metering chip 103 to the data aggregation node 2 through the Bluetooth Mesh networking communication. The first power supply module 105 uses 220V AC power to obtain 5V and 3.3V DC power after capacitor step-down, and supplies power to the metering chip 103 and the first Bluetooth Mesh communication module 104 respectively; Specifically, the device is composed of a perception layer, a network layer and an application layer. The perception layer includes a plurality of power collection terminal nodes 1. Each node has a built-in current transformer 101, a voltage transformer 102, a metering chip 103, a first Bluetooth Mesh communication module 104 and a first power module 105. The current transformer 101 and the voltage transformer 102 respectively collect current and voltage analog signals of the device under test. The metering chip 103 converts the analog signal into a digital signal and calculates the power parameter. The first Bluetooth Mesh communication module 104 packages the power parameter data and transmits it to the data aggregation node 2 through the Bluetooth Mesh network. The first power module 105 supplies power to each component. The perception layer realizes the real-time collection and wireless transmission of multi-point power data on the low-voltage side. The network layer includes at least one data aggregation node 2, which has a first Bluetooth Mesh communication module 104, a WiFi communication module 202 and a first power supply module 105 built in. The data aggregation node 2 receives and aggregates power data packets from the power collection terminal node of the perception layer through the first Bluetooth Mesh communication module 104, and uploads the aggregated data to the cloud platform 3 of the application layer through the WiFi communication module 202. The network layer realizes data transmission and protocol conversion between the perception layer and the application layer.

[0021] The application layer includes a cloud platform 3, a built-in IoT platform 301, a cloud database 302, a cloud server 304 and a visualization terminal 304. The IoT platform 301 receives and parses the power data uploaded by the data aggregation node 2, and forwards the data to the cloud database 302 for storage and the cloud server 304 for processing. The cloud server 304 cleans and analyzes the power data, and the visualization terminal 304 retrieves the data from the cloud database 302 and displays it in charts. The application layer realizes the storage, calculation and visualization of massive power data.

[0022] Through the collaborative work of the perception layer, network layer and application layer, the device can realize the automatic collection, wireless transmission, remote monitoring, intelligent analysis and visual management of power data on the low-voltage side of the distribution network, providing farmers and power supply departments with refined and intelligent electricity consumption monitoring services.

[0023] In this embodiment: the power acquisition terminal node 1 is one or more of the socket type, wiring type or open transformer type, and is used in different application scenarios. The socket type acquisition terminal has a built-in current transformer 101 and a voltage transformer 102, and the outer shell is made of flame-retardant PVC material. The maximum load-bearing current is 15A and the maximum load-bearing power is 3000W. It is suitable for power monitoring of daily household electrical appliances. The wiring type acquisition terminal leads out the current transformer 101 and the voltage transformer 102, connects them to the power supply line of the device under test through a wire, and is installed in a narrow space such as a dark box. It is suitable for distributed power monitoring of equipment in facility agriculture greenhouses. The open transformer type acquisition terminal uses an open and closed current transformer with a maximum diameter of 36mm and a maximum measuring current of 600A. It is mounted on a thick cable and is suitable for power monitoring on the low-voltage outlet side of the transformer.

[0024] Specifically, the power collection terminal node 1 forms a Bluetooth Mesh network through the second Bluetooth Mesh communication module 201 to achieve multi-hop transmission of power data. The second Bluetooth Mesh communication module 201 uses the Nordic nRF52832 chip, with a built-in ARMCortex-M4 core and a 2.4GHz transceiver, and supports the Bluetooth 5.0 protocol. Multiple power collection terminal nodes 1 and data aggregation nodes 2 send and receive data through broadcasting. Each node can relay and forward data without relying on a central node, and has self-organization and self-repair characteristics.

[0025] The Bluetooth Mesh network adopts a flooding routing mechanism. After receiving a data packet, the node broadcasts and forwards it to the surrounding area until the data packet reaches the destination node or exceeds the forwarding number. The Bluetooth Mesh network connects all power collection terminal nodes 1 within the coverage range of the low-voltage side to form a many-to-many communication network, which significantly expands the coverage of power monitoring. The Bluetooth Mesh network is based on BLE low-power technology and has the characteristics of low cost, low power consumption, plug-and-play, etc., which is very suitable for the application environment.

[0026] In this embodiment: the network layer includes at least one data aggregation node 2, the data aggregation node 2 includes a second Bluetooth Mesh communication module 201, a WiFi communication module 202 and a second power module 203, the second Bluetooth Mesh communication module 201 uses the same nRF52832 chip as the power collection terminal node 1, receives power data from multiple power collection terminal nodes 1 through the Bluetooth Mesh networking protocol, and realizes multi-point to multi-point networking communication; the WiFi communication module 202 uploads the aggregated power data to the cloud platform 3 through the MQTT protocol; the second power module 203 is the same as the first power module 105 of the power collection terminal node 1, to ensure reliable power supply of the data aggregation node 2.

[0027] Specifically, the data aggregation node 2 is responsible for data transmission and protocol conversion between the perception layer and the application layer. The data aggregation node 2 accesses the Bluetooth Mesh network through the second Bluetooth Mesh communication module 201 and receives the power data packet from the power collection terminal node 1. The data collected by multiple power collection terminal nodes 1 are aggregated at the data aggregation node 2, which greatly reduces the data volume and improves the transmission efficiency.

[0028] The data aggregation node 2 packages and uploads the aggregated power data to the cloud platform 3 through the WiFi communication module 202. The WiFi communication module 202 uses the Espressif ESP32 chip, supports the IEEE 802.11 b / g / n standard, has a high transmission rate and a long transmission distance. The data aggregation node 2 establishes a publish-subscribe connection with the IoT platform 301 of the cloud platform 3 through the MQTT protocol, and publishes the power data in JSON format to the specified Topic. The IoT platform 301 subscribes to the Topic, receives the data, and parses, stores, and triggers it.

[0029] Data aggregation node 2 achieves seamless connection between Bluetooth Mesh and WiFi, solving the problem of heterogeneous network interconnection between the perception layer and the application layer. Data aggregation node 2 is flexible in configuration. The number and location of data aggregation node 2 can be reasonably set according to factors such as the coverage range of the low-voltage side, terrain characteristics, and distribution of power-consuming equipment to balance communication distance, transmission rate, and power consumption.

[0030] In this embodiment: the application layer includes a cloud platform 3, which includes an Internet of Things platform 301, a cloud database 302, a cloud server 303 and a visualization terminal 304. The Internet of Things platform 301 adopts the Alibaba Cloud Internet of Things platform, receives and parses the power data from the data aggregation node 2 through the MQTT protocol, and forwards the data to the cloud database 302 and the cloud server 303 through the cloud API interface. The cloud database 302 adopts the Alibaba Cloud relational database RDS, supports multiple database engines such as MySQL, SQLServer, PostgreSQL, etc., stores and manages the power data safely and reliably, and provides real-time backup and disaster recovery functions. The cloud server 303 performs secondary processing on the power data in the cloud database 302 by deploying data cleaning and data analysis algorithms to improve data quality and value. The visualization terminal 304 adopts the Alibaba Cloud DataV data visualization tool, retrieves the cleaned power data from the cloud database 302 through a Web page, and displays it in a variety of charts, real-time monitoring of the operating status of the power-consuming equipment in the scene, energy consumption statistics and analysis, and abnormal alarm functions.

[0031] Specifically, the data aggregation node 2 uses the ESP32 chip to implement the WiFi communication function, and uploads the aggregated power data to the Internet of Things platform 301 of the cloud platform 3 through the MQTT protocol. MQTT is a lightweight publish-subscribe mode Internet of Things communication protocol with the characteristics of openness, simplicity, flexibility, etc., which is very suitable for resource-constrained Internet of Things devices and unstable network environments.

[0032] Data aggregation node 2 acts as an MQTT client and establishes a TCP connection with the MQTT server (Broker) preset in the IoT platform 301. Data aggregation node 2 packages the aggregated power data into an MQTT message. The message content is encapsulated in JSON format, including fields such as power collection terminal node 1 ID, timestamp, and power parameters. Data aggregation node 2 publishes the MQTT message to the specified topic. The topic generally adopts a hierarchical naming method, such as "name / transformer number / collection terminal ID / power parameter".

[0033] As an MQTT server and subscriber, the IoT platform 301 subscribes to a pre-configured topic. When the data aggregation node 2 publishes a message, the IoT platform 301 receives the MQTT message in real time and triggers the rule engine to parse the message and transfer the data. The IoT platform 301 forwards the parsed power data to the cloud database 302 for storage and to the cloud server 303 for data processing through the cloud API.

[0034] The MQTT protocol supports three message quality of service (QoS), namely at most once (QoS 0), at least once (QoS 1), and only once (QoS 2), which can balance energy consumption and reliability according to the importance and real-time requirements of power data. The MQTT protocol also supports advanced features such as Topic wildcards, will messages, and reserved messages, which enhances the flexibility and reliability of data transmission.

[0035] In this embodiment: the cloud server 303 uses the improved Chebyshev inequality theorem to remove outliers from the electricity data. The Chebyshev inequality describes the relationship between the probability of a random variable deviating from the mean and its standard deviation. Assuming X is a random variable, E(X) is the mathematical expectation of X, and D(X) is the variance of X, then for any positive number ε, P{|XE(X)|<ε}≥1-D(X) / ε^2, the electricity data set X is divided into n intervals, and the mean E(Xi) and variance D(Xi) are calculated for each interval, and an abnormal threshold ε is set. If the probability of the data in the interval deviating from the mean exceeds the threshold, it is determined to be an outlier and removed. The cloud server 303 uses the neighboring mean interpolation method to fill the missing values ​​of the electricity data. For the missing value Xi, the m data points before and after it are taken to calculate the mean E(Xi), and the missing value Xi is replaced by the mean E(Xi), which can maintain the continuity and smoothness of the data.

[0036] Specifically, the cloud platform 3 includes an Internet of Things platform 301 , a cloud database 302 , a cloud server 303 and a visualization terminal 304 , which respectively undertake the responsibilities of accessing, storing, processing and displaying the power data, and work together to achieve the full life cycle management of the low-voltage power data.

[0037] The Internet of Things platform 301 receives the power data uploaded by the data aggregation node 2 through the MQTT protocol, and performs data parsing and circulation through the preset rule engine. The rule engine supports functions such as message format conversion, data filtering, device mapping, function calculation, etc., and converts the power data into a standardized JSON format. The Internet of Things platform 301 transfers the parsed power data to the cloud database 302 and the cloud server 303 respectively through the AMQP protocol. The AMQP protocol supports multiple data exchange types, such as point-to-point, publish-subscribe, request-response, etc., to meet the diverse needs of data circulation within the cloud platform 3.

[0038] The cloud database 302 uses a relational database such as MySQL, or a time series database such as InfluxDB, to perform structured storage of power data. The cloud database 302 supports PB-level massive data storage, provides data backup, disaster recovery, audit and other data security mechanisms, and performs off-site disaster recovery for important data. The visualization terminal 304 retrieves the required power data from the cloud database 302 through SQL statements or query APIs.

[0039] Cloud server 303 uses a high-performance computing cluster to provide elastic and scalable computing resources to clean, mine and analyze power data. Cloud server 303 deploys a data cleaning module, uses Chebyshev's inequality theorem to identify and eliminate abnormal data, and uses the neighboring mean interpolation method to repair missing data to improve the quality of power data. Cloud server 303 deploys machine learning algorithms to perform load characteristic analysis, equipment abnormality diagnosis, energy efficiency benchmarking, etc. on the cleaned power data to generate data analysis results.

[0040] The visualization terminal 304 uses Web technology stacks such as HTML, JavaScript, CSS, etc. to provide a friendly human-computer interaction interface. The visualization terminal 304 obtains the parsed real-time power data, historical power data and data analysis results by calling the open APIs of the Internet of Things platform 301, cloud database 302, and cloud server 303, and displays them through rich visualization components such as charts, curves, dashboards, etc., to intuitively present the power consumption status and rules on the low-voltage side. The visualization terminal 304 supports multiple access methods such as Web and mobile terminals, and users can view power monitoring data anytime and anywhere. The visualization terminal 304 also provides interactive controls, and users can select data dimensions through drop-down boxes, check boxes, etc. to dynamically generate visualization charts.

[0041] In this embodiment: when the device is applied to the monitoring of the electricity consumption of residents' lives, multiple socket-type electricity collection terminal nodes 1 are arranged in the residents' homes to collect the electricity consumption data of household appliances such as refrigerators, televisions, air conditioners, washing machines, etc. in real time, and upload them to the cloud platform 3 through the data aggregation node 2. Residents can view the electricity consumption, electricity consumption time, electricity consumption habits, etc. of each appliance through the visualization terminal 304, which helps to improve the residents' awareness of electricity safety and energy conservation. When the device is applied to the monitoring of the electricity consumption of facility agriculture, multiple wiring-type electricity collection terminal nodes 1 are arranged in buildings such as agricultural greenhouses and livestock and poultry houses to collect the electricity consumption of lighting, ventilation, heating, feeding and other equipment in real time. The electricity data is collected and uploaded to the cloud platform 3 through the data aggregation node 2. Farmers can remotely monitor the equipment operation status through the visualization terminal 304, and timely discover equipment failures and energy consumption anomalies, which is helpful to improve agricultural production efficiency and economic benefits. When the device is used for electricity monitoring on the low-voltage side of the transformer, multiple open transformer-type electricity collection terminal nodes 1 are arranged in the low-voltage integrated distribution cabinet to collect the electricity data of each low-voltage outgoing line in real time, and upload it to the cloud platform 3 through the data aggregation node 2. The power supply company can use the visualization terminal 304 to finely manage the power load of the substation and timely grasp the fault information, which is helpful to improve the power supply reliability and intelligence level of the power grid.

[0042] Specifically, the cloud server 303 uses a data cleaning module to remove outliers and repair missing values ​​from the electricity big data to improve data quality. Outlier removal adopts the improved Chebyshev inequality theorem. The basic principle is to assume that the electricity data follows a normal distribution. The outlier is a value that deviates from the mean by more than k times the standard deviation, and k is a given threshold. The traditional Chebyshev inequality theorem uses a fixed k value, while the improved method is to divide the time series data into n intervals, calculate the mean and standard deviation for each interval, dynamically adjust the k value, and improve the sensitivity and accuracy of outlier detection.

[0043] Missing value repair uses the neighboring mean interpolation method, that is, the missing value is replaced by the mean of the time points adjacent to the missing value. The traditional mean interpolation method uses a fixed number of adjacent values, while the improved method is to adaptively select the number and weight of adjacent values ​​based on the data characteristics of the interval where the missing value is located, thereby improving the accuracy of missing value repair. For example, during peak power consumption, the fluctuations around the missing value are large, and fewer neighboring points are selected for mean calculation; during stable power consumption, the fluctuations around the missing value are small, and more neighboring points are selected for mean calculation.

[0044] The data cleaning module batch processes the raw power data in the cloud database 302 to generate a cleaned power data set. Parameters such as the threshold, number of intervals, number of neighboring points, etc. for outlier removal and missing value repair can be configured according to the characteristics of the power data and application requirements. After data cleaning, the integrity, accuracy, and consistency of the power data are guaranteed, providing high-quality data support for subsequent data analysis.

[0045] The device can be flexibly deployed in various scenarios on the low-voltage side, and by matching different types of power collection terminals, it can achieve refined collection and monitoring of power consumption information.

[0046] In the scenario of monitoring electricity consumption in residents' daily life, the device deploys multiple socket-type electricity collection terminals in residents' homes, which are connected to electrical appliances through sockets. The electricity collection terminal has built-in mutual inductors and metering chips 103, which can measure parameters such as voltage, current, power, and electricity, and monitor the electricity consumption of household appliances in real time. At the same time, the electricity collection terminal transmits the collected data to the data aggregation node 2 through Bluetooth Mesh networking, and the data aggregation node 2 uploads it to the cloud platform 3. Users can view the electricity consumption data of each device and time period through the visualization terminal 304, understand their own electricity consumption behavior and habits, and help improve residents' awareness of electricity saving and scientific electricity use.

[0047] In the scenario of facility agriculture electricity consumption monitoring, the device deploys multiple wiring-type power collection terminals in buildings such as agricultural greenhouses and farms, and connects them to the power supply lines of agricultural equipment. The power collection terminal can measure the operating electrical parameters of agricultural equipment, evaluate the health status and energy efficiency level of the equipment, and promptly detect equipment failures and energy consumption anomalies. At the same time, the power collection terminal transmits the collected data to the data aggregation node 2 through Bluetooth Mesh networking, and the data aggregation node 2 uploads it to the cloud platform 3. Farmers can remotely view the operating status of agricultural equipment through the visualization terminal 304, optimize equipment scheduling and energy management, and improve agricultural production efficiency and economic benefits.

[0048] In the transformer low-voltage outgoing line monitoring scenario, the device deploys multiple open transformer-type power collection terminals in the low-voltage integrated distribution cabinet and connects them to each low-voltage outgoing line cable. The power collection terminal can measure the current, power and other parameters of each low-voltage outgoing line, grasp the load distribution and change trend in different areas, and evaluate the reliability and balance of power supply. At the same time, the power collection terminal transmits the collected data to the data aggregation node 2 through the Bluetooth Mesh network, and the data aggregation node 2 uploads it to the cloud platform 3. Power supply companies can monitor the operating status of low-voltage outgoing lines in real time through the visualization terminal 304, optimize distribution network scheduling and fault repair, and improve the intelligent management level of the power grid.

[0049] A monitoring method for a low-voltage power Internet of Things, comprising the following steps: S1: an electric quantity collection terminal node 1 is arranged on the low voltage side, and the voltage and current analog signals of the device under test are collected through the current transformer (101) and the voltage transformer 102, and the analog signals are converted into digital signals through the ADC converter and the digital signal processing unit of the metering chip 103, and the voltage, current, power, electric quantity and frequency electric quantity parameters are calculated; S2: The power collection terminal node 1 packages the power parameter data through the first Bluetooth Mesh communication module 104, and transmits it to the data aggregation node 2 through multiple hops using the flooding routing mechanism. The data aggregation node 2 receives the data packets from multiple power collection terminal nodes 1 through the second Bluetooth Mesh communication module 201, and aggregates them; S3: The data aggregation node 2 packages the aggregated power data through the WiFi communication module 202 and uploads it to the Internet of Things platform 301 of the cloud platform 3 through the Internet using the MQTT protocol; S4: The IoT platform 301 of the cloud platform 3 parses the received MQTT data packet, extracts the power data, and forwards the power data to the cloud database 302 for storage through the cloud API interface, and triggers the cloud server 303 to start data cleaning and data analysis tasks; S5: The cloud server 303 reads the original power data from the cloud database 302, uses the improved Chebyshev inequality theorem to remove abnormal values ​​from the power data in each interval, and uses the neighboring mean interpolation method to fill the missing values ​​to obtain the cleaned power data; S6: The cloud server 303 performs data mining and analysis such as energy consumption statistics, power load analysis, and abnormal alarm on the power data after cleaning according to application requirements to obtain power data analysis results; S7: The visualization terminal 304 retrieves the power data after cleaning and the analysis results generated by the cloud server 303 from the cloud database 302 through the WebAPI, and displays them through charts, curves, and indicator card visualization components to provide users with an intuitive and friendly human-computer interaction interface.

[0050] Specifically, the monitoring method includes three stages: perception layer data collection, network layer data transmission, and application layer data processing. Through layered collaboration and process-based operations, full-process monitoring of low-voltage power data can be achieved.

[0051] In the data collection stage of the perception layer, the power collection terminal node 1 distributed on the low-voltage side measures the voltage and current signals in real time, and obtains multi-dimensional power parameters such as voltage, current, power, power, and frequency through the mutual inductor and metering chip 103. The power collection terminal node 1 packages the power parameter data through the second Bluetooth Mesh communication module 201 and transmits it to the data aggregation node 2 through multi-hop using the flooding routing mechanism. The perception layer realizes the automatic collection of multi-source heterogeneous power data on the low-voltage side.

[0052] In the data transmission stage of the network layer, the data aggregation node 2 receives data packets sent by multiple power collection terminal nodes 1 through the second Bluetooth Mesh communication module 201, and performs data aggregation, compression and encryption after unpacking. The data aggregation node 2 packages the aggregated data through the WiFi communication module 202 and uploads it to the Internet of Things platform 301 of the cloud platform 3 through the Internet using the MQTT protocol. The network layer realizes the efficient transmission and secure exchange of massive power monitoring data.

[0053] In the data processing stage of the application layer, the IoT platform 301 parses and verifies the received MQTT data packets, extracts key fields to form structured JSON data, transfers the data to the cloud database 302 InfluxDB for storage through the AMQP protocol, and triggers the cloud server 303 to start the data processing task. The cloud server 303 reads the time series power data from InfluxDB, cleans the data through algorithms such as Chebyshev inequality and neighboring mean interpolation, and mines the data through algorithms such as clustering, association, and prediction to generate data products such as power load curves, equipment health reports, and energy-saving transformation plans. The visualization terminal 304 retrieves the cleaned power monitoring data from InfluxDB through the API gateway, retrieves the data mining results from the cloud server 303, and uses visualization frameworks such as Grafana and Echarts for graphical display, providing end users with comprehensive, accurate, and easy-to-use power monitoring services. The application layer realizes the storage, cleaning, mining, visualization, and business application of power big data.

[0054] In this embodiment: In step S1, according to the power consumption characteristics of different scenarios on the low-voltage side, a suitable type of power collection terminal node 1 is selected: in the residential life scenario, a socket-type power collection terminal node 1 is selected, the measuring power range is 0~3000W, and it is directly connected to household appliances; in the facility agriculture scenario, a wiring-type power collection terminal node 1 is selected, the measuring power range is 0~10000W, and it is connected to the equipment power supply line through a wire; in the transformer low-voltage outlet scenario, an open transformer-type power collection terminal node 1 is selected, the measuring current range is 0~600A, and it is mounted on a thick cable; the measurement accuracy of the power collection terminal node 1 reaches level 0.5, which can meet the needs of low-voltage side power monitoring.

[0055] Specifically, in step S2, Bluetooth Mesh networking communication technology is used to realize many-to-many two-way communication between multiple dispersed power collection terminal nodes 1 and data aggregation nodes 2 in the scene: both the power collection terminal node 1 and the data aggregation node 2 have built-in Bluetooth Mesh protocol stacks and can be used as nodes of the Bluetooth Mesh network; the Bluetooth Mesh network adopts a flooding routing mechanism, and each node can relay and forward data, so that the network coverage can reach hundreds of meters; the Bluetooth Mesh network has self-organization and self-repairing characteristics, and nodes can dynamically join or exit the network; the Bluetooth Mesh network is based on the BLE5.0 standard, realizing low-power, high-reliability, and low-cost wireless networking communication. In steps S4-S6, the massive storage, high-performance computing and data analysis capabilities of the cloud platform 3 are fully utilized to manage the massive power monitoring data of the scene throughout the life cycle: the Internet of Things platform 301 can access millions of data aggregation nodes 2, providing device management, identity authentication, data analysis and other functions; the cloud database 302 adopts a distributed architecture, supports PB-level structured and unstructured data storage, and provides data backup, disaster recovery, auditing and other data.

[0056] Specifically, in the data processing stage, the cloud server 303 queries the original power data from the InfluxDB time series database in the order of timestamps to form a time series. Due to the complex power consumption environment on the low-voltage side, there may be abnormal values ​​(such as abnormal values ​​caused by instantaneous impacts and equipment failures) and missing values ​​(such as data loss caused by unstable transmission) in the original power data. To ensure data quality, the cloud server 303 needs to enable the data cleaning module to pre-process the original power data.

[0057] The data cleaning module uses the improved Chebyshev inequality theorem to detect and remove outliers. The Chebyshev inequality describes the relationship between the probability of a random variable deviating from the mean and its standard deviation. Under certain assumptions, it can be used to determine abnormal points in samples. The traditional Chebyshev inequality uses a fixed threshold, while the improved method introduces a dynamic threshold, that is, the time series electricity data is divided into several intervals according to the time window (such as 1 hour), the mean and standard deviation are calculated for each interval, and the normal value range is determined with the mean as the center and k times the standard deviation as the radius. k is a given confidence coefficient, and points outside the normal value range are outliers. The dynamic threshold comprehensively considers the volatility and trend of electricity data, and improves the adaptability of outlier detection.

[0058] The data cleaning module uses the neighboring mean interpolation method to repair missing values. The mean interpolation method uses the mean of several normal values ​​before and after the missing value to replace the missing value and restore the integrity of the data. The traditional mean interpolation method uses a fixed number of neighboring values, while the improved neighboring mean interpolation method uses an adaptive radius.

[0059] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A low-voltage electricity IoT monitoring device for a distribution network scenario, characterized in that: The invention comprises a perception layer, a network layer and an application layer. The perception layer comprises a plurality of power collection terminal nodes (1). Each power collection terminal node (1) comprises a current transformer (101), a voltage transformer (102), a metering chip (103), a first Bluetooth Mesh communication module (104) and a first power supply module (105). The current transformer (101) obtains a current signal by measuring the current induction of the device under test. The voltage transformer (102) obtains a voltage signal by measuring the live line voltage. The metering chip (103) has a built-in ADC converter and a digital signal processing unit to convert the current transformer (101) and the live line voltage into a voltage signal. The analog signal collected by the voltage transformer (102) is converted into a digital signal and voltage, current, power, electricity quantity, frequency and electricity quantity parameters are calculated. The first Bluetooth Mesh communication module (104) has a built-in ARM Cortex-M4 core and supports the Bluetooth 5.0 protocol, and is used to send the electricity quantity parameters calculated by the metering chip (103) to the data aggregation node (2) through Bluetooth Mesh networking communication. The first power supply module (105) uses 220V alternating current to obtain 5V and 3.3V direct current after capacitor voltage reduction, and supplies power to the metering chip (103) and the first Bluetooth Mesh communication module (104) respectively.

2. A low-voltage electricity IoT monitoring device for a distribution network scenario as claimed in claim 1, characterized in that: The power acquisition terminal node (1) is one or more of a socket type, a wiring type or an open transformer type; wherein the socket type acquisition terminal has a built-in current transformer (101) and a voltage transformer (102), and the housing is made of flame-retardant PVC material, with a maximum load current of 15A and a maximum load power of 3000W; the wiring type acquisition terminal leads out the current transformer (101) and the voltage transformer (102), and connects them to the power supply line of the device under test through a wire; the open transformer type acquisition terminal uses an open-close current transformer, with a maximum diameter of 36mm and a maximum measurement current of 600A, and is mounted on a thick cable.

3. A low-voltage electricity IoT monitoring device for a distribution network scenario as claimed in claim 1, characterized in that: The network layer comprises at least one data aggregation node (2), the data aggregation node (2) comprising a second Bluetooth Mesh communication module (201), a WiFi communication module (202) and a second power module (203), the second Bluetooth Mesh communication module (201) using the same nRF52832 chip as the power collection terminal node (1), receiving power data from a plurality of power collection terminal nodes (1) via a Bluetooth Mesh networking protocol, thereby realizing multi-point to multi-point networking communication; the WiFi communication module (202) uploads the aggregated power data to a cloud platform (3) via an MQTT protocol; the second power module (203) is the same as the first power module (105) of the power collection terminal node (1), thereby ensuring reliable power supply to the data aggregation node (2).

4. A low-voltage electricity IoT monitoring device for a distribution network scenario as claimed in claim 1, characterized in that: The application layer comprises a cloud platform (3), the cloud platform (3) comprises an Internet of Things platform (301), a cloud database (302), a cloud server (303) and a visualization terminal (304), the Internet of Things platform (301) adopts the Alibaba Cloud Internet of Things platform, receives and parses the power data from the data aggregation node (2) through the MQTT protocol, and forwards the data to the cloud database (302) and the cloud server (303) through a cloud API interface; the cloud server (303) performs secondary processing on the power data in the cloud database (302) by deploying data cleaning and data analysis algorithms; the visualization terminal (304) retrieves the cleaned power data from the cloud database (302) through a web page, and displays it in a variety of charts, so as to monitor the operating status of the power-consuming equipment in the scene in real time, perform energy consumption statistics and analysis, and issue abnormal alarms.

5. A low-voltage electricity IoT monitoring device for a distribution network scenario as claimed in claim 4, characterized in that: The cloud server (303) uses an improved Chebyshev inequality theorem to remove abnormal values ​​from the electricity data. The Chebyshev inequality describes the relationship between the probability of a random variable deviating from the mean and its standard deviation. Assuming that X is a random variable, E(X) is the mathematical expectation of X, and D(X) is the variance of X, then for any positive number ε, P{|XE(X)|<ε}≥1-D(X) / ε^2, the electricity data set X is divided into n intervals, and the mean E(Xi) and variance D(Xi) are calculated for each interval, and an abnormal threshold ε is set. If the probability of the data in the interval deviating from the mean exceeds the threshold, it is determined to be an abnormal value and removed. The cloud server (303) uses the neighboring mean interpolation method to fill the missing values ​​of the electricity data. For the missing value Xi, the m data points before and after it are taken to calculate the mean E(Xi), and the missing value Xi is replaced by the mean E(Xi) to maintain the continuity and smoothness of the data.

6. A low-voltage electricity IoT monitoring device for a distribution network scenario as claimed in claim 1, characterized in that: When the device is applied to monitoring the electricity consumption of residents' lives, multiple socket-type electricity collection terminal nodes (1) are arranged in the residents' homes to collect electricity consumption data of household appliances such as refrigerators, televisions, air conditioners and washing machines in real time, and upload the data to the cloud platform (3) through the data aggregation node (2). The residents can view the electricity consumption, electricity consumption time and electricity consumption habit data of each appliance through the visualization terminal (304); when the device is applied to monitoring the electricity consumption of facility agriculture, multiple wiring-type electricity collection terminal nodes (1) are arranged in agricultural greenhouses and livestock and poultry houses to collect electricity consumption data of lighting, ventilation, heating and feeding equipment in real time, and upload the data to the cloud platform (3) through the data aggregation node (2). Farmers can remotely monitor the operating status of the equipment through the visualization terminal (304); when the device is applied to monitoring the low-voltage side electricity consumption of transformers, multiple open transformer-type electricity collection terminal nodes (1) are arranged in the low-voltage integrated power distribution cabinet to collect electricity consumption data of each low-voltage outgoing line in real time, and upload the data to the cloud platform (3) through the data aggregation node (2). The power supply company can manage the power load of the substation in a refined manner through the visualization terminal (304) and obtain fault information in a timely manner.

7. A monitoring method for low-voltage power Internet of Things, characterized in that: The following steps are involved: S1: Arrange an electric quantity collection terminal node (1) on the low voltage side, collect voltage and current analog signals of the device under test through a current transformer (101) and a voltage transformer (102), convert them into digital signals through an ADC converter and a digital signal processing unit of a metering chip (103), and calculate and obtain voltage, current, power, electric quantity, and frequency electric quantity parameters; S2: the power collection terminal node (1) packages the power parameter data through the first Bluetooth Mesh communication module (104), and transmits it to the data aggregation node (2) through multiple hops using a flooding routing mechanism. The data aggregation node (2) receives the data packets from the plurality of power collection terminal nodes (1) through the second Bluetooth Mesh communication module (201), and aggregates the data packets. S3: The data aggregation node (2) packages the aggregated power data through the WiFi communication module (202) and uploads it to the Internet of Things platform (301) of the cloud platform (3) via the Internet using the MQTT protocol; S4: The Internet of Things platform (301) of the cloud platform (3) parses the received MQTT data packet, extracts the power data, and forwards the power data to the cloud database (302) for storage through the cloud API interface, and triggers the cloud server (303) to start data cleaning and data analysis tasks; S5: The cloud server (303) reads the original power data from the cloud database (302), uses the improved Chebyshev inequality theorem to remove abnormal values ​​from the power data in each interval, and uses the neighboring mean interpolation method to fill in the missing values ​​to obtain the cleaned power data; S6: The cloud server (303) performs energy consumption statistics, power load analysis, and abnormal alarm data mining analysis on the cleaned power data according to application requirements to obtain power data analysis results; S7: The visualization terminal (304) retrieves the power data after cleaning and the analysis results generated by the cloud server (303) from the cloud database (302) through the WebAPI, and displays them through charts, curves, and indicator card visualization components.

8. A monitoring method for low-voltage electric quantity Internet of Things according to claim 7, characterized in that: In step S1, according to the power consumption characteristics of different scenarios on the low-voltage side, a suitable type of power collection terminal node (1) is selected: in the residential life scenario, a socket-type power collection terminal node (1) is selected, the measurement power range is 0~3000W, and it is directly connected to household appliances; In the facility agriculture scenario, select the wiring-type power collection terminal node (1), which has a measurement power range of 0 to 10,000 W and is connected to the equipment power supply line through a wire; In the transformer low-voltage outgoing line scenario, an open transformer type power collection terminal node (1) is selected, with a measurement current range of 0~600A, and is mounted on a thick cable; the measurement accuracy of the power collection terminal node (1) reaches level 0.5, which can meet the needs of low-voltage side power monitoring.

9. The monitoring method of a low-voltage electric quantity Internet of Things according to claim 7, characterized in that: In step S2, Bluetooth Mesh networking communication technology is used to realize many-to-many bidirectional communication between multiple dispersed power collection terminal nodes (1) and data aggregation nodes (2) in the scene: the power collection terminal nodes (1) and the data aggregation nodes (2) are both equipped with built-in Bluetooth Mesh protocol stacks and serve as nodes of the Bluetooth Mesh network; the Bluetooth Mesh network adopts a flooding routing mechanism, and each node can relay and forward data, so that the network coverage range can reach hundreds of meters; the Bluetooth Mesh network has self-organization and self-repairing characteristics, and nodes can dynamically join or exit the network; the Bluetooth Mesh network is based on the BLE5.0 standard.

10. A monitoring method for low-voltage and electric quantity Internet of Things according to claim 7, characterized in that: In the steps S4-S6, the cloud platform (3) uses its massive storage, high-performance computing and data analysis capabilities to perform full life cycle management on the massive amount of electricity monitoring data in the scene: the Internet of Things platform (301) can access millions of data aggregation nodes (2) to provide device management, identity authentication, and data analysis functions; the cloud database (302) adopts a distributed architecture, supports PB-level structured and unstructured data storage, and provides data backup, disaster recovery, and audit data.

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