Intelligent power consumption management system and method based on big data

Abstract

The invention discloses an intelligent power consumption management system based on big data, and the system comprises an intelligent sensing module which is provided with a multi-parameter sensor array, an intelligent electric meter cluster and an environment monitoring unit, and achieves the real-time collection of electrical parameters and environment data through wireless networking. The data transmission module integrates the edge computing gateway and the 5G dual-mode communication module and supports multi-protocol conversion and data encryption transmission; and the big data processing module is used for constructing a distributed storage cluster and a real-time calculation engine. The invention further discloses an intelligent power consumption management method based on the big data. During specific use, the intelligent sensing module comprehensively and accurately collects data, and a management foundation is tamped; the data transmission module is efficient and safe in transmission and guarantees data quality; the big data processing module performs intelligent deep analysis and early warning optimization in advance; the application service module flexibly and hierarchically protects equipment safety; the digital twin platform realizes full-life-cycle management, improves operation and maintenance efficiency, and has great application value.

Description

Technical Field

[0001] This invention relates to the field of electricity management, and in particular to a smart electricity management system and method based on big data. Background Technology

[0002] With the rapid development of society and economy, the widespread application of electricity in various fields has led to a continuous increase in electricity consumption, making electricity safety and energy efficiency management issues increasingly prominent.

[0003] Traditional power management relies primarily on regular manual inspections and simple instrument monitoring, which suffers from drawbacks such as incomplete data collection and poor real-time performance. Manual inspections struggle to provide comprehensive, real-time monitoring of electrical parameters and environmental data, failing to promptly identify potential safety hazards and energy waste. This leads to frequent electrical faults, potentially causing serious safety accidents such as fires, resulting in personal injury and property damage. Unplanned equipment downtime also disrupts production and operations. In terms of data transmission, traditional methods often employ wired communication, which suffers from complex wiring, poor flexibility, and high costs, making it difficult to meet the access needs of large-scale distributed power consumption devices. Furthermore, the lack of effective data encryption and multi-protocol conversion mechanisms compromises data transmission security and compatibility. In data processing, traditional methods are often based on simple statistical analysis, failing to uncover the deeper value of the data and hindering accurate prediction of electrical faults and refined energy efficiency management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a smart electricity management system based on big data. In practical use, the intelligent sensing module comprehensively and accurately collects data, strengthening the foundation of management; the data transmission module transmits data efficiently and securely, ensuring data quality; the big data processing module performs intelligent in-depth analysis, providing early warnings and optimization; the application service module flexibly and hierarchically protects equipment security; and the digital twin platform enables full lifecycle management, improving operational efficiency and demonstrating significant application value.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A smart electricity management system based on big data includes an intelligent sensing module, configured with a multi-parameter sensor array, a smart meter cluster, and an environmental monitoring unit, which realizes real-time acquisition of electrical parameters and environmental data through wireless networking; a data transmission module, integrating an edge computing gateway and a 5G dual-mode communication module, supporting multi-protocol conversion and encrypted data transmission; a big data processing module, constructing a distributed storage cluster and a real-time computing engine, combining LSTM neural networks and isolated forest algorithms to achieve electrical fault prediction and energy efficiency analysis; an application service module, deploying a dynamic rule engine and a digital twin platform, supporting energy storage system control and remote assistance based on electricity price off-peak periods; and a terminal module, which receives data from the intelligent sensing module, data transmission module, big data processing module, and application service module.

[0007] Preferably, the multi-parameter sensor array includes a residual current sensor, a temperature sensor, and a three-phase voltage and current transformer; the smart meter cluster is compatible with DL / T645-2007 and IEC62056 protocols, achieving 0.1S-level energy metering; the environmental monitoring unit includes a humidity sensor and a dust concentration detector.

[0008] Preferably, the computing gateway and the 5G dual-mode communication module have a main channel latency of <20ms and a backup channel coverage radius of up to 15km; the data cleaning engine is used to filter outliers, and triggers a rejection mechanism when the current surge exceeds 200% of the rated value.

[0009] Preferably, the LSTM neural network is used to construct an electrical fire prediction model; the isolated forest algorithm is used for energy efficiency anomaly detection.

[0010] Preferably, the application service module adopts a three-level response control strategy, and pushes a notification to the mobile terminal module when the temperature of the application service module is >85℃, with a response time of <2 seconds.

[0011] Preferably, when the temperature of the application service module is >90°C, the load is reduced to 80% of the rated power.

[0012] Preferably, when the temperature of the application service module is >95℃, the circuit breaker is triggered to trip via an IEC61850 GOOSE message.

[0013] This invention also discloses a smart electricity management method based on big data, which employs the aforementioned management system and includes the following steps:

[0014] Step 1: The intelligent sensing module collects electrical parameters and environmental data in real time through a multi-parameter sensor array, smart meter cluster, and environmental monitoring unit, and transmits them to the data transmission module via wireless networking;

[0015] Step 2: The data transmission module uses the edge computing gateway and the 5G dual-mode communication module to achieve multi-protocol conversion and encrypted transmission; the data cleaning engine filters out abnormal values, and triggers a rejection mechanism when the current surge exceeds 200% of the rated value to ensure data quality;

[0016] Step 3: The big data processing module uses a distributed storage cluster and a real-time computing engine to build an electrical fire prediction model in conjunction with an LSTM neural network. It uses the isolated forest algorithm to detect energy efficiency anomalies, generate fault warnings and energy efficiency optimization suggestions, and provide a basis for subsequent control strategies.

[0017] Step 4: The application service module executes actions based on the three-level response control strategy to achieve device protection;

[0018] Step 5: The digital twin platform synchronizes the status of physical equipment, supports intelligent scheduling of energy storage systems based on electricity price troughs; it enables remote parameter configuration and fault diagnosis through a dynamic rule engine, and provides real-time assistance in conjunction with a 5G communication module, completing the closed loop of equipment lifecycle management.

[0019] Compared with the prior art, the beneficial effects of this invention are as follows:

[0020] 1. The intelligent sensing module is equipped with a multi-parameter sensor array, a smart meter cluster, and an environmental monitoring unit, enabling real-time collection of electrical parameters and environmental data. Comprehensive and accurate data collection provides a reliable basis for subsequent electricity management, allowing managers to clearly understand the real-time status of the electricity system.

[0021] 2. The data transmission module integrates an edge computing gateway and a 5G dual-mode communication module, supporting multi-protocol conversion and encrypted transmission. The main channel features low latency, the backup channel offers wide coverage, and a data cleaning engine filters outomas, ensuring efficient, secure, and accurate data transmission and preventing information loss and errors.

[0022] 3. The big data processing module constructs a distributed storage cluster and a real-time computing engine, combining LSTM neural networks and the Isolation Forest algorithm to achieve accurate prediction of electrical faults and in-depth energy efficiency analysis. It generates early fault warnings and energy efficiency optimization suggestions to help managers intervene proactively and improve system safety and energy efficiency.

[0023] 4. The application service module adopts a three-level response control strategy, automatically executing different actions based on temperature changes. From pushing notifications to reducing load and triggering circuit breaker tripping, it provides flexible and hierarchical protection for equipment, preventing damage due to overheating and ensuring the stable operation of the power system.

[0024] 5. The digital twin platform synchronizes the status of physical equipment, supporting intelligent scheduling of energy storage systems. Through a dynamic rule engine, it enables remote parameter configuration and fault diagnosis, and combined with 5G communication, provides real-time assistance, completing a closed-loop management system for the entire equipment lifecycle, improving operation and maintenance efficiency, and reducing costs. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of a smart electricity management system based on big data proposed in this invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] Reference Figure 1 A smart electricity management system based on big data is disclosed. The system includes an intelligent sensing module, a multi-parameter sensor array, a smart meter cluster, and an environmental monitoring unit. It achieves real-time acquisition of electrical parameters and environmental data through wireless networking. The multi-parameter sensor array includes a residual current sensor, a temperature sensor, and three-phase voltage and current transformers. The smart meter cluster is compatible with DL / T645-2007 and IEC62056 protocols, achieving 0.1S-level energy metering. The environmental monitoring unit includes a humidity sensor and a dust concentration detector.

[0029] As one embodiment of the present invention, it also includes a data transmission module that integrates an edge computing gateway and a 5G dual-mode communication module, supports multi-protocol conversion and encrypted data transmission. The computing gateway and the 5G dual-mode communication module have a main channel latency of <20ms and a backup channel coverage radius of up to 15km. The data cleaning engine is used to implement outlier filtering, and triggers a rejection mechanism when the current surge exceeds 200% of the rated value.

[0030] As one embodiment of the present invention, it also includes a big data processing module, which constructs a distributed storage cluster and a real-time computing engine, and combines LSTM neural network and isolated forest algorithm to realize electrical fault prediction and energy efficiency analysis. The LSTM neural network is used to construct an electrical fire prediction model; the isolated forest algorithm is used for energy efficiency anomaly detection.

[0031] As one embodiment of the present invention, it also includes an application service module, which deploys a dynamic rule engine and a digital twin platform to support energy storage system control and remote assistance based on electricity price troughs. The application service module adopts a three-level response control strategy. When the temperature of the application service module is >85°C, it pushes a notification to the mobile terminal module with a response time of <2 seconds. When the temperature of the application service module is >90°C, it reduces the load to 80% of the rated power. When the temperature of the application service module is >95°C, it triggers the circuit breaker to trip via an IEC61850 GOOSE message.

[0032] As one embodiment of the present invention, it also includes a terminal module, which is used to receive data from the control intelligent sensing module, the data transmission module, the big data processing module and the application service module.

[0033] In this invention, a residual current sensor monitors the residual current in the circuit in real time. Once abnormal fluctuations occur in the residual current, the data is immediately recorded and transmitted. A temperature sensor continuously measures the temperature of key parts of the electrical equipment to accurately sense temperature changes. A three-phase voltage and current transformer accurately collects the voltage and current parameters of the three-phase circuit, providing basic electrical data for subsequent analysis. The smart meter cluster measures electrical energy with high precision of 0.1S according to compatible DL / T645-2007 and IEC62056 protocols, records the energy consumption data of the electrical equipment in detail, and transmits this data through a wireless network. A humidity sensor acquires the humidity information of the power supply environment in real time, and a dust concentration detector detects the dust concentration in the environment. The environmental parameter data is also transmitted to subsequent modules through a wireless network to comprehensively consider the impact of the environment on the power supply system.

[0034] The edge computing gateway and 5G dual-mode communication module integrated into the data transmission module begin operation, supporting multiple protocol conversions and uniformly converting data from different protocols transmitted by the intelligent sensing module. Simultaneously, data encryption technology is used to encrypt the converted data, ensuring data security during transmission. Data is then transmitted through the main channel, with a latency of <20ms, guaranteeing real-time data transmission. In the event of a main channel failure, a backup channel quickly activates, with a coverage radius of up to 15km, ensuring continuous data transmission. The data cleaning engine filters outliers from the received data. When a current surge exceeding 200% of the rated value is detected, a rejection mechanism is triggered to remove abnormal data, improving data quality.

[0035] The distributed storage cluster built by the big data processing module efficiently stores cleaned, high-quality data, ensuring data reliability and scalability. The real-time computing engine processes and analyzes the stored data in real time, quickly extracting valuable information and using an LSTM neural network to build an electrical fire prediction model. This model learns the historical variation patterns of electrical parameters and combines them with current data to predict the risk of electrical fires. Simultaneously, the isolated forest algorithm is used to detect energy efficiency anomalies in power consumption data, analyzing whether there are energy efficiency anomalies in electrical equipment or processes, and generating fault warnings and energy efficiency optimization suggestions.

[0036] The application service module's dynamic rule engine automatically adjusts the operating parameters of electrical equipment based on user-defined rules and real-time data. The digital twin platform synchronizes the physical equipment status, enabling virtual mapping and real-time monitoring of the equipment. When the monitored equipment temperature exceeds 85℃, the application service module pushes a notification to the mobile terminal module within a response time of less than 2 seconds, alerting administrators to the abnormal temperature. If the equipment temperature continues to rise above 90℃, the application service module automatically reduces the load to 80% of the rated power to reduce heat generation and protect equipment safety. When the equipment temperature exceeds 95℃, the application service module triggers the circuit breaker to trip via an IEC61850 GOOSE message, quickly disconnecting the circuit to prevent equipment damage due to overheating and ensuring stable operation of the power system. Simultaneously, the application service module supports energy storage system control based on electricity price fluctuations, allowing for efficient energy storage during off-peak hours to reduce electricity costs. It also provides remote assistance functions, facilitating remote parameter configuration and fault diagnosis for administrators.

[0037] The terminal module receives data from the intelligent sensing module, data transmission module, big data processing module, and application service module, integrates and processes this data, and provides managers with a comprehensive and intuitive interface displaying the operating status of the power system, facilitating unified management and decision-making.

[0038] This invention also discloses a smart electricity management method based on big data, which employs the aforementioned management system and includes the following steps:

[0039] Step 1: The intelligent sensing module collects electrical parameters and environmental data in real time through a multi-parameter sensor array, smart meter cluster, and environmental monitoring unit, and transmits them to the data transmission module via wireless networking;

[0040] Step 2: The data transmission module uses the edge computing gateway and the 5G dual-mode communication module to achieve multi-protocol conversion and encrypted transmission; the data cleaning engine filters out abnormal values, and triggers a rejection mechanism when the current surge exceeds 200% of the rated value to ensure data quality;

[0041] Step 3: The big data processing module uses a distributed storage cluster and a real-time computing engine to build an electrical fire prediction model in conjunction with an LSTM neural network. It uses the isolated forest algorithm to detect energy efficiency anomalies, generate fault warnings and energy efficiency optimization suggestions, and provide a basis for subsequent control strategies.

[0042] Step 4: The application service module executes actions based on the three-level response control strategy to achieve device protection;

[0043] Step 5: The digital twin platform synchronizes the status of physical equipment, supports intelligent scheduling of energy storage systems based on electricity price troughs; it enables remote parameter configuration and fault diagnosis through a dynamic rule engine, and provides real-time assistance in conjunction with a 5G communication module, completing the closed loop of equipment lifecycle management.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A smart electricity management system based on big data, characterized in that, include: The intelligent sensing module is equipped with a multi-parameter sensor array, a smart meter cluster, and an environmental monitoring unit, and realizes real-time acquisition of electrical parameters and environmental data through wireless networking. The data transmission module integrates an edge computing gateway and a 5G dual-mode communication module, supporting multi-protocol conversion and encrypted data transmission. The big data processing module constructs a distributed storage cluster and a real-time computing engine, combining LSTM neural networks and isolated forest algorithms to achieve electrical fault prediction and energy efficiency analysis. The application service module deploys a dynamic rule engine and a digital twin platform to support energy storage system control and remote assistance based on electricity price off-peak periods; The terminal module is used to receive data from the intelligent sensing module, data transmission module, big data processing module, and application service module.

2. The smart electricity management system based on big data according to claim 1, characterized in that, The multi-parameter sensor array includes a residual current sensor, a temperature sensor, and a three-phase voltage and current transformer; the smart meter cluster is compatible with DL / T645-2007 and IEC62056 protocols, achieving 0.1S-level energy metering; the environmental monitoring unit includes a humidity sensor and a dust concentration detector.

3. The smart electricity management system based on big data according to claim 1, characterized in that, The computing gateway and 5G dual-mode communication module have a main channel latency of <20ms and a backup channel coverage radius of up to 15km; the data cleaning engine is used to filter outliers, and a rejection mechanism is triggered when the current surge exceeds 200% of the rated value.

4. The smart electricity management system based on big data according to claim 1, characterized in that, The LSTM neural network is used to construct an electrical fire prediction model; the isolated forest algorithm is used for energy efficiency anomaly detection.

5. The smart electricity management system based on big data according to claim 1, characterized in that, The application service module adopts a three-level response control strategy. When the temperature of the application service module is >85℃, it pushes a notification to the mobile terminal module with a response time of <2 seconds.

6. The smart electricity management system based on big data according to claim 1, characterized in that, When the temperature of the application service module is >90℃, reduce the load to 80% of the rated power.

7. The smart electricity management system based on big data according to claim 1, characterized in that, When the temperature of the application service module is greater than 95°C, the circuit breaker is triggered to trip via an IEC61850 GOOSE message.

8. A smart electricity management method based on big data, employing the management system as described in claim 7, characterized in that, Includes the following steps: Step 1: The intelligent sensing module collects electrical parameters and environmental data in real time through a multi-parameter sensor array, smart meter cluster, and environmental monitoring unit, and transmits them to the data transmission module via wireless networking; Step 2: The data transmission module uses the edge computing gateway and the 5G dual-mode communication module to achieve multi-protocol conversion and encrypted transmission; the data cleaning engine filters out abnormal values, and triggers a rejection mechanism when the current surge exceeds 200% of the rated value to ensure data quality; Step 3: The big data processing module uses a distributed storage cluster and a real-time computing engine to build an electrical fire prediction model in conjunction with an LSTM neural network. It uses the isolated forest algorithm to detect energy efficiency anomalies, generate fault warnings and energy efficiency optimization suggestions, and provide a basis for subsequent control strategies. Step 4: The application service module executes actions based on the three-level response control strategy to achieve device protection; Step 5: The digital twin platform synchronizes the status of physical equipment, supports intelligent scheduling of energy storage systems based on electricity price troughs; it enables remote parameter configuration and fault diagnosis through a dynamic rule engine, and provides real-time assistance in conjunction with a 5G communication module, completing the closed loop of equipment lifecycle management.

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