A remote monitoring device for early warning of abnormal energy consumption in smelting

Abstract

This utility model discloses a remote monitoring device for early warning of abnormal energy consumption in smelting, belonging to the field of energy consumption data monitoring technology in industrial electric furnace smelting production processes. It solves the problem of the inability to remotely monitor and warn of abnormal energy consumption in smelting processes manually. The utility model includes a mounting base plate inside a chassis, on which are mounted an intelligent edge computing module, an IoT gateway module, a power supply module, and wiring terminals. The intelligent edge computing module is electrically connected to a power supply unit, a data storage unit, a data acquisition unit, a data processing and calculation unit, and a communication unit. The data acquisition unit has multiple interfaces. The data processing and calculation unit and the communication unit embed data noise interference filtering algorithms, real-time energy consumption index calculation methods for smelting, and machine learning algorithms for judging energy consumption anomalies. The IoT gateway module includes a local area network communication unit and an internet communication unit. This utility model eliminates the need for manual on-site inspections, improving work efficiency and reducing problems such as misjudgments and missed judgments.

Description

Technical Field

[0001] This utility model belongs to the field of energy consumption data monitoring technology in industrial electric furnace smelting production process, specifically involving a remote monitoring smelting energy consumption anomaly early warning device. Background Technology

[0002] Electric arc furnaces are the main equipment in the metallurgical production industry, and are highly energy-intensive. Therefore, the cost of electric arc furnace smelting production is largely linked to energy consumption, making energy management crucial for cost control and energy efficiency. However, current energy consumption monitoring and management in electric arc furnace smelting faces numerous problems. Traditional monitoring methods mostly rely on regular manual inspections, which cannot provide real-time updates on energy consumption changes or promptly detect anomalies. Furthermore, manual monitoring is prone to errors and omissions, making it difficult to detect potential energy consumption issues in a timely manner. Additionally, while some existing monitoring equipment can achieve a certain degree of automation, it lacks remote monitoring and anomaly warning capabilities. Therefore, developing a device capable of remotely monitoring and issuing early warnings for abnormal smelting energy consumption is of significant practical importance. Utility Model Content

[0003] The purpose of this invention is to provide a remote monitoring device for abnormal energy consumption in smelting, so as to solve the problem that it is impossible for humans to remotely monitor and warn of abnormal energy consumption in smelting.

[0004] The technical solution of this utility model is: a remote monitoring device for early warning of abnormal energy consumption in smelting, including a chassis, a mounting base plate inside the chassis, an intelligent edge computing module, an IoT gateway module, a power supply module, and wiring terminals on the mounting base plate; the power supply module is electrically connected to the intelligent edge computing module and the IoT gateway module; the intelligent edge computing module is electrically connected to a power supply unit, a data storage unit, a data acquisition unit, a data processing and calculation unit, and a communication unit; the data acquisition unit has multiple interfaces; the data processing and calculation unit and the communication unit embed data noise interference filtering algorithms, real-time energy consumption index calculation methods for smelting, and machine learning algorithms for judging abnormal energy consumption; the IoT gateway module has a local area network communication unit and an internet communication unit, and the local area network communication unit is electrically connected to the intelligent edge computing module via twisted-pair cable.

[0005] As a further improvement of this utility model, the Internet communication unit is equipped with an Internet of Things (IoT) card.

[0006] As a further improvement of this utility model, a waterproof cable connector is provided on the bottom side wall of the chassis via a threaded connection.

[0007] As a further improvement of this utility model, the side of the chassis is hinged with a door, and there are locking screws at the four corners of the door. The locking screws are connected to the chassis by screws, and there are sealing strips on the inner side of the door.

[0008] As a further improvement of this utility model, the intelligent edge computing module, IoT gateway module, power supply module, and wiring terminals are fixedly connected to the mounting base plate by screws.

[0009] The beneficial effects of this utility model are as follows:

[0010] Through intelligent edge computing modules and IoT gateway modules, real-time energy consumption data and energy consumption early warning information of electric furnace smelting can be obtained anytime and anywhere, eliminating the need for on-site manual inspections and improving work efficiency.

[0011] The machine learning algorithm for energy consumption anomaly detection can accurately and timely obtain energy consumption data and anomaly information in the electric arc furnace smelting process, reducing problems such as misjudgment and missed judgment.

[0012] Energy consumption data and anomaly warning information can be deployed locally in a private environment or on an IoT cloud platform. Access is supported via mobile app, web browser, cloud platform server, or mini-program. Warning notifications offer multiple notification methods, including on-site audible and visual alarms, SMS alarms, email alarms, voice alarms, and WeChat message alarms. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the structure of this utility model;

[0014] Figure 2 This is a schematic diagram of the intelligent edge computing module structure in this utility model;

[0015] Figure 3 This is a schematic diagram of the IoT gateway module structure in this utility model;

[0016] Figure 4 This is a schematic diagram of the middle box door structure of this utility model.

[0017] In the diagram: 1-Chassis; 2-Mounting base plate; 3-Intelligent edge computing module; 31-Power supply unit; 32-Data processing and computing unit; 33-Data storage unit; 34-Communication unit; 35-Data acquisition unit; 4-IoT gateway module; 41-LAN communication unit; 42-Internet communication unit; 43-IoT card; 5-Power supply module; 6-Terminal block; 7-Chassis door; 71-Locking screw; 72-Sealing strip; 8-Waterproof cable connector. Detailed Implementation

[0018] The present invention will be further described below with reference to specific embodiments and accompanying drawings. The accompanying drawings are for illustrative purposes only, representing schematic diagrams only, not actual physical objects, and should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0019] In the accompanying drawings of this utility model, the same or similar reference numerals correspond to the same or similar components. In the description of this utility model, it should be understood that if terms such as "upper," "lower," "left," and "right" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting this application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0020] like Figures 1-4 As shown, a remote monitoring device for abnormal energy consumption in smelting includes a chassis 1, a mounting base 2 inside the chassis 1, an intelligent edge computing module 3, an IoT gateway module 4, a power supply module 5, and a terminal block 6 on the mounting base 2. The power supply module 5 is electrically connected to the intelligent edge computing module 3 and the IoT gateway module 4, and is used to provide a stable power supply to ensure the normal operation of the intelligent edge computing module 3 and the IoT gateway module 4.

[0021] The intelligent edge computing module 3 is electrically connected to a power supply unit 31, a data storage unit 33, a data acquisition unit 35, a data processing and computing unit 32, and a communication unit 34. The data acquisition unit 35 has multiple interfaces, including DB9, RJ45, and terminal blocks, which can be used to connect to multiple field intelligent measuring instruments. The data acquisition unit 35 is used to acquire real-time data from these instruments. The power supply unit 31 is used for voltage conversion at various levels.

[0022] The data processing and calculation unit 32 and the communication unit 34 embed a data noise interference filtering algorithm, a real-time energy consumption index calculation method for smelting, and a machine learning algorithm for energy consumption anomaly judgment. The data noise interference filtering algorithm is used to analyze and calculate the real-time data collected by the acquisition unit to improve the accuracy and reliability of the data. The real-time energy consumption index calculation method for smelting is used to calculate the real-time energy consumption, power, and power factor from the collected current and voltage data. The machine learning algorithm for energy consumption anomaly judgment is used to learn and analyze historical data, establish an energy consumption anomaly judgment standard, compare the energy consumption anomaly judgment standard with the real-time data, and if the real-time data exceeds the energy consumption anomaly judgment standard, it is judged as an energy consumption anomaly, and an anomaly warning information is generated.

[0023] The data storage unit 33 stores real-time data collected by the data acquisition unit 35 and energy consumption data and anomaly warning information analyzed and calculated by the data processing and calculation unit 32. The energy consumption data and anomaly warning information can be deployed locally in a private location or on an IoT cloud platform.

[0024] The IoT gateway module 4 includes a local area network (LAN) communication unit 41 and an internet communication unit 42. The LAN communication unit 41 is connected to the intelligent edge computing module 3 via a twisted-pair electrical signal. This is used to transmit energy consumption data and abnormal warning information processed by the interactive data processing and computing unit.

[0025] The Internet communication unit 42 is equipped with an Internet of Things (IoT) card. The IoT card 43 is used to connect to the Internet, and the Internet is used to push the energy consumption data and abnormal warning information calculated by the data processing and computing unit 32 to the user terminal and generate warning notifications.

[0026] The user end can be a mobile app, a web application, a cloud platform server, or a mini-program. Warning notifications include on-site audible and visual alarms, SMS alarms, email alarms, voice alarms, and WeChat message alarms.

[0027] The bottom side wall of the chassis 1 is provided with a waterproof cable connector 8 via a threaded connection. The waterproof cable connector 8 is used to fix the cable and provide waterproof and dustproof functions.

[0028] The chassis 1 is hinged to a door 7 on its side. Locking screws 71 are located at the four corners of the door 7, and are connected to the chassis 1 by screws. Sealing strips 72 are provided on the inner sides of the door 7. The sealing strips 72 serve to prevent dust and water ingress.

[0029] The intelligent edge computing module 3, the IoT gateway module 4, the power supply module 5, and the wiring terminal 6 are fixedly connected to the mounting base plate 2 with screws.

[0030] When using this device, first install it on-site in a location that is far from high-temperature and strong magnetic fields and easy to install. Then connect the current sensors, voltage sensors, smart meters, temperature sensors and transmitters, flow sensors and transmitters, pressure sensors and transmitters, and harmful gas detectors from the energy-consuming equipment related to electric furnace smelting to this device.

[0031] Next, network and system configurations were performed to ensure that the device was correctly connected to the data of each sensor and instrument, and that the network configuration was normal.

[0032] Finally, the system is started to acquire real-time data and monitor energy consumption. The data acquisition unit 35 collects real-time data from the site, organizes and packages it, and sends it to the data processing and calculation unit 32. The data noise interference filtering algorithm filters the real-time data to remove interference data and improve the accuracy and reliability of the data. The smelting real-time energy consumption index algorithm calculates real-time energy consumption, power, and power factor based on the acquired current and voltage data, and stores the processed and calculated data.

[0033] Meanwhile, the machine learning algorithm for judging energy consumption anomalies learns and analyzes historical data to establish energy consumption anomaly judgment standards. The energy consumption anomaly judgment standards are compared with real-time data to determine whether the real-time data exceeds the energy consumption anomaly judgment standards. If an anomaly is found, an anomaly warning message is generated immediately.

[0034] When an abnormal warning is issued, the on-site audible and visual alarm will sound and flash, prompting on-site staff to address the energy consumption anomaly promptly. Simultaneously, SMS, email, voice, and WeChat message alerts will push the energy consumption anomaly warning information to relevant personnel.

[0035] The specific embodiments of this utility model described above do not constitute a limitation on the scope of protection of this utility model. Any other corresponding changes and modifications made based on the technical concept of this utility model should be included in the technical description of this utility model.

Claims

1. A remote monitoring smelting energy consumption anomaly early warning device, characterized in that: The system includes a chassis (1), which has a mounting base plate (2) inside. The mounting base plate (2) has an intelligent edge computing module (3), an IoT gateway module (4), a power supply module (5), and a wiring terminal (6). The power supply module (5) is electrically connected to the intelligent edge computing module (3) and the IoT gateway module (4). The intelligent edge computing module (3) is electrically connected to a power supply unit (31), a data storage unit (33), a data acquisition unit (35), a data processing and computing unit (32), and a communication unit (34). The data acquisition unit (35) has multiple interfaces. The data processing and computing unit (32) and the communication unit (34) are embedded with a data noise interference filtering algorithm, a real-time energy consumption index calculation method for smelting, and a machine learning algorithm for judging energy consumption anomalies. The Internet of Things gateway module (4) is equipped with a local area network communication unit (41) and an Internet communication unit (42). The local area network communication unit (41) is connected to the intelligent edge computing module (3) via twisted-pair electrical signals.

2. The remote monitoring smelting energy consumption anomaly early warning device according to claim 1, characterized in that: The Internet communication unit (42) is equipped with an Internet of Things card (43).

3. The remote monitoring smelting energy consumption anomaly early warning device according to claim 1 or 2, characterized in that: The bottom side wall of the chassis (1) is provided with a waterproof cable connector (8) by threaded connection.

4. The remote monitoring smelting energy consumption anomaly early warning device according to claim 3, characterized in that: The chassis (1) is hinged to the side and has a door (7). The door (7) has locking screws (71) at the four corners. The locking screws (71) are connected to the chassis (1) by screws. The door (7) has a sealing strip (72) on the inner side.

5. The remote monitoring smelting energy consumption anomaly early warning device according to claim 4, characterized in that: The intelligent edge computing module (3), IoT gateway module (4), power supply module (5), and wiring terminal (6) are fixedly connected to the mounting base plate (2) by screws.

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