Intelligent safety socket based on machine learning

Abstract

The utility model relates to the technical field of electrical safety, in particular to an intelligent safety socket based on machine learning, which comprises a socket unit, a power supply line, a control module and a data acquisition unit, a relay unit is electrically connected between the power supply line and the socket unit, and the data acquisition unit is arranged between the relay unit and the socket unit. The relay unit and the data acquisition unit are electrically connected with the control module. The relay unit is arranged to realize automatic control of connection and disconnection between the socket unit and a power supply line, the action is rapid and reliable, and further expansion of accidents is avoided; the power utilization change condition of the corresponding socket is acquired through the arranged data acquisition unit, so that the power utilization condition of the corresponding socket is more transparent, the reliable measurement of the power utilization safety is realized by acquiring the real-time change of the current, and the power utilization data support is provided for the control module.

Description

Technical Field

[0001] The utility model relates to the technical field of electricity safety, and in particular to an intelligent safety socket based on machine learning. Background Art

[0002] With the rapid development of modern technology, various household appliances are increasing day by day, and electricity has become an indispensable part of our daily life. The safety of household electricity use is related to the happiness and peace of every family. It is not only a technical issue, but also a responsibility for the safety of family members. However, incorrect electricity use habits, aging wires, unqualified electrical equipment, etc. may become hidden dangers to household electricity safety. Therefore, household electricity safety is of vital importance.

[0003] Electricity safety is an important research direction in the field of smart home. One of the important directions in the field of smart home is to analyze the types of household appliances used by users based on household electricity consumption data. Currently, there is little research content in this direction. The power feature analysis method in electricity monitoring mainly uses the transient characteristics and steady-state characteristics of power to identify the electricity consumption data of household appliances. Common transient characteristics include: transient power waveform characteristics, starting current waveform characteristics, voltage noise characteristics, etc.; common steady-state characteristics include: power step characteristics, steady-state current waveform characteristics, VI trajectory characteristics, high-order harmonic characteristics, etc.

[0004] As a common power access device for various household appliances, sockets are used and touched by users very frequently. The safety and reliability of sockets are very important. The functions of existing sockets are relatively simple and their safety needs to be further improved. They are gradually unable to meet the development needs of smart homes. Utility Model Content

[0005] The technical problem to be solved by the utility model is to provide an intelligent safety socket based on machine learning in response to the above-mentioned technical deficiencies. The relay unit provided can realize automatic control of the on and off between the socket unit and the power supply line. The operation is fast and reliable, avoiding further expansion of accidents. Automatic control is realized in conjunction with the control module, solving the problem that the existing sockets have low safety and cannot realize intelligent safety control.

[0006] In order to solve the above technical problems, the technical solution adopted by the utility model is: an intelligent safety socket based on machine learning, including a socket unit and a power supply line, and also including a control module and a data acquisition unit. A relay unit is electrically connected between the power supply line and the socket unit, and the data acquisition unit is arranged between the relay unit and the socket unit. The relay unit and the data acquisition unit are electrically connected to the control module respectively.

[0007] To further optimize the technical solution, the control module includes a microcontroller, a time module and a data entry module that are communicatively connected to each other;

[0008] The data acquisition unit is used to collect the current change of the corresponding socket and provide the collected information to the control module;

[0009] The data entry module is used to enter parameter information of the device connected to the socket unit into the microcontroller;

[0010] The time module is used to provide year, month, day and clock information to the microcontroller.

[0011] To further optimize the technical solution, the control module includes a wireless communication module that is communicatively connected to the microcontroller, and the wireless communication module is communicatively connected to the data cloud server. The communication connection between the smart safety socket and the data cloud server is realized through the wireless communication module, thereby realizing the upload and download of machine learning data.

[0012] To further optimize the technical solution, the microcontroller is communicatively connected to a positioning module, and the positioning module is used to provide geographic location information for use by the smart safety socket.

[0013] Compared with the prior art, the utility model has the following advantages: 1. The relay unit can realize the automatic control of the on and off between the socket unit and the power supply line, and the action is fast and reliable, avoiding the further expansion of the accident; 2. The data acquisition unit is set to collect the power consumption changes of the corresponding socket, so that the power consumption of the corresponding socket is more transparent, and the reliable measurement of whether the power consumption is safe or not is realized by collecting the real-time changes of the current, and the power consumption data support is provided to the control module; 3. The control module monitors the power consumption of the electrical equipment through the data acquisition unit and records and stores the operation data to complete the learning of the corresponding operation data; 4. By using the big data collected by the data cloud server to collect and analyze the massive power consumption data, the power consumption behavior and rules of the electrical appliances can be better understood, and the data basis for training and optimization of the smart socket can be provided, and the power safety level can be further improved. It can predict and identify potential safety risks, such as circuit overload, short circuit, etc., so as to provide early warning and intervention. 5. Through the time module and positioning module set, the time information and location information can be bound to the operation data of the socket, and the law of equipment operation can be grasped more accurately, and the adaptability and learning ability of the socket in use can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a smart safety socket based on machine learning.

[0015] Reference numerals:

[0016] 1. Socket unit; 2. Power supply line; 3. Relay unit; 4. Data acquisition unit; 5. Control module; 51. Time module; 52. Data entry module; 53. Wireless communication module; 54. Positioning module. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the utility model clearer, the utility model is further described in detail below in conjunction with the reference drawings of specific embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the utility model. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the utility model.

[0018] Combination Figure 1 As shown, a smart safety socket based on machine learning includes a power supply line 2, a socket unit 1, a control module 5 and a data acquisition unit 4. A relay unit 3 is electrically connected between the power supply line 2 and the socket unit 1, and the data acquisition unit 4 is arranged between the relay unit 3 and the socket unit 1. The relay unit 3 and the data acquisition unit 4 are electrically connected to the control module 5 respectively. The control module 5 includes a microcontroller, a time module 51, a positioning module 54, a wireless communication module 53 and a data entry module 52 that are mutually communicatively connected, and the wireless communication module 53 is communicatively connected to the microcontroller. The wireless communication module 53 is communicatively connected to the data cloud server.

[0019] When using, combine Figure 1 As shown, the socket unit 1, the relay unit 3 and the data acquisition unit 4 are provided in multiple groups and are electrically connected to the control module 5 respectively. The power supply line 2 supplies power to the socket unit 1 and the control module 5. The data acquisition unit 4 is used to collect the current change of the corresponding socket and provide the collected information to the microcontroller of the control module 5. The data entry module 52 is used to enter the model and other parameter information of the device connected to the socket unit 1 into the microcontroller; the time module 51 is used to provide the year, month, day, season and clock information to the microcontroller. The positioning module 54 is used to locate the geographical location of the use of the intelligent safety socket.

[0020] The microcontroller processes the current data collected by the data acquisition unit 4, and calculates the power change of the corresponding socket unit 1 in combination with the collected real-time voltage. The microcontroller uses the data acquisition unit 4 and the time module 51 to calculate and draw a power change curve over time, and binds the obtained curve with the electrical equipment information entered by the data entry module 52 to form machine learning results data. The wireless communication module 53 binds the learning results data to the positioning information of the positioning module 54 and uploads it to the data cloud server. Multiple smart sockets share multiple groups of machine learning results data through the wireless communication module 53.

[0021] During normal operation, the microcontroller compares the data acquisition unit 4 with the existing learning data. When abnormal power consumption occurs, the microcontroller can quickly control the relay unit 3 to cut off the power supply to the socket unit 1 to avoid accidents. The smart socket can monitor and control the power consumption of electrical appliances in real time. Combined with the machine learning algorithm, it can realize intelligent analysis and control of power consumption behavior. The working state and power safety of electrical appliances are judged by the recognition and analysis of waveform signals such as current and voltage through waveform recognition technology. It can accurately identify whether the electrical appliances are working properly and whether there are potential power hazards. By using the big data collected by the data cloud server to collect and analyze massive power consumption data, we can better understand the power consumption behavior and rules of electrical appliances, provide a data basis for training and optimization of machine learning algorithms, and further improve the level of power safety. It can predict and identify potential safety risks, such as circuit overload and short circuit, so as to provide early warning and intervention.

[0022] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation on the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the claims attached to the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the attached claims, or within the equivalent forms of such scope and boundaries.

Claims

1. A smart safety socket based on machine learning, comprising a socket unit (1) and a power supply line (2), characterized in that: It also comprises a control module (5) and a data acquisition unit (4); a relay unit (3) is electrically connected between the power supply line (2) and the socket unit (1); the data acquisition unit (4) is arranged between the relay unit (3) and the socket unit (1); and the relay unit (3) and the data acquisition unit (4) are electrically connected to the control module (5) respectively.

2. The intelligent safety socket based on machine learning according to claim 1, characterized in that: The control module (5) comprises a microcontroller, a time module (51) and a data entry module (52) which are communicatively connected to each other; The data collection unit (4) is used to collect current changes of the corresponding socket and provide the collected information to the control module (5); The data entry module (52) is used to enter parameter information of a device connected to the socket unit (1) into the microcontroller; The time module (51) is used to provide year, month, day and clock information to the microcontroller.

3. The intelligent safety socket based on machine learning according to claim 2, characterized in that: The control module (5) comprises a wireless communication module (53) which is communicatively connected to the microcontroller, and the wireless communication module (53) is communicatively connected to the data cloud server. The communication connection between the intelligent safety socket and the data cloud server is realized through the wireless communication module (53), thereby realizing the uploading and downloading of machine learning data.

4. The intelligent safety socket based on machine learning according to claim 2, characterized in that: The microcontroller is communicatively connected to a positioning module (54), and the positioning module (54) is used to provide geographical location information used by the intelligent safety socket.

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