An intelligent power strip and control method thereof

Abstract

The application discloses an intelligent patch board and a control method thereof, relates to the technical field of patch board control, and realizes accurate state analysis and early warning of branch loads, and can help users to accurately monitor loads and timely report early warning control.

Description

Technical Field

[0001] This invention relates to the field of power strip control technology, specifically an intelligent power strip and its control method. Background Technology

[0002] Smart power strip control technology is one of the core underlying control technologies of smart homes. It relies on the Internet of Things, embedded control and power detection technologies to gradually upgrade from traditional mechanical timed power strips to smart terminals that integrate power supply, control and monitoring. Currently, most power strips used in home settings are traditional mechanical power strips, whose function is to expand the use and transmission of household power through a connection structure. However, with the popularization of smart home technology, traditional mechanical power strips can no longer meet the smart needs of today's users. They lack real-time status monitoring and data interaction for load devices. When load devices malfunction, it often leads to circuit interruption or damage to the load devices, and cannot help users accurately monitor and control smart home devices in a timely manner. Summary of the Invention

[0003] The purpose of this invention is to provide a smart power strip and its control method to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A control method comprising the following steps: Real-time determination of the power strip branch status and synchronous acquisition of energy consumption data of the load devices on each branch; Based on the energy consumption data of the load devices on each branch, the monitoring period is divided and the load status matrix at each time point within the period is constructed; and the status of the load status matrix at each time point within the period is evaluated to obtain the load status index at each time point of the period. Based on the load state index at each branch cycle time, the periodic load state development curve is fitted, and an offset comparison model is constructed to perform offset comparison analysis on the periodic load state index at continuous time points on the curve to obtain the periodic load state offset index. By combining the periodic load state offset index of the branch load, the offset risk assessment of the real-time load state matrix within the period is carried out to determine the degree of real-time load offset risk. Anomaly judgment analysis is performed on the analysis data to determine the abnormal load state and output the prompt. Furthermore, by deploying a sensor network on the power strip, the operating status of the power strip circuits can be detected in real time; and based on the operating status judgment data of each power strip circuit, real-time energy consumption data of the load devices on the circuits can be extracted. It should be noted that the operating states of a power strip branch include open circuit and closed circuit; an open circuit means that no load device is connected to the power strip branch and no operating current flows through the branch; a closed circuit means that a load device is connected to the power strip branch and operating current flows through the branch; operating current refers to the current flowing through the power strip branch when a load device is operating. When the power strip's branch circuit operation status is determined to be closed, real-time energy consumption data of the load devices on the branch circuit is extracted; the energy consumption data of the load devices includes, but is not limited to, power data, current data, voltage data, power factor, etc. Furthermore, based on the energy consumption data of the load devices extracted from each branch of the power strip, a monitoring period T is created, and the energy consumption data of the load devices at each moment within the monitoring period is determined; and a load status matrix is ​​constructed based on the energy consumption data of the load devices on each branch of the power strip at each moment within the monitoring period. By performing a status assessment analysis on the load status matrix of the load devices on each branch of the power strip at each time point within the monitoring period, the periodic time load status index PMS(t) of the corresponding load devices is determined; the analysis is as follows: ; Wherein, PMS(c,t) is the load status index of the load device in the power strip branch c at time t within the monitoring period; c is the number of the power strip branch; t is the time point within the monitoring period; n is the data type number of the load device; w c,n In the power strip branch c, the weighting of energy consumption data for the load device type number n; e c,n For the power strip branch c, the value of the energy consumption data corresponding to the type number n of the load device; e bt For the power strip branch c, the rated value of the energy consumption data corresponding to the type number n of the load device; Furthermore, by coordinating the periodic load state indices of the load devices in each branch of the power strip, a time-series curve model is constructed for curve fitting to obtain the periodic load state development curves of the load devices in the corresponding branch. Based on the cyclic load state development curve of the load equipment, a load offset comparison model is constructed, and offset comparison analysis is performed on the load state index at consecutive time points on the curve. The analysis steps are as follows: S1. Locate the curve points of the periodic load state index at each time point on the periodic load state development curve of the load equipment. S2. Based on the load state index curve points at each cycle time, and combined with the coordinate origin and the horizontal axis projection points of the load state index curve points at each cycle time, construct the load state distribution area. It should be noted that the horizontal axis projection point of the load state index curve point at each cycle time is the horizontal axis point corresponding to the vertical projection of the horizontal axis projection point of the load state index curve point at each cycle time on the horizontal axis. S3. Analyze the load state distribution shift changes between adjacent load state distribution areas on the cyclic load state development curve. This is done by determining the projection difference region between adjacent load state distribution areas and analyzing the load state distribution shift degree SDS(t,t+1) based on the area of ​​the load state distribution region. The analysis is as follows: ; Wherein, SDS(t,t+1) is the degree of load state distribution offset of the load state distribution region at adjacent times t and t+1 on the periodic load state development curve; S(t,t+1) is the area of ​​the projected difference region between the load state distribution regions at adjacent times t and t+1 on the periodic load state development curve; S(t) and S(t+1) are the areas of the load state distribution region at time t and time t+1 on the periodic load state development curve, respectively. Furthermore, based on the comparative analysis data of the load state index offset at consecutive time points within the monitoring period, the comprehensive offset degree of the load state index between adjacent time points within the period of each power strip branch is analyzed to obtain the periodic load state offset index SDD(c); its analysis is as follows: ; Wherein, SDD(c) is the periodic load state offset index of the load device in the power strip branch c; m(T) is the number of time points within the monitoring period T; SDS(t,t+1) is the degree of load state distribution offset of the load state distribution area at adjacent times t and t+1 on the periodic load state development curve; PMS(c,t) and PMS(c,t+1) are the load state indices of the load device in the power strip branch c at time t and time t+1 within the monitoring period, respectively. Furthermore, based on the periodic load state offset index analysis data of each branch load of the power strip, the offset risk assessment is performed on the real-time state matrix of each branch load in the current period, and the real-time load state offset risk level RSD(c) of the current power strip branch load device is output; its calculation is as follows: ; Where RSD(c) is the real-time load state offset risk level of the load device in power strip branch c; F(c) is the real-time state matrix of the load device in power strip branch c; f(c) is the rated state matrix of the load device in power strip branch c; E is the covariance matrix of the rated state matrix of the load device in power strip branch c; SDD(c) is the periodic load state offset index of the load device in power strip branch c; exp(·) is the natural exponential function; Based on the risk assessment results of the real-time load status deviation of the load devices on each branch of the power strip, the risk comparison constant RSD(g) is introduced to conduct anomaly comparison analysis. If RSD(c)≥RSD(g), then it is determined that there is an abnormal risk in the load device on the current power strip branch c, and the risk is marked. If RSD(c) < RSD(g), then the load device on the current power strip branch c is considered to be normal. Locate each branch circuit of the power strip of the load device with abnormal risk markers, and generate an abnormal operation data report of the load device to provide early warning prompts; In this embodiment, when issuing an early warning for a power strip circuit containing an abnormally operating load device, the abnormal operation data report of the load device can be sent to the user's mobile terminal via data communication; or a display screen device can be installed on the power strip to issue the warning. The abnormal operation data of the load equipment includes the power strip circuit number, the real-time status matrix data of the load equipment, and the abnormal risk marker data. A smart power strip, including a processor and memory; The processor implements a smart power strip control method when executing a computer program stored in the memory.

[0005] Compared with the prior art, the beneficial effects of the present invention are: Compared to traditional power strips that only provide distance extension and mechanical interruption of electrical power, this invention highlights a more intelligent load monitoring and early warning function. It obtains the operating energy consumption data of the load devices by determining the branch status of the power strip; it analyzes the load status of the load devices by constructing a matrix; it performs offset comparison analysis of the load device status within a period by constructing a load offset comparison model; and it conducts anomaly risk assessment by combining real-time device status matrix data. This invention improves upon the shortcomings of traditional power strip branch monitoring, enabling accurate status analysis and early warning of branch loads, and helping users to perform precise load monitoring and timely reporting and early warning control. Attached Figure Description

[0006] Figure 1 This is a flowchart illustrating a smart power strip control method according to the present invention. Detailed Implementation

[0007] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0008] Example 1: As Figure 1 As shown, the present invention provides a technical solution: A control method comprising the following steps: Real-time determination of the power strip branch status and synchronous acquisition of energy consumption data of the load devices on each branch; Based on the energy consumption data of the load devices on each branch, the monitoring period is divided and the load status matrix at each time point within the period is constructed; and the status of the load status matrix at each time point within the period is evaluated to obtain the load status index at each time point of the period. Based on the load state index at each branch cycle time, the periodic load state development curve is fitted, and an offset comparison model is constructed to perform offset comparison analysis on the periodic load state index at continuous time points on the curve to obtain the periodic load state offset index. By combining the periodic load state offset index of the branch load, the offset risk assessment of the real-time load state matrix within the period is carried out to determine the degree of real-time load offset risk. Anomaly judgment analysis is performed on the analysis data to determine the abnormal load state and output the prompt. Furthermore, by deploying a sensor network on the power strip, the operating status of the power strip circuits can be detected in real time; and based on the operating status judgment data of each power strip circuit, real-time energy consumption data of the load devices on the circuits can be extracted. It should be noted that the operating states of a power strip branch include open circuit and closed circuit; an open circuit means that no load device is connected to the power strip branch and no operating current flows through the branch; a closed circuit means that a load device is connected to the power strip branch and operating current flows through the branch; operating current refers to the current flowing through the power strip branch when a load device is operating. In this embodiment, since a normal household power strip includes multiple socket interfaces for connecting load devices, and each socket interface on the power strip is a branch of a single power strip, under normal circumstances, a single socket interface connects to a single load device, which corresponds to a single power strip branch connecting only a single load device at the same time. Therefore, this embodiment does not consider the connection between power strips. When the power strip's branch circuit operation status is determined to be closed, real-time energy consumption data of the load devices on the branch circuit is extracted; the energy consumption data of the load devices includes, but is not limited to, power data, current data, voltage data, power factor, etc. Furthermore, based on the energy consumption data of the load devices extracted from each branch of the power strip, a monitoring period T is created, and the energy consumption data of the load devices at each moment within the monitoring period is determined; and a load status matrix is ​​constructed based on the energy consumption data of the load devices on each branch of the power strip at each moment within the monitoring period. In this embodiment, the load status matrix of the load device at each moment within the monitoring period is an n×1 matrix; where n is the number of energy consumption data types of the load device. By performing a status assessment analysis on the load status matrix of the load devices on each branch of the power strip at each time point within the monitoring period, the periodic time load status index PMS(t) of the corresponding load devices is determined; the analysis is as follows: ; Wherein, PMS(c,t) is the load status index of the load device in the power strip branch c at time t within the monitoring period; c is the number of the power strip branch; t is the time point within the monitoring period; n is the data type number of the load device; w c,n In the power strip branch c, the weighting of energy consumption data for the load device type number n; e c,n For the power strip branch c, the value of the energy consumption data corresponding to the type number n of the load device; e bt For the power strip branch c, the rated value of the energy consumption data corresponding to the type number n of the load device; In this embodiment, it should be noted that the weighting of various types of energy consumption data of load devices in load status assessment is an empirical value, which is determined by manual preset. Furthermore, by coordinating the periodic load state indices of the load devices in each branch of the power strip, a time-series curve model is constructed for curve fitting to obtain the periodic load state development curves of the load devices in the corresponding branch. The time-series curve model is a curve model that fits the load state index of the load equipment at the periodic time on the horizontal axis and the time value on the vertical axis. It uses time as the development clue to determine the development trend of the state index of the load equipment and generates the curve model. Based on the cyclic load state development curve of the load equipment, a load offset comparison model is constructed, and offset comparison analysis is performed on the load state index at consecutive time points on the curve. The analysis steps are as follows: S1. Locate the curve points of the periodic load state index at each time point on the periodic load state development curve of the load equipment. S2. Based on the load state index curve points at each cycle time, and combined with the coordinate origin and the horizontal axis projection points of the load state index curve points at each cycle time, construct the load state distribution area. It should be noted that the horizontal axis projection point of the load state index curve point at each cycle time is the horizontal axis point corresponding to the vertical projection of the horizontal axis projection point of the load state index curve point at each cycle time on the horizontal axis. S3. Analyze the load state distribution shift changes between adjacent load state distribution areas on the cyclic load state development curve. This is done by determining the projection difference region between adjacent load state distribution areas and analyzing the load state distribution shift degree SDS(t,t+1) based on the area of ​​the load state distribution region. The analysis is as follows: ; Wherein, SDS(t,t+1) is the degree of load state distribution offset of the load state distribution region at adjacent times t and t+1 on the periodic load state development curve; S(t,t+1) is the area of ​​the projected difference region between the load state distribution regions at adjacent times t and t+1 on the periodic load state development curve; S(t) and S(t+1) are the areas of the load state distribution region at time t and time t+1 on the periodic load state development curve, respectively. In this embodiment, the projection difference region of adjacent load state distribution areas refers to determining the load state index curve points at adjacent cycle times on the periodic load state development curve, and projecting the load state index curve point of the previous time moment horizontally onto the vertical axis where the load state index curve point of the next time moment is located to obtain the difference projection points of the load state index curve points at adjacent cycle times; and connecting the load state index curve points at adjacent cycle times and the corresponding difference projection points to obtain the projection difference region of adjacent load state distribution areas. Furthermore, based on the comparative analysis data of the load state index offset at consecutive time points within the monitoring period, the comprehensive offset degree of the load state index between adjacent time points within the period of each power strip branch is analyzed to obtain the periodic load state offset index SDD(c); its analysis is as follows: ; Wherein, SDD(c) is the periodic load state offset index of the load device in the power strip branch c; m(T) is the number of time points within the monitoring period T; SDS(t,t+1) is the degree of load state distribution offset of the load state distribution area at adjacent times t and t+1 on the periodic load state development curve; PMS(c,t) and PMS(c,t+1) are the load state indices of the load device in the power strip branch c at time t and time t+1 within the monitoring period, respectively. Furthermore, based on the periodic load state offset index analysis data of each branch load of the power strip, the offset risk assessment is performed on the real-time state matrix of each branch load in the current period, and the real-time load state offset risk level RSD(c) of the current power strip branch load device is output; its calculation is as follows: ; Where RSD(c) is the real-time load state offset risk level of the load device in power strip branch c; F(c) is the real-time state matrix of the load device in power strip branch c; f(c) is the rated state matrix of the load device in power strip branch c; E is the covariance matrix of the rated state matrix of the load device in power strip branch c; SDD(c) is the periodic load state offset index of the load device in power strip branch c; exp(·) is the natural exponential function; Based on the risk assessment results of the real-time load status deviation of the load devices on each branch of the power strip, the risk comparison constant RSD(g) is introduced to conduct anomaly comparison analysis. If RSD(c)≥RSD(g), then it is determined that there is an abnormal risk in the load device on the current power strip branch c, and the risk is marked. If RSD(c) < RSD(g), then the load device on the current power strip branch c is considered to be normal. Locate each branch circuit of the power strip of the load device with abnormal risk markers, and generate an abnormal operation data report of the load device to provide early warning prompts; In this embodiment, when issuing an early warning for a power strip circuit containing an abnormally operating load device, the abnormal operation data report of the load device can be sent to the user's mobile terminal via data communication; or a display screen device can be installed on the power strip to issue the warning. The abnormal operation data of the load device includes the power strip branch number, the real-time status matrix data of the load device, and the abnormal risk marker data.

[0009] Example 2: The present invention provides another technical solution: A smart power strip, including a processor and memory; The processor implements a smart power strip control method when executing a computer program stored in the memory.

[0010] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A control method, characterized in that: Real-time determination of the power strip branch status and synchronous acquisition of energy consumption data of the load devices on each branch; Based on the energy consumption data of the load devices on each branch, the monitoring period is divided and the load status matrix at each time point within the period is constructed; and the status of the load status matrix at each time point within the period is evaluated to obtain the load status index at each time point of the period. Based on the load state index at each branch cycle time, the periodic load state development curve is fitted, and a load offset comparison model is constructed to perform offset comparison analysis on the periodic load state index at continuous time points on the curve to obtain the periodic load state offset index. By combining the periodic load state offset index of the branch load, the offset risk assessment of the real-time load state matrix within the period is performed to determine the degree of real-time load offset risk. Anomaly judgment analysis is performed on the analyzed data to identify abnormal load states and output prompts.

2. The control method according to claim 1, characterized in that: Based on the energy consumption data of the load devices extracted from each branch of the power strip, a monitoring period T is created, and the energy consumption data of the load devices at each moment within the monitoring period is determined; and a load status matrix is ​​constructed based on the energy consumption data of the load devices on each branch of the power strip at each moment within the monitoring period. By performing a status assessment analysis on the load status matrix of the load devices on each branch of the power strip at each time point within the monitoring period, the periodic time load status index PMS(t) of the corresponding load devices is determined; the analysis is as follows: ； Wherein, PMS(c,t) is the load status index of the load device in the power strip branch c at time t within the monitoring period; c is the number of the power strip branch; t is the time point within the monitoring period; n is the data type number of the load device; w c,n In the power strip branch c, the weighting of energy consumption data for the load device type number n; e c,n For the power strip branch c, the value of the energy consumption data corresponding to the type number n of the load device; e bt For the power strip branch c, the rated value of the energy consumption data for the load device corresponding to the type number n.

3. The control method according to claim 1, characterized in that: By coordinating the periodic load state index of the load devices in each branch of the power strip, constructing a time-series curve model and performing curve fitting, the periodic load state development curve of the load devices in the corresponding branch is obtained. Based on the periodic load state development curve of the load device, a load offset comparison model is constructed to perform offset comparison analysis on the load state index at continuous time points on the curve.

4. The control method according to claim 1, characterized in that: The steps for constructing and analyzing the load offset comparison model are as follows: S1. Locate the curve points of the periodic load state index at each time point on the periodic load state development curve of the load equipment. S2. Based on the load state index curve points at each cycle time, and combined with the coordinate origin and the horizontal axis projection points of the load state index curve points at each cycle time, construct the load state distribution area. S3. Analyze the load state distribution shift changes between adjacent load state distribution areas on the cyclic load state development curve. This is done by determining the projection difference region between adjacent load state distribution areas and analyzing the load state distribution shift degree SDS(t,t+1) based on the area of ​​the load state distribution region. The analysis is as follows: ； Wherein, SDS(t,t+1) is the degree of load state distribution offset of the load state distribution region at adjacent times t and t+1 on the periodic load state development curve; S(t,t+1) is the area of ​​the projected difference region between the load state distribution regions at adjacent times t and t+1 on the periodic load state development curve; S(t) and S(t+1) are the areas of the load state distribution region at time t and time t+1 on the periodic load state development curve, respectively.

5. The control method according to claim 1, characterized in that: Based on the comparative analysis of the load state index offset at consecutive time points within the monitoring period, the comprehensive offset of the load state index between adjacent time points within the period of each power strip branch is analyzed to obtain the periodic load state offset index SDD(c); the analysis is as follows: ； Wherein, SDD(c) is the periodic load state offset index of the load device in the power strip branch c; m(T) is the number of time points within the monitoring period T; SDS(t,t+1) is the degree of load state distribution offset of the load state distribution area at adjacent times t and t+1 on the periodic load state development curve; PMS(c,t) and PMS(c,t+1) are the load state indices of the load device in the power strip branch c at time t and time t+1 within the monitoring period, respectively.

6. The control method according to claim 1, characterized in that: Based on the periodic load state offset index analysis data of each branch load of the power strip, the offset risk assessment is performed on the real-time state matrix of each branch load in the current period, and the real-time load state offset risk level RSD(c) of the current power strip branch load device is output; its calculation is as follows: ； Where RSD(c) is the real-time load state offset risk level of the load device in power strip branch c; F(c) is the real-time state matrix of the load device in power strip branch c; f(c) is the rated state matrix of the load device in power strip branch c; E is the covariance matrix of the rated state matrix of the load device in power strip branch c; SDD(c) is the periodic load state offset index of the load device in power strip branch c; and exp(·) is the natural exponential function.

7. The control method according to claim 1, characterized in that: Based on the risk assessment results of the real-time load status deviation of the load devices on each branch of the power strip, the risk comparison constant RSD(g) is introduced to conduct anomaly comparison analysis. If RSD(c)≥RSD(g), then it is determined that there is an abnormal risk in the load device on the current power strip branch c, and the risk is marked. If RSD(c) < RSD(g), then the load device on the current power strip branch c is considered to be normal. Locate each branch circuit of the power strip of the load device with abnormal risk markers, and generate an abnormal operation data report of the load device to provide early warning prompts.

8. The control method according to claim 1, characterized in that: By deploying a sensor network on the power strip, the operating status of the power strip circuits can be detected in real time; and based on the operating status judgment data of each power strip circuit, real-time energy consumption data of the load devices on the circuits can be extracted.

9. A smart power strip, characterized in that: Including processor and memory; When the processor executes the computer program stored in the memory, it implements a control method as described in any one of claims 1-8.

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