Household ammeter early warning method using state analysis

Abstract

The invention relates to a household electricity meter early warning method utilizing state analysis. The method comprises the steps that an intelligent efficiency evaluation model is adopted at a household electricity meter to intelligently evaluate the compressor efficiency of a current refrigerator in a current time segment; a built-in wireless communication interface is adopted at the position of a household electricity meter, and when the received compressor efficiency is too low, compressor low-efficiency early warning information is wirelessly transmitted to an after-sales management server of a refrigerator manufacturer. The household ammeter early warning method is simple in structure, convenient and practical. According to the change condition of each frame of visual picture of the inner wall of the current refrigerator in the previous time segment and the fluctuation condition of the electricity consumption in the previous time segment, the state analysis can be carried out on whether the compressor efficiency of the current time segment of the current refrigerator is reduced and exceeds the expectation or not at the household ammeter; and the low-efficiency early warning operation of the compressor is executed when the expectation is exceeded, so that the existing software and hardware resources of the household electric meter are fully utilized while the performance of the refrigerator is ensured.

Description

Technical Field

[0001] This invention relates to the field of household electricity meters, and more particularly to a method for early warning of household electricity meters using state analysis. Background Technology

[0002] Every household has at least one electricity meter, also known as a home electricity meter. Home electricity meters come in several types, including mechanical meters, IC card meters, and smart meters, each with its own reading method. For example, a smart meter has only an LCD screen; if it's single-phase, it directly displays the total electricity consumption and remaining electricity, which can be read directly. An IC card meter, on the other hand, only has an LED display (single-display meter), and there will be a small red dot on the meter. The red dot indicates the total consumption when it lands on the "total consumption" section and the remaining electricity when it lands on the "remaining" section. In China, the voltage of residential power lines is 220V and the frequency is 50Hz. The rated voltage and applicable frequency of the selected electricity meter should be consistent with this line voltage and frequency, which should also be 220V and 50Hz.

[0003] Many people may only know that a household electricity meter is an instrument used to measure electricity consumption and help each family pay their electricity bill on time. In fact, due to the special installation location of the household electricity meter, it can collect a large amount of data related to household electricity consumption. If this data is used to manage household appliances, it will save a lot of hardware and software resources for the regulators of household appliances. For example, this data can be used to evaluate the performance of commonly used household appliances such as refrigerators. Obviously, there is a lack of reliable technical solutions in this sub-field in the current technology. Summary of the Invention

[0004] To overcome the technical problems in the prior art, this invention proposes a household electricity meter early warning method using state analysis. This method analyzes the compressor efficiency of the refrigerator in the current time segment (which is considered a future time segment) based on changes in the visual images of the refrigerator's inner wall over past time segments and fluctuations in the refrigerator's electricity consumption over past time segments. If the state analysis shows an unexpected decrease, a compressor inefficiency early warning operation is executed, allowing after-sales departments or users to intervene in advance and provide protection against sudden refrigerator malfunctions later.

[0005] According to the present invention, a method for early warning of household electricity meters using state analysis is provided, the method comprising: The system captures the internal wall imaging data of each frame of the refrigerator's interior from the automatic defrosting camera at the household electricity meter, taken at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior consists of the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame. The duration of each time segment is the same. Extract the electricity consumption of the refrigerator at the household electricity meter for each sub-time segment within the most recent time segment before the current time. Each sub-time segment within the most recent time segment before the current time is obtained by dividing the most recent time segment before the current time into evenly spaced time segments. At the household electricity meter, an intelligent efficiency evaluation model is used to intelligently evaluate the compressor efficiency of the refrigerator in the current time segment starting from the current time, based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the refrigerator corresponding to each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time. When the compressor efficiency of the refrigerator is lower than the corresponding lower limit threshold of the compressor efficiency in the current time segment starting from the current time, the built-in wireless communication interface at the household electricity meter will wirelessly transmit the refrigerator's product code, the current time segment, and the compressor inefficiency warning information into a network data packet and then transmit it to the refrigerator manufacturer's after-sales management server at a remote location. The intelligent efficiency evaluation model is a convolutional neural network that has undergone multiple learning actions, and the number of learning actions performed by the convolutional neural network is monotonically positively correlated with the numerical difference between the lower limit threshold of the compressor efficiency corresponding to the current refrigerator and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator. Among them, the current configuration information of the refrigerator includes the current service life of the refrigerator, the internal volume of the cabinet, the rated voltage and the applicable frequency.

[0006] Therefore, it can be seen that the present invention has at least the following four important inventive points: The first invention point: Based on the changes in the visual images of the inner wall of the refrigerator in each frame of the past time segment and the fluctuations in the power consumption of the refrigerator in the past time segment, the system analyzes whether the compressor efficiency of the refrigerator in the current time segment, which is a future time segment, has decreased beyond expectations. When the state analysis exceeds expectations, the system executes a compressor inefficiency warning operation, thereby providing early intervention for after-sales departments or users to avoid sudden failures of the refrigerator in the future. The second invention point is: an intelligent efficiency evaluation model specifically designed for the current refrigerator is introduced to perform state analysis on whether the compressor efficiency in the current time segment has decreased beyond expectations. Specifically, the intelligent efficiency evaluation model is a convolutional neural network after multiple learning actions, and the number of learning actions of the convolutional neural network is monotonically positively correlated with the numerical difference between the lower limit threshold and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator. The third invention point: Introducing multiple basic data to participate in the state analysis of whether the compressor efficiency drops beyond expectations in the current time segment. Specifically, the multiple basic data include the configuration information of the refrigerator, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at a uniform interval in the most recent time segment before the current time, the power consumption of the refrigerator corresponding to each sub-time segment in the most recent time segment before the current time, and the total number of time segments at a uniform interval in the most recent time segment before the current time. The fourth invention point: More specifically, the internal wall imaging data of each frame of the refrigerator's interior is captured at a household electricity meter. These frames are taken at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior includes the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame, as well as the current refrigerator's configuration information, such as the refrigerator's service life, internal volume, rated voltage, and applicable frequency. This completes the data structure design of the input content for the intelligent efficiency evaluation model.

[0007] The household electricity meter early warning method of the present invention is simple in structure, convenient and practical. By analyzing the changes in the visual image of the refrigerator's inner wall over past time segments and the fluctuations in the refrigerator's electricity consumption over past time segments at the household electricity meter, the method determines whether the compressor efficiency of the refrigerator, which is considered a future time segment, has decreased beyond expectations. If the state analysis shows an unexpected decrease, a compressor inefficiency early warning operation is executed, thereby providing early intervention for after-sales departments or users to prevent sudden refrigerator malfunctions later. Detailed Implementation

[0008] The following will provide a detailed description of the implementation scheme of the household electricity meter early warning method based on state analysis of the present invention.

[0009] Implementation Plan 1 The household electricity meter early warning method based on state analysis shown in Embodiment 1 of the present invention specifically includes the following steps: The system captures the internal wall imaging data of each frame of the refrigerator's interior from the automatic defrosting camera at the household electricity meter, taken at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior consists of the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame. The duration of each time segment is the same. Extract the electricity consumption of the refrigerator at the household electricity meter for each sub-time segment within the most recent time segment before the current time. Each sub-time segment within the most recent time segment before the current time is obtained by dividing the most recent time segment before the current time into evenly spaced time segments. At the household electricity meter, an intelligent efficiency evaluation model is used to intelligently evaluate the compressor efficiency of the refrigerator in the current time segment starting from the current time, based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the refrigerator corresponding to each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time. When the compressor efficiency of the refrigerator is lower than the corresponding lower limit threshold of the compressor efficiency in the current time segment starting from the current time, the built-in wireless communication interface at the household electricity meter will wirelessly transmit the refrigerator's product code, the current time segment, and the compressor inefficiency warning information into a network data packet and then transmit it to the refrigerator manufacturer's after-sales management server at a remote location. The intelligent efficiency evaluation model is a convolutional neural network that has undergone multiple learning actions, and the number of learning actions performed by the convolutional neural network is monotonically positively correlated with the numerical difference between the lower limit threshold of the compressor efficiency corresponding to the current refrigerator and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator. Among them, the current configuration information of the refrigerator includes the current service life of the refrigerator, the internal volume of the cabinet, the rated voltage and the applicable frequency; In addition, the internal wall imaging data of each frame of the refrigerator's interior is captured at the household electricity meter at each time interval within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior includes the depth value, gray value, and coordinate value of each pixel in the internal wall imaging area of ​​the frame of the refrigerator's interior. This includes: identifying the internal wall imaging area of ​​the refrigerator's interior in each frame of the refrigerator's interior based on the imaging characteristics of the refrigerator's interior.

[0010] Implementation Plan 2 Compared to embodiment 1 of the present invention, the household electricity meter early warning method based on state analysis shown in embodiment 2 of the present invention further includes the following steps: Receive the compressor efficiency of the current refrigerator within the current time segment starting from the current time, and when the compressor efficiency of the current refrigerator within the current time segment starting from the current time is lower than the corresponding lower limit threshold of compressor efficiency, execute the on-site broadcast of compressor inefficiency warning information.

[0011] Implementation Plan 3 Compared to embodiment 1 of the present invention, the household electricity meter early warning method based on state analysis shown in embodiment 3 of the present invention further includes the following steps: Receive the compressor efficiency of the current refrigerator within the current time segment starting from the current time, and when the compressor efficiency of the current refrigerator within the current time segment starting from the current time is lower than the corresponding lower limit threshold of compressor efficiency, wirelessly transmit the compressor inefficiency warning information to the nearby user's mobile device.

[0012] Next, the specific structure of the household electricity meter early warning method utilizing state analysis of the present invention will be further described.

[0013] In the household electricity meter early warning method based on utilization status analysis according to any embodiment of the present invention: Based on the imaging characteristics of the inner wall of the refrigerator, the imaging area of ​​the inner wall of the refrigerator in each frame of the internal image is identified, including: the imaging characteristics of the inner wall of the refrigerator are the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator. Among them, the imaging characteristics of the inner wall of the refrigerator are the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator, including: the shape imaging characteristics of the inner wall of the refrigerator are the standard shape pattern corresponding to the inner wall of the refrigerator. Among them, the imaging characteristics of the inner wall of the refrigerator are the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator, and also include: the color imaging characteristics of the inner wall of the refrigerator are the R color channel distribution value range, G color channel distribution value range and B color channel distribution value range corresponding to the inner wall of the refrigerator in the RGB color space. The process involves capturing the internal wall imaging data of each frame of the refrigerator's interior from a household electricity meter. This data is obtained by capturing the internal wall imaging data of each frame of the refrigerator's interior from the automatic defrosting camera at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior consists of the depth-of-field value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame. It also includes the coordinate value of each pixel in the internal wall imaging area, which is the horizontal coordinate value and the vertical coordinate value of the pixel. In addition, when the compressor efficiency of the current refrigerator in the current time segment, which is the current time as the starting point, is lower than the lower limit threshold of the compressor efficiency of the current refrigerator, the product code of the current refrigerator, the current time segment, and the compressor inefficiency warning information are all included in the network data packet and wirelessly transmitted to the refrigerator manufacturer's after-sales management server at the remote end, the network data packet is an IP data packet.

[0014] And in the household electricity meter early warning method based on utilization status analysis according to any embodiment of the present invention: A smart efficiency evaluation model is used at the household electricity meter to intelligently evaluate the compressor efficiency of the refrigerator in the current time segment starting from the current time. This evaluation is based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's interior image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the refrigerator in each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time. The process of using an intelligent efficiency evaluation model at the household electricity meter to intelligently evaluate the compressor efficiency of the current refrigerator in the current time segment starting from the current time, based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the current refrigerator in each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time, also includes: running the intelligent efficiency evaluation model to obtain the compressor efficiency of the current refrigerator in the current time segment starting from the current time.

[0015] Furthermore, in the aforementioned household electricity meter early warning method utilizing state analysis, the intelligent efficiency evaluation model is a convolutional neural network that has undergone multiple learning actions. The monotonically positive correlation between the number of learning actions performed by the convolutional neural network and the numerical difference between the lower limit threshold and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator includes: using a content transformation function that takes the lower limit threshold and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator as inputs and the number of learning actions performed by the convolutional neural network as output to represent the content transformation relationship between the number of learning actions performed by the convolutional neural network and the numerical difference between the lower limit threshold and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator.

[0016] The foregoing description of exemplary embodiments of the invention is provided for illustrative and descriptive purposes. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will obviously be apparent to those skilled in the art. Exemplary embodiments were chosen and described in order to best illustrate the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the various embodiments and variations of the invention suitable for the contemplated particular purpose. The scope of the invention is intended to be defined by the appended claims and their equivalents.

Claims

1. A method for early warning of household electricity meters using state analysis, characterized in that, The method includes: The system captures the internal wall imaging data of each frame of the refrigerator's interior from the automatic defrosting camera at the household electricity meter, taken at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame of the refrigerator's interior consists of the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame. The duration of each time segment is the same. Extract the electricity consumption of the refrigerator at the household electricity meter for each sub-time segment within the most recent time segment before the current time. Each sub-time segment within the most recent time segment before the current time is obtained by dividing the most recent time segment before the current time into evenly spaced time segments. At the household electricity meter, an intelligent efficiency evaluation model is used to intelligently evaluate the compressor efficiency of the refrigerator in the current time segment starting from the current time, based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the refrigerator corresponding to each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time. When the compressor efficiency of the refrigerator is lower than the corresponding lower limit threshold of the compressor efficiency in the current time segment starting from the current time, the built-in wireless communication interface at the household electricity meter will wirelessly transmit the refrigerator's product code, the current time segment, and the compressor inefficiency warning information into a network data packet and then transmit it to the refrigerator manufacturer's after-sales management server at a remote location. The intelligent efficiency evaluation model is a convolutional neural network that has undergone multiple learning actions, and the number of learning actions performed by the convolutional neural network is monotonically positively correlated with the numerical difference between the lower limit threshold of the compressor efficiency corresponding to the current refrigerator and the upper limit threshold of the compressor efficiency corresponding to the current refrigerator. Among them, the current configuration information of the refrigerator includes the current service life of the refrigerator, the internal volume of the cabinet, the rated voltage and the applicable frequency.

2. The household electricity meter early warning method using state analysis as described in claim 1, characterized in that: The system captures the internal wall imaging data of each frame of the refrigerator's interior from the household electricity meter. The internal wall imaging data of each frame consists of the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​the refrigerator in that frame. It includes: identifying the internal wall imaging area of ​​the refrigerator in each frame based on the imaging characteristics of the refrigerator's interior wall.

3. The household electricity meter early warning method using state analysis as described in claim 2, characterized in that, The method further includes: Receive the compressor efficiency of the current refrigerator within the current time segment starting from the current time, and when the compressor efficiency of the current refrigerator within the current time segment starting from the current time is lower than the corresponding lower limit threshold of compressor efficiency, execute the on-site broadcast of compressor inefficiency warning information.

4. The household electricity meter early warning method using state analysis as described in claim 2, characterized in that, The method further includes: Receive the compressor efficiency of the current refrigerator within the current time segment starting from the current time, and when the compressor efficiency of the current refrigerator within the current time segment starting from the current time is lower than the corresponding lower limit threshold of compressor efficiency, wirelessly transmit the compressor inefficiency warning information to the nearby user's mobile device.

5. The household electricity meter early warning method using state analysis as described in any one of claims 2-4, characterized in that: Based on the imaging characteristics of the inner wall of the refrigerator, the imaging area of ​​the inner wall of the refrigerator in each frame of the internal image is identified, including: the imaging characteristics of the inner wall of the refrigerator are the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator. Among them, the imaging characteristics of the inner wall of the refrigerator are the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator, including: the shape imaging characteristics of the inner wall of the refrigerator are the standard shape pattern corresponding to the inner wall of the refrigerator. The imaging characteristics of the inner wall of the refrigerator include the shape imaging characteristics and / or color imaging characteristics of the inner wall of the refrigerator, and also include the color imaging characteristics of the inner wall of the refrigerator as the R color channel distribution value range, G color channel distribution value range and B color channel distribution value range corresponding to the inner wall of the refrigerator in the RGB color space.

6. The household electricity meter early warning method using state analysis as described in claim 5, characterized in that: The system captures the internal wall imaging data of each frame of the refrigerator's interior from a household electricity meter. The frame is captured by an automatic defrosting camera inside the refrigerator at evenly spaced intervals within the most recent time segment before the current time. The internal wall imaging data of each frame consists of the depth value, grayscale value, and coordinate value of each pixel in the internal wall imaging area of ​​that frame. It also includes the horizontal and vertical coordinate values ​​of each pixel in the internal wall imaging area.

7. The household electricity meter early warning method using state analysis as described in claim 6, characterized in that: When the compressor efficiency of the refrigerator in the current time segment, starting from the current moment, is lower than the lower limit threshold of the compressor efficiency of the refrigerator, the product code of the refrigerator, the current time segment, and the compressor inefficiency warning information are all included in the network data packet and wirelessly transmitted to the refrigerator manufacturer's after-sales management server at the remote location using the built-in wireless communication interface at the household electricity meter. The network data packet is an IP data packet.

8. The household electricity meter early warning method using state analysis as described in any one of claims 2-4, characterized in that: A smart efficiency evaluation model is used at the household electricity meter to intelligently evaluate the compressor efficiency of the refrigerator in the current time segment starting from the current time. This evaluation is based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's interior image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the refrigerator in each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time.

9. The household electricity meter early warning method using state analysis as described in claim 8, characterized in that: The intelligent efficiency evaluation model at the household electricity meter intelligently evaluates the compressor efficiency of the current refrigerator in the current time segment starting from the current time based on the refrigerator's configuration information, the internal wall imaging data of each frame of the refrigerator's internal image corresponding to each time segment at even intervals within the most recent time segment before the current time, the electricity consumption of the current refrigerator corresponding to each sub-time segment within the most recent time segment before the current time, and the total number of time segments at even intervals within the most recent time segment before the current time. The model also includes running the intelligent efficiency evaluation model to obtain the compressor efficiency of the current refrigerator in the current time segment starting from the current time.