Electricity meter identification and registration method

Abstract

The invention provides an electricity meter identification and registration method which is realized through image acquisition equipment and a mobile phone, and the method comprises the steps that an optical lens acquires an electricity meter bar code image, a main control chip carries out light compensation, light filtering, temperature difference compensation and jitter elimination processing, and an AD conversion module converts an analog image signal into a digital image signal. The image processing chip compresses the digital image signal into an MJPEG format and transmits the digital image signal to the mobile phone through a UVC protocol, an application program in the mobile phone decompresses the image and identifies the identity of the electric meter through a deep learning model, and then registration management of installation or disassembly of the electric meter is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to an electric meter identification and registration method. Background Art

[0002] With the convenience of modern life and the improvement of the happiness index of life, new household appliances are being updated and upgraded, and new types of household appliances are also increasing. The power consumption capacity of homes is also increasing accordingly, and the requirements for the accuracy of electricity metering are becoming higher and higher. The power supply service of the power grid is increasing accordingly. In order to provide a better user experience, new and old metering meters and transformers need to be updated and upgraded, and meters with new functions and new capacities are developed and used, resulting in an increase in the number of old and new meters. However, according to the relevant requirements of asset management, it is necessary to register the use of newly installed and removed meters. Each meter uses its own identity ID, which requires the corresponding personnel to register, but because the number is extremely large each time, the labor cost and work errors will increase exponentially. Summary of the invention

[0003] The object of the present invention is to provide an electric meter identification and registration method to improve the work efficiency of using and registering newly installed and recently removed electric meters.

[0004] To achieve the purpose of the present invention, a method for identifying and registering an electric meter is provided according to the present invention. The method is implemented based on an image acquisition device and a mobile phone. The image acquisition device includes an optical lens, a main control chip, an AD conversion module and an image processing chip. The image acquisition device and the mobile phone are connected via a data cable.

[0005] The method comprises:

[0006] The optical lens collects a barcode image of the meter barcode;

[0007] The main control chip performs light compensation, light filtering, temperature difference compensation and de-jittering on the barcode image to obtain an analog image signal and then transmits it to the AD conversion module;

[0008] The AD conversion module converts the analog image signal into a digital image signal, and transmits the digital image signal to the image processing chip through the MIPI protocol;

[0009] The image processing chip compresses the digital image signal to obtain a compressed image in MJPEG format, and transmits the compressed image to the mobile phone through the UVC standard protocol;

[0010] The mobile phone is installed with a pre-developed application, which is used to decompress the compressed image to obtain a barcode image of a preset size by running the application, and to identify the identity of the electric meter based on the decompressed barcode image of the preset size based on a pre-trained deep learning model, and to register and manage the installation of a new electric meter or the removal of an old electric meter according to the identity of the electric meter.

[0011] The electric meter identification and registration method of the present invention has the following beneficial effects:

[0012] The high-definition barcode image is collected through the optical lens, and the main control chip is combined to perform image preprocessing such as light compensation, filtering, temperature difference compensation and de-jittering, which significantly improves the image quality, so that the deep learning model can more accurately identify the identity of the meter. The image preprocessing step can effectively overcome the influence of factors such as different lighting conditions, ambient temperature changes and hand-held jitter on the image quality, so that the method can maintain a high recognition rate in different environments. The automated image acquisition and meter identity recognition process reduces manual intervention and greatly improves the efficiency of meter installation and removal registration. Compared with manual barcode recognition, the deep learning model can more reliably identify the meter identity and reduce recognition errors caused by human errors. The real-time processing and transmission of data through the mobile phone application realizes the instant update of meter information, which is convenient for real-time monitoring and management. Users can complete the identification and registration management of the meter identity through the mobile phone application, which is easy to operate and does not require professional training. The integrated image acquisition device and mobile phone application reduce additional hardware investment and operation and maintenance costs. Using mobile phone applications for data transmission and management, encryption technology can be used to protect data security and prevent information leakage. The software-based solution facilitates subsequent functional expansion and system maintenance, and can easily adapt to new business needs and technology upgrades. The registration management system can effectively track the entire life cycle of the meter, providing strong support for the asset management of power companies. In summary, the method provided by the present invention not only improves the accuracy and efficiency of meter identification, but also optimizes the management process of meter installation and removal, providing an efficient, reliable and economical solution for the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0014] Figure 1 The present invention is a flowchart of an electric meter identification and registration method in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The detailed description of the drawings is intended as an illustration of the current embodiment of the present application, and is not intended to represent the only form in which the present application can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments intended to be included in the spirit and scope of the present application.

[0016] The embodiment of the present application proposes a method for identifying and registering an electric meter, which is implemented based on an image acquisition device and a mobile phone. The image acquisition device includes an optical lens, a main control chip, an AD conversion module and an image processing chip. The image acquisition device and the mobile phone are connected via a data cable.

[0017] The method comprises:

[0018] Step S10: the optical lens collects a barcode image of the electric meter barcode.

[0019] Specifically, the optical lens is the core part of the image acquisition device, which is responsible for capturing the barcode image on the electricity meter. The barcode is a graphical code that can contain the identity information of the electricity meter.

[0020] Step S20: the main control chip performs light compensation, light filtering, temperature difference compensation and de-jittering on the barcode image to obtain an analog image signal and then transmits it to the AD conversion module.

[0021] Specifically, due to the uneven ambient light, the image may have areas of varying brightness and darkness. Light compensation adjusts the brightness and contrast of the image to make the brightness distribution of the entire image more uniform, thereby improving the readability of the image. Light filtering removes light of specific wavelengths during image acquisition to reduce the impact of ambient light on image quality, such as removing glare or reflected light. Due to changes in ambient temperature, the performance of the optical lens may be affected, resulting in blurred images. Temperature difference compensation corrects this blur by adjusting the lens or image processing algorithm. During handheld shooting, hand shaking can cause blurred images.

[0022] Step S30: The AD conversion module converts the analog image signal into a digital image signal, and transmits the digital image signal to the image processing chip through the MIPI protocol.

[0023] Specifically, the AD conversion module converts the analog image signal processed by the main control chip into a digital image signal, because digital signals are more suitable for processing and transmission by electronic equipment; the digital image signal is transmitted to the image processing chip through the Mobile Industry Processor Interface (MIPI) protocol. MIPI is an interface standard designed specifically for mobile devices that can efficiently transmit image data.

[0024] Step S40: the image processing chip compresses the digital image signal to obtain a compressed image in MJPEG format, and transmits the compressed image to the mobile phone via the UVC standard protocol.

[0025] Specifically, the image processing chip compresses the digital image signal into an image in MJPEG format, which is a common image compression format that can effectively reduce the size of image data and facilitate transmission. The compressed image is transmitted to the mobile phone via the Universal Serial Bus Video Class (UVC) standard protocol. UVC is a widely used standard that allows image and video data to be transmitted through the USB interface.

[0026] Step S50, the mobile phone is installed with a pre-developed application, and the compressed image is decompressed by running the application to obtain a barcode image of a preset size, and the identity of the electric meter is identified based on a pre-trained deep learning model for the decompressed barcode image of the preset size, and registration and management of the installation of a new electric meter or the removal of an old electric meter are performed according to the identity of the electric meter.

[0027] Specifically, the application in the mobile phone receives the compressed image and decompresses it into a barcode image of a preset size to prepare for the subsequent recognition steps; the application uses a pre-trained deep learning model to recognize the decompressed barcode image and extract the identity information of the electricity meter; the deep learning model can accurately recognize and classify different electricity meter barcodes by learning a large number of barcode images. Once the identity of the electricity meter is recognized, the application will perform corresponding registration and management operations, such as updating the database and generating reports, based on whether the meter is newly installed or dismantled.

[0028] Through the above steps, the entire method realizes the process from the acquisition of the meter barcode image to the recognition of the meter identity and then to the final registration management, forming an efficient and accurate meter recognition and management process.

[0029] In some embodiments, the light compensation comprises the following steps:

[0030] Calculate the histogram of the initially acquired barcode image, and analyze the histogram to determine the brightness distribution of the initially acquired barcode image; specifically, the histogram is a chart of the frequency of occurrence of each brightness value in the image. By observing the histogram, it can be determined whether the image is too dark, too bright, or uneven in brightness. For example, if the histogram is concentrated on the left side, the image may be too dark; if it is concentrated on the right side, it may be too bright.

[0031] The image brightness of the initially acquired barcode image is adjusted according to the brightness distribution to obtain a first brightness adjusted image, so that the histogram brightness distribution of the adjusted first brightness adjusted image is uniform; specifically, according to the result of the histogram analysis, the brightness of the image is adjusted, which involves multiplying or adding each pixel value of the image to make the overall brightness distribution of the image more uniform.

[0032] A sliding window is used to analyze the local area of ​​the adjusted barcode image, and adaptive adjustment is performed according to the local brightness to achieve brightness optimization in the local area to obtain a second brightness adjusted image; specifically, the sliding window technology allows each local area of ​​the image to be analyzed separately, and the brightness of each area can be evaluated by sliding a small window on the image. For each local area, the brightness is adaptively adjusted according to its brightness situation, which can ensure that each part of the image has an appropriate brightness level.

[0033] The light intensity of the current environment is obtained, and a corresponding gamma value is selected according to the light intensity to correct the second brightness adjustment image to obtain a light-compensated barcode image, so as to further optimize the brightness and contrast of the image. Specifically, a sensor or other method is used to measure the light intensity of the current environment. Gamma correction is a method for adjusting the brightness and contrast of an image. Under different light intensity conditions, different gamma values ​​can be selected to make more precise adjustments to the image. For example, under low light conditions, a gamma value less than 1 may be selected to increase the brightness; under high light conditions, a gamma value greater than 1 may be selected to reduce the brightness. The image after gamma correction is further optimized in terms of brightness and contrast, so that the barcode can maintain a high readability under various lighting conditions.

[0034] Through the above steps, the light compensation process significantly improves the quality of the image and provides a clear and uniformly bright image input for subsequent meter barcode recognition.

[0035] In some embodiments, the filtering comprises the following steps:

[0036] The noise type of the light-compensated barcode image is analyzed, and a corresponding filter is selected according to the noise type to filter the light-compensated barcode image to remove light of a specific wavelength and reduce the impact of ambient light on image quality.

[0037] Specifically, various types of noise may be introduced during image acquisition and processing, such as Gaussian noise, salt and pepper noise, random noise, etc. Analyzing the noise type is the first step in determining how to effectively remove these noises. This usually involves analyzing the statistical characteristics of the image, such as the distribution and frequency characteristics of the noise; different noise types require different filters to process.

[0038] Here are some common filters and the types of noise they are suitable for:

[0039] Gaussian filter: suitable for removing Gaussian noise, it smoothes the image by weighted averaging the pixel values ​​in the neighborhood;

[0040] Median filter: Suitable for removing salt and pepper noise. It removes noise by replacing each pixel value with the median value in its neighborhood.

[0041] Bilateral filter: Suitable for removing noise while maintaining edge information. It considers both spatial proximity and pixel value similarity.

[0042] Wavelet transform: Suitable for removing random noise, it removes noise by decomposing the image into different frequency layers and processing these layers.

[0043] The image is processed using the selected filter. The filter operates on each pixel in the image according to the principle for which it is designed to reduce the effects of noise. For example, the median filter looks for a certain area (window) around each pixel and replaces the value of the pixel with the median value within the area.

[0044] Through the above steps, the light filtering process can effectively improve the image quality, making the meter barcode clearer and facilitating subsequent image recognition and processing, which is crucial to ensure that the meter information can be read accurately and quickly in various environments.

[0045] In some embodiments, the temperature difference compensation comprises the following steps:

[0046] The ambient temperature when the meter barcode image is captured is obtained, and the image quality is optimized according to the ambient temperature and a preset relationship model between temperature change and image quality, wherein the relationship model between temperature change and image quality is established according to historical data and experimental results, and the image quality includes at least one of contrast, brightness and noise level.

[0047] Specifically, in image acquisition devices, temperature sensors are usually integrated to monitor the ambient temperature during shooting. Changes in ambient temperature may affect the performance of the optical lens, such as the focal length and refractive index of the lens, and thus affect the quality of the image. By analyzing historically collected image data and ambient temperature records, and experimentally observing changes in image quality at different temperatures, a model can be established to describe the relationship between temperature changes and image quality (contrast, brightness, and noise level). This model can be linear or nonlinear, and the specific form depends on the experimental results and data analysis. After establishing a relationship model between temperature changes and image quality, this model can be used to predict possible problems with the image at the current ambient temperature and take corresponding compensation measures.

[0048] The following are possible optimizations:

[0049] Contrast optimization: If the model predicts a loss in contrast, it can be compensated by adjusting the contrast of the image.

[0050] Brightness optimization: If the model predicts that the image will be too dark or too bright, it can compensate by adjusting the brightness of the image.

[0051] Noise level optimization: If the model predicts that the noise level will increase, a more aggressive noise filtering algorithm can be applied to reduce the noise.

[0052] By implementing temperature compensation, the image acquisition device can automatically adjust the image processing parameters to adapt to changes in ambient temperature, thereby maintaining stable image quality and ensuring accurate recognition of meter barcodes. This is very important for using image acquisition devices in a wide range of environmental conditions.

[0053] In some embodiments, the debouncing comprises the following steps:

[0054] When shooting a barcode image, the image acquisition device will shoot continuous frame images and use the last frame image as the initially acquired barcode image. Motion estimation is performed based on the continuous frame images to calculate the displacement vector between images. Motion compensation is performed on the barcode image after temperature difference compensation based on the displacement vector to eliminate the displacement caused by jitter.

[0055] Specifically, the image acquisition device does not take a single image, but takes multiple images continuously. These continuous frame images can be used to analyze the motion trajectory of the device. Among the continuous frame images, the last frame is usually selected as the object to be processed, because it is assumed that during the continuous shooting process, the device jitter will gradually stabilize, and the last frame may be relatively clearer. Motion estimation is to analyze the continuous frame images and calculate the relative displacement between adjacent frames. This can be achieved through image processing algorithms, such as block matching algorithms, which compare the position changes of the same blocks in continuous frames to estimate the displacement vector. Once the displacement vector is calculated, this information can be used to perform motion compensation on the image. The purpose of motion compensation is to correct the image displacement caused by device jitter so that the image looks like it has no jitter.

[0056] Through the above-mentioned motion compensation steps, the image blur and displacement caused by device shaking can be effectively reduced or eliminated, thereby obtaining a clearer barcode image, which is convenient for subsequent barcode recognition processing.

[0057] In some embodiments, the deep learning model includes:

[0058] The input layer is used to receive the barcode image of the preset size as the input of the model. In this model, the input layer accepts a grayscale image of 128*128 pixels.

[0059] The first convolutional layer is used to perform convolution operations on the input image using 32 3*3 convolution kernels to extract low-level features in the image, such as edges and textures. The convolutional layer reduces the number of parameters through weight sharing and local receptive field while maintaining the spatial hierarchical structure of the image.

[0060] The first maximum pooling layer is used to downsample the feature map output by the convolutional layer. It uses a 2*2 pooling window and selects the maximum value in each local area as the output, reducing the size of the feature map while retaining the most significant features.

[0061] The second convolutional layer is used to further extract more complex features. 64 3*3 convolution kernels are used to convolve the feature map output by the first pooling layer to enhance the network's ability to represent image features.

[0062] The second maximum pooling layer is used to downsample the output of the second convolutional layer similar to the first maximum pooling layer, using a 2*2 pooling window to continue reducing the size of the feature map.

[0063] The third convolutional layer is used to convolve the feature map output by the second pooling layer using 128 3*3 convolution kernels to extract higher-level features that are helpful for subsequent classification tasks.

[0064] The third maximum pooling layer is used to downsample the output of the third convolutional layer, using a 2*2 pooling window to further reduce the size of the feature map while retaining key features.

[0065] The flattening layer is used to convert the three-dimensional feature map output by the third maximum pooling layer into a one-dimensional vector so that it can be input into the fully connected layer. The flattening layer does not change the content of the data, but only changes the structure of the data.

[0066] The first fully connected layer is used to map the feature vector output by the flattening layer to 1024 neurons. Each neuron is connected to all neurons in the previous layer to achieve nonlinear combination of features.

[0067] The first batch normalization layer is used to batch normalize the output of the first fully connected layer to accelerate the network training process, reduce the gradient disappearance and gradient explosion problems, and improve the stability of the model.

[0068] The first Dropout layer is used to randomly discard a certain proportion (e.g. 50%) of the neuron outputs in the first fully connected layer to prevent overfitting and enhance the generalization ability of the network.

[0069] The second fully connected layer is used to further combine and abstract the features. It uses 512 neurons to process the feature vector output by the first Dropout layer.

[0070] The second batch normalization layer is used to perform batch normalization on the output of the second fully connected layer similar to the first batch normalization layer.

[0071] The second Dropout layer is used in the same way as the first Dropout layer, randomly discarding a certain proportion of neuron outputs in the second fully connected layer to continue to prevent overfitting.

[0072] The output layer is used to set the number of neurons according to the number of types of electricity meter identities, and use the Softma* activation function to output the probability distribution of each category for the final classification decision. The output of each neuron corresponds to the recognition probability of an electricity meter identity.

[0073] In some embodiments, the meter identity is a meter serial number or an asset number.

[0074] In some embodiments, the registration management of installing a new meter or removing an old meter according to the meter identity includes:

[0075] The step of querying the meter database according to the meter identity to determine whether the meter is a new meter or an old meter;

[0076] If it is a new meter, the meter installation registration process is entered to obtain the installation date, location information and user information entered by the user, and the status of the meter is updated to installed in the meter database;

[0077] If it is an old meter, the meter removal registration process is entered to obtain the removal date, location information and user information entered by the user, and the status of the meter is updated to removed in the meter database.

[0078] In summary, the method of the embodiment of the present invention collects high-definition barcode images through an optical lens, and combines the main control chip to perform image preprocessing such as light compensation, filtering, temperature difference compensation and de-jittering, which significantly improves the image quality, so that the deep learning model can more accurately identify the identity of the meter. The image preprocessing step can effectively overcome the influence of factors such as different lighting conditions, ambient temperature changes and hand-held jitter on the image quality, so that the method can maintain a high recognition rate in different environments. The automated image acquisition and meter identity recognition process reduces manual intervention and greatly improves the work efficiency of meter installation and removal registration. Compared with manual barcode recognition, the deep learning model can more reliably identify the meter identity and reduce recognition errors caused by human errors. Through real-time processing and transmission of data by mobile phone applications, instant update of meter information is realized, which is convenient for real-time monitoring and management. Users can complete the identification and registration management of the meter identity only through mobile phone applications, which is easy to operate and does not require professional training. Through integrated image acquisition equipment and mobile phone applications, additional hardware investment and operation and maintenance costs are reduced. Using mobile phone applications for data transmission and management, encryption technology can be used to protect data security and prevent information leakage. The software-based solution facilitates subsequent functional expansion and system maintenance, and can easily adapt to new business needs and technology upgrades. The registration management system can effectively track the entire life cycle of the meter, providing strong support for the asset management of power companies. In summary, the method provided by the embodiment of the present invention not only improves the accuracy and efficiency of meter identification, but also optimizes the management process of meter installation and removal, providing an efficient, reliable and economical solution for the power industry.

[0079] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for identifying and registering an electric meter, characterized in that: The method is implemented based on an image acquisition device and a mobile phone. The image acquisition device includes an optical lens, a main control chip, an AD conversion module and an image processing chip. The image acquisition device and the mobile phone are connected via a data cable. The method comprises: The optical lens collects a barcode image of the meter barcode; The main control chip performs light compensation, light filtering, temperature difference compensation and de-jittering on the barcode image to obtain an analog image signal and then transmits it to the AD conversion module; The AD conversion module converts the analog image signal into a digital image signal, and transmits the digital image signal to the image processing chip through the MIPI protocol; The image processing chip compresses the digital image signal to obtain a compressed image in MJPEG format, and transmits the compressed image to the mobile phone through the UVC standard protocol; The mobile phone is installed with a pre-developed application, which is used to decompress the compressed image to obtain a barcode image of a preset size by running the application, and to identify the identity of the electric meter based on the decompressed barcode image of the preset size based on a pre-trained deep learning model, and to register and manage the installation of a new electric meter or the removal of an old electric meter according to the identity of the electric meter.

2. The method according to claim 1, characterized in that: The light compensation comprises the following steps: Calculating a histogram of the initially acquired barcode image, and analyzing the histogram to determine a brightness distribution of the initially acquired barcode image; Adjusting the image brightness of the initially acquired barcode image according to the brightness distribution to obtain a first brightness-adjusted image, so that the histogram brightness distribution of the adjusted first brightness-adjusted image is uniform; Using a sliding window to analyze a local area of ​​the adjusted barcode image, and adaptively adjusting the local brightness to optimize the brightness of the local area to obtain a second brightness-adjusted image; The light intensity of the current environment is obtained, and a corresponding gamma value is selected according to the light intensity to correct the second brightness adjusted image to obtain a light compensated barcode image, so as to further optimize the brightness and contrast of the image.

3. The method according to claim 2, characterized in that The filtering comprises the following steps: The noise type of the light-compensated barcode image is analyzed, and a corresponding filter is selected according to the noise type to filter the light-compensated barcode image to remove light of a specific wavelength and reduce the impact of ambient light on image quality.

4. The method according to claim 3, characterized in that The temperature difference compensation comprises the following steps: The ambient temperature when the meter barcode image is captured is obtained, and the image quality is optimized according to the ambient temperature and a preset relationship model between temperature change and image quality, wherein the relationship model between temperature change and image quality is established according to historical data and experimental results, and the image quality includes at least one of contrast, brightness and noise level.

5. The method according to claim 4, characterized in that The debouncing comprises the following steps: When shooting a barcode image, the image acquisition device will shoot continuous frame images and use the last frame image as the initially acquired barcode image. Motion estimation is performed based on the continuous frame images to calculate the displacement vector between images. Motion compensation is performed on the barcode image after temperature difference compensation based on the displacement vector to eliminate the displacement caused by jitter.

6. The method according to claim 5, characterized in that The deep learning model includes: An input layer, used for receiving the barcode image of the preset size as a model input; The first convolutional layer is used to perform convolution operations on the input image using 32 3*3 convolution kernels to extract low-level features in the image; The first maximum pooling layer is used to downsample the feature map output by the convolutional layer, using a 2*2 pooling window to select the maximum value in each local area as the output; The second convolutional layer is used to convolve the feature map output by the first pooling layer using 64 3x3 convolution kernels; The second maximum pooling layer is used to downsample the output of the second convolutional layer, using a 2*2 pooling window; The third convolutional layer is used to convolve the feature map output by the second pooling layer using 128 3*3 convolution kernels; The third maximum pooling layer is used to downsample the output of the third convolutional layer, using a 2*2 pooling window; A flattening layer is used to convert the three-dimensional feature map output by the third maximum pooling layer into a one-dimensional vector; The first fully connected layer is used to map the feature vector output by the flattening layer to 1024 neurons. Each neuron is connected to all neurons in the previous layer to achieve nonlinear combination of features. The first batch normalization layer is used to batch normalize the output of the first fully connected layer; The first Dropout layer is used to randomly discard a certain proportion of neuron outputs in the first fully connected layer; The second fully connected layer is used to process the feature vector output by the first Dropout layer using 512 neurons; A second batch normalization layer, used to batch normalize the output of the second fully connected layer; The second Dropout layer is used to randomly discard a certain proportion of neuron outputs in the second fully connected layer; The output layer is used to set the number of neurons according to the number of types of electricity meter identities. The Softmax activation function is used to output the probability distribution of each category for the final classification decision. The output of each neuron corresponds to the recognition probability of an electricity meter identity.

7. The method according to claim 6, characterized in that The meter identity is the meter serial number or asset number.

8. The method according to claim 6, characterized in that The registration management of installing a new meter or removing an old meter according to the meter identity includes: The step of querying the meter database according to the meter identity to determine whether the meter is a new meter or an old meter; If it is a new meter, the meter installation registration process is entered to obtain the installation date, location information and user information entered by the user, and the status of the meter is updated to installed in the meter database; If it is an old meter, the meter removal registration process is entered to obtain the removal date, location information and user information entered by the user, and the status of the meter is updated to removed in the meter database.

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