Intelligent analysis system for remote meter reading management

Abstract

The invention relates to an intelligent analysis system for remote meter reading management, which comprises an intelligent analysis device, the data sharpening module is used for sharpening a data sharpening image according to the contrast of the data sharpening image and the number of pixel points occupied by a background area in the data sharpening image; intelligently analyzing whether a binary numerical value of the table cover exists in the data sharpening image or not according to the total number of pixel points in the data sharpening image and each gray numerical value of each pixel point of an image block matched with the standard outline of the table cover in the data sharpening image; and the wireless reporting device is used for packaging meter cover missing information and a meter body number of the gas meter into a network data packet and wirelessly reporting the network data packet when the binary numerical value shows that the meter cover does not exist in the data sharpening image. The intelligent analysis system for remote meter reading management is compact in structure and intelligent in operation. The deep neural network model can be adopted to intelligently analyze whether the gas meter subjected to remote meter reading has the meter cover or not, so that the intelligent level of remote meter reading management is improved.

Description

Technical Field

[0001] This invention relates to the field of remote meter reading, and more particularly to an intelligent analysis system for remote meter reading management. Background Technology

[0002] The gas meter only has a small glass window showing a digital dial with seven digits: the first four digits are black, and the last three are red. People's daily cooking fuels have shifted from conventional energy sources like firewood and coal, which are wasteful and polluting, to clean energy sources like natural gas, coal gas, and even electricity. This is where the gas meter comes in. Its automatic accumulation function allows households using natural gas or piped coal gas to easily know how much gas they've used, enabling them to pay their bills based on the monthly gas consumption in cubic meters.

[0003] For gas meters currently being remotely read, if the meter cover is missing, on the one hand, remote meter reading will lack the necessary reference point, affecting the reliability and effectiveness of the meter reading results; on the other hand, dust and moisture will easily accumulate inside the gas meter, causing damage to its operation. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an intelligent analysis system for remote meter reading management. The technical solution employs a deep neural network model to intelligently analyze the presence of a meter cover in a sharpened image based on factors such as the contrast of the image, the number of pixels occupied by the background region, the total number of pixels in the image, and the grayscale values ​​of each pixel in the image block matching the standard outline of the meter cover. This enhances the intelligence level of image analysis. Furthermore, a wireless reporting device is used to package the missing meter cover information and the gas meter's body number into a network data packet and wirelessly report it to a remote device management server when the binary numerical representation of the missing meter cover in the sharpened image is used. This completes the intelligent analysis of whether a gas meter is missing a meter cover and the corresponding meter body location in remote meter reading.

[0005] According to the technical solution of the present invention, the system includes:

[0006] The device is connected to the gas meter being remotely read and is used to send a first control command when the gas meter is detected to be in operation, and to send a second control command when the gas meter is detected to be in paused state.

[0007] A directional image capture device is installed at the front end of the remote meter reading terminal and wirelessly connected to the operation identification device. It is used to perform a directional image capture operation on the operation scene of the gas meter when the first control command is received, so as to obtain and output the corresponding scene capture image.

[0008] The content enhancement device is installed inside the remote meter reading terminal and connected to the directional capture device. It is used to perform image SVD enhancement processing on the received scene capture images to obtain and output the corresponding content enhancement images.

[0009] A real-time filtering device, connected to the content enhancement device, is used to perform Gaussian high-pass filtering on the received content enhancement image to obtain and output a corresponding real-time filtered image.

[0010] A data sharpening device, connected to the real-time filtering device, is used to perform spatial domain differential sharpening processing on the received real-time filtered image to obtain and output the corresponding data sharpened image.

[0011] The model building device is installed inside the remote meter reading terminal. It is used to perform training on the deep neural network to obtain the deep neural network that has completed each training and output it as the deep neural network model. The number of training sessions is proportional to the maximum curvature of the edge curve of the standard profile of the gas meter cover.

[0012] An intelligent analysis device, installed inside the remote meter reading terminal and connected to both the data sharpening device and the model building device, is used to take the contrast of the data sharpened image, the number of pixels occupied by the background area in the data sharpened image, the total number of pixels in the data sharpened image, and the grayscale values ​​of each pixel in the image block that matches the standard contour of the meter cover in the data sharpened image as inputs to the deep neural network model, so as to execute the deep neural network model and obtain the binary value output by the deep neural network model that indicates whether the meter cover exists in the data sharpened image;

[0013] A wireless reporting device, connected to the intelligent analysis device, is used to package the missing cover information and the gas meter body number together into a network data packet and wirelessly report it to a remote device management server when the binary numerical representation of the data sharpening image does not show the cover.

[0014] The intelligent analysis system for remote meter reading management of this invention has a compact structure and intelligent operation. It improves the intelligence level of remote meter reading management by employing a deep neural network model to intelligently analyze whether the gas meter being remotely read has a cover. Furthermore, it utilizes a wireless reporting device to package the cover-missing information and the gas meter's serial number into a network data packet and wirelessly report it to a remote device management server when the intelligent analysis indicates the gas meter is missing a cover. This completes the intelligent analysis of whether the gas meter is missing a cover and the corresponding meter location. Attached Figure Description

[0015] The embodiments of the present invention will now be described with reference to the accompanying drawings, wherein:

[0016] Figure 1 This is a schematic diagram of the internal structure of an intelligent analysis system for remote meter reading management according to an embodiment A of the present invention.

[0017] Figure 2 This is a schematic diagram of the internal structure of an intelligent analysis system for remote meter reading management according to embodiment B of the present invention.

[0018] Figure 3 This is a schematic diagram of the internal structure of an intelligent analysis system for remote meter reading management according to embodiment C of the present invention. Detailed Implementation

[0019] The embodiments of the intelligent analysis system for remote meter reading management of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] Example A

[0021] Figure 1 The diagram illustrates the internal structure of an intelligent analysis system for remote meter reading management according to an embodiment A of the present invention. The system includes:

[0022] The device is connected to the gas meter being remotely read and is used to send a first control command when the gas meter is detected to be in operation, and to send a second control command when the gas meter is detected to be in paused state.

[0023] For example, the running identification device is connected to the gas meter currently being remotely read, and is used to send a first control command when the gas meter is detected to be in a running state, and to send a second control command when the gas meter is detected to be in a paused state. The running identification device can be implemented using an FPGA device, connected to the gas meter currently being remotely read, and is used to send a first control command when the gas meter is detected to be in a running state, and to send a second control command when the gas meter is detected to be in a paused state.

[0024] A directional image capture device is installed at the front end of the remote meter reading terminal and wirelessly connected to the operation identification device. It is used to perform a directional image capture operation on the operation scene of the gas meter when the first control command is received, so as to obtain and output the corresponding scene capture image.

[0025] The content enhancement device is installed inside the remote meter reading terminal and connected to the directional capture device. It is used to perform image SVD enhancement processing on the received scene capture images to obtain and output the corresponding content enhancement images.

[0026] A real-time filtering device, connected to the content enhancement device, is used to perform Gaussian high-pass filtering on the received content enhancement image to obtain and output a corresponding real-time filtered image.

[0027] A data sharpening device, connected to the real-time filtering device, is used to perform spatial domain differential sharpening processing on the received real-time filtered image to obtain and output the corresponding data sharpened image.

[0028] The model building device is installed inside the remote meter reading terminal. It is used to perform training on the deep neural network to obtain the deep neural network that has completed each training and output it as the deep neural network model. The number of training sessions is proportional to the maximum curvature of the edge curve of the standard profile of the gas meter cover.

[0029] An intelligent analysis device, installed inside the remote meter reading terminal and connected to both the data sharpening device and the model building device, is used to take the contrast of the data sharpened image, the number of pixels occupied by the background area in the data sharpened image, the total number of pixels in the data sharpened image, and the grayscale values ​​of each pixel in the image block that matches the standard contour of the meter cover in the data sharpened image as inputs to the deep neural network model, so as to execute the deep neural network model and obtain the binary value output by the deep neural network model that indicates whether the meter cover exists in the data sharpened image;

[0030] A wireless reporting device, connected to the intelligent analysis device, is used to package the missing cover information and the gas meter body number together into a network data packet and wirelessly report it to a remote device management server when the binary value indicates that the cover is not present in the data sharpening image.

[0031] The wireless reporting device is also used to temporarily suspend packaging the missing cover information and the gas meter body number together into a network data packet and wirelessly reporting it to a remote device management server when the binary value indicates that a cover is present in the data sharpening image.

[0032] The directional capturing device is also used to stop performing directional capturing operations on the operating scene of the gas meter when it receives the second control command;

[0033] The directional capture device is installed at the front end of the remote meter reading terminal and wirelessly connected to the operation identification device. It is used to perform directional capture operation on the operation scene of the gas meter when the first control command is received, so as to obtain and output the corresponding scene capture image. The directional capture device and the operation identification device conduct wireless network data interaction through a WIFI communication network.

[0034] Example B

[0035] Figure 2 This is a schematic diagram of the internal structure of an intelligent analysis system for remote meter reading management according to embodiment B of the present invention.

[0036] and Figure 1 different, Figure 2 The intelligent analysis system for remote meter reading management can also include the following components:

[0037] A humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively.

[0038] The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. The multiple humidity measurement units used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device are multiple non-contact humidity sensors.

[0039] The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors with identical internal structures used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively.

[0040] The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, each having the same upper and lower humidity measurement thresholds.

[0041] The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, each at an equal distance from the respective device.

[0042] Example C

[0043] Figure 3 This is a schematic diagram of the internal structure of an intelligent analysis system for remote meter reading management according to embodiment C of the present invention.

[0044] and Figure 1 different, Figure 3 The intelligent analysis system for remote meter reading management can also include the following components:

[0045] The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively, and is used to execute corresponding humidity alarm actions based on the humidity measurement results of the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively.

[0046] The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It is used to execute corresponding humidity alarm actions based on the humidity measurement results of the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. The instant notification mechanism includes a built-in humidity receiving unit, a humidity judgment unit, and a notification execution unit.

[0047] The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It is used to execute corresponding humidity alarm actions based on the humidity measurement results of the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device. The mechanism further includes the humidity receiving unit, the humidity judging unit, and the notification execution unit being sequentially connected within the instant notification mechanism.

[0048] In addition, in the intelligent analysis system for remote meter reading management, an identification device is connected to the gas meter currently being remotely read. It is used to send a first control command when it is detected that the gas meter is currently in a running state, and to send a second control command when it is detected that the gas meter is currently in a paused state. The identification device identifies whether the gas meter is currently in a running state or a paused state based on whether the gas meter is powered on.

[0049] Therefore, the present invention has at least the following three beneficial technical effects:

[0050] First: Perform training on the deep neural network to obtain the deep neural network that has completed each training session and output it as the deep neural network model. The number of training sessions is proportional to the maximum curvature of the edge curve of the standard profile of the gas meter cover corresponding to the gas meter currently being remotely read, thereby completing the targeted training of the deep neural network model.

[0051] Secondly, a deep neural network model is used to intelligently analyze whether a meter cover exists in the data-sharpened image based on the contrast of the data-sharpened image, the number of pixels occupied by the background area in the data-sharpened image, the total number of pixels in the data-sharpened image, and the grayscale values ​​of each pixel in the image block that matches the standard contour of the meter cover in the data-sharpened image, thereby improving the level of intelligence of remote meter reading management.

[0052] Again: The wireless reporting device is used to package the missing cover information and the gas meter body number together into a network data packet and wirelessly report it to the remote device management server when the cover is missing in the sharpened image of the binary numerical representation data. This completes the intelligent analysis of whether the gas meter in the remote meter reading is missing a cover and the corresponding meter body location.

[0053] Although the invention has been described with reference to the structure disclosed herein, the invention is not limited to the details given, and this application is intended to cover such modifications or variations that fall within the scope of the following claims for improvement purposes.

Claims

1. An intelligent analysis system for remote meter reading management, characterized in that, The system includes: The device is connected to the gas meter being remotely read and is used to send a first control command when the gas meter is detected to be in operation, and to send a second control command when the gas meter is detected to be in paused state. A directional image capture device is installed at the front end of the remote meter reading terminal and wirelessly connected to the operation identification device. It is used to perform a directional image capture operation on the operation scene of the gas meter when the first control command is received, so as to obtain and output the corresponding scene capture image. The content enhancement device is installed inside the remote meter reading terminal and connected to the directional capture device. It is used to perform image SVD enhancement processing on the received scene capture images to obtain and output the corresponding content enhancement images. A real-time filtering device, connected to the content enhancement device, is used to perform Gaussian high-pass filtering on the received content enhancement image to obtain and output a corresponding real-time filtered image. A data sharpening device, connected to the real-time filtering device, is used to perform spatial domain differential sharpening processing on the received real-time filtered image to obtain and output the corresponding data sharpened image. The model building device is installed inside the remote meter reading terminal. It is used to perform training on the deep neural network to obtain the deep neural network that has completed each training and output it as the deep neural network model. The number of training sessions is proportional to the maximum curvature of the edge curve of the standard profile of the gas meter cover. An intelligent analysis device, installed inside the remote meter reading terminal and connected to both the data sharpening device and the model building device, is used to take the contrast of the data sharpened image, the number of pixels occupied by the background area in the data sharpened image, the total number of pixels in the data sharpened image, and the grayscale values ​​of each pixel in the image block that matches the standard contour of the meter cover in the data sharpened image as inputs to the deep neural network model, so as to execute the deep neural network model and obtain the binary value output by the deep neural network model that indicates whether the meter cover exists in the data sharpened image; A wireless reporting device, connected to the intelligent analysis device, is used to package the missing cover information and the gas meter body number together into a network data packet and wirelessly report it to a remote device management server when the binary numerical representation of the data sharpening image does not show the cover.

2. The intelligent analysis system for remote meter reading management as described in claim 1, characterized in that: The wireless reporting device is also used to temporarily suspend packaging the missing cover information and the gas meter body number together into a network data packet and wirelessly reporting it to a remote device management server when the binary numerical representation of the data sharpening image shows the presence of a cover. The directional capturing device is also used to stop performing directional capturing operations on the operating scene of the gas meter when it receives the second control command; The directional capture device is installed at the front end of the remote meter reading terminal and wirelessly connected to the operation identification device. It is used to perform directional capture operation on the operation scene of the gas meter when the first control command is received, so as to obtain and output the corresponding scene capture image. The directional capture device and the operation identification device conduct wireless network data interaction through a WIFI communication network.

3. The intelligent analysis system for remote meter reading management as described in claim 2, characterized in that, The system also includes: A humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. The multiple humidity measurement units used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device are multiple non-contact humidity sensors.

4. The intelligent analysis system for remote meter reading management as described in claim 3, characterized in that: The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors with identical internal structures used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device.

5. The intelligent analysis system for remote meter reading management as described in claim 3, characterized in that: The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, each having the same upper and lower humidity measurement thresholds.

6. The intelligent analysis system for remote meter reading management as described in claim 5, characterized in that: The humidity measurement mechanism includes multiple humidity measurement units for measuring the current surface humidity values ​​of the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It also includes multiple non-contact humidity sensors used by the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, each at an equidistant distance from the respective device.

7. The intelligent analysis system for remote meter reading management as described in any one of claims 3-6, characterized in that, The system also includes: The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively, and is used to execute corresponding humidity alarm actions based on the humidity measurement results of the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device.

8. The intelligent analysis system for remote meter reading management as described in claim 7, characterized in that: The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It is used to execute corresponding humidity alarm actions based on the humidity measurement results from the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device. The instant notification mechanism includes a built-in humidity receiving unit, a humidity judgment unit, and a notification execution unit.

9. The intelligent analysis system for remote meter reading management as described in claim 8, characterized in that: The instant notification mechanism is connected to multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device, respectively. It is used to execute corresponding humidity alarm actions based on the humidity measurement results of the multiple non-contact humidity sensors used in the content enhancement device, the real-time filtering device, the data sharpening device, and the model building device. The mechanism further includes the humidity receiving unit, the humidity judging unit, and the notification execution unit being sequentially connected within the instant notification mechanism.

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