Ammeter damage determination system

Abstract

The invention relates to an electricity meter damage judgment system which comprises a network reconstruction device, a pattern analysis device, a content extraction device, a judgment processing mechanism, a state recognition mechanism and the like. According to the electricity meter damage judgment system, the damage area of the front end surface of the target electricity meter in the current state can be effectively judged by adopting the different damage judgment models designed for different electricity meter types in a targeted mode, and therefore normal operation of the different types of electricity meters is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of electric meters, and in particular to an electric meter damage determination system. Background Art

[0002] When choosing an energy meter, the current value selection is the most important and the most complicated. The first is the starting current, that is, the minimum current that can make the turntable rotate continuously; the second is the multiple of the maximum rated current relative to the basic current. In addition, there are differences in performance between old and new meters. The old energy meters still used in old houses have a relatively large starting current, generally (5% to 10%); the maximum rated current is small, generally ≤2, and only one current value is marked on the dial, which is generally called the rated current. Therefore, when in use, the current of the load circuit should be greater than 10% of the rated current, less than 120% or less than 125%.

[0003] Generally, the starting current of the electric energy meter used in the newly built house is small, which is 0.5% for the grade meter; the maximum rated current is large, which is generally (2~4), and some can reach (6~8). There are two current values ​​marked on the dial of the new electric energy meter, such as 5 (20A). When choosing this electric energy meter, on the one hand, it should be noted that the minimum load current cannot be lower than the starting current, that is, 0.5%×≥5A=0.025A; on the other hand, the current meter value used for a long time cannot be higher than the maximum rated current value of 20A. When choosing an electric energy meter, it should be considered that the various electrical appliances entering the home are increasing day by day, and a margin should be left, and it should also be reasonable and moderate, because the larger the multiple, the higher the price of the meter.

[0004] In actual use, it is necessary to ensure that the meters of different types are not damaged to ensure normal operation. However, the existing technology is unable to customize damage judgment models with different structures for different meter types, resulting in a lack of reliable, effective and stable judgment schemes for the damage of meters of different meter types. Summary of the invention

[0005] In order to overcome the technical problems in the prior art, the present invention proposes a meter damage judgment system, which adopts a network reconstruction device to perform multiple learning operations on a convolutional neural network to complete multiple reconstructions of the convolutional neural network, and outputs the convolutional neural network that has completed multiple reconstructions as a damage judgment model. The number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target meter, so as to customize damage judgment models of different structures for different types of meters, and then selectively screen sufficient and comprehensive multiple basic data for the damage judgment model to ensure the effectiveness and stability of the judgment result. The sufficient and comprehensive multiple basic data are the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area, wherein the benchmark overhead pattern is the front end surface of the target meter with the front end surface intact. The overhead pattern obtained only includes the front end surface of a single target meter, and the imaging area is the picture area obtained by performing the segmentation processing of the front end surface on the overhead picture of the target meter in the current state. Finally, the damage judgment model is used to intelligently judge the damaged area of ​​the front end surface of the target meter in the current state based on the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel of the imaging area. The damage level of the front end surface of the target meter in the current state is also determined based on the percentage of the damaged area of ​​the front end surface of the target meter in the current state to the distribution area of ​​the front end surface of the target meter. The higher the percentage value, the higher the damage level of the front end surface of the target meter in the current state, thereby completing the intelligent judgment of the real-time damage status of each meter based on the artificial intelligence model and visualization information.

[0006] According to the present invention, a system for determining damage of an electric meter is provided, the system comprising: A network reconstruction device, used to perform multiple learning operations on the convolutional neural network to complete multiple reconstructions of the convolutional neural network, and output the convolutional neural network that has completed the multiple reconstructions as a damage determination model, wherein the number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target electric meter; A pattern analysis device is used to perform visual data analysis on a reference overhead pattern of a front surface of a target electric meter in a state where the front surface is intact, so as to obtain various visualization data of the reference overhead pattern, wherein the various visualization data of the reference overhead pattern are the total number of pixels of the reference overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel; A content extraction device is used to perform a front surface segmentation process on the overhead view of the target electric meter in the current state to obtain an imaging area of ​​the front surface, and output the total number of pixels in the imaging area and the respective cyan component values, magenta component values, yellow component values, and black component values ​​corresponding to the respective pixels as various visualization data of the imaging area; A determination processing mechanism, connected to the network reconstruction device, the pattern analysis device and the content extraction device respectively, for intelligently determining the damaged area of ​​the front end surface of the target electric meter in the current state by using the damage determination model based on the distribution area of ​​the front end surface of the target electric meter, various visual data of the reference overhead pattern, various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the reference overhead pattern, and various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the imaging area; a state recognition mechanism connected to the determination processing mechanism, for determining a damage level of the front end surface of the target meter in the current state based on a percentage of the damaged area of ​​the front end surface of the target meter in the current state to the distribution area of ​​the front end surface of the target meter, wherein the higher the percentage value, the higher the damage level of the front end surface of the target meter in the current state; Among them, the pattern analysis device is used to perform visual data analysis on the benchmark overhead pattern of the front end surface of the target electric meter when the front end surface is intact, so as to obtain various visualization data of the benchmark overhead pattern. The various visualization data of the benchmark overhead pattern are the total number of pixels of the benchmark overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel, including: the benchmark overhead pattern only includes the front end surface of a single target electric meter.

[0007] It can be seen that the present invention has at least the following three important inventive features: Firstly, a network reconstruction device is used to perform multiple learning operations on a convolutional neural network to complete multiple reconstructions of the convolutional neural network, and the convolutional neural network that has completed the multiple reconstructions is output as a damage determination model, wherein the number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target electric meter, thereby customizing damage determination models of different structures for different types of electric meters; Secondly: targeted screening of multiple sufficient and comprehensive basic data for the damage judgment model to ensure the validity and stability of the judgment result, wherein the multiple sufficient and comprehensive basic data are the distribution area of ​​the front end surface of the target meter, various visualization data of the benchmark overhead pattern, various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the benchmark overhead pattern, and various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the imaging area, wherein the benchmark overhead pattern is an overhead pattern of the front end surface of the target meter in a complete front end state, which only includes the front end surface of a single target meter, and the imaging area is a screen area obtained by performing front end surface segmentation processing on the overhead screen of the target meter in the current state; Again: a damage judgment model is used to intelligently judge the damaged area of ​​the front end surface of the target meter in the current state based on the distribution area of ​​the front end surface of the target meter, various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area. The damage level of the front end surface of the target meter in the current state is also determined based on the percentage of the damaged area of ​​the front end surface of the target meter in the current state to the distribution area of ​​the front end surface of the target meter. The higher the percentage value, the higher the damage level of the front end surface of the target meter in the current state, thereby completing the intelligent judgment of the real-time damage status of each meter based on the artificial intelligence model and visualization information.

[0008] The meter damage determination system of the present invention has compact logic and wide application. Since different damage determination models designed specifically for different types of meters can be used to effectively determine the damage area of ​​the front surface of the target meter in the current state, the normal operation of different types of meters is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0010] Figure 1 1 is a block diagram showing the internal structure of an electric meter damage determination system according to Embodiment 1 of the present invention.

[0011] Figure 2 1 is a block diagram showing the internal structure of an electric meter damage determination system according to Embodiment 2 of the present invention.

[0012] Figure 3 1 is a block diagram showing the internal structure of an electric meter damage determination system according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0013] The implementation scheme of the electric meter damage determination system of the present invention will be described in detail below with reference to the accompanying drawings.

[0014] Figure 1 The internal structure block diagram of the electric meter damage determination system according to Embodiment 1 of the present invention is shown, and the system includes: A network reconstruction device, used to perform multiple learning operations on the convolutional neural network to complete multiple reconstructions of the convolutional neural network, and output the convolutional neural network that has completed the multiple reconstructions as a damage determination model, wherein the number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target electric meter; For example, a network reconstruction device is used to perform multiple learning operations on a convolutional neural network to complete multiple reconstructions of the convolutional neural network, and output the convolutional neural network that has completed the multiple reconstructions as a damage determination model, wherein the number of times the convolutional neural network performs reconstructions is positively correlated with the distribution area of ​​the front end surface of the target electric meter, including: selecting a numerical conversion formula to represent a numerical conversion relationship in which the number of times the convolutional neural network performs reconstructions is positively correlated with the distribution area of ​​the front end surface of the target electric meter; A pattern analysis device is used to perform visual data analysis on a reference overhead pattern of a front surface of a target electric meter in a state where the front surface is intact, so as to obtain various visualization data of the reference overhead pattern, wherein the various visualization data of the reference overhead pattern are the total number of pixels of the reference overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel; A content extraction device is used to perform a front surface segmentation process on the overhead view of the target electric meter in the current state to obtain an imaging area of ​​the front surface, and output the total number of pixels in the imaging area and the respective cyan component values, magenta component values, yellow component values, and black component values ​​corresponding to the respective pixels as various visualization data of the imaging area; A determination processing mechanism, connected to the network reconstruction device, the pattern analysis device and the content extraction device respectively, for intelligently determining the damaged area of ​​the front end surface of the target electric meter in the current state by using the damage determination model based on the distribution area of ​​the front end surface of the target electric meter, various visual data of the reference overhead pattern, various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the reference overhead pattern, and various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the imaging area; a state recognition mechanism connected to the determination processing mechanism, for determining a damage level of the front end surface of the target meter in the current state based on a percentage of the damaged area of ​​the front end surface of the target meter in the current state to the distribution area of ​​the front end surface of the target meter, wherein the higher the percentage value, the higher the damage level of the front end surface of the target meter in the current state; The pattern analysis device is used to perform visual data analysis on a reference overhead pattern of the front surface of a target electric meter in a state where the front surface is intact, so as to obtain various visualization data of the reference overhead pattern, wherein the various visualization data of the reference overhead pattern are the total number of pixels of the reference overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel, including: the reference overhead pattern only includes the front surface of a single target electric meter; The network reconstruction device is used to perform multiple learning operations on the convolutional neural network to complete multiple reconstructions of the convolutional neural network, and output the convolutional neural network that has completed multiple reconstructions as a damage determination model, and the number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target electric meter, including: the smaller the distribution area of ​​the front end surface of the target electric meter, the fewer the number of times the convolutional neural network performs reconstruction; Among them, the damage judgment model is used to intelligently judge the damaged area of ​​the front end surface of the target meter in the current state based on the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area, including: the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area are input in parallel into the damage judgment model to run the damage judgment model, and obtain the damaged area of ​​the front end surface of the target meter in the current state output by the damage judgment model.

[0015] Figure 2 1 is a block diagram showing the internal structure of an electric meter damage determination system according to Embodiment 2 of the present invention.

[0016] Compared to Figure 1 , Figure 2 The meter damage determination system in the invention may also include the following components: A voltage supply interface, connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, respectively, for providing the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with their respective required working voltages; The voltage supply interface is connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device respectively, and is used to provide the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with the required working voltages respectively, including: the working voltages required by the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device respectively include 3.3 volts and 5 volts; The voltage supply interface is connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device respectively, and is used to provide the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with the required working voltages respectively, and further includes: the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are arranged around the voltage supply interface and are isolated from the voltage supply interface by different electromagnetic shielding mechanisms respectively; The determination and processing mechanism, the network reconstruction device, the pattern analysis device, and the content extraction device are arranged around the voltage supply interface and are isolated from the voltage supply interface by using different electromagnetic shielding mechanisms respectively, including: the distances from the different electromagnetic shielding mechanisms respectively used by the determination and processing mechanism, the network reconstruction device, the pattern analysis device, and the content extraction device to the voltage supply interface are equal; And wherein the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are arranged around the voltage supply interface and are isolated from the voltage supply interface by different electromagnetic shielding mechanisms respectively, and also includes: the internal structures of the different electromagnetic shielding mechanisms respectively used by the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are the same.

[0017] Figure 3 1 is a block diagram showing the internal structure of an electric meter damage determination system according to Embodiment 3 of the present invention.

[0018] Compared to Figure 1 , Figure 3 The meter damage determination system in the invention may also include the following components: a radiation sensing device, connected to the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, respectively, for providing the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation amounts; The radiation sensing device is connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device respectively, and is used to provide the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation quantities, including: the radiation sensing device includes a plurality of radiation sensing units, which are used to provide respective numerical sensing operations of real-time electromagnetic radiation quantities for the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device; The radiation sensing device includes a plurality of radiation sensing units, which are used to respectively provide the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation amounts, including: the internal structures of the plurality of radiation sensing units are the same; And wherein, the radiation sensing device includes a plurality of radiation sensing units, which are used to provide respective numerical sensing operations of real-time electromagnetic radiation quantities for the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, respectively, including: the plurality of radiation sensing units are respectively equipped with respective radiation alarm components.

[0019] In addition, in the meter damage judgment system, the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the various horizontal coordinate values ​​and the various vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the various horizontal coordinate values ​​and the various vertical coordinate values ​​corresponding to each pixel point of the imaging area are input in parallel to the damage judgment model to run the damage judgment model, and obtaining the damage area of ​​the front end surface of the target meter in the current state output by the damage judgment model includes: using a parallel control interface to execute the parallel input of the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the various horizontal coordinate values ​​and the various vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the various horizontal coordinate values ​​and the various vertical coordinate values ​​corresponding to each pixel point of the imaging area.

[0020] Although the present invention has been comprehensively described by way of example, it should be understood that various changes and modifications are apparent to those skilled in the art. Therefore, unless otherwise indicated, changes and modifications depart from the scope of the present invention, such changes and modifications should all be considered to be included in the scope of the present invention.

Claims

1. A system for determining damage to an electric meter, characterized in that: The system comprises: A network reconstruction device, used to perform multiple learning operations on the convolutional neural network to complete multiple reconstructions of the convolutional neural network, and output the convolutional neural network that has completed the multiple reconstructions as a damage determination model, wherein the number of times the convolutional neural network performs reconstruction is positively correlated with the distribution area of ​​the front end surface of the target electric meter; A pattern analysis device is used to perform visual data analysis on a reference overhead pattern of a front surface of a target electric meter in a state where the front surface is intact, so as to obtain various visualization data of the reference overhead pattern, wherein the various visualization data of the reference overhead pattern are the total number of pixels of the reference overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel; A content extraction device is used to perform a front surface segmentation process on the overhead view of the target electric meter in the current state to obtain an imaging area of ​​the front surface, and output the total number of pixels in the imaging area and the respective cyan component values, magenta component values, yellow component values, and black component values ​​corresponding to the respective pixels as various visualization data of the imaging area; A determination processing mechanism, connected to the network reconstruction device, the pattern analysis device and the content extraction device respectively, for intelligently determining the damaged area of ​​the front end surface of the target electric meter in the current state by using the damage determination model based on the distribution area of ​​the front end surface of the target electric meter, various visual data of the reference overhead pattern, various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the reference overhead pattern, and various horizontal coordinate values ​​and various vertical coordinate values ​​corresponding to various pixel points of the imaging area; a state recognition mechanism connected to the determination processing mechanism, for determining a damage level of the front end surface of the target meter in the current state based on a percentage of the damaged area of ​​the front end surface of the target meter in the current state to the distribution area of ​​the front end surface of the target meter, wherein the higher the percentage value, the higher the damage level of the front end surface of the target meter in the current state; Among them, the pattern analysis device is used to perform visual data analysis on the benchmark overhead pattern of the front end surface of the target electric meter when the front end surface is intact, so as to obtain various visualization data of the benchmark overhead pattern. The various visualization data of the benchmark overhead pattern are the total number of pixels of the benchmark overhead pattern and the respective cyan component values, magenta component values, yellow component values ​​and black component values ​​corresponding to each pixel, including: the benchmark overhead pattern only includes the front end surface of a single target electric meter.

2. The electric meter damage determination system according to claim 1, characterized in that: A network reconstruction device, used for performing multiple learning operations on a convolutional neural network to complete multiple reconstructions of the convolutional neural network, and outputting the convolutional neural network that has completed multiple reconstructions as a damage determination model, wherein the number of times the convolutional neural network performs reconstructions is positively correlated with the distribution area of ​​the front end surface of the target electric meter, including: the smaller the distribution area of ​​the front end surface of the target electric meter, the fewer the number of times the convolutional neural network performs reconstructions; Among them, the damage judgment model is used to intelligently judge the damaged area of ​​the front end surface of the target meter in the current state based on the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area, including: the distribution area of ​​the front end surface of the target meter, the various visualization data of the benchmark overhead pattern, the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the benchmark overhead pattern, and the horizontal coordinate values ​​and the vertical coordinate values ​​corresponding to each pixel point of the imaging area are input in parallel into the damage judgment model to run the damage judgment model, and obtain the damaged area of ​​the front end surface of the target meter in the current state output by the damage judgment model.

3. The electric meter damage determination system according to claim 2, characterized in that: The system further comprises: A voltage supply interface, connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, respectively, for providing the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with their respective required working voltages; Among them, the voltage supply interface is respectively connected to the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, and is used to provide the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with the required working voltages respectively, including: the working voltages required by the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device respectively include 3.3 volts and 5 volts.

4. The electric meter damage determination system according to claim 3, characterized in that: A voltage supply interface is respectively connected to the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, and is used to provide the determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with their respective required working voltages. The determination processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are also configured around the voltage supply interface and are isolated from the voltage supply interface by different electromagnetic shielding mechanisms.

5. The electric meter damage determination system according to claim 4, characterized in that: The determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are arranged around the voltage supply interface and are isolated from the voltage supply interface by using different electromagnetic shielding mechanisms respectively, including: the distances from the different electromagnetic shielding mechanisms respectively used by the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device to the voltage supply interface are equal.

6. The electric meter damage determination system according to claim 4, characterized in that: The determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are arranged around the voltage supply interface and are isolated from the voltage supply interface by using different electromagnetic shielding mechanisms respectively. It also includes: the internal structures of the different electromagnetic shielding mechanisms respectively used by the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device are the same.

7. The electric meter damage determination system according to claim 2, characterized in that: The system further comprises: a radiation sensing device, connected to the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, respectively, for providing the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation amounts; Among them, the radiation sensing device is respectively connected to the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, and is used to provide the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation quantities, including: the radiation sensing device includes multiple radiation sensing units, which are used to provide the judgment processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation quantities.

8. The electric meter damage determination system according to claim 7, characterized in that: The radiation sensing device includes a plurality of radiation sensing units, which are used to provide respective numerical sensing operations of real-time electromagnetic radiation quantities for the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device, including: the internal structures of the plurality of radiation sensing units are the same.

9. The electric meter damage determination system according to claim 7, characterized in that: The radiation sensing device includes a plurality of radiation sensing units, which are used to provide the determination and processing mechanism, the network reconstruction device, the pattern analysis device and the content extraction device with respective numerical sensing operations of real-time electromagnetic radiation quantities, including: the plurality of radiation sensing units are respectively equipped with respective radiation alarm components.

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