Distribution Box Data Analysis System Using BP Neural Network

Through the BP neural network model combined with visual acquisition and signal recognition technology, the appearance aesthetic problem caused by the change in the size of the distribution box is solved, and the intelligent analysis of the front width of the distribution box is realized, which improves the reliability and operation of management. stability.

CN117952954BActive Publication Date: 2024-12-27HENAN XJ INSTR
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Patent Information

Application Number
CN202410209416.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-02-26
Publication Date
2024-12-27
Estimated Expiration
2044-02-26

AI Technical Summary

Technical Problem

During long-term use of the power distribution box, the box size is prone to change, resulting in the appearance aesthetics being affected, especially the width data on the front is more obvious, and how to perform contactless intelligent analysis has become an important issue in management.

Method used

The BP neural network model is used to combine the visual acquisition mechanism and signal recognition mechanism to obtain pixel point information and curvature data through color imaging feature detection of the distribution box shell, and real-time width analysis of the front of the distribution box is performed, using the BP neural network The model completes training to set the total number to realize intelligent analysis of the front width of the distribution box.

Benefits of technology

It realizes intelligent analysis of the front width of the distribution box, with stable operation and intelligent design, provides more reliable basic data, provides high-quality imaging images for subsequent operations, and improves the reliability of distribution box management.

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Abstract

The present invention relates to a distribution box data analysis system using a BP neural network, comprising: a visual acquisition mechanism, which is arranged opposite to a visual inspection station and is used to perform visual picture acquisition on the front of a distribution box whose front width is to be inspected; a content mapping mechanism, which is used to intelligently analyze the real-time width value of the front of the distribution box according to various pixel point information corresponding to the front of the distribution box and item-by-item curvature data corresponding to the front of the distribution box using a BP neural network model. The distribution box data analysis system using a BP neural network of the present invention has stable operation and intelligent design. Since the real-time width value of the front of the distribution box can be intelligently analyzed using a BP neural network model based on various visual information of the distribution box shell, the BP neural network model is a BP neural network that has been trained a set total number of times, thereby completing the intelligent analysis of the front width of the current distribution box.
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Description

Technical Field

[0001] The invention relates to the field of power distribution boxes, and in particular to a power distribution box data analysis system using a BP neural network. Background Art

[0002] The distribution box has the characteristics of small size, easy installation, special technical performance, fixed position, unique configuration function, no site restrictions, wide application, stable and reliable operation, high space utilization, small footprint and environmental protection. It is the control center for commanding various components in the power supply line to reasonably distribute electric energy, the control link for reliably receiving the upper power supply and correctly feeding the load electric energy, and the key to obtaining the user's satisfaction with the power supply quality. Improving the operational reliability of the power distribution box is the goal of creating high-quality projects. The purpose of the distribution box is to reasonably distribute electric energy and facilitate the opening and closing operation of the circuit. It has a high level of safety protection and can intuitively display the conduction status of the circuit.

[0003] The size of the distribution box is prone to change during long-term use, which has a certain impact on the appearance of the distribution box during normal use. This size change is more obvious in the width and length data on the front of the distribution box. How to perform contactless intelligent analysis of the appearance data of the distribution box is one of the important issues that need to be solved in the current distribution box management. Summary of the invention

[0004] In order to solve the technical problems in the related fields, the present invention provides a distribution box data analysis system using a BP neural network, which can detect the number of pixel rows, pixel columns and total number of pixel points occupied in the sequential mapping picture obtained after targeted optimization processing on the front of the distribution box based on the color imaging characteristics of the distribution box shell as the information of various pixel points corresponding to the front of the distribution box, and analyze the maximum curvature, minimum curvature and the curvature with the most occurrences of the edge curve of the distribution pattern of the front of the distribution box in the sequential mapping picture as the item-by-item curvature data output corresponding to the front of the distribution box, and then use the BP neural network model to intelligently analyze the real-time width value of the front of the distribution box according to the various pixel point information corresponding to the front of the distribution box and the item-by-item curvature data corresponding to the front of the distribution box. The BP neural network model is a BP neural network after completing a set total number of trainings, and the training number of each training is monotonically positively correlated with the total number of pixels corresponding to the front of the distribution box, thereby completing the intelligent analysis of the front width of the current distribution box.

[0005] According to the present invention, a distribution box data analysis system using a BP neural network is provided, the system comprising:

[0006] A box pushing mechanism is used to push the distribution box whose front width is to be inspected to the visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the working parameters of the positioning unit, the timing unit and the pushing unit;

[0007] A visual acquisition mechanism is arranged opposite to the visual inspection station and is used to acquire visual images from the front of the distribution box body whose front width is to be inspected, so as to obtain and output a corresponding directional acquisition image;

[0008] A sequential mapping mechanism, connected to the visual acquisition mechanism and comprising a front-end mapping component, a secondary mapping component and a terminal mapping component, for sequentially performing tilt correction processing, harmonic mean filtering processing and density function-based histogram equalization processing on the received directional acquisition pictures to obtain and output corresponding sequential mapping pictures;

[0009] A signal recognition mechanism is connected to the sequential mapping mechanism, and is used to receive the sequential mapping picture, and detect the number of pixel rows, the number of pixel columns, and the total number of pixel points occupied on the front side of the distribution box in the sequential mapping picture based on the color imaging characteristics of the distribution box shell, so as to output various pixel point information corresponding to the front side of the distribution box;

[0010] A data capture mechanism, connected to the signal recognition mechanism, for analyzing the maximum curvature, the minimum curvature and the curvature with the most occurrences of the edge curve of the distribution pattern on the front of the distribution box in the sequential mapping screen to output as item-by-item curvature data corresponding to the front of the distribution box;

[0011] The content mapping mechanism is connected to the signal recognition mechanism and the data capture mechanism respectively, and is used to adopt a BP neural network model to intelligently analyze the real-time width value of the front of the distribution box according to the information of each pixel point corresponding to the front of the distribution box and the curvature data corresponding to the front of the distribution box. The BP neural network model is a BP neural network after completing a set total number of trainings, and the training number of each training is monotonically positively correlated with the total number of pixels corresponding to the front of the distribution box;

[0012] Among them, the sequential mapping mechanism is connected to the visual acquisition mechanism and includes a front-end mapping component, a secondary-end mapping component and a terminal mapping component, which is used to sequentially perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function on the received directional acquisition picture to obtain and output the corresponding sequential mapping picture, including: the front-end mapping component, the secondary-end mapping component and the terminal mapping component are used to respectively perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function.

[0013] Therefore, the present invention has at least the following three beneficial technical effects:

[0014] Firstly: based on the color imaging characteristics of the distribution box shell, the number of pixel rows, the number of pixel columns and the total number of pixel points occupied in the sequential mapping picture obtained after the targeted optimization process on the front of the distribution box are detected as the information of each item of pixel point corresponding to the front of the distribution box, and the maximum curvature, the minimum curvature and the curvature with the most occurrences of the edge curve of the distribution pattern of the front of the distribution box in the sequential mapping picture are analyzed as the item-by-item curvature data output corresponding to the front of the distribution box;

[0015] Secondly: a BP neural network model is used to intelligently analyze the real-time width value of the front of the distribution box according to the information of various pixel points corresponding to the front of the distribution box and the curvature data corresponding to the front of the distribution box. The BP neural network model is a BP neural network after completing a set total number of trainings, and the number of trainings for each training is monotonically positively correlated with the total number of pixel points corresponding to the front of the distribution box, thereby completing the intelligent analysis of the front width of the current distribution box;

[0016] Again: a sequential mapping mechanism is introduced to sequentially perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function on the front imaging picture of the distribution box whose front width is to be inspected, so as to complete the targeted optimization of the front imaging picture of the distribution box, obtain and output a sequential mapping picture with better image quality, thereby providing more reliable basic data for subsequent intelligent analysis operations.

[0017] The distribution box data analysis system using BP neural network of the present invention is stable in operation and intelligent in design. Since the real-time width value of the front of the distribution box can be intelligently analyzed by using BP neural network model based on various visual information of the distribution box shell, the BP neural network model is a BP neural network after each training for a set total number of times, thereby completing the intelligent analysis of the front width of the current distribution box. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0019] Figure 1 It is a schematic diagram of the internal structure of a distribution box data analysis system using a BP neural network according to an embodiment of the distribution box of the present invention.

[0020] Figure 2 It is a schematic diagram of the internal structure of a distribution box data analysis system using a BP neural network according to Embodiment B of the present invention.

[0021] Figure 3It is a schematic diagram of the internal structure of a distribution box data analysis system using a BP neural network according to Embodiment C of the present invention. DETAILED DESCRIPTION

[0022] The following will describe in detail an embodiment of the distribution box data analysis system using a BP neural network according to the present invention with reference to the accompanying drawings.

[0023] Figure 1 The internal structure diagram of the distribution box data analysis system using BP neural network according to the distribution box embodiment of the present invention is shown, and the system includes:

[0024] A box pushing mechanism is used to push the distribution box whose front width is to be inspected to the visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the working parameters of the positioning unit, the timing unit and the pushing unit;

[0025] For example, a box pushing mechanism is used to push a distribution box to be inspected for front width to a visual inspection station, and the box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller, and the main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize the real-time configuration of the respective working parameters of the positioning unit, the timing unit and the pushing unit. The box pushing mechanism includes a mechanical pushing arm, which is used to specifically perform the pushing operation of the distribution box to be inspected for front width;

[0026] A visual acquisition mechanism is arranged opposite to the visual inspection station and is used to acquire visual images from the front of the distribution box body whose front width is to be inspected, so as to obtain and output a corresponding directional acquisition image;

[0027] A sequential mapping mechanism, connected to the visual acquisition mechanism and comprising a front-end mapping component, a secondary mapping component and a terminal mapping component, for sequentially performing tilt correction processing, harmonic mean filtering processing and density function-based histogram equalization processing on the received directional acquisition pictures to obtain and output corresponding sequential mapping pictures;

[0028] A signal recognition mechanism is connected to the sequential mapping mechanism, and is used to receive the sequential mapping picture, and detect the number of pixel rows, the number of pixel columns, and the total number of pixel points occupied on the front side of the distribution box in the sequential mapping picture based on the color imaging characteristics of the distribution box shell, so as to output various pixel point information corresponding to the front side of the distribution box;

[0029] A data capture mechanism, connected to the signal recognition mechanism, for analyzing the maximum curvature, the minimum curvature and the curvature with the most occurrences of the edge curve of the distribution pattern on the front of the distribution box in the sequential mapping screen to output as item-by-item curvature data corresponding to the front of the distribution box;

[0030] The content mapping mechanism is connected to the signal recognition mechanism and the data capture mechanism respectively, and is used to adopt a BP neural network model to intelligently analyze the real-time width value of the front of the distribution box according to the information of each pixel point corresponding to the front of the distribution box and the curvature data corresponding to the front of the distribution box. The BP neural network model is a BP neural network after completing a set total number of trainings, and the training number of each training is monotonically positively correlated with the total number of pixels corresponding to the front of the distribution box;

[0031] Wherein, the sequential mapping mechanism is connected to the visual acquisition mechanism and includes a front-end mapping component, a secondary mapping component and a terminal mapping component, and is used to sequentially perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function on the received directional acquisition picture to obtain and output the corresponding sequential mapping picture, including: the front-end mapping component, the secondary mapping component and the terminal mapping component are used to perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function respectively;

[0032] Among them, the box pushing mechanism is used to push the distribution box whose front width is to be inspected to the visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the respective working parameters of the positioning unit, the timing unit and the pushing unit, including: the pushing unit pushes the distribution box whose front width is to be inspected to the visual inspection station based on the positioning data of the positioning unit.

[0033] Figure 2 It is a schematic diagram of the internal structure of a distribution box data analysis system using a BP neural network according to Embodiment B of the present invention.

[0034] and Figure 1 different, Figure 2 The distribution box data analysis system using BP neural network can also include the following components:

[0035] a voltage conversion device, arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively;

[0036] Among them, the voltage conversion device is arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and is respectively connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component, including: the voltage conversion device is used to provide the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component with their respective required working voltages.

[0037] Figure 3 It is a schematic diagram of the internal structure of a distribution box data analysis system using a BP neural network according to Embodiment C of the present invention.

[0038] and Figure 1 different, Figure 3 The distribution box data analysis system using BP neural network can also include the following components:

[0039] a quartz oscillator device, arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively;

[0040] Among them, the quartz oscillator device is arranged near the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and is respectively connected to the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component, including: the quartz oscillator device is used to provide the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component with the reference clock pulses they need respectively.

[0041] Next, the specific structure of the distribution box data analysis system using the BP neural network of the present invention will be further described.

[0042] In the distribution box data analysis system using BP neural network according to various embodiments of the present invention:

[0043] A programmable logic device is used to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

[0044] In the distribution box data analysis system using BP neural network according to various embodiments of the present invention:

[0045] Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image sharpening processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

[0046] In the distribution box data analysis system using BP neural network according to various embodiments of the present invention:

[0047] Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image enhancement processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

[0048] In the distribution box data analysis system using BP neural network according to various embodiments of the present invention:

[0049] Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image filtering processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

[0050] And in the distribution box data analysis system using BP neural network according to various embodiments of the present invention:

[0051] Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing distortion correction processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

[0052] In addition, in the distribution box data analysis system using the BP neural network, the box pushing mechanism is used to push the distribution box whose front width is to be inspected to the visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the respective working parameters of the positioning unit, the timing unit and the pushing unit. It also includes: the main controller is an MCU controller, and the timing unit is based on a quartz oscillation component.

[0053] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications, equivalent structures and functions.

Claims

1. A distribution box data analysis system using BP neural network, characterized in that: The system comprises: A box pushing mechanism is used to push the distribution box whose front width is to be inspected to the visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the working parameters of the positioning unit, the timing unit and the pushing unit; A visual acquisition mechanism is arranged opposite to the visual inspection station and is used to acquire visual images from the front of the distribution box body whose front width is to be inspected, so as to obtain and output a corresponding directional acquisition image; A sequential mapping mechanism, connected to the visual acquisition mechanism and comprising a front-end mapping component, a secondary mapping component and a terminal mapping component, for sequentially performing tilt correction processing, harmonic mean filtering processing and density function-based histogram equalization processing on the received directional acquisition pictures to obtain and output corresponding sequential mapping pictures; A signal recognition mechanism is connected to the sequential mapping mechanism, and is used to receive the sequential mapping picture, and detect the number of pixel rows, the number of pixel columns, and the total number of pixel points occupied by the front face of the distribution box in the sequential mapping picture based on the color imaging characteristics of the distribution box shell, so as to output various pixel point information corresponding to the front face of the distribution box; A data capture mechanism, connected to the signal recognition mechanism, for analyzing the maximum curvature, the minimum curvature and the curvature with the most occurrences of the edge curve of the distribution pattern on the front of the distribution box in the sequential mapping screen to output as item-by-item curvature data corresponding to the front of the distribution box; The content mapping mechanism is connected to the signal recognition mechanism and the data capture mechanism respectively, and is used to adopt a BP neural network model to intelligently analyze the real-time width value of the front of the distribution box according to the information of each pixel point corresponding to the front of the distribution box and the curvature data corresponding to the front of the distribution box. The BP neural network model is a BP neural network after completing a set total number of trainings, and the training number of each training is monotonically positively correlated with the total number of pixels corresponding to the front of the distribution box; Among them, the sequential mapping mechanism is connected to the visual acquisition mechanism and includes a front-end mapping component, a secondary-end mapping component and a terminal mapping component, which is used to sequentially perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function on the received directional acquisition picture to obtain and output the corresponding sequential mapping picture, including: the front-end mapping component, the secondary-end mapping component and the terminal mapping component are used to respectively perform tilt correction processing, harmonic mean filtering processing and histogram equalization processing based on a density function.

2. The distribution box data analysis system using BP neural network as claimed in claim 1, characterized in that: A box pushing mechanism is used to push the distribution box whose front width is to be inspected to a visual inspection station. The box pushing mechanism includes a positioning unit, a timing unit, a pushing unit and a main controller. The main controller is respectively connected to the positioning unit, the timing unit and the pushing unit, and is used to respectively realize real-time configuration of the respective working parameters of the positioning unit, the timing unit and the pushing unit. The pushing unit includes: the pushing unit pushes the distribution box whose front width is to be inspected to the visual inspection station based on the positioning data of the positioning unit.

3. The distribution box data analysis system using BP neural network as claimed in claim 2 is characterized in that: The system further comprises: a voltage conversion device, arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively; Among them, the voltage conversion device is arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and is respectively connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component, including: the voltage conversion device is used to provide the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component with their respective required working voltages.

4. The distribution box data analysis system using BP neural network as claimed in claim 2, characterized in that: The system further comprises: a quartz oscillator device, arranged near the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and connected to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively; Among them, the quartz oscillator device is arranged near the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component and is respectively connected to the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component, including: the quartz oscillator device is used to provide the signal identification mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component with the reference clock pulses they need respectively.

5. The distribution box data analysis system using BP neural network according to any one of claims 2 to 4, characterized in that: A programmable logic device is used to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

6. The distribution box data analysis system using BP neural network as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image sharpening processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

7. The distribution box data analysis system using BP neural network as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image enhancement processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

8. The distribution box data analysis system using BP neural network as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing image filtering processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.

9. The distribution box data analysis system using BP neural network as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively includes: performing distortion correction processing on the output data of the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component to obtain the output processing data corresponding to the signal recognition mechanism, the front-end mapping component, the secondary-end mapping component and the terminal mapping component respectively.