Intelligent analysis system for visual data of active electricity meter

Abstract

The invention relates to an intelligent analysis system for visual data of an active electricity meter, and the system comprises a target collection mechanism which is used for carrying out the visual collection of a current active electricity meter under the control of a remote meter reading instruction, so as to obtain an instant collection image corresponding to a current timestamp; and the state judgment device is used for intelligently judging the surrounding image block with the minimum coding operation amount when the reference coding block serving as the current block to be coded performs intra-frame coding on the current block to be coded by adopting an AI judgment model according to various image data of the immediately acquired image. According to the invention, the AI judgment model designed in a targeted structure can be adopted to intelligently judge the surrounding image block with the least coding operation amount when the reference coding block of the current block to be coded performs intra-frame coding on the current block to be coded; therefore, an automatic and intelligent solution is adopted to meet the coding requirement of minimum intra-frame coding operation amount.

Description

Technical Field

[0001] The present invention relates to the field of active electric meters, and in particular to an intelligent analysis system for visual data of active electric meters. Background Art

[0002] Active watt-hour meter is an instrument that measures active electric energy by integrating active power over time. There are the following main differences between active watt-hour meter and reactive watt-hour meter:

[0003] 1. Conceptual differences:

[0004] 1. Active power meter: An instrument specifically used to measure active power. It calculates active energy by integrating the relationship between active power and time.

[0005] 2. Reactive energy meter: used to measure and record the electric energy required to generate magnetic field and electric field in AC circuit, that is, reactive energy, which reflects the reactive power consumption in the circuit.

[0006] 2. Recording different types of electric energy:

[0007] 1. Active power meter: It records the active power, that is, the actual power consumed. It is the basic data for calculating electricity bills and the operating efficiency of the power system.

[0008] 2. Reactive energy meter: It records reactive energy, which is related to the power factor of the circuit and is used to evaluate and control the reactive power of the circuit.

[0009] 3. Differences in power calculation methods:

[0010] 1. Active power meter: Active power is calculated based on the cosine value of the phase difference between voltage and current when AC load is applied, thereby measuring active electrical energy.

[0011] 2. Reactive power meter: Calculates reactive power based on the sine value of the phase difference, and then measures the consumption of reactive power in the circuit.

[0012] In the prior art, the image processing of active electric meters lacks specificity. For example, for each image block to be encoded, it is necessary to test each surrounding block as a reference image block of the image block to be encoded, so as to obtain the surrounding blocks with the smallest encoding operation amount as the reference image block of the image block to be encoded. Obviously, this image processing mode wastes too much time and operation cost. Summary of the invention

[0013] In order to solve the technical problems in the prior art, the present invention provides an active electric meter visual data intelligent analysis system, which performs multiple training on a deep neural network to obtain a deep neural network after multiple training and uses it as an AI judgment model, wherein the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the real-time acquisition image, thereby completing the customized structural design of the AI ​​judgment model for intelligent judgment of surrounding image blocks with the least coding operation, and also performs a visual acquisition action on the current active electric meter under the control of a remote meter reading instruction to obtain a real-time acquisition image corresponding to a current timestamp, and outputs the number of noise types, the total number of pixels, the mean square error of each pixel value corresponding to each pixel point, and the contrast of the real-time acquisition image as various image data of the real-time acquisition image, and obtains the current block to be encoded in the real-time acquisition image. The related information of each block corresponding to each surrounding image block, and the related information of the block corresponding to the current block to be encoded are obtained, so as to realize the targeted screening of various basic data of the intelligent judgment mechanism for executing the surrounding image blocks with the least encoding operation, and adopt the AI ​​judgment model to intelligently judge the surrounding image blocks with the least encoding operation when performing intra-frame encoding on the current block to be encoded as the reference coding block of the current block to be encoded according to various image data of the real-time acquisition image, the related information of each block corresponding to each surrounding image block of the current block to be encoded in the real-time acquisition image, and the related information of the block corresponding to the current block to be encoded, wherein the AI ​​judgment model outputs the position number of the surrounding image blocks with the least encoding operation when performing intra-frame encoding on the current block to be encoded as the reference coding block of the current block to be encoded, thereby meeting the encoding requirement with the least intra-frame encoding operation.

[0014] According to the present invention, a system for intelligent analysis of active electric meter visual data is provided, the system comprising: The object mapping mechanism is used for mapping the corresponding AI judgment model based on the deep neural network, and the corresponding AI judgment model based on the deep neural network mapping includes: training the deep neural network multiple times to obtain the deep neural network after completing the multiple trainings and outputting it as the AI ​​judgment model; The target acquisition mechanism is used to perform visual acquisition actions on the current active electric meter under the control of the remote meter reading instruction to obtain an instant acquisition image corresponding to the current timestamp; An image analysis mechanism, connected to the target acquisition mechanism, for outputting the number of noise types, the total number of pixels, the mean square error of each pixel value corresponding to each pixel, and the contrast of the real-time acquisition image as various image data of the real-time acquisition image; A surrounding detection mechanism is used to obtain the block-related information corresponding to each surrounding image block of the current block to be encoded in the instant captured image, and obtain the block-related information corresponding to the current block to be encoded; A state judgment device is connected to the object mapping mechanism, the image analysis mechanism and the surrounding detection mechanism respectively, and is used to use the AI ​​judgment model to intelligently judge the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded according to the image data of the real-time collected image, the respective block-related information corresponding to the respective surrounding image blocks of the current block to be coded in the real-time collected image, and the block-related information corresponding to the current block to be coded; Among them, the AI ​​judgment model is used to intelligently judge the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded according to the image data of the real-time acquisition image, the relevant information of each block corresponding to each surrounding image block of the current block to be coded in the real-time acquisition image, and the relevant information of the block corresponding to the current block to be coded, including: the AI ​​judgment model outputs the position number of the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded; Among them, the deep neural network is trained multiple times to obtain the deep neural network after multiple trainings and output as the AI ​​judgment model, including: the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the real-time acquired image.

[0015] It can be seen that the present invention has at least the following important invention points: Invention point 1: The deep neural network is trained multiple times to obtain a deep neural network after multiple training and used as an AI judgment model, wherein the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the instant collected image, thereby completing the customized structural design of the AI ​​judgment model for intelligent judgment of surrounding image blocks with the least amount of coding operations; Invention point 2: Under the control of the remote meter reading instruction, a visual acquisition action is performed on the current active electric meter to obtain a real-time acquisition image corresponding to the current timestamp, and the number of noise types, the total number of pixels, the mean square error of each pixel value corresponding to each pixel, and the contrast of the real-time acquisition image are output as various image data of the real-time acquisition image, and the respective block-related information corresponding to each surrounding image block of the current block to be encoded in the real-time acquisition image is obtained, and the block-related information corresponding to the current block to be encoded is obtained, thereby realizing the targeted screening of various basic data of the intelligent judgment mechanism for executing the surrounding image blocks with the least encoding operation amount; Invention point three: An AI judgment model is used to intelligently judge the surrounding image blocks with the least coding operation amount when performing intra-frame coding on the current block to be encoded as the reference coding block of the current block to be encoded, based on various image data of the real-time acquired image, the block-related information corresponding to each surrounding image block of the current block to be encoded in the real-time acquired image, and the block-related information corresponding to the current block to be encoded, wherein the AI ​​judgment model outputs the position number of the surrounding image blocks with the least coding operation amount when performing intra-frame coding on the current block to be encoded as the reference coding block of the current block to be encoded, thereby meeting the coding requirement of minimizing the intra-frame coding operation amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0017] Figure 1 It is a structural block diagram of an active electricity meter visual data intelligent analysis system according to implementation scheme A of the present invention.

[0018] Figure 2 It is a structural block diagram of an active electricity meter visual data intelligent analysis system according to implementation scheme B of the present invention.

[0019] Figure 3 It is a structural block diagram of an active electricity meter visual data intelligent analysis system according to implementation scheme C of the present invention. DETAILED DESCRIPTION

[0020] The implementation scheme of the active electricity meter visual data intelligent analysis system of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 The structure block diagram of the active electric meter visual data intelligent analysis system according to the A embodiment of the present invention is shown, and the system includes: The object mapping mechanism is used for mapping the corresponding AI judgment model based on the deep neural network, and the corresponding AI judgment model based on the deep neural network mapping includes: training the deep neural network multiple times to obtain the deep neural network after completing the multiple trainings and outputting it as the AI ​​judgment model; Specifically, the object mapping mechanism is used to map the corresponding AI judgment model based on the deep neural network, and the corresponding AI judgment model based on the deep neural network mapping includes: training the deep neural network multiple times to obtain the deep neural network after multiple trainings and outputting it as the AI ​​judgment model, including: selecting and adopting a numerical simulation mode to implement the testing and simulation of the data processing process of training the deep neural network multiple times to obtain the deep neural network after multiple trainings and outputting it as the AI ​​judgment model; The target acquisition mechanism is used to perform visual acquisition actions on the current active electric meter under the control of the remote meter reading instruction to obtain an instant acquisition image corresponding to the current timestamp; An image analysis mechanism, connected to the target acquisition mechanism, for outputting the number of noise types, the total number of pixels, the mean square error of each pixel value corresponding to each pixel, and the contrast of the real-time acquisition image as various image data of the real-time acquisition image; A surrounding detection mechanism is used to obtain the block-related information corresponding to each surrounding image block of the current block to be encoded in the instant captured image, and obtain the block-related information corresponding to the current block to be encoded; A state judgment device is connected to the object mapping mechanism, the image analysis mechanism and the surrounding detection mechanism respectively, and is used to use the AI ​​judgment model to intelligently judge the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded according to the image data of the real-time collected image, the respective block-related information corresponding to the respective surrounding image blocks of the current block to be coded in the real-time collected image, and the block-related information corresponding to the current block to be coded; Among them, the AI ​​judgment model is used to intelligently judge the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded according to the image data of the real-time acquisition image, the relevant information of each block corresponding to each surrounding image block of the current block to be coded in the real-time acquisition image, and the relevant information of the block corresponding to the current block to be coded, including: the AI ​​judgment model outputs the position number of the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded; The deep neural network is trained multiple times to obtain a deep neural network after multiple training and output as an AI judgment model, including: the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the instant acquisition image; The surrounding detection mechanism is used to obtain the block-related information corresponding to each surrounding image block of the current block to be encoded in the real-time acquisition image, and the block-related information corresponding to the current block to be encoded includes: the block-related information corresponding to each image block is the pixel value gradients corresponding to each pixel point of the image block, the content repetition of the image block, and the position number of the image block in the real-time acquisition image; The block-related information corresponding to each image block is the gradients of each pixel value respectively corresponding to each pixel point of the image block, the content repetition of the image block and the position number of the image block in the real-time acquired image, including: the content repetition of each image block is the reciprocal of the ratio obtained by dividing the number of remaining multiple pixel values ​​obtained after performing a deduplication operation on each pixel value respectively corresponding to each pixel point of the image block by the number of each pixel point of the image block; The block-related information corresponding to each image block is the pixel value gradients corresponding to each pixel point of the image block, the content repetition of the image block, and the position number of the image block in the real-time acquisition image, and also includes: the position number of the image block in the real-time acquisition image is the coordinate value of the pixel point at the center position of the image block in the real-time acquisition image; Among them, the coordinate values ​​of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image include: the horizontal coordinate value of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image immediately follows the vertical coordinate value of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image.

[0022] Figure 2 It is a structural block diagram of an active electricity meter visual data intelligent analysis system according to implementation scheme B of the present invention.

[0023] Compared with the A embodiment, the active electric meter visual data intelligent analysis system shown in the B embodiment of the present invention may also include: A service execution mechanism, connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, respectively, for providing the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need respectively; The service execution mechanism is connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, respectively, and is used to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need, including: the service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need; Among them, the service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need respectively, including: the service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with timing services of various changing waveforms of the same timing clock.

[0024] Figure 3 It is a structural block diagram of an active electricity meter visual data intelligent analysis system according to implementation scheme C of the present invention.

[0025] Compared with implementation scheme A, the active electric meter visual data intelligent analysis system shown in implementation scheme C of the present invention may also include: A multi-bit data bus, connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, respectively, for providing a multi-bit parallel data communication link between any two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism; Wherein, the multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting and using a field logic gate array device to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism; Wherein, the multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting a CPLD chip to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism; Wherein, the multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting and using a GAL device to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism; And wherein, a multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between each two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and also includes: the distance between each two of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism is less than or equal to a preset distance limit.

[0026] In addition, in the active electric meter visual data intelligent analysis system, the AI ​​judgment model is used to intelligently judge the surrounding image block with the least coding operation amount when performing intra-frame coding on the current block to be coded as the reference coding block of the current block to be coded according to the image data of the real-time acquisition image, the block-related information corresponding to each surrounding image block of the current block to be coded in the real-time acquisition image, and the block-related information corresponding to the current block to be coded, and further includes: synchronously inputting the image data of the real-time acquisition image, the block-related information corresponding to each surrounding image block of the current block to be coded in the real-time acquisition image, and the block-related information corresponding to the current block to be coded into the AI ​​judgment model; And wherein, training the deep neural network multiple times to obtain the deep neural network after completing multiple trainings and outputting it as an AI judgment model also includes: using an information transformation formula to represent an information transformation relationship in which the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the real-time acquired image.

[0027] The active electricity meter visual data intelligent analysis system of the present invention solves the technical problem that the analysis of the coding mechanism with the least intra-frame coding operation in the prior art wastes too much time and computing cost. By adopting an AI judgment model with targeted structural design, the surrounding image blocks with the least coding operation when performing intra-frame coding on the current block to be encoded are intelligently judged as the reference coding blocks of the current block to be encoded, thereby adopting an automated and intelligent solution to meet the coding requirements with the least intra-frame coding operation and solve the above-mentioned technical problems.

[0028] The benefits, other advantages and solutions to the problems have been described above in conjunction with specific embodiments. However, the benefits, advantages, solutions to the problems, and any other elements that may produce any benefit, advantage, or solution or make any benefit, advantage, or solution more apparent should not be interpreted as key, necessary, or basic features or elements of any or all claims. The term "comprises," "comprising," or any other variation thereof as used herein is intended to cover non-exclusive inclusions, such as processes, methods, articles, or devices, which include not only those elements but also a series of elements that are not explicitly listed or are inherent to such processes, methods, articles, or devices.

Claims

1. An active electric meter visual data intelligent analysis system, characterized in that: The system comprises: An object mapping mechanism, used for mapping a corresponding AI judgment model based on a deep neural network, wherein the corresponding AI judgment model based on the deep neural network mapping includes: training the deep neural network multiple times to obtain a deep neural network after multiple trainings and outputting it as an AI judgment model, wherein the number of times the deep neural network is trained is positively correlated with the mean square error of each pixel value corresponding to each pixel point of the instant collected image; The target acquisition mechanism is used to perform visual acquisition actions on the current active electric meter under the control of the remote meter reading instruction to obtain an instant acquisition image corresponding to the current timestamp; An image analysis mechanism, connected to the target acquisition mechanism, for outputting the number of noise types, the total number of pixels, the mean square error of each pixel value corresponding to each pixel, and the contrast of the real-time acquisition image as various image data of the real-time acquisition image; A surrounding detection mechanism is used to obtain the block-related information corresponding to each surrounding image block of the current block to be encoded in the instant captured image, and obtain the block-related information corresponding to the current block to be encoded; A state judgment device is respectively connected to the object mapping mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to use the AI ​​judgment model to intelligently judge the surrounding image block with the least coding operation when performing intra-frame coding on the current block to be encoded as the reference coding block of the current block to be encoded, according to the image data of the real-time acquisition image, the block-related information corresponding to the respective surrounding image blocks of the current block to be encoded in the real-time acquisition image, and the block-related information corresponding to the current block to be encoded, including: the AI ​​judgment model outputs the position number of the surrounding image block with the least coding operation when performing intra-frame coding on the current block to be encoded as the reference coding block of the current block to be encoded.

2. The active electric meter visual data intelligent analysis system according to claim 1, characterized in that: The surrounding detection mechanism is used to obtain the block-related information corresponding to each surrounding image block of the current block to be encoded in the real-time acquisition image, and the block-related information corresponding to the current block to be encoded includes: the block-related information corresponding to each image block is the pixel value gradients corresponding to each pixel point of the image block, the content repetition of the image block, and the position number of the image block in the real-time acquisition image; Among them, the block-related information corresponding to each image block is the gradient of each pixel value corresponding to each pixel point of the image block, the content repetition of the image block and the position number of the image block in the real-time acquired image, including: the content repetition of each image block is the reciprocal of the ratio of the number of remaining multiple pixel values ​​obtained after deduplication operation is performed on the pixel values ​​corresponding to each pixel point of the image block, divided by the number of each pixel point of the image block.

3. The active electric meter visual data intelligent analysis system according to claim 2, characterized in that: The block-related information corresponding to each image block is the pixel value gradients corresponding to each pixel point of the image block, the content repetition of the image block, and the position number of the image block in the real-time acquisition image, and also includes: the position number of the image block in the real-time acquisition image is the coordinate value of the pixel point at the center position of the image block in the real-time acquisition image; Among them, the coordinate values ​​of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image include: the horizontal coordinate value of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image immediately follows the vertical coordinate value of the pixel point whose position number of the image block in the real-time acquisition image is the center position of the image block in the real-time acquisition image.

4. The active electric meter visual data intelligent analysis system according to claim 3, characterized in that: The system further comprises: A service execution mechanism, connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, respectively, for providing the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need respectively; Among them, the service execution mechanism is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with their respective required timing services, including: the service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with their respective required timing services.

5. The active electric meter visual data intelligent analysis system according to claim 4, characterized in that: The service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with the timing services they need respectively, including: the service execution mechanism uses the same timing clock to provide the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism with timing services of various changing waveforms of the same timing clock.

6. The active electric meter visual data intelligent analysis system according to claim 4, characterized in that: The system further comprises: A multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between any two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism.

7. The active electric meter visual data intelligent analysis system according to claim 6, characterized in that: A multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting and using a field logic gate array device to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism.

8. The active electric meter visual data intelligent analysis system according to claim 6, characterized in that: A multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting a CPLD chip to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism.

9. The active electric meter visual data intelligent analysis system according to claim 6, characterized in that: A multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, including: selecting and adopting a GAL device to implement the multi-bit data bus, and is used to provide a multi-bit parallel data communication link between two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism.

10. The active electric meter visual data intelligent analysis system according to claim 6, characterized in that: A multi-bit data bus is respectively connected to the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism, and is used to provide a multi-bit parallel data communication link between each two devices of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism. It also includes: the distance between each two of the object mapping mechanism, the target acquisition mechanism, the image analysis mechanism and the surrounding detection mechanism is less than or equal to a preset distance limit.

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