CN117799483B - Intelligent charging pile electric quantity allocation system utilizing type analysis - Google Patents
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Abstract
The invention discloses an intelligent charging pile electric quantity allocation system utilizing type analysis, and relates to the field of intelligent charging piles. The system comprises: the electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type; and the orientation recognition mechanism is used for sending the current electric vehicle type which is intelligently analyzed to the electric quantity allocation mechanism. Through this system, can adopt the current electric motor car of intelligent analysis mode analysis, confirm the biggest electric quantity that charges that corresponds with current electric motor car to for the corresponding electric quantity that prestores of this charge allotment of intelligent charging stake of biggest electric quantity that corresponds based on the received, thereby promoted the utilization ratio of charging electric energy, promoted the intelligent transformation level of charging stake.
Description
Technical Field
The invention relates to the field of intelligent charging piles, in particular to an intelligent charging pile electric quantity allocation system utilizing type analysis.
Background
The charging pile can be divided into a floor type charging pile and a wall-hanging type charging pile according to the installation mode. The floor type charging pile is suitable for being installed on a parking space which is not close to a wall body. The wall-hanging type charging pile is suitable for being installed on a parking space close to a wall body.
The charging piles may be divided into public charging piles and private charging piles according to installation sites. The public charging pile is a charging pile which is built in a public parking lot (garage) and combined with a parking berth to provide public charging service for social vehicles. The special charging pile is a charging pile used by personnel in a building unit (enterprise) and is a parking lot (warehouse) of the building unit (enterprise). The self-charging pile is built in an individual parking space (warehouse) and provides charging for private users. The charging piles are generally built in conjunction with parking spaces of parking lots (libraries). The protection level of the charging pile installed outdoors should not be lower than IP54. The protection level of the charging pile installed indoors should not be lower than IP32.
However, in the charging layout of the array of charging piles with a plurality of charging piles, if too much surplus power is distributed in advance for one charging pile which is not used for a long time, or if relatively sufficient power is not provided for one charging pile which is frequently used, site charging is unsmooth or the site power utilization rate is low, and the requirement of intelligent modification of the charging piles is obviously not met.
Disclosure of Invention
Aiming at the technical problems, the invention provides an intelligent charging pile electric quantity allocation system utilizing type analysis, which comprises the following components:
The electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
The environment acquisition mechanism is arranged at the top end of the intelligent charging pile and is used for executing image data acquisition action on the environment where the intelligent charging pile is positioned before each charging so as to obtain and output a corresponding charging environment image;
The artifact removing device is arranged in the intelligent charging pile and connected with the environment acquisition mechanism, and is used for performing artifact removing processing on the received charging environment image so as to obtain and output a corresponding artifact removed image;
a content enhancement device connected with the artifact removal device and used for performing image content enhancement processing based on logarithmic transformation on the received artifact removal image so as to obtain and output a corresponding content enhancement image;
The real-time filtering device is connected with the content enhancement device and is used for performing Gaussian high-pass filtering processing on the received content enhancement image so as to obtain and output a corresponding real-time filtering image;
The orientation recognition mechanism is arranged in the intelligent charging pile and is respectively connected with the electric quantity allocation mechanism and the real-time filtering equipment, and is used for detecting standard deviation of each gray value corresponding to each pixel point of each image block in the received real-time filtering image, judging that the image block is a foreground image block when the standard deviation is more than or equal to a set standard deviation limit, judging that the image block is a background image block when the standard deviation is less than the set standard deviation limit, inputting binary representation values of each standard contour pattern corresponding to each electric vehicle and binary representation values of pixel values of each pixel point of the foreground image block into a learned neural network to output the type of the electric vehicle which is the most matched with the foreground image block, and sending the type of the electric vehicle to the electric quantity allocation mechanism as a current electric vehicle type;
The method for inputting the binary representation values of the standard outline patterns respectively corresponding to various electric vehicles and the binary representation values of the pixel points of the foreground image blocks into the learned neural network to output the types of the electric vehicles with the best matching foreground image blocks comprises the following steps: the number of times the neural network learns is proportional to the total number of types of various electric vehicles.
Therefore, the invention has at least the following four beneficial technical effects:
First place: detecting standard deviation of gray values corresponding to pixel points of each image block in a received real-time filtered image, judging the image block as a foreground image block when the standard deviation is larger than or equal to a set standard deviation limit, and judging the image block as a background image block when the standard deviation is smaller than the set standard deviation limit;
Second place: the binary representation values of all standard outline patterns corresponding to all types of electric vehicles and the binary representation values of the pixel values of all pixel points of the foreground image blocks are input into the learned neural network to output the type of the electric vehicle with the best matching foreground image blocks, so that intelligent analysis of the type of the electric vehicle in a charging site is realized;
third place: determining the maximum charging electric quantity corresponding to the received current electric vehicle type, and allocating corresponding pre-stored electric quantity for the current charging of the intelligent charging pile based on the received maximum charging electric quantity corresponding to the current electric vehicle type, so that the utilization rate of the charging electric energy is improved;
Fourth place: the number of times of learning the neural network for intelligent analysis of the types of the electric vehicles is proportional to the total number of types of various electric vehicles, so that the reliability and the effectiveness of intelligent analysis results are ensured.
Drawings
Numerous advantages of the present invention may be better understood by those skilled in the art by reference to the accompanying figures.
Fig. 1 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a first embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a third embodiment of the present invention.
Detailed Description
Example 1
Fig. 1 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a first embodiment of the present invention, where the system includes:
The electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
For example, an ASIC chip may be selectively used to implement the electric quantity allocation mechanism, and the electric quantity allocation mechanism is disposed in the intelligent charging pile, and is configured to determine a maximum charging electric quantity corresponding to the received current electric vehicle type, and allocate a corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
The environment acquisition mechanism is arranged at the top end of the intelligent charging pile and is used for executing image data acquisition action on the environment where the intelligent charging pile is positioned before each charging so as to obtain and output a corresponding charging environment image;
The artifact removing device is arranged in the intelligent charging pile and connected with the environment acquisition mechanism, and is used for performing artifact removing processing on the received charging environment image so as to obtain and output a corresponding artifact removed image;
a content enhancement device connected with the artifact removal device and used for performing image content enhancement processing based on logarithmic transformation on the received artifact removal image so as to obtain and output a corresponding content enhancement image;
The real-time filtering device is connected with the content enhancement device and is used for performing Gaussian high-pass filtering processing on the received content enhancement image so as to obtain and output a corresponding real-time filtering image;
The orientation recognition mechanism is arranged in the intelligent charging pile and is respectively connected with the electric quantity allocation mechanism and the real-time filtering equipment, and is used for detecting standard deviation of each gray value corresponding to each pixel point of each image block in the received real-time filtering image, judging that the image block is a foreground image block when the standard deviation is more than or equal to a set standard deviation limit, judging that the image block is a background image block when the standard deviation is less than the set standard deviation limit, inputting binary representation values of each standard contour pattern corresponding to each electric vehicle and binary representation values of pixel values of each pixel point of the foreground image block into a learned neural network to output the type of the electric vehicle which is the most matched with the foreground image block, and sending the type of the electric vehicle to the electric quantity allocation mechanism as a current electric vehicle type;
The method for inputting the binary representation values of the standard outline patterns respectively corresponding to various electric vehicles and the binary representation values of the pixel points of the foreground image blocks into the learned neural network to output the types of the electric vehicles with the best matching foreground image blocks comprises the following steps: the number of times of learning of the neural network is proportional to the total number of types of various electric vehicles;
The method for inputting the binary representation values of the standard outline patterns respectively corresponding to various electric vehicles and the binary representation values of the pixel points of the foreground image blocks into the learned neural network to output the types of the electric vehicles with the best matching foreground image blocks comprises the following steps: and (3) expressing the numerical value correspondence relationship between the learning times of the neural network and the total types of various electric vehicles by adopting a numerical value conversion formula.
Example two
Fig. 2 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a second embodiment of the present invention, where the structure includes the following components:
The electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
The environment acquisition mechanism is arranged at the top end of the intelligent charging pile and is used for executing image data acquisition action on the environment where the intelligent charging pile is positioned before each charging so as to obtain and output a corresponding charging environment image;
The artifact removing device is arranged in the intelligent charging pile and connected with the environment acquisition mechanism, and is used for performing artifact removing processing on the received charging environment image so as to obtain and output a corresponding artifact removed image;
a content enhancement device connected with the artifact removal device and used for performing image content enhancement processing based on logarithmic transformation on the received artifact removal image so as to obtain and output a corresponding content enhancement image;
The real-time filtering device is connected with the content enhancement device and is used for performing Gaussian high-pass filtering processing on the received content enhancement image so as to obtain and output a corresponding real-time filtering image;
The orientation recognition mechanism is arranged in the intelligent charging pile and is respectively connected with the electric quantity allocation mechanism and the real-time filtering equipment, and is used for detecting standard deviation of each gray value corresponding to each pixel point of each image block in the received real-time filtering image, judging that the image block is a foreground image block when the standard deviation is more than or equal to a set standard deviation limit, judging that the image block is a background image block when the standard deviation is less than the set standard deviation limit, inputting binary representation values of each standard contour pattern corresponding to each electric vehicle and binary representation values of pixel values of each pixel point of the foreground image block into a learned neural network to output the type of the electric vehicle which is the most matched with the foreground image block, and sending the type of the electric vehicle to the electric quantity allocation mechanism as a current electric vehicle type;
The voltage measuring mechanism is respectively connected with the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identifying mechanism and is used for respectively measuring the current real-time voltage values of the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identifying mechanism;
The voltage measurement mechanism is respectively connected with the artifact removal device, the content enhancement device, the real-time filtering device and the directional identification mechanism, and is used for respectively measuring current real-time voltage values of the artifact removal device, the content enhancement device, the real-time filtering device and the directional identification mechanism, wherein the current real-time voltage values comprise: the voltage measurement mechanism comprises a plurality of voltage measurement units which are respectively connected with the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the orientation identification mechanism to finish the respective measurement of the current real-time voltage values of the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the orientation identification mechanism;
The voltage measurement mechanism comprises a plurality of voltage measurement units, and the voltage measurement units are respectively connected with the artifact removal device, the content enhancement device, the real-time filtering device and the orientation identification mechanism to finish the respective measurement of the current real-time voltage values of the artifact removal device, the content enhancement device, the real-time filtering device and the orientation identification mechanism, and the respective measurement comprises the following steps: the voltage measuring units are a plurality of voltage sensing circuits and are respectively connected with the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the orientation recognition mechanism so as to finish the respective measurement of the current real-time voltage values of the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the orientation recognition mechanism;
The voltage measurement units are a plurality of voltage sensing circuits and are used for being respectively connected with the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism so as to finish the respective measurement of the current real-time voltage values of the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism, and the respective measurement comprises the following steps: the structures of the voltage sensing circuits are the same;
And wherein the plurality of voltage measurement units are a plurality of voltage sensing circuits, and are configured to be respectively connected to the artifact removal device, the content enhancement device, the real-time filtering device, and the orientation recognition mechanism, so as to complete respective measurements of current real-time voltage values of the artifact removal device, the content enhancement device, the real-time filtering device, and the orientation recognition mechanism, and further include: the plurality of voltage sensing circuits have the same upper voltage measurement limit value and lower voltage measurement limit value.
Example III
Fig. 3 is a schematic structural diagram of an intelligent charging pile power allocation system using type analysis according to a third embodiment of the present invention, where the structure includes the following components:
The electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
The environment acquisition mechanism is arranged at the top end of the intelligent charging pile and is used for executing image data acquisition action on the environment where the intelligent charging pile is positioned before each charging so as to obtain and output a corresponding charging environment image;
The artifact removing device is arranged in the intelligent charging pile and connected with the environment acquisition mechanism, and is used for performing artifact removing processing on the received charging environment image so as to obtain and output a corresponding artifact removed image;
a content enhancement device connected with the artifact removal device and used for performing image content enhancement processing based on logarithmic transformation on the received artifact removal image so as to obtain and output a corresponding content enhancement image;
The real-time filtering device is connected with the content enhancement device and is used for performing Gaussian high-pass filtering processing on the received content enhancement image so as to obtain and output a corresponding real-time filtering image;
The orientation recognition mechanism is arranged in the intelligent charging pile and is respectively connected with the electric quantity allocation mechanism and the real-time filtering equipment, and is used for detecting standard deviation of each gray value corresponding to each pixel point of each image block in the received real-time filtering image, judging that the image block is a foreground image block when the standard deviation is more than or equal to a set standard deviation limit, judging that the image block is a background image block when the standard deviation is less than the set standard deviation limit, inputting binary representation values of each standard contour pattern corresponding to each electric vehicle and binary representation values of pixel values of each pixel point of the foreground image block into a learned neural network to output the type of the electric vehicle which is the most matched with the foreground image block, and sending the type of the electric vehicle to the electric quantity allocation mechanism as a current electric vehicle type;
The on-site configuration interface is respectively connected with the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the voltage sensing circuits of the directional identification mechanism and is used for configuring operation configuration parameters of the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the voltage sensing circuits of the directional identification mechanism in real time;
The on-site configuration interface is respectively connected with the artifact removal device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism, and is used for configuring operation configuration parameters of the artifact removal device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism in real time, wherein the operation configuration parameters comprise: the field configuration interface is a serial configuration interface;
And a field configuration interface connected to the artifact removal device, the content enhancement device, the real-time filtering device, and the voltage sensing circuits of the orientation recognition mechanism, respectively, wherein the operation configuration parameters for configuring the artifact removal device, the content enhancement device, the real-time filtering device, and the voltage sensing circuits of the orientation recognition mechanism in real time include: and aiming at the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism, the on-site configuration interface adopts different configuration addresses to configure operation configuration parameters of a plurality of voltage sensing circuits of the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism in real time.
The method for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating the corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type comprises the following steps: allocating corresponding pre-stored electric quantity for the current charging to be equal to or slightly larger than the received maximum electric quantity corresponding to the current electric vehicle type;
In addition, in the intelligent charging pile power allocation system for analyzing the utilization type, the method for inputting the binary representation value of each standard outline pattern corresponding to each electric vehicle and the binary representation value of the pixel value of each pixel point of the foreground image block into the learned neural network to output the type of the electric vehicle with the foreground image block being the best match, and sending the type of the electric vehicle to the power allocation mechanism as the current electric vehicle type comprises the following steps: and (3) inputting binary representation values of all standard outline patterns corresponding to all types of electric vehicles respectively and binary representation values of pixel values of all pixel points of the foreground image blocks into the learned neural network by adopting a MATLAB tool box to output the type of the electric vehicle with the best matching foreground image blocks, and sending the type of the electric vehicle to the electric quantity allocation mechanism as the current electric vehicle type.
The intelligent charging pile electric quantity allocation system utilizing type analysis is compact in logic and intelligent in design. The current electric vehicle type can be analyzed by adopting the intelligent analysis mode, the maximum charging electric quantity corresponding to the current electric vehicle type is determined, and the corresponding pre-stored electric quantity is allocated for the current charging of the intelligent charging pile based on the received maximum charging electric quantity corresponding to the current electric vehicle type, so that the utilization rate of the charging electric energy is improved, and the intelligent transformation level of the charging pile is improved.
The foregoing description of the exemplary embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. It is evident that many modifications and variations will be apparent to those skilled in the art. The exemplary embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the invention for various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the following claims and their equivalents.
Claims (8)
1. An intelligent charging pile electricity allocation system utilizing type analysis, which is characterized by comprising:
The electric quantity allocation mechanism is arranged in the intelligent charging pile and is used for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type;
The environment acquisition mechanism is arranged at the top end of the intelligent charging pile and is used for executing image data acquisition action on the environment where the intelligent charging pile is positioned before each charging so as to obtain and output a corresponding charging environment image;
The artifact removing device is arranged in the intelligent charging pile and connected with the environment acquisition mechanism, and is used for performing artifact removing processing on the received charging environment image so as to obtain and output a corresponding artifact removed image;
a content enhancement device connected with the artifact removal device and used for performing image content enhancement processing based on logarithmic transformation on the received artifact removal image so as to obtain and output a corresponding content enhancement image;
The real-time filtering device is connected with the content enhancement device and is used for performing Gaussian high-pass filtering processing on the received content enhancement image so as to obtain and output a corresponding real-time filtering image;
The orientation recognition mechanism is arranged in the intelligent charging pile and is respectively connected with the electric quantity allocation mechanism and the real-time filtering equipment, and is used for detecting standard deviation of each gray value corresponding to each pixel point of each image block in the received real-time filtering image, judging that the image block is a foreground image block when the standard deviation is more than or equal to a set standard deviation limit, judging that the image block is a background image block when the standard deviation is less than the set standard deviation limit, inputting binary representation values of each standard outline pattern corresponding to each electric vehicle and binary representation values of pixel values of each pixel point of the foreground image block into a learned neural network to output the type of the electric vehicle which is most matched with the foreground image block, and sending the type of the electric vehicle to the electric quantity allocation mechanism as a current electric vehicle type;
The method for inputting the binary representation values of the standard outline patterns respectively corresponding to various electric vehicles and the binary representation values of the pixel points of the foreground image blocks into the learned neural network to output the types of the electric vehicles with the best matching foreground image blocks comprises the following steps: the number of times of learning of the neural network is proportional to the total number of types of various electric vehicles;
The method for inputting binary representation values of standard outline patterns respectively corresponding to various electric vehicles and binary representation values of pixel points of foreground image blocks into the learned neural network to output the type of the electric vehicle with the foreground image blocks being the best matched, and sending the type of the electric vehicle to the electric quantity allocation mechanism as the current electric vehicle type comprises the following steps: the method comprises the steps that a MATLAB tool box is adopted to realize that binary representation values of standard outline patterns corresponding to various electric vehicles respectively and binary representation values of pixel points of foreground image blocks are input into a learned neural network to output the type of the electric vehicle with the foreground image blocks being matched most, and the type is used as a current electric vehicle type to be sent to simulation processing of an electric quantity allocation mechanism;
the method for inputting the binary representation numerical values of the standard outline patterns respectively corresponding to the various electric vehicles and the binary representation numerical values of the pixel points of the foreground image blocks into the learned neural network to output the types of the electric vehicles with the foreground image blocks being matched with each other comprises the following steps: and (3) expressing the numerical value correspondence relationship between the learning times of the neural network and the total types of various electric vehicles by adopting a numerical value conversion formula.
2. The intelligent charging pile electricity distribution system utilizing type resolution of claim 1, wherein the system further comprises:
The voltage measuring mechanism is respectively connected with the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identifying mechanism and is used for respectively measuring the current real-time voltage values of the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identifying mechanism;
the voltage measurement mechanism is respectively connected with the artifact removal device, the content enhancement device, the real-time filtering device and the directional identification mechanism, and is used for respectively measuring current real-time voltage values of the artifact removal device, the content enhancement device, the real-time filtering device and the directional identification mechanism, wherein the current real-time voltage values comprise: the voltage measurement mechanism comprises a plurality of voltage measurement units which are respectively connected with the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism so as to finish the respective measurement of the current real-time voltage values of the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism.
3. The intelligent charging pile electricity allocation system utilizing type analysis according to claim 2, wherein:
the voltage measurement mechanism comprises a plurality of voltage measurement units, and is used for being respectively connected with the artifact removal device, the content enhancement device, the real-time filtering device and the orientation identification mechanism to finish the respective measurement of the current real-time voltage values of the artifact removal device, the content enhancement device, the real-time filtering device and the orientation identification mechanism, and the respective measurement comprises the following steps: the voltage measuring units are a plurality of voltage sensing circuits and are used for being respectively connected with the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism so as to finish the respective measurement of the current real-time voltage values of the artifact removing equipment, the content enhancement equipment, the real-time filtering equipment and the directional identification mechanism.
4. The intelligent charging pile electricity allocation system utilizing type analysis according to claim 3, wherein:
The voltage measuring units are voltage sensing circuits and are used for being respectively connected with the artifact removing device, the content enhancing device, the real-time filtering device and the directional identifying mechanism to finish the respective measurement of the current real-time voltage values of the artifact removing device, the content enhancing device, the real-time filtering device and the directional identifying mechanism, and the measurement comprises the following steps: the plurality of voltage sensing circuits are identical in structure.
5. The intelligent charging pile electricity allocation system utilizing type analysis according to claim 4, wherein:
The voltage measuring units are voltage sensing circuits and are used for being respectively connected with the artifact removing device, the content enhancing device, the real-time filtering device and the directional identifying mechanism to finish the respective measurement of the current real-time voltage values of the artifact removing device, the content enhancing device, the real-time filtering device and the directional identifying mechanism, and the voltage measuring units further comprise: the plurality of voltage sensing circuits have the same upper voltage measurement limit value and lower voltage measurement limit value.
6. The intelligent charging pile electricity distribution system utilizing type resolution according to any one of claims 1-5, wherein the system further comprises:
And the field configuration interface is respectively connected with the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the voltage sensing circuits of the directional identification mechanism and is used for configuring operation configuration parameters of the artifact removal equipment, the content enhancement equipment, the real-time filtering equipment and the voltage sensing circuits of the directional identification mechanism in real time.
7. The intelligent charging pile electricity allocation system utilizing type analysis according to claim 6, wherein:
The on-site configuration interface is respectively connected with the artifact removing device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism, and is used for configuring operation configuration parameters of the artifact removing device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism in real time, wherein the operation configuration parameters comprise: the field configuration interface is a serial configuration interface.
8. The intelligent charging pile electricity allocation system utilizing type analysis according to claim 6, wherein:
The on-site configuration interface is respectively connected with the artifact removing device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism, and is used for configuring operation configuration parameters of the artifact removing device, the content enhancement device, the real-time filtering device and the voltage sensing circuits of the directional identification mechanism in real time, wherein the operation configuration parameters comprise: the on-site configuration interface adopts different configuration addresses to configure operation configuration parameters of a plurality of voltage sensing circuits of the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identification mechanism in real time aiming at the artifact removing equipment, the content enhancing equipment, the real-time filtering equipment and the directional identification mechanism;
the method for determining the maximum charging electric quantity corresponding to the received current electric vehicle type and allocating the corresponding pre-stored electric quantity for the current charging based on the received maximum charging electric quantity corresponding to the current electric vehicle type comprises the following steps: and allocating corresponding pre-stored electric quantity for the current charging to be equal to or slightly larger than the received maximum charging electric quantity corresponding to the current electric vehicle type.
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