Charging pile ultra-low energy consumption control method, system and equipment based on AI regulation and control and medium

Abstract

The invention discloses a charging pile ultra-low energy consumption control method, system and device based on AI regulation and control and a medium, belongs to the technical field of charging piles, and aims to solve the technical problem of how to adjust charging power and improve the energy use efficiency of the charging pile. According to the technical scheme, the method comprises the steps that a power grid load historical data table, meteorological data and dates are obtained through a charging operation platform, current power configuration and temperature data are collected through a sensor of a charging pile, then data related to the charging pile are collected, the collected data related to the charging pile are circularly processed, and the data are stored in a storage battery. And obtaining the overall network parameters, predicting the charging power load through the overall network parameters, and then adjusting the charging power of the charging pile in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and specifically to an ultra-low energy consumption control method, system, device and medium for charging piles based on AI regulation. Background Technique

[0002] At present, the dual-gun DC all-in-one machine on the market integrates charging control, network access, human-computer interaction, heat dissipation management, charging indication, and power management. These functions increase the power consumption of the charging pile to a certain extent during the use of the charging pile. In addition, most charging piles do not support load prediction, resulting in 75% of the stations still operating at full power during the peak period of the power grid, additionally increasing the distribution loss by 9%-15%, and the temperature rise problem of high-power charging will also cause the charging efficiency to decrease by about 1.2%.

[0003] Therefore, how to adjust the charging power and improve the energy use efficiency of the charging pile is a technical problem to be solved urgently at present. Summary of the Invention

[0004] The technical task of the present invention is to provide an ultra-low energy consumption control method, system, device and medium for charging piles based on AI regulation to solve the problem of how to adjust the charging power and improve the energy use efficiency of the charging pile.

[0005] The technical task of the present invention is realized in the following way. An ultra-low energy consumption control method for a charging pile based on AI regulation. This method obtains the historical data table of the power grid load, meteorological data and date through the charging operation platform, and uses the sensors of the charging pile itself to collect the current power configuration and temperature data, and then collects the data related to the charging pile. Then, the data related to the charging pile collected is processed cyclically to obtain the overall network parameters, and the charging power load is predicted through the overall network parameters, and then the charging power of the charging pile is adjusted in real time to prevent the current charging pile from exceeding the power grid carrying level.

[0006] Preferably, the cyclic processing of the data related to the charging pile uses the LSTM algorithm.

[0007] Preferably, the main control board of the charging pile detects the change of the gun line plugging signal in real time, and analyzes whether to control the charging pile to enter the sleep state through the signal change.

[0008] Preferably, during the charging process of the charging pile, according to the analysis of the temperature change of the current charging module, the start and stop of the charging module are intelligently controlled;

[0009] For the charging module that has not been started, the relay inside the charging module is used to control it to enter the sleep state, reducing the standby power consumption.

[0010] Preferably, during the charging process of the charging pile, by comprehensively analyzing the charging curve and heat, the intelligent regulation of the constant voltage, constant current, and constant power charging duration is carried out to ensure the balanced increase of heat. At the same time, according to the statistical data of the internal temperature sensor of the charging pile, the fan power is automatically adjusted to ensure that the ambient temperature of the charging module is maintained at the optimal value, further improving the charging power and reducing the power loss.

[0011] More preferably, after the charging pile stops charging, by detecting the output voltage and current of the charging module and the detection of the charging gun in place, if the target charging pile is in the stopped charging state, the charging pile is controlled to enter the low-power operation mode.

[0012] More preferably, the charging pile entering the low-power operation mode means that the main control board enters the low-power mode, the screen enters the standby mode, the charging module enters the sleep mode, and the brightness of the indicator light is reduced.

[0013] An ultra-low energy consumption control system for a charging pile based on AI regulation, which is used to implement the ultra-low energy consumption control method for a charging pile based on AI regulation as described above; the system includes:

[0014] A data acquisition module, which is used to obtain the historical data table of the grid load, meteorological data, and date through the charging operation platform, and collect the current power configuration and temperature data using the sensors of the charging pile itself, and then collect the data related to the charging pile;

[0015] A data loop processing module, which is used to loop process the data related to the charging pile collected to obtain the overall network parameters;

[0016] A prediction module, which is used to predict the charging power load through the overall network parameters, and then adjust the charging power of the charging pile in real time to avoid the current charging pile exceeding the grid carrying capacity.

[0017] An electronic device, including: a memory and at least one processor;

[0018] Wherein, a computer program is stored on the memory;

[0019] The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the ultra-low energy consumption control method for a charging pile based on AI regulation as described above.

[0020] A computer-readable storage medium, in which a computer program is stored, and the computer program can be executed by a processor to implement the ultra-low energy consumption control method for a charging pile based on AI regulation as described above.

[0021] The ultra-low energy consumption control method, system, device, and medium for a charging pile based on AI regulation of the present invention have the following advantages:

[0022] (1) The present invention adjusts the control strategy in a timely manner by monitoring the entire charging process of the charging pile. On the one hand, it adjusts the charging power of the entire pile according to the power grid load prediction. On the other hand, it analyzes the charging status, adjusts the charging power in a timely manner, and selectively sets the module and some board cards to sleep, thereby improving the energy utilization efficiency of the charging pile;

[0023] (2) Through the monitoring of the entire charging process and intelligent management of the present invention, the charging pile can adaptively adjust according to the charging requirements sent by the electric vehicle BMS, adjust the heat dissipation control in a timely manner, reduce energy loss, and at the same time adjust the sleep time of the charging pile in a timely manner to further reduce the standby power consumption and reduce the electricity cost expenditure;

[0024] (3) Through the monitoring of the entire charging process and intelligent management of the present invention, it adaptively adjusts the charging power and sleep state, reduces energy loss, and at the same time, a camera can be added to the charging pile to monitor the charging vehicles in the station, and the charging pile can be scheduled to end the sleep state according to the charging intention, improving the charging satisfaction of customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below with reference to the accompanying drawings.

[0026] Appendix Figure 1 It is a structural block diagram of a charging pile ultra-low energy consumption control system based on AI regulation. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The method, system, device and medium for ultra-low energy consumption control of a charging pile based on AI regulation of the present invention will be described in detail below with reference to the accompanying drawings of the specification and specific embodiments.

[0028] Embodiment 1:

[0029] This embodiment provides a method for ultra-low energy consumption control of a charging pile based on AI regulation. The method obtains the historical data table of the power grid load, meteorological data and date through the charging operation platform, collects the current power configuration and temperature data by using the sensors of the charging pile itself, and then collects the data related to the charging pile. Then, the collected data related to the charging pile is processed in a loop to obtain the overall network parameters, and the charging power load is predicted through the overall network parameters, and then the charging power of the charging pile is adjusted in real time to avoid the current charging pile exceeding the power grid carrying level.

[0030] In this embodiment, the loop processing of the data related to the charging pile uses the LSTM algorithm.

[0031] In this embodiment, the main control board of the charging pile detects the change of the gun line plugging signal in real time, and analyzes whether to control the charging pile to enter the sleep state through the signal change.

[0032] During the charging start-up process of the charging pile in this embodiment, based on the analysis of the current temperature change of the charging module, the start and stop of the charging module are intelligently controlled.

[0033] For the charging modules that have not been started, the internal relays of the charging modules are used to control them to enter the sleep state, reducing the standby power consumption.

[0034] During the charging process of the charging pile in this embodiment, by comprehensively analyzing the charging curve and heat, the constant voltage, constant current, and constant power charging duration are intelligently adjusted to ensure the balanced increase of heat. At the same time, according to the statistical data of the temperature sensors inside the charging pile, the fan power is automatically adjusted to ensure that the ambient temperature of the charging module is maintained at the optimal value, further increasing the charging power and reducing the power loss.

[0035] After the charging pile in this embodiment stops charging, by detecting the output voltage and current of the charging module and the detection of the charging gun in place, if the target charging pile is in the stop-charging state, the charging pile is controlled to enter the low-power operation mode.

[0036] The charging pile in this embodiment entering the low-power operation mode means that the main control board enters the low-power mode, the screen enters the standby mode, the charging module enters the sleep mode, and the brightness of the indicator light is reduced.

[0037] Embodiment 2:

[0038] As shown in the appendix Figure 1 This embodiment provides an ultra-low energy consumption control system for a charging pile based on AI regulation. This system is used to implement the ultra-low energy consumption control method for a charging pile based on AI regulation as in Embodiment 1. The system includes:

[0039] A data acquisition module, which is used to obtain the historical power grid load data table, meteorological data, and date through the charging operation platform, and collect the current power configuration and temperature data using the sensors of the charging pile itself, thereby collecting data related to the charging pile.

[0040] A data loop processing module, which is used to perform loop processing on the data related to the charging pile collected to obtain the overall network parameters.

[0041] A prediction module, which is used to predict the charging power load through the overall network parameters, and then adjust the charging power of the charging pile in real time to prevent the current charging pile from exceeding the power grid carrying capacity.

[0042] In this embodiment, the loop processing of the data related to the charging pile collected adopts the LSTM algorithm.

[0043] The main control board of the charging pile in this embodiment detects the change of the gun line plugging signal in real time, and analyzes whether to control the charging pile to enter the sleep state through the signal change.

[0044] During the charging start-up process of the charging pile in this embodiment, according to the analysis of the current charging module temperature change, the start and stop of the charging module are intelligently controlled.

[0045] For the unstarted charging module, it is controlled by the relay inside the charging module to enter the sleep state, reducing the standby power consumption.

[0046] During the charging process of the charging pile in this embodiment, by comprehensively analyzing the charging curve and heat, the constant voltage, constant current, and constant power charging duration are intelligently adjusted to ensure the balanced increase of heat. At the same time, according to the statistical data of the temperature sensor inside the charging pile, the fan power is automatically adjusted to ensure that the ambient temperature of the charging module is maintained at the optimal value, further increasing the charging power and reducing the power loss.

[0047] After the charging pile in this embodiment stops charging, by detecting the output voltage and current of the charging module and the charging gun in-place detection, if the target charging pile is in the stopped charging state, the charging pile is controlled to enter the low-power operation mode.

[0048] The charging pile in this embodiment entering the low-power operation mode means that the main control board enters the low-power mode, the screen enters the standby mode, the charging module enters the sleep mode, and the indicator light brightness is reduced.

[0049] Embodiment 3:

[0050] This embodiment also provides an electronic device, including: a memory and a processor;

[0051] Wherein, the memory stores computer execution instructions;

[0052] The processor executes the computer execution instructions stored in the memory, so that the processor executes the ultra-low energy consumption control method of the charging pile based on AI regulation in any embodiment of the present invention.

[0053] The processor can be a central processing unit (CPU), or can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0054] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include high-speed random access memory, and can also include non-volatile memory, such as hard disks, memory, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash memory cards, at least one magnetic disk storage period, flash memory devices, or other volatile solid-state storage devices.

[0055] Embodiment 4:

[0056] This embodiment also provides a computer-readable storage medium, which stores multiple instructions. The instructions are loaded by the processor to enable the processor to execute the ultra-low energy consumption control method of the charging pile based on AI regulation in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided. On this storage medium, software program codes for realizing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device is made to read and execute the program codes stored in the storage medium.

[0057] In this case, the program code read from the storage medium itself can realize the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0058] Embodiments of the storage medium for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0059] In addition, it should be clear that not only can the actual operations be completed in part or in whole by executing the program codes read by the computer, but also by instructions based on the program codes to cause the operating system, etc. operating on the computer to complete the actual operations, so as to realize the functions of any one of the above embodiments.

[0060] In addition, it can be understood that the program code read out from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU etc. installed on the expansion board or the expansion unit are made to execute part or all of the actual operations, thereby implementing the functions of any one of the above embodiments.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An ultra-low energy consumption control method for charging piles based on AI regulation, characterized in that, This method obtains the historical data table of grid load, meteorological data and date through the charging operation platform, and uses the sensors of the charging pile itself to collect the current power configuration and temperature data, and then collects the data related to the charging pile. Then, the data related to the charging pile collected is processed cyclically to obtain the overall network parameters, and the charging power load is predicted through the overall network parameters, and then the charging power of the charging pile is adjusted in real time.

2. The ultra-low energy consumption control method of the charging pile based on AI regulation according to claim 1, characterized in that The cyclic processing of the data related to the charging pile uses the LSTM algorithm.

3. The ultra-low energy consumption control method of the charging pile based on AI regulation according to claim 1, characterized in that The main control board of the charging pile detects the change of the gun line insertion signal in real time, and analyzes whether to control the charging pile to enter the sleep state through the signal change.

4. The ultra-low energy consumption control method for a charging pile based on AI regulation according to claim 1, wherein During the charging process of the charging pile, according to the analysis of the current temperature change of the charging module, the start and stop of the charging module are intelligently controlled; For the charging module that has not been started, the relay inside the charging module is used to control it to enter the sleep state.

5. The ultra-low energy consumption control method of the charging pile based on AI regulation according to claim 1, characterized in that, During the charging process of the charging pile, by comprehensively analyzing the charging curve and heat, the constant voltage, constant current and constant power charging duration are intelligently adjusted; at the same time, according to the statistical data of the internal temperature sensor of the charging pile, the fan power is automatically adjusted to ensure that the ambient temperature of the charging module is maintained at the optimal value, further improving the charging power.

6. The ultra-low energy consumption control method for a charging pile based on AI regulation according to any one of claims 1-5, characterized in that, After the charging pile stops charging, by detecting the output voltage and current of the charging module and the charging gun in-place detection, if the target charging pile is in the stop charging state, the charging pile is controlled to enter the low-power operation mode.

7. The ultra-low energy consumption control method for a charging pile based on AI regulation according to claim 6, wherein The charging pile entering the low-power operation mode means that the main control board enters the low-power mode, the screen enters the standby mode, the charging module enters the sleep mode, and the brightness of the indicator light is reduced.

8. An ultra-low energy consumption control system for a charging pile based on AI regulation, characterized in that, This system is used to implement the ultra-low energy consumption control method of the charging pile based on AI regulation as described in any one of claims 1 to 7; this system includes: A data acquisition module, which is used to obtain the historical data table of grid load, meteorological data and date through the charging operation platform, and use the sensors of the charging pile itself to collect the current power configuration and temperature data, and then collect the data related to the charging pile; A data cyclic processing module, which is used to cyclically process the data related to the charging pile collected to obtain the overall network parameters; A prediction module, which is used to predict the charging power load through the overall network parameters, and then adjust the charging power of the charging pile in real time.

9. An electronic device, characterized in that, Including: A memory and at least one processor; Wherein, a computer program is stored on the memory; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the ultra-low energy consumption control method of the charging pile based on AI regulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the computer program can be executed by the processor to implement the ultra-low energy consumption control method of the charging pile based on AI regulation as described in any one of claims 1 to 7.

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