Real-time monitoring and analysis system of energy consumption of intelligent charging piles based on the Internet of Things

Abstract

The present invention belongs to the field of charging pile energy consumption monitoring and relates to data analysis technology, which is used to solve the problem that the prior art cannot optimize energy consumption according to the operating status of the charging pile. Specifically, it is a real-time monitoring and analysis system for energy consumption of an intelligent charging pile based on the Internet of Things, including a real-time monitoring subsystem and a cycle optimization subsystem. The real-time monitoring subsystem includes a state monitoring module, a standby monitoring module and a charging monitoring module. The cycle optimization subsystem includes an operation analysis module, a loss monitoring module and a standby optimization module. The state monitoring module is used to monitor and analyze the operating status of the intelligent charging pile; the operation analysis module is used to analyze the operating mode of the intelligent charging pile; the present invention can perform real-time monitoring and analysis of the energy consumption of the intelligent charging pile in the standby state and the charging state respectively, adopt different early warning standards to monitor and feedback abnormal energy consumption phenomena in different states, and ensure the accuracy of the energy consumption monitoring results.

Description

Technical Field

[0001] The present invention belongs to the field of charging pile energy consumption monitoring, and relates to data analysis technology, and specifically to a real-time monitoring and analysis system for energy consumption of intelligent charging piles based on the Internet of Things. Background Art

[0002] With the popularity of electric vehicles, the demand for smart charging piles is growing. In order to effectively manage the energy consumption of charging piles, achieve energy conservation and emission reduction, and improve energy utilization efficiency, it is necessary to use an energy consumption monitoring and analysis system to monitor, analyze and manage the energy consumption of charging piles.

[0003] The real-time monitoring and analysis system for energy consumption of intelligent charging piles in the prior art can only monitor and analyze the energy consumption of the charging piles, but cannot optimize the energy consumption according to the operating status of the charging piles, resulting in that the energy consumption of the charging piles cannot be effectively controlled.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring and analysis system for energy consumption of an intelligent charging pile based on the Internet of Things, which is used to solve the problem that the prior art cannot optimize energy consumption according to the operating status of the charging pile;

[0006] The technical problem to be solved by the present invention is: how to provide a real-time monitoring and analysis system for energy consumption of an intelligent charging pile based on the Internet of Things, which can optimize energy consumption according to the operating status of the charging pile.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The real-time monitoring and analysis system of energy consumption of intelligent charging piles based on the Internet of Things includes a real-time monitoring subsystem and a cycle optimization subsystem. The real-time monitoring subsystem includes a state monitoring module, a standby monitoring module and a charging monitoring module. The cycle optimization subsystem includes an operation analysis module, a loss monitoring module and a standby optimization module.

[0009] The operation analysis module is used to analyze the operation mode of the smart charging pile: generate an analysis cycle and obtain the charging occupancy coefficient of the analysis cycle, and mark the operation mode of the smart charging pile in the analysis cycle as a standby mode or a charging mode according to the charging occupancy coefficient;

[0010] The loss monitoring module is used to monitor and analyze the charging loss of the smart charging pile and obtain the loss coefficient and stability coefficient, compare the loss coefficient and stability coefficient with the preset loss threshold and stability threshold respectively, and determine whether the charging loss state of the smart charging pile in the analysis period meets the requirements through the comparison results;

[0011] The standby optimization module is used to optimize and analyze the standby state of the smart charging pile and mark the mode of the standby optimization processing as an automatic optimization mode or a manual optimization mode.

[0012] As a preferred embodiment of the present invention, the state monitoring module is used to monitor and analyze the operating state of the smart charging pile: obtain the charging power of the smart charging pile and mark it as a charging monitoring value, obtain the current value, voltage value and power value of the power supply line of the smart charging pile, and obtain the energy consumption coefficient of the smart charging pile by numerically calculating the current value, voltage value and power value;

[0013] Determine whether the charging monitoring value is zero: if so, determine that the smart charging pile is in standby state, generate a standby monitoring signal, and send the standby monitoring signal and the energy consumption coefficient to the standby monitoring module; if not, determine that the smart charging pile is in charging state, generate a charging monitoring signal, and send the charging monitoring signal and the energy consumption coefficient to the charging monitoring module.

[0014] As a preferred embodiment of the present invention, the process of obtaining the charging occupancy coefficient of the analysis period includes: dividing into several sub-periods, each sub-period is twenty-four hours long, setting several analysis time points in the sub-period, and the time difference between every two adjacent analysis time points is equal; marking the analysis time points when the smart charging pile is in the charging state as charging time points, marking the analysis time points when the smart charging pile is in the standby state as standby time points, marking the ratio of the number of charging time points to the analysis time points as the charging occupancy value of the sub-period, and summing and averaging the charging occupancy values ​​of all sub-periods to obtain the charging occupancy coefficient of the analysis period.

[0015] As a preferred embodiment of the present invention, the specific process of marking the operating mode of the smart charging pile during the analysis period includes: comparing the charging occupancy coefficient with the preset charging occupancy threshold: if the charging occupancy coefficient is less than the charging occupancy threshold, then the operating mode of the smart charging pile during the analysis period is determined to be standby mode, and a standby optimization signal is generated and sent to the standby optimization module; if the charging occupancy coefficient is greater than or equal to the charging occupancy threshold, then the operating mode of the smart charging pile during the analysis period is determined to be charging mode, and a loss monitoring signal is generated and sent to the loss monitoring module.

[0016] As a preferred embodiment of the present invention, the process of obtaining the loss coefficient and the stability coefficient includes: a charging period is composed of two consecutive adjacent charging time points, the difference between the power consumption and the charging amount of the smart charging pile in the charging period is marked as the loss amount, the ratio of the loss amount to the charging amount is marked as the loss value of the charging period, the loss values ​​of all charging periods in the analysis period are summed and averaged to obtain the loss coefficient, and the loss values ​​of all charging periods in the analysis period are calculated by variance.

[0017] As a preferred embodiment of the present invention, the specific process of comparing the loss coefficient and the stability coefficient with the preset loss threshold and the stability threshold respectively includes: if the loss coefficient is less than the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period meets the requirements; if the loss coefficient is greater than or equal to the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, a line optimization signal is generated and the line optimization signal is sent to the mobile phone terminal of the administrator; if the stability coefficient is greater than or equal to the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, an abnormal maintenance signal is generated and the abnormal maintenance signal is sent to the mobile phone terminal of the administrator.

[0018] As a preferred embodiment of the present invention, the specific process of the standby optimization module optimizing and analyzing the standby state of the smart charging pile includes: forming an analysis period by two adjacent analysis time points, arranging the analysis periods in the same sub-cycle in order of time from first to last to obtain an analysis sequence, forming an analysis set by the serial numbers of all charging periods in the analysis period in the analysis sequence, marking the number of elements of the serial numbers of the charging period in the analysis set as the element value of the charging period, marking the charging period with an element value not less than L1 as a regular period, marking the ratio of the number of regular periods to the number of analysis periods in the sub-cycle as a regularity coefficient, and comparing the regularity coefficient with a preset regularity threshold: if the regularity coefficient is less than the regularity threshold, the manual optimization mode is used for standby optimization processing; if the regularity coefficient is greater than or equal to the regularity threshold, the automatic optimization mode is used for standby optimization processing.

[0019] The present invention has the following beneficial effects:

[0020] 1. The real-time monitoring subsystem can monitor and analyze the energy consumption of smart charging piles in standby and charging states in real time, and adopt different early warning standards to monitor and feedback abnormal energy consumption in different states, ensuring the accuracy of energy consumption monitoring results while providing data support for optimizing the analysis process;

[0021] 2. The operation analysis module can analyze the operation mode of the smart charging pile, monitor the working frequency and the overall working time of the smart charging pile in a periodic monitoring and analysis manner, so as to mark the operation mode according to the charging occupancy coefficient, and then take targeted optimization measures according to different operation modes to optimize energy consumption;

[0022] 3. The loss monitoring module can monitor and analyze the charging loss of the smart charging pile. The loss coefficient and stability coefficient are obtained by monitoring and processing the energy consumption and charging amount during the charging process. The corresponding optimization processing signal is generated according to the values ​​of the loss coefficient and stability coefficient to improve the processing efficiency when the charging loss is abnormal;

[0023] 4. The standby state of the smart charging pile can be optimized and analyzed through the standby optimization module. During the analysis period, it is determined whether there is a regularity in the sequence number of the analysis sequence of the charging period of the smart charging pile, so as to mark the standby optimization processing mode according to its value, taking into account the energy consumption control and ease of use of the smart charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 is the overall system block diagram of the present invention;

[0026] Figure 2 is a system block diagram of a real-time monitoring subsystem in Embodiment 1 of the present invention;

[0027] Figure 3 This is a system block diagram of the period optimization subsystem in the second embodiment of the present invention;

[0028] Figure 4 This is a workflow diagram of the period optimization subsystem in the second embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] like Figure 1As shown, the real-time monitoring and analysis system of energy consumption of intelligent charging piles based on the Internet of Things includes a real-time monitoring subsystem and a cycle optimization subsystem. The real-time monitoring subsystem includes a state monitoring module, a standby monitoring module and a charging monitoring module. The cycle optimization subsystem includes an operation analysis module, a loss monitoring module and a standby optimization module.

[0031] Embodiment 1

[0032] like Figure 2 As shown, the status monitoring module is communicatively connected with the standby monitoring module and the charging monitoring module.

[0033] The status monitoring module is used to monitor and analyze the operating status of the smart charging pile: obtain the charging power of the smart charging pile and mark it as the charging monitoring value, obtain the current value DL, voltage value DY and power value GL of the smart charging pile power supply line, and use the formula The energy consumption coefficient NH of the smart charging pile is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1, and e is a natural constant, and the value of e is 2.73;

[0034] Determine whether the charging monitoring value is zero: if so, determine that the smart charging pile is in standby state, generate a standby monitoring signal, and send the standby monitoring signal and the energy consumption coefficient NH to the standby monitoring module; if not, determine that the smart charging pile is in charging state, generate a charging monitoring signal, and send the charging monitoring signal and the energy consumption coefficient NH to the charging monitoring module.

[0035] The standby monitoring module is used to monitor and analyze the energy consumption of the smart charging pile in the standby state: compare the energy consumption coefficient NH of the smart charging pile with the preset standby threshold DJmax: if the energy consumption coefficient NH is less than the standby threshold DJmax, it is determined that the energy consumption state of the smart charging pile in the standby state meets the requirements; if the energy consumption coefficient NH is greater than or equal to the standby threshold DJmax, it is determined that the energy consumption state of the smart charging pile in the standby state does not meet the requirements, generate a standby energy consumption abnormality signal and send the standby energy consumption abnormality signal to the mobile terminal of the manager.

[0036] The charging monitoring module is used to monitor and analyze the energy consumption of the smart charging pile in the charging state: compare the energy consumption coefficient NH of the smart charging pile with the preset charging threshold CDmax: if the energy consumption coefficient NH is less than the charging threshold CDmax, it is determined that the energy consumption state of the smart charging pile in the charging state meets the requirements; if the energy consumption coefficient NH is greater than or equal to the charging threshold CDmax, it is determined that the energy consumption state of the smart charging pile in the charging state does not meet the requirements, and an abnormal charging energy consumption signal is generated and sent to the mobile phone terminal of the manager; real-time monitoring and analysis of energy consumption of the smart charging pile in the standby state and the charging state are performed respectively, and different early warning standards are adopted to monitor and feedback abnormal energy consumption phenomena in different states, so as to ensure the accuracy of the energy consumption monitoring results while providing data support for the optimization analysis process.

[0037] Embodiment 2

[0038] like Figure 3-4 As shown, the operation analysis module is communicatively connected with the loss monitoring module and the standby optimization module.

[0039] The operation analysis module is used to analyze the operation mode of the smart charging pile: generate an analysis cycle and divide the analysis cycle into several sub-cycles, each sub-cycle is 24 hours long, set several analysis time points in the sub-cycle, and the time difference between every two adjacent analysis time points is equal; mark the analysis time point when the smart charging pile is in the charging state as the charging time point, mark the analysis time point when the smart charging pile is in the standby state as the standby time point, mark the ratio of the number of charging time points to the analysis time points as the charging occupancy value of the sub-cycle, sum and average the charging occupancy values ​​of all sub-cycles to obtain the charging occupancy coefficient of the analysis cycle, and compare the charging occupancy coefficient with the preset charging occupancy threshold: If the charging occupancy coefficient is less than the charging occupancy threshold, the operation mode of the smart charging pile during the analysis period is determined to be the standby mode, and a standby optimization signal is generated and sent to the standby optimization module; if the charging occupancy coefficient is greater than or equal to the charging occupancy threshold, the operation mode of the smart charging pile during the analysis period is determined to be the charging mode, and a loss monitoring signal is generated and sent to the loss monitoring module; the operation mode of the smart charging pile is analyzed, and the working frequency and the overall working time ratio of the smart charging pile are monitored in a periodic monitoring and analysis manner, so as to mark the operation mode according to the charging occupancy coefficient, and then targeted optimization measures are taken according to different operation modes to optimize energy consumption.

[0040] The loss monitoring module is used to monitor and analyze the charging loss of the smart charging pile: a charging period is composed of two consecutive adjacent charging time points, the difference between the power consumption and the charging amount of the smart charging pile in the charging period is marked as the loss amount, and the ratio of the loss amount to the charging amount is marked as the loss value of the charging period. The loss values ​​of all charging periods in the analysis period are summed and averaged to obtain the loss coefficient, and the variance of the loss values ​​of all charging periods in the analysis period is calculated to obtain the stability coefficient. The loss coefficient and stability coefficient are compared with the preset loss threshold and stability threshold respectively: if the loss coefficient is less than the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile in the analysis period meets the requirements, and a normal charging loss signal is generated and the charging loss is normalized. A normal signal is sent to the mobile terminal of the manager; if the loss coefficient is greater than or equal to the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, and a line optimization signal is generated and sent to the mobile terminal of the manager; if the stability coefficient is greater than or equal to the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, and an abnormal maintenance signal is generated and sent to the mobile terminal of the manager; the charging loss of the smart charging pile is monitored and analyzed, and the loss coefficient and stability coefficient are obtained by monitoring and processing the energy consumption and charging amount during the charging process, and the corresponding optimization processing signal is generated according to the values ​​of the loss coefficient and the stability coefficient to improve the processing efficiency when the charging loss is abnormal.

[0041] The standby optimization module is used to optimize and analyze the standby state of the smart charging pile: an analysis period is composed of two adjacent analysis time points, and the analysis periods in the same sub-cycle are arranged in order of time from first to last to obtain an analysis sequence. The sequence numbers of all charging periods in the analysis cycle in the analysis sequence constitute an analysis set, and the number of elements of the sequence numbers of the charging period in the analysis set is marked as the element value of the charging period, and the charging period with an element value not less than L1 is marked as a regular period. The ratio of the number of regular periods to the number of analysis periods in the sub-cycle is marked as a regularity coefficient, and the regularity coefficient is compared with a preset regularity threshold: if the regularity coefficient is less than the regularity threshold, the manual optimization mode is used for standby optimization processing; if the regularity coefficient is greater than or equal to the regularity threshold, the automatic optimization mode is used for standby optimization processing; the standby state of the smart charging pile is optimized and analyzed, and it is determined whether there is a regularity in the sequence number of the analysis sequence where the charging period of the smart charging pile is located within the analysis cycle, so as to mark the standby optimization processing mode according to its value, taking into account the energy consumption control and ease of use of the smart charging pile.

[0042] The specific process of using the manual optimization mode for standby optimization processing includes: disconnecting the power supply of the smart charging pile in the standby state, and when the vehicle needs to be charged, the charging personnel presses the power switch of the smart charging pile to power it on, and the power is automatically cut off after charging is completed;

[0043] The specific process of using the automatic optimization mode for standby optimization processing includes: automatically connecting the power supply line of the smart charging pile at the beginning of a regular period, and automatically cutting off the power of the smart charging pile in the standby state at the beginning of an irregular period.

[0044] The real-time monitoring and analysis system for energy consumption of smart charging piles based on the Internet of Things obtains the charging power of the smart charging pile and marks it as the charging monitoring value when working, obtains the current value DL, voltage value DY and power value GL of the power supply line of the smart charging pile and performs numerical calculation to obtain the energy consumption coefficient NH of the smart charging pile; determines whether the charging monitoring value is zero: if so, uses the standby monitoring module to monitor and analyze the energy consumption of the smart charging pile in the standby state; if not, uses the charging monitoring module to monitor and analyze the energy consumption of the smart charging pile in the charging state; generates an analysis cycle and obtains the charging occupancy coefficient of the analysis cycle, marks the operation mode of the smart charging pile as charging mode or standby mode through the charging occupancy coefficient, monitors and analyzes the charging loss of the smart charging pile in the charging mode; optimizes and analyzes the standby state of the smart charging pile in the standby mode.

[0045] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0046] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets a corresponding energy consumption coefficient for each group of sample data; substitutes the set energy consumption coefficient and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, and the calculated coefficients are screened and averaged to obtain the values ​​of k1, k2 and k3, which are 4.35, 2.62 and 2.12 respectively;

[0047] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding energy consumption coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the energy consumption coefficient is proportional to the current value.

[0048] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0049] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The real-time monitoring and analysis system of energy consumption of intelligent charging piles based on the Internet of Things is characterized by: It includes a real-time monitoring subsystem and a cycle optimization subsystem. The real-time monitoring subsystem includes a state monitoring module, a standby monitoring module and a charging monitoring module. The cycle optimization subsystem includes an operation analysis module, a loss monitoring module and a standby optimization module. The operation analysis module is used to analyze the operation mode of the smart charging pile: generate an analysis cycle and obtain the charging occupancy coefficient of the analysis cycle, and mark the operation mode of the smart charging pile in the analysis cycle as a standby mode or a charging mode according to the charging occupancy coefficient; The loss monitoring module is used to monitor and analyze the charging loss of the smart charging pile and obtain the loss coefficient and stability coefficient, compare the loss coefficient and stability coefficient with the preset loss threshold and stability threshold respectively, and determine whether the charging loss state of the smart charging pile in the analysis period meets the requirements through the comparison results; The standby optimization module is used to optimize and analyze the standby state of the smart charging pile and mark the mode of the standby optimization processing as an automatic optimization mode or a manual optimization mode; The acquisition process of the charging occupancy coefficient of the analysis period includes: dividing into several sub-periods, each sub-period is 24 hours long, setting several analysis time points in the sub-period, and the time difference between every two adjacent analysis time points is equal; marking the analysis time point when the smart charging pile is in the charging state as the charging time point, marking the analysis time point when the smart charging pile is in the standby state as the standby time point, marking the ratio of the number of charging time points to the analysis time points as the charging occupancy value of the sub-period, and summing and averaging the charging occupancy values ​​of all sub-periods to obtain the charging occupancy coefficient of the analysis period; The specific process of marking the operation mode of the smart charging pile in the analysis period includes: comparing the charging occupancy coefficient with the preset charging occupancy threshold: if the charging occupancy coefficient is less than the charging occupancy threshold, it is determined that the operation mode of the smart charging pile in the analysis period is the standby mode, and a standby optimization signal is generated and sent to the standby optimization module; if the charging occupancy coefficient is greater than or equal to the charging occupancy threshold, it is determined that the operation mode of the smart charging pile in the analysis period is the charging mode, and a loss monitoring signal is generated and sent to the loss monitoring module; The process of obtaining the loss coefficient and the stability coefficient includes: forming a charging period by two consecutive adjacent charging time points, marking the difference between the power consumption and the charging amount of the smart charging pile in the charging period as the loss amount, marking the ratio of the loss amount to the charging amount as the loss value of the charging period, summing and averaging the loss values ​​of all charging periods in the analysis period to obtain the loss coefficient, and performing variance calculation on the loss values ​​of all charging periods in the analysis period to obtain the stability coefficient; The specific process of comparing the loss coefficient and the stability coefficient with the preset loss threshold and the stability threshold respectively includes: if the loss coefficient is less than the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period meets the requirements; if the loss coefficient is greater than or equal to the loss threshold and the stability coefficient is less than the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, a line optimization signal is generated and the line optimization signal is sent to the mobile phone terminal of the manager; if the stability coefficient is greater than or equal to the stability threshold, it is determined that the charging loss state of the smart charging pile during the analysis period does not meet the requirements, an abnormal maintenance signal is generated and the abnormal maintenance signal is sent to the mobile phone terminal of the manager; The specific process of the standby optimization module optimizing and analyzing the standby state of the smart charging pile includes: forming an analysis period from two adjacent analysis time points, arranging the analysis periods in the same sub-cycle in order of time from first to last to obtain an analysis sequence, forming an analysis set from the serial numbers of all charging periods in the analysis period in the analysis sequence, marking the number of elements of the serial numbers of the charging period in the analysis set as the element value of the charging period, marking the charging period with an element value not less than L1 as a regular period, marking the ratio of the number of regular periods to the number of analysis periods in the sub-cycle as a regularity coefficient, and comparing the regularity coefficient with a preset regularity threshold: if the regularity coefficient is less than the regularity threshold, the manual optimization mode is used for standby optimization processing; if the regularity coefficient is greater than or equal to the regularity threshold, the automatic optimization mode is used for standby optimization processing.

2. According to the Internet of Things-based smart charging pile energy consumption real-time monitoring and analysis system according to claim 1, it is characterized in that: The state monitoring module is used to monitor and analyze the operating state of the smart charging pile: obtain the charging power of the smart charging pile and mark it as a charging monitoring value, obtain the current value, voltage value and power value of the power supply line of the smart charging pile, and obtain the energy consumption coefficient of the smart charging pile by numerically calculating the current value, voltage value and power value; Determine whether the charging monitoring value is zero: if so, determine that the smart charging pile is in standby state, generate a standby monitoring signal, and send the standby monitoring signal and the energy consumption coefficient to the standby monitoring module; if not, determine that the smart charging pile is in charging state, generate a charging monitoring signal, and send the charging monitoring signal and the energy consumption coefficient to the charging monitoring module.

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