Intelligent management method and system for new energy automobile charging pile

Abstract

The invention relates to the technical field of automobile charging pile management, in particular to an intelligent management method and system for a new energy automobile charging pile. Extracting built-in navigation information and historical charging record information of a central control system of a charging vehicle corresponding to each charging pile at the current time, and analyzing a charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and in combination with the load demand coefficient corresponding to each charging pile in the corresponding charging station at the current time, generating a dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time. The charging power of the charging pile is dynamically regulated and controlled by considering the endurance demand difference of the charging vehicle and combining the actual vehicle condition and the load demand of the charging pile, the purpose of intelligent scheduling of the charging pile is achieved, and intelligent management of the charging pile of the new energy vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging pile management, and particularly to an intelligent management method and system for new energy vehicle charging piles. Background Art

[0002] With the rapid growth of the penetration rate of new energy vehicles (NEVs), charging piles, as the core infrastructure, have seen a significant increase in both quantity and management complexity. Currently, the number of public charging piles has exceeded 2 million, but problems such as low charging efficiency and uneven resource allocation have become increasingly prominent.

[0003] Traditional charging piles lack intelligent scheduling. The concentrated charging behavior of users exacerbates the peak load of the power grid. Additionally, the cost of constructing additional substations is high, and the utilization rate of charging stations during off-peak hours is low. Existing technologies are unable to combine the reservation and queuing information of each charging pile in the charging station and the load differences (such as the number of charging vehicles) of charging piles during different time periods in historical data to quantify the charging demand of charging piles. At the same time, the difference in the range requirements of charging vehicles (such as the relationship between the distance to the destination and the driving range in the navigation information) is not considered, and it is impossible to dynamically adjust the charging power of charging piles in combination with the actual vehicle situation. Therefore, there are significant deficiencies in existing technologies. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management method and system for new energy vehicle charging piles to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: An intelligent management method for new energy vehicle charging piles, the method comprising: S1. Real-time collect the usage information of the charging piles to be measured and the status information of the charging station to which the charging piles to be measured belong through the Internet of Things collaborative network, and construct a charging pile collaborative information network; S2. Calculate the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time according to the usage information of each charging pile in the same charging station at the current time, the charging reservation and queuing information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in historical data; S3. Under the authorization of the vehicle user, extract the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time, and analyze the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and combine the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time; S4. Generate the charging power regulation decisions for each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic equilibrium distribution coefficients respectively corresponding to each charging pile in the corresponding charging station.

[0006] Further, the Internet of Things collaborative network in S1 includes multiple bound sensors; the usage information of the to-be-tested charging pile includes the usage status, the actual charging power at the current time, and the duration of the current charging status; the usage status includes the non-charging status and the charging status; if the usage status is the non-charging status, it is determined that the actual charging power at the current time is 0 and the duration of the current charging status is 0; if the usage status is the charging status, the duration of the current charging status represents the time interval from the start time when the corresponding charging vehicle is connected to the corresponding charging pile to the current time. The status information in the charging station to which the to-be-tested charging pile belongs includes the time period to which the current time belongs, the usage status of each charging pile, and the charging reservation queue number of each charging pile; the time period is preset, and a day is evenly divided into multiple consecutive time periods in sequence. The charging pile collaborative information network is composed of charging piles in one or more charging stations, and in the charging pile collaborative information network, each charging station is bound with the real-time status information of the corresponding charging station, and each charging pile in the corresponding charging station is bound with the real-time usage information of the corresponding charging pile.

[0007] In the construction of the Internet of Things collaborative network in the present invention, sensors are used to monitor the data of the charging station and the charging piles inside it in real time, providing a theoretical basis and data support for quantifying and calculating the load demand coefficients respectively corresponding to the charging piles in the subsequent steps; at the same time, the Internet of Things collaborative network also provides data references for the load demand coefficients respectively corresponding to each charging station passed by on the midway of the to-be-navigated road section in the built-in navigation information of the central control system of the charging vehicle and each charging pile in each charging station passed by at the current time.

[0008] Further, the vehicle charging information respectively corresponding to each preset time period in the historical data in S2 includes the total number of charging vehicles in each preset time period of each day in the historical data, the average value of the total number of charging vehicles in the same preset time period every day, and the charging duration of each vehicle on average. Calculating the load demand coefficients respectively corresponding to each charging pile in the corresponding charging station at the current time in S2 involves the following calculation formula: , where DM i represents the load demand coefficient respectively corresponding to the i-th charging pile in the corresponding charging station at the current time; APT iIndicates the number of charging reservation queues corresponding to the i-th charging pile in the corresponding charging station within the time segment to which the current time belongs; ND indicates the total number of charging vehicles within the time segment to which the current time belongs in the vehicle charging information in the historical data; NDN indicates the total number of charging vehicles within the next time segment of the time segment to which the current time belongs in the vehicle charging information in the historical data; and both ND and NDN are not equal to 0; TP indicates the average charging time per vehicle in the vehicle charging information in the historical data; TC i Indicates the duration of the current charging state in the usage information of the i-th charging pile in the corresponding charging station at the current time.

[0009] Furthermore, the built-in navigation information of the central control system of the charging vehicle in S3 includes the road section to be navigated, the cruising range, each charging station passed in the road section to be navigated, and the load demand coefficient corresponding to each charging pile in each charging station passed at the current time; the road section to be navigated represents the route section between the current position and the navigation end point in the navigation route; The historical charging record information includes the vehicle charging positions corresponding to the latest preset times of the corresponding vehicle and the proportion of charging times belonging to the current charging station; The calculation formula for analyzing the charging demand bias value of each charging pile corresponding to the charging vehicle at the current time in S3 is as follows: , Among them, CD i Indicates the charging demand bias value of the charging vehicle corresponding to the i-th charging pile of the corresponding charging station at the current time; SQ i Indicates the number of charging stations within the range of the vehicle's driving range in the navigation section for the charging vehicle at the i-th charging pile of the corresponding charging station at the current time; SUM{SQ i} represents the sum of the number of charging piles in the charging station within the cruising range in the navigation section for the charging vehicle at the i-th charging pile of the corresponding charging station at the current time; SUM{SQ i ,DM i} indicates that the corresponding load demand coefficient is less than or equal to DM in the navigation section of the charging station for the charging vehicle at the i-th charging pile of the corresponding charging station at the current time, and is within the range of the charging station. i The total number of charging piles; DM i represents the load demand coefficient corresponding to the i-th charging pile in the corresponding charging station at the current time; PC i Indicates the proportion of the charging times of the corresponding vehicle charging positions in the current charging station in the historical charging record information of the corresponding vehicle at the i-th charging pile of the corresponding charging station at the current time; LB i It represents the quotient of the current endurance process of the charging vehicle at the i-th charging pile of the corresponding charging station at the current time and the distance to be navigated; when LB i ≥1, then it is determined that G{LBi {LB, 1} = LB i Conversely, it is determined that G{LB i , 1} = 1.

[0010] Furthermore, in step S3, generating the dynamic equilibrium distribution coefficient of the charging piles corresponding to the current time involves the following calculation formula: , where represents the dynamic equilibrium distribution coefficient of the i1-th charging pile in the charging station corresponding to the current time; DM i represents the load demand coefficient corresponding to the i-th charging pile in the charging station corresponding to the current time; CD i represents the charging demand deviation value of the charging vehicle corresponding to the i-th charging pile in the charging station corresponding to the current time; b represents the total number of charging piles in the charging station corresponding to the current time; DM i1 represents the load demand coefficient corresponding to the i1-th charging pile in the charging station corresponding to the current time; CD i1 represents the charging demand deviation value of the charging vehicle corresponding to the i1-th charging pile in the charging station corresponding to the current time; r represents a preset conversion factor.

[0011] Furthermore, in the process of generating the charging power regulation decision for each charging pile in the charging station at the current time in step S4, obtain the upper limit value of the power supply of the charging station corresponding to the current time, denoted as PWX; calculate the product of the dynamic equilibrium distribution coefficient of the i-th charging pile in the charging station corresponding to the current time and W, denoted as PW i ; Take PW i Greater than the upper limit value of the charging power corresponding to the i-th charging pile in the charging station corresponding to the current time is denoted as PWS i ; If PW i is less than or equal to PWS i , then output PW i and bind it to the i-th charging pile in the charging station corresponding to the current time, and determine that the over-limit regulation power bound to the i-th charging pile in the charging station corresponding to the current time is 0; if PW i is greater than PWS i , then output PWS i and bind it to the i-th charging pile in the charging station corresponding to the current time; and take the difference between PW i and PWS i as the over-limit regulation power bound to the i-th charging pile in the charging station corresponding to the current time; Extract the sequence formed by the power supply values bound to each charging pile in descending order of the dynamic equilibrium distribution coefficient to obtain a charging power regulation sequence; obtain the sum of the over-limit regulation powers bound to each charging pile in the corresponding charging station at the current time, denoted as the candidate regulation power; and sequentially supplement the power supply of each element in the charging power regulation sequence with the candidate regulation power until the sum of the power supply supplement results is equal to the candidate regulation power or the power supply corresponding to each element in the charging power regulation sequence is equal to the charging power upper limit value corresponding to the corresponding charging pile; during the process of supplementing the charging power of each element in the charging power regulation sequence, when the power supply corresponding to the corresponding element is supplemented to the charging power upper limit value corresponding to the corresponding charging pile, then jump to the next element to supplement the charging power; use the charging power regulation sequence supplemented based on the candidate regulation power as the charging power regulation decision for each charging pile in the charging station at the current time.

[0012] In the process of generating the charging power regulation strategy for the charging pile, the present invention not only takes into account the differences in the power supply requirements corresponding to different charging piles and introduces the dynamic equilibrium distribution coefficient to achieve the quantitative regulation of the power supply of each charging pile, but also takes into account the charging power upper limit problem of the charging pile, and introduces the over-limit regulation power to dynamically allocate and manage the part of the quantitatively regulated power supply that exceeds the charging power upper limit, so as to effectively control the charging power of each charging pile in the charging station.

[0013] An intelligent management system for a new energy vehicle charging pile, the system includes: a charging pile collaborative information network construction module, a load demand analysis module, a dynamic equilibrium distribution coefficient regulation module, and a charging power regulation strategy management module; The charging pile collaborative information network construction module constructs a charging pile collaborative information network by collecting the usage information of the to-be-tested charging pile and the status information in the charging station to which the to-be-tested charging pile belongs through the Internet of Things collaborative network in real time; The load demand analysis module calculates the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time according to the usage information of each charging pile in the same charging station at the current time, the charging reservation queue information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in the historical data; The dynamic equilibrium distribution coefficient regulation module extracts the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time with the authorization of the vehicle user, and analyzes the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and combines the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time. The charging power regulation strategy management module generates charging power regulation decisions for each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic equilibrium distribution coefficients corresponding to each charging pile in the corresponding charging station.

[0014] Furthermore, the dynamic equilibrium distribution coefficient regulation module includes a charging demand bias analysis unit and a dynamic equilibrium distribution coefficient calculation unit; The charging demand bias analysis unit extracts the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time under the authorization of the vehicle user, and analyzes the charging demand bias value of the charging vehicle corresponding to each charging pile at the current time; The dynamic equilibrium distribution coefficient calculation unit generates the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time in combination with the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: (1) Considering the situation that the concentrated charging behavior of users intensifies the peak load of the power grid, the present invention can combine the reservation queue information of each charging pile in the charging station and the load differences of the charging piles in different time periods in the historical data to realize the quantification of the charging demand of the charging piles; (2) Considering the differences in the endurance requirements of charging vehicles, and combining the actual vehicle conditions and the load requirements of the charging piles, the present invention realizes the dynamic regulation of the charging power of the charging piles, achieves the purpose of intelligent scheduling of the charging piles, and realizes the intelligent management of new energy vehicle charging piles. Description of the Drawings

[0016] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a schematic structural diagram of an intelligent management system for a new energy vehicle charging pile of the present invention; Figure 2 is a schematic flowchart of an intelligent management method for a new energy vehicle charging pile of the present invention. Detailed Embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Please refer toFigure 1 - Figure 2 , the present invention provides a technical solution: As Figure 1 shown, this embodiment provides an intelligent management system for a new energy vehicle charging pile. The system includes: a charging pile collaborative information network construction module, a load demand analysis module, a dynamic balance distribution coefficient regulation module, and a charging power regulation strategy management module; The charging pile collaborative information network construction module collects the usage information of the to-be-tested charging pile and the status information of the charging station to which the to-be-tested charging pile belongs in real time through the Internet of Things collaborative network, and constructs a charging pile collaborative information network; The load demand analysis module calculates the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time according to the usage information of each charging pile in the same charging station at the current time, the charging reservation queue information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in the historical data; The dynamic balance distribution coefficient regulation module includes a charging demand deviation analysis unit and a dynamic balance distribution coefficient calculation unit; The charging demand deviation analysis unit extracts the built-in navigation information of the central control system of the charging vehicle corresponding to each charging pile at the current time and the historical charging record information under the authorization of the vehicle user, and analyzes the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; The dynamic balance distribution coefficient calculation unit combines the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic balance distribution coefficient of the corresponding charging pile at the current time; The charging power regulation strategy management module generates a charging power regulation decision for each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic balance distribution coefficients corresponding to each charging pile in the corresponding charging station;

[0019] As Figure 2 shown, this embodiment provides an intelligent management method for a new energy vehicle charging pile. The method includes: S1. Collect the usage information of the to-be-tested charging pile and the status information of the charging station to which the to-be-tested charging pile belongs in real time through the Internet of Things collaborative network, and construct a charging pile collaborative information network; The Internet of Things collaborative network in S1 includes multiple bound sensors; the usage information of the to-be-tested charging pile includes the usage status, the actual charging power at the current time, and the duration of the current charging state; the usage status includes the uncharged state and the charged state; if the usage status is the uncharged state, it is determined that the actual charging power at the current time is 0 and the duration of the current charging state is 0; if the usage status is the charged state, the duration of the current charging state represents the time interval from the start time when the corresponding charging vehicle is connected to the corresponding charging pile to the current time; The status information of the charging station to which the charging pile to be measured belongs includes the time period to which the current time belongs, the usage status of each charging pile, and the charging reservation queue number of each charging pile; the time period is preset, and a day is evenly divided into multiple consecutive time periods in sequence; The charging pile collaborative information network is composed of charging piles in one or more charging stations, and the real-time status information of the corresponding charging station and the real-time usage information of each charging pile in the corresponding charging station are bound to each charging station in the charging pile collaborative information network.

[0020] S2. According to the usage information of each charging pile in the same charging station at the current time, the charging reservation queue information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in the historical data, calculate the load demand coefficient corresponding to each charging pile in the corresponding charging station at the current time; The vehicle charging information corresponding to each preset time period in the historical data in S2 includes the total number of charging vehicles in each preset time period of each day in the historical data, the average value of the total number of charging vehicles per day in the same preset time period, and the charging duration of each vehicle on average; When calculating the load demand coefficient corresponding to each charging pile in the corresponding charging station at the current time in S2, the involved calculation formula is as follows: , where DM i represents the load demand coefficient corresponding to the i-th charging pile in the corresponding charging station at the current time; APT i represents the charging reservation queue number corresponding to the i-th charging pile in the corresponding charging station within the time period to which the current time belongs; ND represents the total number of charging vehicles within the time period to which the current time belongs in the vehicle charging information in the historical data; NDN represents the total number of charging vehicles within the next time period to which the current time belongs in the vehicle charging information in the historical data; and both ND and NDN are not equal to 0; TP represents the average charging duration of each vehicle in the vehicle charging information in the historical data; TC i represents the duration of the current charging state in the usage information of the i-th charging pile in the corresponding charging station at the current time.

[0021] S3. With the authorization of the vehicle user, extract the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time, and analyze the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and combine the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time; The built-in navigation information of the central control system of the charging vehicle in S3 includes the section to be navigated, the cruising range, each charging station passed through on the way of the section to be navigated, and the load demand coefficient corresponding to each charging pile in each charging station at the current time; the section to be navigated represents the route section between the current position and the navigation end point in the navigation route. The historical charging record information includes the charging position of the corresponding vehicle in the most recent preset number of times and the proportion of the charging times belonging to the current charging station. The calculation formula for analyzing the charging demand deviation value of each charging pile corresponding to the charging vehicle at the current time in S3 is as follows: , where CD i represents the charging demand deviation value of the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time; SQ i represents the number of charging stations passed through within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time; SUM{SQ i} represents the sum of the number of charging piles in each charging station passed through within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time; SUM{SQ i , DM i} represents the total number of charging piles with a corresponding load demand coefficient less than or equal to DM i in the charging stations passed through within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time; DM i represents the load demand coefficient corresponding to the i-th charging pile in the corresponding charging station at the current time; PC i represents the proportion of the charging times with the charging position of the corresponding vehicle belonging to the current charging station in the historical charging record information of the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time within the most recent preset number of times; LB i represents the quotient of the current cruising process and the distance to be navigated of the charging vehicle corresponding to the i-th charging pile in the corresponding charging station at the current time; when LB i ≥1, it is determined that G{LB i , 1}=LB i ; otherwise, it is determined that G{LB i , 1}=1.

[0022] The calculation formula for generating the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time in S3 is as follows: , where represents the dynamic equilibrium distribution coefficient of the i1-th charging pile in the corresponding charging station at the current time; DM iIt represents the load demand coefficient corresponding to the i-th charging pile in the charging station at the current time; CD i It represents the charging demand deviation value of the charging vehicle corresponding to the i-th charging pile in the charging station at the current time; b represents the total number of charging piles in the charging station at the current time; DM i1 It represents the load demand coefficient corresponding to the i1-th charging pile in the charging station at the current time; CD i1 It represents the charging demand deviation value of the charging vehicle corresponding to the i1-th charging pile in the charging station at the current time; r represents a preset conversion factor.

[0023] S4. Generate the charging power regulation decision for each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic equilibrium distribution coefficient corresponding to each charging pile in the charging station; In the process of generating the charging power regulation decision for each charging pile in the charging station at the current time in S4, obtain the upper limit value of the power supply of the charging station corresponding to the current time, denoted as PWX; calculate the product of the dynamic equilibrium distribution coefficient of the i-th charging pile in the charging station corresponding to the current time and W, denoted as PW i ; Take PW i Greater than the upper limit value of the charging power corresponding to the i-th charging pile in the charging station at the current time is denoted as PWS i ; If PW i Is less than or equal to PWS i , then output PW i Bind it to the i-th charging pile in the charging station at the current time, and determine that the over-limit regulation power bound to the i-th charging pile in the charging station at the current time is 0; If PW i Is greater than PWS i , then output PWS i Bind it to the i-th charging pile in the charging station at the current time; And take the difference between PW i And PWS i As the over-limit regulation power bound to the i-th charging pile in the charging station at the current time; Extract the sequence composed of the power supply power values bound to each charging pile in descending order according to the dynamic equilibrium distribution coefficient to obtain the charging power regulation sequence; obtain the sum of the over-limit regulation powers bound to each charging pile in the charging station corresponding to the current time, denoted as the candidate regulation power; and supplement the power supply power of each element in the charging power regulation sequence in turn with the candidate regulation power until the sum of the power supply power supplement results is equal to the candidate regulation power or the power supply power corresponding to each element in the charging power regulation sequence is equal to the charging power upper limit value corresponding to the corresponding charging pile; during the process of supplementing the charging power of each element in the charging power regulation sequence, when the power supply power corresponding to the corresponding element is supplemented to the charging power upper limit value corresponding to the corresponding charging pile, then jump to the next element to supplement the charging power; use the charging power regulation sequence supplemented based on the candidate regulation power as the charging power regulation decision for each charging pile in the charging station at the current time.

[0024] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0025] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent management method for a new energy vehicle charging pile, characterized in that, The method includes: S1. Real-time collect the usage information of the to-be-tested charging pile and the status information of the charging station to which the to-be-tested charging pile belongs through the Internet of Things collaborative network, and construct a charging pile collaborative information network; S2. Calculate the load demand coefficient corresponding to each charging pile in the corresponding charging station at the current time according to the usage information of each charging pile in the same charging station at the current time, the charging reservation queue information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in the historical data; S3. Under the condition of vehicle user authorization, extract the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time, and analyze the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and combine the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic balance distribution coefficient of the corresponding charging pile at the current time; S4. Generate the charging power regulation decision of each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic balance distribution coefficients corresponding to each charging pile in the corresponding charging station.

2. The intelligent management method for a new energy vehicle charging pile according to claim 1, characterized in that: In the S1, the Internet of Things collaborative network includes multiple bound sensors; the usage information of the to-be-tested charging pile includes the usage status, the actual charging power at the current time, and the duration of the current charging state; the usage status includes the non-charging state and the charging state; if the usage status is the non-charging state, it is determined that the actual charging power at the current time is 0 and the duration of the current charging state is 0; if the usage status is the charging state, the duration of the current charging state represents the interval duration from the start time when the corresponding charging vehicle is connected to the corresponding charging pile to the current time; The status information of the charging station to which the to-be-tested charging pile belongs includes the time period to which it belongs at the current time, the usage status of each charging pile, and the charging reservation queue number of each charging pile; the time period is preset, and a day is evenly divided into multiple consecutive time periods in sequence; The charging pile collaborative information network is composed of charging piles in one or more charging stations, and in the charging pile collaborative information network, each charging station is bound with the real-time status information of the corresponding charging station and each charging pile in the corresponding charging station is bound with the real-time usage information of the corresponding charging pile.

3. An intelligent management method for a new energy vehicle charging pile according to claim 1, characterized in that: In the S2, the vehicle charging information corresponding to each preset time period in the historical data includes the total number of charging vehicles in each preset time period of each day in the historical data, the average value of the total number of charging vehicles in the same preset time period of each day, and the charging duration of each vehicle on average; In S2, calculate the load demand coefficient corresponding to each charging pile in the charging station at the current time. The involved calculation formula is as follows: , Among them, DM i represents the load demand coefficient corresponding to the i-th charging pile in the charging station at the current time; APT i represents the charging reservation queue number corresponding to the i-th charging pile in the charging station within the time period to which the current time belongs; ND represents the total number of charging vehicles within the time period to which the current time belongs in the vehicle charging information in the historical data; NDN represents the total number of charging vehicles in the next time period to which the current time belongs in the vehicle charging information in the historical data; and both ND and NDN are not equal to 0; TP represents the average charging duration per vehicle in the vehicle charging information in the historical data; TC i represents the duration of the current charging state in the usage information of the i-th charging pile in the charging station at the current time.

4. An intelligent management method for a new energy vehicle charging pile according to claim 1, characterized in that: In the S3, the built-in navigation information of the central control system of the charging vehicle includes the to-be-navigated road section, the cruising range, each charging station passed through on the to-be-navigated road section, and the load demand coefficients corresponding to each charging pile in each charging station passed through at the current time; the to-be-navigated road section represents the road section between the current position and the navigation end point in the navigation route; The historical charging record information includes the vehicle charging positions corresponding to the corresponding vehicle in the recent preset number of times and the proportion of the number of charging times belonging to the current charging station; The calculation formula for analyzing the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time in S3 is as follows: , Among them, CD i represents the charging demand deviation value of the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time; SQ i represents the number of charging stations passed within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time; SUM{SQ i} represents the sum of the number of charging piles of each charging station passed within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time; SUM{SQ i , DM i} represents the total number of charging piles corresponding to the load demand coefficient less than or equal to DM i in the charging stations passed within the cruising range in the section to be navigated by the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time; DM i represents the load demand coefficient corresponding to the i-th charging pile in the corresponding charging station at the current time; PC i represents the proportion of the number of charging times when the charging positions of the corresponding vehicles in the recent preset number of times of the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time belong to the current charging station; LB i represents the quotient of the current cruising range process and the distance to be navigated of the charging vehicle corresponding to the i-th charging pile at the corresponding charging station at the current time; when LB i ≥1, then it is determined that G{LB i , 1}=LB i ; otherwise, it is determined that G{LB i , 1}=1.

5. The intelligent management method for a new energy vehicle charging pile according to claim 1, wherein: The dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time generated in S3 involves the following calculation formula: , Among them, represents the dynamic equilibrium distribution coefficient of the i1-th charging pile in the charging station corresponding to the current time; DM i represents the load demand coefficient corresponding to the i-th charging pile in the charging station corresponding to the current time; CD i represents the charging demand deviation value of the charging vehicle corresponding to the i-th charging pile in the charging station corresponding to the current time; b represents the total number of charging piles in the charging station corresponding to the current time; DM i1 represents the load demand coefficient corresponding to the i1-th charging pile in the charging station corresponding to the current time; CD i1 represents the charging demand deviation value of the charging vehicle corresponding to the i1-th charging pile in the charging station corresponding to the current time; r represents a preset conversion factor.

6. The intelligent management method for a new energy vehicle charging pile according to claim 1, characterized in that: In the process of generating the charging power regulation decision of each charging pile in the charging station at the current time in S4, obtain the upper limit value of the power supply of the corresponding charging station at the current time, denoted as PWX; calculate the product of the dynamic equilibrium distribution coefficient of the i-th charging pile in the corresponding charging station at the current time and W, denoted as PW i ; Let PW i greater than the upper limit value of the charging power corresponding to the i-th charging pile in the corresponding charging station at the current time be denoted as PWS i ; If PW i is less than or equal to PWS i , then output PW i and bind it to the i-th charging pile in the charging station corresponding to the current time, and determine that the over-limit regulation power bound to the i-th charging pile in the charging station corresponding to the current time is 0; if PW i is greater than PWS i , then output PWS i and bind it to the i-th charging pile in the charging station corresponding to the current time; and use the difference between PW i and PWS i as the over-limit regulation power bound to the i-th charging pile in the charging station corresponding to the current time; Extract the sequence composed of the power supply values bound to each charging pile in descending order of the dynamic equilibrium distribution coefficient to obtain the charging power regulation sequence; obtain the sum of the over-limit regulation powers bound to each charging pile in the corresponding charging station at the current time, denoted as the candidate regulation power; and supplement the power supply for each element in the charging power regulation sequence in turn with the candidate regulation power until the sum of the power supply supplement results is equal to the candidate regulation power or the power supply corresponding to each element in the charging power regulation sequence is equal to the charging power upper limit value corresponding to the corresponding charging pile; during the process of supplementing the charging power for each element in the charging power regulation sequence, when the power supply corresponding to the corresponding element is supplemented to the charging power upper limit value corresponding to the corresponding charging pile, then jump to the next element for charging power supplement. Use the charging power regulation sequence supplemented based on the candidate regulation power as the charging power regulation decision for each charging pile in the charging station at the current time.

7. An intelligent management system for a new energy vehicle charging pile, applying the intelligent management method for a new energy vehicle charging pile described in any one of claims 1-6, characterized in that, The system includes: a charging pile collaborative information network construction module, a load demand analysis module, a dynamic equilibrium distribution coefficient regulation module, and a charging power regulation strategy management module. The charging pile collaborative information network construction module constructs a charging pile collaborative information network by collecting the usage information of the to-be-tested charging piles and the status information of the charging stations to which the to-be-tested charging piles belong in real time through the Internet of Things collaborative network. The load demand analysis module calculates the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time according to the usage information of each charging pile in the same charging station at the current time, the charging reservation queue information in the corresponding charging station at the current time, and the vehicle charging information corresponding to each preset time period in the historical data. The dynamic equilibrium distribution coefficient regulation module, with the authorization of the vehicle user, extracts the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time, and analyzes the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time; and combines the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time. The charging power regulation strategy management module generates the charging power regulation decision for each charging pile in the charging station at the current time according to the load power in the charging station at the current time and the dynamic equilibrium distribution coefficients corresponding to each charging pile in the corresponding charging station.

8. The intelligent management system for a new energy vehicle charging pile according to claim 7, wherein: The dynamic equilibrium distribution coefficient regulation module includes a charging demand deviation analysis unit and a dynamic equilibrium distribution coefficient calculation unit. The charging demand deviation analysis unit, with the authorization of the vehicle user, extracts the built-in navigation information and historical charging record information of the central control system of the charging vehicle corresponding to each charging pile at the current time, and analyzes the charging demand deviation value of the charging vehicle corresponding to each charging pile at the current time. The dynamic equilibrium distribution coefficient calculation unit combines the load demand coefficients corresponding to each charging pile in the corresponding charging station at the current time to generate the dynamic equilibrium distribution coefficient of the corresponding charging pile at the current time.

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