Alternating current charging pile health state prediction method and system based on artificial intelligence

Abstract

The invention discloses an AC charging pile health state prediction method and system based on artificial intelligence, and relates to the technical field of charging pile health prediction, and the method comprises the steps: obtaining the charging data of all historical orders of an AC charging pile, and carrying out the data screening of the charging data of all historical orders of the AC charging pile, and determining charging data of the control group order. The charging data of all historical orders of the AC charging pile are subjected to temperature analysis, and the change condition of the temperature rise speed of the AC charging pile during charging each time is determined, because after the AC charging pile is put into use, the AC charging pile may be affected by the external environment in the charging process, and the temperature rise is abnormal, so that the temperature rise is abnormal. Through the temperature change of the AC charging pile during charging, the health state of the AC charging pile is analyzed, whether the state of the AC charging pile is abnormal is determined, continuous use of the abnormal AC charging pile is avoided, and the accident risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of charging pile health prediction technology, specifically to an artificial intelligence-based method and system for predicting the health status of AC charging piles. Background Technology

[0002] Charging piles are energy-charging devices that provide charging services for electric vehicles.

[0003] Charging stations are mainly divided into ground-mounted and wall-mounted charging stations, primarily using time-based, energy-based, and cost-based charging methods. Charging stations can be fixed to the ground or walls and installed in public buildings (public buildings, shopping malls, public parking lots, etc.) and residential parking lots or charging stations. They can charge various models of electric vehicles according to different voltage levels. The input end of the charging station is directly connected to the AC power grid, and the output end is equipped with a charging plug for charging electric vehicles. Most charging stations are public charging stations, generally providing both regular charging and fast charging methods. Users can use a specific charging card to swipe on the human-machine interface provided by the charging station to perform operations such as selecting the charging method, charging time, and printing cost data. The charging station's display screen shows data such as charging amount, cost, and charging time.

[0004] When AC charging piles have been in use for an extended period, their temperature can rise too quickly while charging devices. To prevent the charging pile from overheating and damaging its internal electronic components, it is necessary to predict the health status of the AC charging pile. Summary of the Invention

[0005] To address the aforementioned technical problems, this paper provides a method and system for predicting the health status of AC charging piles based on artificial intelligence. This technical solution solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An artificial intelligence-based method for predicting the health status of AC charging piles includes: Obtain charging data for all historical orders of AC charging piles, perform data filtering on the charging data of all historical orders of AC charging piles, and determine the charging data of control group orders; Based on the charging data of the control group orders, the charging data of the remaining historical orders are compared and analyzed to obtain the set of temperature rise rates of AC charging piles; wherein, the charging data of the remaining historical orders specifically refers to the charging data of the control group orders removed from the charging data of all historical orders of AC charging piles. The data in the set of AC charging pile temperature rise rate are calculated and processed to determine the set of AC charging pile temperature rise rate difference values. By comparing and analyzing the set of temperature rise rate differences of AC charging piles, the real-time status of AC charging piles can be determined.

[0007] Preferably, the step of obtaining charging data from all historical orders of AC charging piles, and performing data filtering processing on the charging data from all historical orders of AC charging piles to determine the charging data of the control group orders specifically includes the following steps: Information is extracted and processed from the AC charging piles to be tested to determine the number of the AC charging piles to be tested; Based on the number information of the AC charging pile to be detected, data extraction and processing are performed on the database system to obtain all data of the AC charging pile to be detected. Using order information as a feature, all data of the AC charging pile to be tested is extracted and processed to obtain charging data of all historical orders of the AC charging pile; The charging data of all historical orders for AC charging piles were sorted to determine the charging data of the control group orders.

[0008] Preferably, the step of sorting the charging data of all historical orders for AC charging piles to determine the charging data of the control group orders specifically includes the following steps: Data extraction and processing are performed on all historical charging orders of AC charging piles to obtain the order number and charging duration data for each order; Based on the order number data, the charging time of each order is uniquely assigned a number to obtain the charging time data containing the unique number; Based on the minimum value function, the charging time data containing unique numbers is sorted to determine the minimum value of the charging time data. Based on the unique number corresponding to the minimum value of the charging duration data, the charging data of all historical orders of AC charging piles are matched to determine the charging data of the control group orders.

[0009] Preferably, the step of comparing and analyzing the charging data of the remaining historical orders based on the charging data of the control group orders to obtain the set of AC charging pile temperature rise rates specifically includes the following steps: Information extraction and processing were performed on the charging data of the control group orders to obtain the remaining energy of the control group charging equipment when it was not charging and the temperature data of the control group AC charging piles; the temperature data of the control group AC charging piles included the temperature data of the control group AC charging piles before charging and the temperature data of the control group AC charging piles after charging was completed. Based on the remaining energy of the control group's charging equipment when it was not charging, the charging data of the remaining historical orders was processed to obtain partial charging data corresponding to the remaining orders. Based on the temperature data and charging duration data of the control group's AC charging piles, data calculation and processing were performed on the partial charging data corresponding to the remaining orders to determine the set of AC charging pile temperature rise rates.

[0010] Preferably, the step of extracting and processing the charging data of the remaining historical orders based on the remaining energy of the control group's charging equipment when it is not charging, and obtaining the partial charging data corresponding to the remaining orders, specifically includes the following steps: Based on the remaining energy of the control group's charging equipment when it was not charging, the charging data of the remaining historical orders were processed to determine the time information of the remaining historical orders when they reached the remaining energy of the control group's charging equipment when it was not charging. Based on the remaining energy information of the remaining historical orders when they arrived at the charging equipment of the control group but were not yet charged, the charging data of the remaining historical orders was processed to obtain partial charging data corresponding to the remaining orders.

[0011] Preferably, the step of performing data calculation and processing on the partial charging data corresponding to the remaining orders based on the temperature data and charging time data of the control group AC charging piles to determine the set of AC charging pile temperature rise rates specifically includes the following steps: The temperature data and charging time data of the control group AC charging piles were calculated and processed to determine the temperature rise rate of the control group AC charging piles. Read and process the partial charging data corresponding to the remaining orders to obtain the charging completion time of the charging devices for the remaining orders; Based on the charging completion time of the remaining order charging equipment, the time information of the remaining energy when the remaining historical orders arrived at the control group charging equipment before charging was completed was calculated by subtracting the remaining energy information to obtain the charging completion time of the remaining order charging equipment. The remaining orders' partial charging data is read and processed to obtain the remaining historical orders' AC charging pile temperature data when the charging equipment of the control group was not charging, and the AC charging pile temperature data when the charging equipment of the remaining orders was fully charged. The difference between the remaining historical orders' AC charging pile temperature data when the charging equipment of the control group was not charging and the remaining orders' AC charging pile temperature data when the charging equipment of the remaining orders was fully charged was calculated to obtain the difference in AC charging pile temperature change of the remaining orders. The temperature change difference of the remaining AC charging piles and the charging completion time of the remaining charging equipment are calculated and processed to obtain the temperature rise rate of the remaining AC charging piles. The temperature rise rate of the control group AC charging piles and the temperature rise rate of the remaining order AC charging piles are set as the set of AC charging pile temperature rise rates.

[0012] Preferably, the step of calculating and processing the data in the set of AC charging pile temperature rise rate to determine the set of AC charging pile temperature rise rate difference values ​​specifically includes the following steps: Based on the unique ID, the data in the set of AC charging pile temperature rise rate is sorted to obtain the ordered set of AC charging pile temperature rise rate. The temperature rise rate of AC charging piles is calculated by performing a difference calculation on adjacent data in the ordered set of AC charging pile temperature rise rate to obtain several sets of temperature rise rate differences of AC charging piles. Based on the continuity of unique IDs, the temperature rise rate differences of several groups of AC charging piles are sorted to obtain a set of temperature rise rate differences for AC charging piles.

[0013] Preferably, the step of comparing and analyzing the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles specifically includes the following steps: The data in the set of temperature rise rate difference values ​​of AC charging piles are judged and processed against the set temperature rise rate difference threshold. Data with temperatures below a set threshold for the rate of temperature rise in the AC charging piles are removed from the set of data sets showing abnormal temperature rise in the AC charging piles. Weighted analysis is performed on the data in the abnormal temperature rise data set of AC charging piles to determine the real-time status of AC charging piles.

[0014] Preferably, the step of performing weighted analysis on the data in the abnormal temperature rise data set of the AC charging pile to determine the real-time status of the AC charging pile specifically includes the following steps: The data in the abnormal temperature rise data set of AC charging piles and the data in the temperature rise rate difference set of AC charging piles are counted separately to obtain the total number of abnormal data and the total number of all data. The total number of abnormal data and the total number of all data are calculated and processed to obtain the percentage of abnormal data. Judgment and processing of abnormal data proportion information and set abnormal data proportion threshold; If the percentage of abnormal data is greater than or equal to the set threshold for abnormal data, the AC charging pile is in an unhealthy state. If the percentage of abnormal data is less than the set threshold for abnormal data percentage, the AC charging pile is considered to be in a healthy state.

[0015] Furthermore, an AI-based AC charging pile health status prediction system is proposed to implement the aforementioned AI-based AC charging pile health status prediction method, including: The intelligent control terminal is used to control the data transmission and information interaction between various modules. The intelligent control terminal is used to perform data filtering, data comparison, data calculation and status analysis on the charging data of all historical orders of AC charging piles to determine the real-time status of AC charging piles. A database system for storing all data of the AC charging piles to be tested; The data filtering module is used to filter the charging data of all historical orders of AC charging piles and determine the charging data of control group orders. The temperature rise rate calculation module extracts and calculates the charging data of the remaining historical orders using the charging data of the control group orders to obtain a set of temperature rise rates for AC charging piles. Temperature rise rate difference calculation module, which is used to perform difference calculation on the data in the temperature rise rate set of AC charging piles to obtain the temperature rise rate difference set of AC charging piles; The status analysis module is used to compare and analyze the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles.

[0016] Compared with existing technologies, this invention provides an artificial intelligence-based method and system for predicting the health status of AC charging piles, which has the following beneficial effects: This invention performs temperature analysis on charging data from all historical orders of AC charging piles to determine the rate of temperature rise during each charging session. Since AC charging piles may be affected by the external environment during charging, leading to abnormal temperature increases, this invention analyzes the temperature changes during charging to determine the health status of the AC charging piles, identify any abnormalities, and prevent the continued use of malfunctioning charging piles, thus reducing the risk of accidents. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating steps S100-S400 in an AI-based method for predicting the health status of AC charging piles proposed in this invention. Figure 2 This is a structural block diagram of an AI-based AC charging pile health status prediction system proposed in this invention. Detailed Implementation

[0018] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0019] Reference Figure 1 As shown, an artificial intelligence-based method for predicting the health status of AC charging piles includes: S100. Obtain charging data for all historical orders of AC charging piles, perform data filtering on the charging data for all historical orders of AC charging piles, and determine the charging data for the control group orders. S200. Based on the charging data of the control group orders, the charging data of the remaining historical orders are compared and analyzed to obtain the set of temperature rise rates of AC charging piles; wherein, the charging data of the remaining historical orders is specifically the charging data of the control group orders removed from the charging data of all historical orders of AC charging piles. S300: Calculate and process the data in the set of AC charging pile temperature rise rate to determine the set of AC charging pile temperature rise rate difference values. S400: Compare and analyze the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles. Those skilled in the art will understand that after AC charging piles are put into use, they may sometimes be affected by the external environment, causing the internal temperature of the AC charging pile to rise too quickly. However, not all rapid temperature rises are caused by the external environment; they may also be due to the AC charging pile itself. For example, severely aged electronic components inside the charging pile may cause increased resistance, leading to abnormal temperature rise inside the AC charging pile. Therefore, it is necessary to analyze the temperature data of the AC charging pile to determine its health status. If the rapid temperature rise of the AC charging pile is not caused by the external environment, it indicates that the AC charging pile is not in good condition and needs to be maintained in a timely manner to prevent spontaneous combustion due to excessive temperature rise and reduce the risk of charging equipment during charging.

[0020] Step S100: Obtain charging data for all historical orders of AC charging piles, and perform data filtering processing on the charging data of all historical orders of AC charging piles to determine the charging data of the control group orders. This specifically includes the following steps: S101. Extract and process information from the AC charging pile to be tested to determine the AC charging pile number information. S102. Based on the number information of the AC charging pile to be detected, perform data extraction processing on the database system to obtain all data of the AC charging pile to be detected. S103. Using order information as a feature, perform data extraction and processing on all data of the AC charging pile to be tested to obtain charging data of all historical orders of the AC charging pile. S104. Sort the charging data of all historical orders for AC charging piles to determine the charging data of the control group orders. Step S104, which involves sorting the charging data of all historical orders for AC charging piles to determine the charging data of the control group orders, specifically includes the following steps: S1041. Extract and process the charging data of all historical orders of AC charging piles to obtain the order number and charging duration data of each order. S1042. Based on the order number data, perform unique numbering on the charging time of each order to obtain charging time data containing unique numbers; Understandably, in order to better distinguish the data, each data item is assigned a unique number. This unique number is generated based on the order generation time, meaning that the unique number contains time information. S1043. Based on the minimum value function, sort the charging time data containing unique numbers to determine the minimum value of the charging time data. S1044. Based on the unique number corresponding to the minimum value of the charging duration data, perform data matching processing on the charging data of all historical orders of AC charging piles to determine the charging data of the control group orders. In this embodiment, the charging data of all historical orders for AC charging piles are not entirely the same. Some charging devices are charged when they still have a lot of energy left, while others are charged when they are almost out of energy. Therefore, the charging time of AC charging piles is different, and the temperature of AC charging piles after charging is also different. If the state of AC charging piles is healthy, then the heating rate of AC charging piles is the same, that is, the temperature change is regular. Therefore, the temperature data of the control group is the same starting point of these data, that is, the same energy starting point. Therefore, it is necessary to filter from the charging data of all historical orders of AC charging piles to determine a control group. The charging time of the shortest charging time in all historical orders of AC charging piles is the required control group. If the longest charging time is selected, then the temperature of AC charging piles after charging is not reached by the shortest charging time, so data comparison and analysis cannot be performed, and the state of AC charging piles cannot be determined.

[0021] Step S200: Based on the charging data of the control group orders, perform comparative analysis on the charging data of the remaining historical orders to obtain the set of AC charging pile temperature rise rates. This specifically includes the following steps: S201. Extract and process the charging data of the control group orders to obtain the remaining energy of the control group charging equipment when it is not charging and the temperature data of the control group AC charging pile; the temperature data of the control group AC charging pile includes the temperature data of the control group AC charging pile before it is not charging and the temperature data of the control group AC charging pile after it is fully charged. S202. Based on the remaining energy of the control group's charging equipment when it is not charging, perform data extraction processing on the charging data of the remaining historical orders to obtain partial charging data corresponding to the remaining orders. S203. Based on the temperature data of the control group AC charging piles and the charging time data of the control group, perform data calculation and processing on the partial charging data corresponding to the remaining orders to determine the set of temperature rise rates of the AC charging piles. Specifically, step S202, based on the remaining energy of the control group's charging equipment when it was not charging, involves data extraction processing of the charging data of the remaining historical orders to obtain the partial charging data corresponding to the remaining orders. This includes the following steps: S2021. Based on the remaining energy of the control group's charging equipment when it was not charging, perform data positioning processing on the charging data of the remaining historical orders to determine the time information of the remaining historical orders when they reached the remaining energy of the control group's charging equipment when it was not charging. S2022. Based on the time information of the remaining energy when the remaining historical orders arrive at the charging equipment of the control group but are not yet charged, the charging data of the remaining historical orders is processed by data extraction to obtain the partial charging data corresponding to the remaining orders. Understandably, to analyze the temperature data of AC charging piles, it is necessary to select a data starting point (i.e., the remaining energy of the control group charging equipment when it is not charging) from the charging data of all historical orders of AC charging piles, and the data ending point is when the charging equipment is fully charged. This is because the remaining energy of the control group charging equipment when it is not charging is the data of the order with the shortest charging time. To conduct temperature analysis of AC charging piles, it is necessary to have the same data starting point. Therefore, the order with the shortest charging time is set as the control group, and the subsequent data is extracted with reference to the control group. Specifically, step S203, based on the temperature data and charging duration data of the control group's AC charging piles, performs data calculation and processing on the partial charging data corresponding to the remaining orders to determine the set of AC charging pile temperature rise rates, includes the following steps: S2031. Calculate and process the temperature data and charging time data of the control group AC charging piles to determine the temperature rise rate of the control group AC charging piles. S2032. Read and process the partial charging data corresponding to the remaining orders to obtain the charging completion time of the charging equipment for the remaining orders. S2033. Based on the charging completion time of the remaining order charging equipment, perform a difference calculation on the time information of the remaining energy when the remaining historical orders arrive at the control group charging equipment before charging, and obtain the charging completion time of the remaining order charging equipment. S2034. Perform data reading and processing on the partial charging data corresponding to the remaining orders, and obtain the AC charging pile temperature data of the remaining energy when the remaining historical orders arrive at the control group charging equipment without charging and the AC charging pile temperature data when the remaining order charging equipment is fully charged. S2035. Perform a difference calculation on the AC charging pile temperature data of the remaining energy when the remaining historical orders arrive at the control group charging equipment before charging and the AC charging pile temperature data of the remaining orders when charging equipment is completed, to obtain the difference in AC charging pile temperature change of the remaining orders. S2036. Calculate the temperature change difference of the remaining AC charging piles and the charging completion time of the remaining charging equipment to obtain the temperature rise rate of the remaining AC charging piles. S2037. Set the temperature rise rate of the control group AC charging piles and the temperature rise rate of the remaining order AC charging piles as the set of AC charging pile temperature rise rates. Understandably, if the AC charging station is in a healthy state, the temperature rise rate of the remaining order AC charging stations should be similar to that of the control group. Therefore, the difference in temperature rise rate between the remaining order AC charging stations and the control group should be within a reasonable range. However, sometimes AC charging stations are affected by the external environment, causing them to heat up too quickly. For example, in hot weather, the electronic components inside the AC charging station may be affected by the external temperature, causing them to heat up too quickly. However, the external environment is not always at a high temperature. Therefore, the data on abnormal temperature rise of AC charging stations is limited. Subsequent weighted analysis of the temperature rise rate difference can determine the status of the AC charging station.

[0022] Step S300: Calculate and process the data in the set of AC charging pile temperature rise rate to determine the set of AC charging pile temperature rise rate difference values. This specifically includes the following steps: S301. Based on the unique number, sort the data in the set of temperature rise rates of AC charging piles to obtain an ordered set of temperature rise rates of AC charging piles. S302. Perform difference calculation on adjacent data in the ordered set of temperature rise rate of AC charging piles to obtain several sets of temperature rise rate differences of AC charging piles. S303. Based on the continuity of the unique number, sort the temperature rise rate difference of several groups of AC charging piles to obtain the set of temperature rise rate difference of AC charging piles. In this embodiment, if the AC charging pile is in a healthy state, the number of times the AC charging pile's temperature rise rate is affected by the external environment is limited. Therefore, the difference between the temperature rise rate data of adjacent time periods is calculated to obtain the temperature rise rate difference between adjacent time periods. If the AC charging pile is in a healthy state, the number of abnormal data in the temperature rise rate difference between adjacent time periods will be very small. If the AC charging pile is in an unhealthy state, the number of abnormal data in the temperature rise rate difference between adjacent time periods will be very large.

[0023] Step S400: Comparatively analyze the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles. This specifically includes the following steps: S401. Perform judgment processing on the data in the set of temperature rise rate difference values ​​of AC charging piles and the set temperature rise rate difference threshold. S402. Remove data from the set of temperature rise rate difference values ​​of AC charging piles that are less than the set temperature rise rate difference threshold, and obtain the set of abnormal temperature rise data of AC charging piles. S403. Perform weighted analysis on the data in the abnormal temperature rise data set of AC charging piles to determine the real-time status of AC charging piles. It is understandable that when an AC charging station is affected by the external environment or is in an unhealthy state, the difference in the rate of temperature rise of the AC charging station will be abnormal and will exceed the threshold for the rate of temperature rise difference. Specifically, step S403, which involves performing weighted analysis on the data in the abnormal temperature rise data set of the AC charging pile to determine the real-time status of the AC charging pile, includes the following steps: S4031. Count the data in the abnormal temperature rise data set of AC charging piles and the data in the temperature rise rate difference set of AC charging piles respectively, and obtain the total number of abnormal data and the total number of all data. S4032. Calculate and process the total number of abnormal data and the total number of all data to obtain the percentage of abnormal data. S4033, Perform judgment and processing on the abnormal data percentage information and the set abnormal data percentage threshold; S4034. If the percentage of abnormal data is greater than or equal to the set threshold for the percentage of abnormal data, the AC charging pile is in an unhealthy state. S4035. If the percentage of abnormal data is less than the set threshold for the percentage of abnormal data, the AC charging pile is in a healthy state. In this embodiment, if the AC charging pile is in an unhealthy state, the percentage of abnormal data will be greater than the set threshold for abnormal data percentage. If the AC charging pile is in a healthy state, the percentage of abnormal data will be less than the set threshold for abnormal data percentage. At this time, the percentage of abnormal data indicates that the AC charging pile is experiencing abnormal heating rate due to the influence of the external environment. When the external environment returns to normal, the heating rate of the AC charging pile will return to normal. Therefore, the health status of the AC charging pile can be determined by the percentage of abnormal data.

[0024] Reference Figure 2 As shown, an AI-based AC charging pile health status prediction system is used to implement the AI-based AC charging pile health status prediction method described above, including: The intelligent control terminal is used to control the data transmission and information interaction between various modules. The intelligent control terminal is used to perform data filtering, data comparison, data calculation and status analysis on the charging data of all historical orders of AC charging piles to determine the real-time status of AC charging piles. A database system for storing all data of the AC charging piles to be tested; The data filtering module is used to filter the charging data of all historical orders of AC charging piles and determine the charging data of control group orders. The temperature rise rate calculation module extracts and calculates the charging data of the remaining historical orders using the charging data of the control group orders to obtain a set of temperature rise rates for AC charging piles. Temperature rise rate difference calculation module, which is used to perform difference calculation on the data in the temperature rise rate set of AC charging piles to obtain the temperature rise rate difference set of AC charging piles; The status analysis module is used to compare and analyze the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles.

[0025] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for predicting the health status of AC charging piles based on artificial intelligence, characterized in that, include: Obtain charging data for all historical orders of AC charging piles, perform data filtering on the charging data of all historical orders of AC charging piles, and determine the charging data of control group orders; Based on the charging data of the control group orders, the charging data of the remaining historical orders are compared and analyzed to obtain the set of temperature rise rates of AC charging piles; specifically, the charging data of the remaining historical orders is the charging data of the control group orders removed from the charging data of all historical orders of AC charging piles. The data in the set of AC charging pile temperature rise rate are calculated and processed to determine the set of AC charging pile temperature rise rate difference values. By comparing and analyzing the set of temperature rise rate differences of AC charging piles, the real-time status of AC charging piles can be determined.

2. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 1, characterized in that, The process of obtaining charging data from all historical orders of AC charging piles, and then filtering and processing this data to determine the charging data for the control group orders, specifically includes the following steps: Information is extracted and processed from the AC charging piles to be tested to determine the number of the AC charging piles to be tested; Based on the number information of the AC charging pile to be detected, data extraction and processing are performed on the database system to obtain all data of the AC charging pile to be detected. Using order information as a feature, all data of the AC charging pile to be tested is extracted and processed to obtain charging data of all historical orders of the AC charging pile; The charging data of all historical orders for AC charging piles were sorted to determine the charging data of the control group orders.

3. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 2, characterized in that, The process of sorting and processing the charging data of all historical orders for AC charging piles to determine the charging data of the control group orders specifically includes the following steps: Data extraction and processing are performed on all historical charging orders of AC charging piles to obtain the order number and charging duration data for each order; Based on the order number data, the charging time of each order is uniquely assigned a number to obtain the charging time data containing the unique number; Based on the minimum value function, the charging time data containing unique numbers is sorted to determine the minimum value of the charging time data. Based on the unique number corresponding to the minimum value of the charging duration data, the charging data of all historical orders of AC charging piles are matched to determine the charging data of the control group orders.

4. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 3, characterized in that, The process of comparing and analyzing the charging data of the remaining historical orders based on the charging data of the control group orders to obtain the set of AC charging pile temperature rise rates specifically includes the following steps: Information extraction and processing were performed on the charging data of the control group orders to obtain the remaining energy of the control group charging equipment when it was not charging and the temperature data of the control group AC charging piles; the temperature data of the control group AC charging piles included the temperature data of the control group AC charging piles before charging and the temperature data of the control group AC charging piles after charging was completed. Based on the remaining energy of the control group's charging equipment when it was not charging, the charging data of the remaining historical orders was processed to obtain partial charging data corresponding to the remaining orders. Based on the temperature data and charging duration data of the control group's AC charging piles, data calculation and processing were performed on the partial charging data corresponding to the remaining orders to determine the set of AC charging pile temperature rise rates.

5. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 4, characterized in that, The process of extracting and processing the charging data of the remaining historical orders based on the remaining energy of the control group's charging equipment when it is not charging, and obtaining the partial charging data corresponding to the remaining orders, specifically includes the following steps: Based on the remaining energy of the control group's charging equipment when it was not charging, the charging data of the remaining historical orders were processed to determine the time information of the remaining historical orders when they reached the remaining energy of the control group's charging equipment when it was not charging. Based on the remaining energy information of the remaining historical orders when they arrived at the charging equipment of the control group but were not yet charged, the charging data of the remaining historical orders was processed to obtain partial charging data corresponding to the remaining orders.

6. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 5, characterized in that, The process of calculating and processing partial charging data for the remaining orders based on the temperature data and charging duration data of the control group AC charging piles to determine the set of AC charging pile temperature rise rates specifically includes the following steps: The temperature data and charging time data of the control group AC charging piles were calculated and processed to determine the temperature rise rate of the control group AC charging piles. Read and process the partial charging data corresponding to the remaining orders to obtain the charging completion time of the charging devices for the remaining orders; Based on the charging completion time of the remaining order charging equipment, the difference calculation is performed on the remaining energy time information of the remaining historical orders when they arrived at the control group charging equipment before charging, to obtain the charging completion time of the remaining order charging equipment. The remaining orders' partial charging data is read and processed to obtain the remaining historical orders' AC charging pile temperature data when the charging equipment of the control group was not charging, and the AC charging pile temperature data when the charging equipment of the remaining orders was fully charged. The difference between the remaining historical orders' AC charging pile temperature data when the charging equipment of the control group was not charging and the remaining orders' AC charging pile temperature data when the charging equipment of the remaining orders was fully charged was calculated to obtain the difference in AC charging pile temperature change for the remaining orders. The temperature change difference of the remaining AC charging piles and the charging completion time of the remaining charging equipment are calculated and processed to obtain the temperature rise rate of the remaining AC charging piles. The temperature rise rate of the control group AC charging piles and the temperature rise rate of the remaining order AC charging piles are set as the set of AC charging pile temperature rise rates.

7. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 6, characterized in that, The process of calculating and processing the data in the set of AC charging pile temperature rise rate differences to determine the set of AC charging pile temperature rise rate differences specifically includes the following steps: Based on the unique ID, the data in the set of AC charging pile temperature rise rate is sorted to obtain the ordered set of AC charging pile temperature rise rate. The temperature rise rate of AC charging piles is calculated by performing a difference calculation on adjacent data in the ordered set of AC charging pile temperature rise rate to obtain several sets of temperature rise rate differences of AC charging piles. Based on the continuity of unique IDs, the temperature rise rate differences of several groups of AC charging piles are sorted to obtain a set of temperature rise rate differences for AC charging piles.

8. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 7, characterized in that, The process of comparing and analyzing the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles specifically includes the following steps: The data in the set of temperature rise rate difference values ​​of AC charging piles are judged and processed against the set temperature rise rate difference threshold. Data with temperatures below a set threshold for the rate of temperature rise in the AC charging piles are removed from the set of data sets showing abnormal temperature rise in the AC charging piles. Weighted analysis is performed on the data in the abnormal temperature rise data set of AC charging piles to determine the real-time status of AC charging piles.

9. The method for predicting the health status of AC charging piles based on artificial intelligence according to claim 8, characterized in that, The process of performing weighted analysis on the data in the abnormal temperature rise data set of AC charging piles to determine the real-time status of AC charging piles specifically includes the following steps: The data in the abnormal temperature rise data set of AC charging piles and the data in the temperature rise rate difference set of AC charging piles are counted separately to obtain the total number of abnormal data and the total number of all data. The total number of abnormal data and the total number of all data are calculated and processed to obtain the percentage of abnormal data. Judgment and processing of abnormal data proportion information and set abnormal data proportion threshold; If the percentage of abnormal data is greater than or equal to the set threshold for abnormal data, the AC charging pile is in an unhealthy state. If the percentage of abnormal data is less than the set threshold for abnormal data percentage, the AC charging pile is considered to be in a healthy state.

10. An AI-based AC charging pile health status prediction system, used to implement the AI-based AC charging pile health status prediction method as described in any one of claims 1-9, characterized in that, include: The intelligent control terminal is used to control the data transmission and information interaction between various modules. The intelligent control terminal is used to perform data filtering, data comparison, data calculation and status analysis on the charging data of all historical orders of AC charging piles to determine the real-time status of AC charging piles. A database system for storing all data of the AC charging piles to be tested; The data filtering module is used to filter the charging data of all historical orders of AC charging piles and determine the charging data of control group orders. The temperature rise rate calculation module extracts and calculates the charging data of the remaining historical orders using the charging data of the control group orders to obtain a set of temperature rise rates for AC charging piles. Temperature rise rate difference calculation module, which is used to perform difference calculation on the data in the temperature rise rate set of AC charging piles to obtain the temperature rise rate difference set of AC charging piles; The status analysis module is used to compare and analyze the set of temperature rise rate differences of AC charging piles to determine the real-time status of AC charging piles.

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