An artificial intelligence-based charging pile monitoring system

Abstract

The application discloses a kind of charging pile monitoring systems based on artificial intelligence, it is related to charging pile monitoring field, including data acquisition module, user demand analysis module, charging index analysis module, temperature monitoring adjustment module, charging state monitoring early warning module, man-machine interaction platform and database.The demand parameter of the behavior characteristic of charging pile user and the demand parameter of the charging of charging pile user are determined and analyzed, and then the temperature state parameter of charging pile and the charging state parameter of charging pile are counted and analyzed, to obtain the charging demand candidate set corresponding to the user of charging pile, temperature state value and charging state value of charging pile, which can ensure that charging pile operates in a suitable temperature range interval, can make charging pile immediately remind staff in abnormal working state, reduce the working pressure and heating of internal elements of charging pile in low charging peak period, ensure the stability of charging pile power grid in high charging peak period, and improve the service life of charging pile.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of charging pile monitoring, and particularly relates to a charging pile monitoring system based on artificial intelligence. BACKGROUND

[0002] With the continuous development of economy and the continuous emphasis on sustainable development, energy transformation has become an inevitable trend. As a clean and convenient means of transportation, electric vehicles have become an increasingly popular choice, but the development of electric vehicles also cannot do without charging infrastructure, and a charging pile monitoring system based on artificial intelligence has emerged as the times require.

[0003] At present, the charging pile monitoring system cannot comprehensively consider the charging behavior characteristics of the charging pile users and the charging associated demand when in operation, cannot adjust the charging pile temperature when the charging pile is in operation, and thus the burning time of the charging pile occurs from time to time.

[0004] At present, the charging pile monitoring system cannot monitor the charging state of the charging pile when in operation, cannot timely adjust the abnormal charging state, and thus the safety accident rate is greatly improved. In addition, the charging pile cannot be adjusted according to the low peak and high peak of power consumption when charging, and thus the power grid is idle and wasted and the power grid of the charging pile is unstable.

[0005] In order to solve the above defects, the present application provides a technical scheme. SUMMARY

[0006] In order to solve the technical problems in the background art, the present application is proposed. The embodiment of the present application provides a charging pile monitoring system based on artificial intelligence.

[0007] The object of the present application can be achieved by the following technical scheme:

[0008] A charging pile monitoring system based on artificial intelligence comprises a data acquisition module, a user demand analysis module, a charging index analysis module, a temperature monitoring and adjusting module, a charging state monitoring and early warning module, a man-machine interaction platform and a database.

[0009] The user demand analysis module is used for receiving charging pile user behavior characteristic demand parameters and charging pile user charging associated demand parameters, and performing charging pile user behavior characteristic demand and charging pile user charging associated demand determination analysis, and sending the same to the charging state monitoring and early warning module.

[0010] The charging pile user behavior characteristic demand and charging pile user charging associated demand determination analysis is specifically analyzed in the following manner:

[0011] Obtaining the charging records of each charging pile user, wherein the charging records include the charging times and the charging start time and the charging end time of each charging, calculating the time difference value of the charging start time and the charging end time to obtain the charging duration and the charging times of the charging pile user, and recording as Cs i and Cc i , i is the number of the charging pile user; the charging start time is taken as the charging time of this time; a coordinate system is constructed with time as the horizontal axis and charging duration as the vertical axis, the charging time corresponding to the charging duration is plotted, and the charging record change broken line graph is obtained by connecting the broken lines, and the slope of the adjacent two points is calculated and recorded as r is the number of the charging times of the charging pile user, wherein r takes the value range of 1, 2, 3,..., R, R is the maximum value of the charging time number, and the average value of all slopes is taken and recorded as The charging start time and the adjacent last charging end time are calculated to obtain the charging time interval of the charging pile user, recorded as According to the set formula , the charging fluctuation value Cb i of the charging pile user is calculated.

[0012] According to the set formula , the charging behavior characteristic demand index TH i of the charging pile user is obtained, wherein a1, a2, a3, a4 are the set weight factor coefficients, e is a constant, and Z1, Z2, Z3 are the set reference charging duration, reference slope and reference charging times of the charging pile user;

[0013] The gradient comparison intervals T1, T2 and T3 of the charging behavior characteristic demand index are set, and the charging behavior characteristic demand index of each charging pile user is respectively substituted into the preset gradient comparison intervals T1, T2 and T3 for comparison and analysis.

[0014] When the charging behavior characteristic demand index is within the preset gradient comparison interval T1, the charging behavior characteristic value of the charging pile user is recorded as x1 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T2, the charging behavior characteristic value of the charging pile user is recorded as x2 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T3, the charging behavior characteristic value of the charging pile user is recorded as x3 points.

[0015] The residual power of the charging pile user's vehicle is obtained by the power detection sensor, recorded as Sd i ; the vehicle type of the charging pile user is obtained, including electric buses, electric taxis, electric family cars, hybrid electric vehicles and other vehicles, corresponding to w1, w2, w3, w4 and w5 values, and recorded as Cz i; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg i ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0016] ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0017] ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0018] ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0019] ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0020] ; obtain the distance between the home address and the company address of the charging pile user, denoted as Jg

[0021] temperature monitoring adjustment module for statistics and analysis of the temperature state value of the charging pile, and automatic adjustment of the temperature of the charging pile;

[0022] charging state monitoring and early warning module for statistics and analysis of the charging state value of the charging pile, and generating corresponding signals and adjusting the working power of the charging pile accordingly.

[0023] Further, the specific operation steps of the temperature state value of the charging pile are as follows:

[0024] The temperature of the power module of the charging pile is measured by a thermocouple. The measured time is taken as the horizontal axis, and the temperature of the power module is taken as the vertical axis. A two-dimensional coordinate system is established, and each measurement time is plotted with the corresponding temperature. The temperature line is obtained by connecting each point with a broken line. The slope of each temperature line value and the included angle with the horizontal line are calculated. When the included angle between the temperature line value and the horizontal line is acute, it is recorded as the first slope, and is recorded as Wy t , t is the number of measurement time, where t takes the value of 1, 2, 3,..., T, T represents the maximum number of numbers, and takes the value of positive integer, when the included angle between the temperature line value and the horizontal line is obtuse, it is recorded as the second slope, and is recorded as We t The highest temperature value in the broken line graph is read and recorded as Tg, and the lowest temperature value in the broken line graph is read and recorded as Tx. The temperature state value Wp of the charging pile is calculated, where b1, b2, b3, b4 are the set proportion coefficients, are the average values of the first slope of the temperature corresponding to different measurement times, are the average values of the second slope of the temperature corresponding to different measurement times, are the average values of the slope of the temperature corresponding to different measurement times, BW are the standard values of the slope of the temperature corresponding to different measurement times, Tg' is the maximum value of the temperature reference value, and Tx' is the minimum value of the temperature reference value.

[0025] Further, the specific operation steps of the charging state value of the charging pile are as follows:

[0026] The actual electrical operating parameters of the charging pile are acquired, including the current and voltage values. These real-time current and voltage values ​​are displayed as curves in a coordinate system, and standard current and voltage lines are established. The enclosed areas formed by the two curves and the two standard lines are measured and summed to obtain a reference value, which is used as the standard for the actual electrical operating parameters of the charging pile. A smart energy meter measures the energy consumption of the charging pile and calculates the active power value based on the measurement time. After normalizing the reference value, active power value, and temperature status value, ellipses are constructed with the reference value and active power value as the major and minor axes, respectively, and a cone is constructed with the temperature status value as the height. The volume of the cone is extracted as a custom result and calibrated as the charging pile's charging status value.

[0027] Furthermore, the specific steps for statistically analyzing the temperature status values ​​of the charging piles are as follows:

[0028] The operating temperature of the charging pile is divided into n temperature values, each corresponding to a range of values: Q1(0, q1), Q2(q1, q2), Q3(q2, q3)...Qn(qn-1, qn), where Q1, Q2, Q3...Qn are specific temperature value numbers, and q1, q2, q3...qn are the temperature values. The temperature status values ​​of various charging piles are matched with the range of temperature values. If the temperature status value of the charging pile is within the range of temperature values, then that range is the adjustment temperature value for the charging pile's operation, and the charging pile automatically adjusts its temperature according to the adjustment temperature value.

[0029] Furthermore, the specific steps for statistical analysis of the charging status values ​​of charging piles are as follows:

[0030] Set gradient reference thresholds Yu1, Yu2, Yu3, and Yu4 for the charging status value of the charging pile, and compare and analyze the charging status value of the charging pile with the preset gradient reference thresholds Yu1, Yu2, Yu3, and Yu4. The gradient reference thresholds Yu1, Yu2, Yu3, and Yu4 increase in a gradient.

[0031] When the charging status value is less than the preset gradient reference threshold Yu1, an abnormal signal of insufficient working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of insufficient working current and voltage. Please have staff check the line fault".

[0032] When the charging status value is between the preset gradient reference thresholds Yu1 and Yu2, an underutilization identification signal for the charging pile is generated. The difference between the value and the reference threshold Yu2 is calculated to obtain a custom value. The custom value is multiplied by the charging pile working power conversion coefficient and matched with the charging demand candidate set to obtain the working power adjustment value corresponding to the charging demand candidate set. The adjustment is as follows: the first-level charging demand candidate set B1 is multiplied by the adjustment coefficient m1, the second-level charging demand candidate set B2 is multiplied by the adjustment coefficient m2, the third-level charging demand candidate set B3 is multiplied by the adjustment coefficient m3, the fourth-level charging demand candidate set B4 is multiplied by the adjustment coefficient m4, and the unused charging pile is multiplied by the adjustment coefficient m5, where m1>m2>m3>m4>m5.

[0033] When the charging status value is between the preset gradient reference thresholds Yu2 and Yu3, a normal status signal for the charging pile is generated, and the charging pile is not operated.

[0034] When the charging status value is between the preset gradient reference thresholds Yu3 and Yu4, a signal indicating that the charging pile is under heavy charging load is generated. The difference between the value and the reference threshold Yu3 is calculated to obtain a custom value. The custom value is multiplied by the charging pile working power conversion coefficient and matched with the charging demand candidate set to obtain the working power adjustment value corresponding to the charging demand candidate set. The adjustment is the multiplication of the first-level charging demand candidate set B1 by the adjustment coefficient d1, the second-level charging demand candidate set B2 by the adjustment coefficient d2, the third-level charging demand candidate set B3 by the adjustment coefficient d3, and the fourth-level charging demand candidate set B4 by the adjustment coefficient d4, where d1>d2>d3>d4.

[0035] When the charging status value is greater than the preset gradient reference threshold Yu4, an abnormal signal of excessive working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of excessive working current and voltage. Please have staff check the line fault".

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. This invention collects user behavior characteristics and charging-related demand parameters of charging piles, and performs judgment and analysis on them to obtain a set of charging demand candidates for each level. It integrates various situations of charging pile users, enabling the system to more accurately monitor charging users at different levels.

[0038] 2. This invention obtains the charging status value of a charging pile by statistically analyzing the current, voltage, active power, and temperature values ​​during its operation. It also detects, warns, and adjusts the charging status value of the charging pile, which helps reduce the working pressure and heat generation of the internal components of the charging pile during off-peak charging periods, and reduces the waste of idle power grid during off-peak periods. During peak charging periods, it helps ensure the stability of the power grid for the charging pile and reduces the occurrence of safety accidents. When the charging pile is malfunctioning, it alerts the staff to promptly investigate the corresponding line faults.

[0039] 3. This invention obtains and analyzes the temperature status value of the charging pile by detecting the temperature of the charging pile. The charging pile automatically adjusts its temperature according to the adjusted temperature value, ensuring that the temperature of the charging pile is within a suitable range, and at the same time greatly reducing the incidence of safety accidents. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not drawn to scale according to the actual size, but are intended to illustrate the main idea of ​​the present invention.

[0041] Figure 1 This is a system block diagram of the present invention;

[0042] Figure 2 This is a line graph showing the changes in charging records according to the present invention.

[0043] Figure 3 This is a line graph of the temperature curve of the present invention;

[0044] Figure 4 This is a schematic diagram of the vertebral body of the present invention. Detailed Implementation

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of the present invention.

[0046] like Figure 1 As shown, an artificial intelligence-based charging pile monitoring system includes a data acquisition module, a user demand analysis module, a charging index analysis module, a temperature monitoring and adjustment module, a charging status monitoring and early warning module, a human-computer interaction platform, and a database.

[0047] The data acquisition module collects user behavior characteristics and demand parameters, charging-related demand parameters, charging pile temperature status parameters, and charging pile charging status parameters, and sends these to the user demand analysis module and the charging index analysis module. User behavior characteristics and demand parameters include the user's charging duration, number of charges, and charging interval; charging-related demand parameters include the user's vehicle's remaining battery power, the user's vehicle type, and the distance between the user's home address and work address; charging pile temperature status parameters include the temperature value of the charging pile's power module; and charging pile charging status parameters include the real-time current, voltage, and active power values ​​of the charging pile.

[0048] The user demand analysis module receives user behavior characteristic demand parameters and charging-related demand parameters, performs user behavior characteristic demand and charging-related demand judgment analysis, and sends them to the charging status monitoring and early warning module. The specific analysis is as follows:

[0049] Obtain the charging records for each charging station user. These records include the number of charging attempts and the start and end times of each charge. Calculate the time difference between the start and end times to obtain the charging duration for each user, denoted as Cs. i The number of charging cycles, denoted as Cc. i , where i is the user ID of the charging station, and i takes the value of a positive integer; the charging start time is taken as the charging time for this charging session; for example... Figure 2 As shown, a coordinate system is constructed with time as the horizontal axis and charging duration as the vertical axis. Charging times corresponding to the charging duration are plotted as points, and these points are connected by a broken line to obtain a line graph showing the changes in charging records. The slope between adjacent points is calculated and denoted as... r represents the charging station user's charging number, where r ranges from 1, 2, 3, ..., R, and R is the maximum value of the charging number, which is a positive integer. The average of all slopes is denoted as r. The charging start time is compared with the previous charging end time to calculate the time difference, resulting in the charging time interval for the charging station user, denoted as . According to the set formula Calculate the charging fluctuation value Cb for charging station users. i .

[0050] According to the set formula The charging behavior characteristics and demand index of charging pile users were obtained. iIn this formula, a1, a2, a3, and a4 are set weighting factor coefficients, with customizable values ​​of 10.1, 10.2, 16.2, and 9.8 respectively. These weighting factor coefficients are used to balance the weight of various data points in the formula calculation, thereby improving the accuracy of the calculation results. e is a constant with a specific value of 2.718. Z1, Z2, and Z3 are the set reference charging time, reference slope, and reference number of charging sessions for charging station users. The formula shows that the longer the charging time, the closer the charging time slope between adjacent points is to the reference slope; the more charging sessions, the greater the charging behavior characteristic demand index.

[0051] Set up gradient comparison intervals T1, T2, and T3 for the charging behavior characteristic demand index, and substitute the charging behavior characteristic demand index of each charging pile user into the preset gradient comparison intervals T1, T2, and T3 for comparison and analysis. The comparison intervals T1, T2, and T3 increase in a gradient.

[0052] It should be noted that, assuming that T1, T2 and T3 increase in a gradient of 10, when the comparison interval T1 is set to [10, 20), then the comparison interval T2 is [20, 30) and the comparison interval T3 is [30, 40). The interval values ​​of the comparison intervals T1, T2 and T3, as well as the gradient increment value, shall be set by those skilled in the art in specific cases.

[0053] When the charging behavior characteristic demand index is within the preset gradient comparison interval T1, the charging behavior characteristic value of the charging pile user is recorded as x1 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T2, the charging behavior characteristic value of the charging pile user is recorded as x2 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T3, the charging behavior characteristic value of the charging pile user is recorded as x3 points, where x1 < x2 < x3.

[0054] The remaining battery level of the user's vehicle is obtained through a battery detection sensor and denoted as Sd. i Obtain the vehicle types of charging station users. These types can be categorized as electric buses, electric taxis, electric passenger cars, hybrid electric vehicles, and other vehicles, corresponding to values ​​w1, w2, w3, w4, and w5, respectively, where w5 < w4 < w3 < w2 < w1, and denoted as Cz. i ; Obtain the distance between the home address and company address of the charging station user, denoted as Jg i According to the set formula The charging demand index for charging station users is obtained, where f1, f2, and f3 are set weighting factor coefficients with customizable values ​​of 5.2, 5.7, and 9.2 respectively. Sd′, Cz′, and Jg′ represent the reference remaining battery capacity, reference vehicle type value, and reference distance between home and work addresses for the charging station user's vehicle, respectively. ΔSd, ΔCz, and ΔJg represent the difference in reference remaining battery capacity, difference in reference vehicle type value, and difference in distance between home and work addresses for the charging station user's vehicle, respectively. As can be seen from the formula, the smaller the remaining battery capacity, the larger the vehicle type value, and the greater the distance between home and work addresses, the higher the charging demand index.

[0055] Set a complete reference threshold TH1 for the charging-related demand index, and compare and analyze the charging-related demand index of charging pile users by substituting them into the preset complete reference threshold TH1.

[0056] When the charging demand index of a charging pile user is less than the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y1; when the charging demand index of a charging pile user is equal to the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y2; when the charging demand index of a charging pile user is greater than the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y3, where y1 < y2 < y3.

[0057] The scores of the charging behavior characteristic demand data items and the charging related demand index data items of each charging pile user are added together to obtain the total score of each charging pile user. Based on the total score, each charging pile user is classified into levels. When the total score is x3+y3 or x2+y3 or x3+y2, the corresponding charging pile user is classified into the first-level charging demand candidate set B1. When the total score is x2+y2 or x1+y3 or x3+y1, the corresponding charging pile user is classified into the second-level charging demand candidate set B2. When the total score is x1+y2 or x2+y1, the corresponding charging pile user is classified into the third-level charging demand candidate set B3. When the total score is x1+y1, the corresponding charging pile user is classified into the fourth-level charging demand candidate set B4.

[0058] The obtained first-level charging demand candidate set B1, second-level charging demand candidate set B2, third-level charging demand candidate set B3 and fourth-level charging demand candidate set B4 are sent to the charging status monitoring and early warning module.

[0059] The charging index analysis module receives and analyzes the charging pile's temperature and charging status parameters to obtain the charging pile's temperature and charging status values. The temperature monitoring and adjustment module and the charging status monitoring and early warning module are detailed below:

[0060] The temperature of the charging pile's power module is measured using thermocouples. A two-dimensional coordinate system is established with measurement time as the horizontal axis and power module temperature as the vertical axis. Temperature points corresponding to each measurement time are plotted, and temperature curves are obtained by connecting these points with a broken line. Figure 3 As shown, calculate the slope of each temperature line value and the angle between it and the horizontal line. When the angle between the temperature line value and the horizontal line is acute, it is recorded as the first slope, and denoted as Wy. t t represents the measurement time number, where t ranges from 1, 2, 3, ..., T, where T represents the maximum value of the number and is a positive integer. When the angle between the temperature line value and the horizontal line is obtuse, it is recorded as the second slope and denoted as We. t Read the highest temperature value in the line graph and record it as Tg; read the lowest temperature value in the line graph and record it as Tx. The temperature status value Wp of the charging pile is calculated, where b1, b2, b3, and b4 are set proportional coefficients with custom values ​​of 1.2, 1.6, 2.5, and 1.8 respectively. These represent the average first slope of the temperature at different measurement times. This represents the average of the second slope of the temperature at different measurement times. The slope of the temperature is the average value corresponding to different measurement times. BW represents the standard value of the temperature slope corresponding to different measurement times, the maximum reference value of temperature Tg′, and the minimum reference value of temperature Tx′, respectively. It should be noted that when the slope of the temperature data change of the charging pile is more concentrated, and the maximum and minimum temperature values ​​are closer to the reference values, the temperature status value is larger.

[0061] The charging status of charging pile equipment is analyzed as follows: The actual electrical operating parameters of the charging pile are acquired, including the current and voltage values. The real-time current and voltage values ​​are displayed as curves in a coordinate system. Standard current and voltage lines are established in the coordinate system. The enclosed areas formed by the two curves and the two standard lines are measured and summed to obtain a reference value, which is used as the standard for the actual electrical operating parameters of the charging pile. The energy consumption of the charging pile is measured using a smart energy meter, and the active power value is calculated based on the measurement time. After normalizing the reference value, active power value, and temperature status value, ellipses are constructed with the reference value and active power value as the major and minor axes, respectively, and a cone is constructed with the temperature status value as the height. The volume of the cone is extracted, and the volume value is calibrated as the charging status value of the charging pile. Figure 4 As shown.

[0062] The temperature monitoring and adjustment module statistically analyzes the temperature status values ​​of the charging pile and automatically adjusts the temperature of the charging pile accordingly. The specific analysis is as follows:

[0063] The operating temperature of the charging pile is divided into n temperature values, each corresponding to a range of values: Q1(0, q1), Q2(q1, q2), Q3(q2, q3)...Qn(qn-1, qn), where Q1, Q2, Q3...Qn are specific temperature value numbers, and q1, q2, q3...qn are the temperature values. The temperature status values ​​of various charging piles are matched with the range of temperature values. If the temperature status value of the charging pile is within the range of temperature values, then that range is the adjustment temperature value for the charging pile's operation, and the charging pile automatically adjusts its temperature according to the adjustment temperature value.

[0064] The charging status monitoring and early warning module statistically analyzes the charging status values ​​of the charging pile, generates corresponding signals, and adjusts the operating power of the charging pile accordingly. The specific analysis is as follows:

[0065] Set gradient reference thresholds Yu1, Yu2, Yu3, and Yu4 for the charging status value of the charging pile, and compare and analyze the charging status value of the charging pile with the preset gradient reference thresholds Yu1, Yu2, Yu3, and Yu4. The gradient reference thresholds Yu1, Yu2, Yu3, and Yu4 increase in a gradient.

[0066] When the charging status value is less than the preset gradient reference threshold Yu1, an abnormal signal of insufficient working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of insufficient working current and voltage. Please have staff check the line fault".

[0067] When the charging status value is between the preset gradient reference thresholds Yu1 and Yu2, an underutilization of the charging pile is identified as a signal. The difference between this signal and the reference threshold Yu2 is calculated to obtain a custom value. This custom value is then multiplied by the charging pile's operating power conversion factor and matched against a set of alternative charging demands to obtain the corresponding operating power adjustment value. Specifically, the adjustment is as follows: for the first-level charging demand alternative set B1, the adjustment factor is m1; for the second-level charging demand alternative set B2, the adjustment factor is m2; and for the third-level charging demand alternative set B3, the adjustment factor is m3. The four-level charging demand candidate set B4 is multiplied by the adjustment factor m4, and the unused charging piles are multiplied by the adjustment factor m5, where m1>m2>m3>m4>m5. Specifically, m1 can be 95-100%, m2 90-95%, m3 90-95%, m4 85-90%, and m5 60-70%. It should be noted that for underutilized charging piles or during off-peak charging periods, reducing the operating power of the charging piles helps reduce the working pressure and heat generation of the internal components of the charging piles, and reduces the waste of grid idleness during off-peak periods.

[0068] When the charging status value is between the preset gradient reference thresholds Yu2 and Yu3, a normal status signal for the charging pile is generated, and the charging pile is not operated.

[0069] When the charging status value is between the preset gradient reference thresholds Yu3 and Yu4, a signal indicating that the charging pile is under heavy charging load is generated. The difference between the value and the reference threshold Yu3 is calculated to obtain a custom value. This custom value is multiplied by the charging pile's operating power conversion coefficient and matched with the charging demand candidate set to obtain the corresponding operating power adjustment value. Specifically, the adjustment is as follows: Level 1 charging demand candidate set B1 multiplied by adjustment coefficient d1; Level 2 charging demand candidate set B2 multiplied by adjustment coefficient d2; Level 3 charging demand candidate set B3 multiplied by adjustment coefficient d3; Level 4 charging demand candidate set B4 multiplied by adjustment coefficient d4, where d1>d2>d3>d4. The specific values ​​can be d1 90-100%, d2 80-90%, d3 70-80%, and d4 65-70%. It should be noted that reducing the charging pile's operating power during periods of heavy charging load or peak charging is beneficial to ensuring the stability of the charging pile's power grid.

[0070] When the charging status value is greater than the preset gradient reference threshold Yu4, an abnormal signal of excessive working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of excessive working current and voltage. Please have staff check the line fault".

[0071] The human-computer interaction platform is used to receive text messages such as "The charging pile is in an abnormal state of insufficient operating current and voltage. Please have staff check the line fault" and "The charging pile is in an abnormal state of excessive operating current and voltage. Please have staff check the line fault." The platform displays the message and reminds the relevant staff.

[0072] The database stores the reference remaining battery level of the user's vehicle, the reference vehicle type value, the distance between the reference home address and the company address, and the gradient reference threshold for the charging status value of the charging pile.

[0073] The foregoing description is illustrative of the invention and should not be construed as limiting it. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Therefore, all such modifications are intended to be included within the scope of the invention as defined in the claims. It should be understood that the foregoing description is illustrative of the invention and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. An artificial intelligence-based charging pile monitoring system, comprising a data acquisition module, a human-computer interaction platform, and a database, characterized in that, Also includes: The user demand analysis module is used to receive charging pile user behavior characteristic demand parameters and charging pile user charging related demand parameters, and to perform charging pile user behavior characteristic demand and charging pile user charging related demand judgment analysis, and send them to the charging status monitoring and early warning module. The specific analysis method for determining and analyzing the behavioral characteristics and charging-related needs of charging pile users is as follows: Obtain the charging records of each charging station user. These records include the number of charging attempts, the start time of each charge, and the end time. Calculate the time difference between the start and end times to obtain the charging duration and number of charges for each user, denoted as Cs. i and Cc i Let 'i' be the user ID of the charging station; the charging start time is taken as the charging time for this charging session; a coordinate system is constructed with time as the horizontal axis and charging duration as the vertical axis; the charging times corresponding to the charging duration are plotted, and a line graph is obtained by connecting the points to obtain the charging record change; the slope between adjacent points is calculated and denoted as . r represents the charging station user's charging number, where r ranges from 1, 2, 3, ..., R, and R is the maximum value of the charging number. The average of all slopes is denoted as r. The charging time interval for a user is calculated by taking the time difference between the start time of charging and the end time of the previous charging session. This time difference is denoted as . According to the set formula Calculations are performed to obtain the charging fluctuation value Cb for charging station users. i ; According to the set formula The charging behavior characteristics and demand index of charging pile users were obtained. i Where a1, a2, a3, and a4 are the set weight factor coefficients, e is a constant, and Z1, Z2, and Z3 are the set reference charging time, reference slope, and reference charging times for charging pile users. Set up gradient comparison intervals T1, T2 and T3 for the charging behavior characteristic demand index, and substitute the charging behavior characteristic demand index of each charging pile user into the preset gradient comparison intervals T1, T2 and T3 for comparison and analysis. When the charging behavior characteristic demand index is within the preset gradient comparison interval T1, the charging behavior characteristic value of the charging pile user is recorded as x1 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T2, the charging behavior characteristic value of the charging pile user is recorded as x2 points; when the charging behavior characteristic demand index is within the preset gradient comparison interval T3, the charging behavior characteristic value of the charging pile user is recorded as x3 points. The remaining battery level of the user's vehicle is obtained through a battery detection sensor and denoted as Sd. i Obtain the vehicle types of charging station users, including electric buses, electric taxis, electric passenger cars, hybrid electric vehicles, and other vehicles, corresponding to w1, w2, w3, w4, and w5 values ​​respectively, and denoted as Cz. i ; Obtain the distance between the home address and company address of the charging station user, denoted as Jg i According to the set formula The charging demand index of charging pile users is obtained, where f1, f2, and f3 are set weight factor coefficients, Sd′, Cz′, and Jg′ represent the set reference remaining battery power, reference vehicle type value, and reference distance between home address and company address of charging pile users, respectively, and ΔSd, ΔCz, and ΔJg represent the set reference remaining battery power difference, reference vehicle type value difference, and reference distance difference between home address and company address of charging pile users, respectively. Set a complete reference threshold TH1 for the charging-related demand index, and compare and analyze the charging-related demand index of charging pile users by substituting them into the preset complete reference threshold TH1. When the charging demand index of a charging pile user is less than the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y1; when the charging demand index of a charging pile user is equal to the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y2; when the charging demand index of a charging pile user is greater than the preset complete reference threshold TH1, the corresponding charging pile user will be assigned an additional value of y3. The scores of the charging behavior characteristic demand data items and the charging related demand index data items of each charging pile user are added together to obtain the total score of each charging pile user. Based on the total score, each charging pile user is classified into levels. When the total score is x3+y3 or x2+y3 or x3+y2, the corresponding charging pile user is classified into the first-level charging demand candidate set B1. When the total score is x2+y2 or x1+y3 or x3+y1, the corresponding charging pile user is classified into the second-level charging demand candidate set B2. When the total score is x1+y2 or x2+y1, the corresponding charging pile user is classified into the third-level charging demand candidate set B3. When the total score is x1+y1, the corresponding charging pile user is classified into the fourth-level charging demand candidate set B4. The obtained first-level charging demand candidate set B1, second-level charging demand candidate set B2, third-level charging demand candidate set B3 and fourth-level charging demand candidate set B4 are sent to the charging status monitoring and early warning module. The charging index analysis module is used to receive and analyze the temperature status parameters and charging status parameters of the charging pile, obtain the temperature status value and charging status value of the charging pile, and send them to the temperature monitoring and adjustment module and the charging status monitoring and early warning module. The temperature monitoring and adjustment module is used to statistically analyze the temperature status values ​​of the charging pile and automatically adjust the temperature of the charging pile. The charging status monitoring and early warning module is used to statistically analyze the charging status values ​​of the charging pile, generate corresponding signals, and adjust the working power of the charging pile accordingly.

2. The charging pile monitoring system based on artificial intelligence according to claim 1, characterized in that, The specific analysis method for the temperature status value of the charging pile is as follows: The temperature of the charging pile's power module is measured using thermocouples. A two-dimensional coordinate system is established with measurement time as the horizontal axis and power module temperature as the vertical axis. Temperature points corresponding to each measurement time are plotted, and temperature lines are obtained by connecting these points with broken lines. The slope of each temperature line and its angle with the horizontal line are calculated. When the angle between the temperature line value and the horizontal line is acute, it is recorded as the first slope, and denoted as Wy. t t represents the measurement time number, where t ranges from 1, 2, 3, ..., T, and T represents the maximum value of the number. When the angle between the temperature line value and the horizontal line is obtuse, it is denoted as the second slope We. t Read the highest temperature value in the line graph and record it as Tg; read the lowest temperature value in the line graph and record it as Tx. Calculations are performed to obtain the temperature status value Wp of the charging pile, where b1, b2, b3, and b4 are set proportional coefficients. These represent the average first slope of the temperature at different measurement times. This represents the average of the second slope of the temperature at different measurement times. Let Tg' be the average slope of the temperature at different measurement times, and BW be the standard value of the temperature slope at different measurement times, the maximum reference value of the temperature Tg', and the minimum reference value of the temperature Tx', respectively.

3. The charging pile monitoring system based on artificial intelligence according to claim 1, characterized in that, The specific analysis method for the charging status value of the charging pile is as follows: The actual electrical operating parameters of the charging pile are acquired, including the current and voltage values ​​during operation. The real-time current and voltage values ​​are displayed as curves in a coordinate system, and standard current and voltage lines are established. The enclosed areas formed by the two curves and the two standard lines are measured and summed to obtain a reference value, which is used as the standard for the actual electrical operating parameters of the charging pile. The energy consumption of the charging pile is measured using a smart energy meter, and the active power value is calculated based on the measurement time. After normalizing the reference value, active power value, and temperature status value, ellipses are constructed with the reference value and active power value as the major and minor axes, respectively, and a cone is constructed with the temperature status value as the height. The volume of the cone is extracted, and the volume value is calibrated as the charging status value of the charging pile.

4. The charging pile monitoring system based on artificial intelligence according to claim 1, characterized in that, The temperature status values ​​of the charging piles are statistically analyzed, and the specific analysis method is as follows: The operating temperature of the charging pile is divided into n temperature values, each corresponding to a range of values: Q1(0, q1), Q2(q1, q2), Q3(q2, q3)...Qn(qn-1, qn), where Q1, Q2, Q3...Qn are specific temperature value numbers, and q1, q2, q3...qn are the temperature values. The temperature status values ​​of various charging piles are matched with the range of temperature values. If the temperature status value of the charging pile is within the range of temperature values, then that range is the adjustment temperature value for the charging pile's operation, and the charging pile automatically adjusts its temperature according to the adjustment temperature value.

5. The charging pile monitoring system based on artificial intelligence according to claim 1, characterized in that, The charging status values ​​of the charging piles are statistically analyzed, and the specific analysis method is as follows: Set gradient reference thresholds Yu1, Yu2, Yu3, and Yu4 for the charging status value of the charging pile, and compare and analyze the charging status value of the charging pile with the preset gradient reference thresholds Yu1, Yu2, Yu3, and Yu4. When the charging status value is less than the preset gradient reference threshold Yu1, an abnormal signal of insufficient working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of insufficient working current and voltage. Please have staff check the line fault". When the charging status value is between the preset gradient reference thresholds Yu1 and Yu2, an underutilization identification signal for the charging pile is generated. The difference between the value and the reference threshold Yu2 is calculated to obtain a custom value. The custom value is multiplied by the charging pile working power conversion coefficient and matched with the charging demand candidate set to obtain the working power adjustment value corresponding to the charging demand candidate set. The adjustment is as follows: the working power corresponding to the first-level charging demand candidate set B1 is multiplied by the adjustment coefficient m1; the working power corresponding to the second-level charging demand candidate set B2 is multiplied by the adjustment coefficient m2; the working power corresponding to the third-level charging demand candidate set B3 is multiplied by the adjustment coefficient m3; the working power corresponding to the fourth-level charging demand candidate set B4 is multiplied by the adjustment coefficient m4; and the working power corresponding to the unused charging pile is multiplied by the adjustment coefficient m5. When the charging status value is between the preset gradient reference thresholds Yu3 and Yu4, a signal indicating that the charging pile is under heavy charging load is generated. The difference between the value and the reference threshold Yu3 is calculated to obtain a custom value. The custom value is multiplied by the charging pile working power conversion coefficient and matched with the charging demand candidate set to obtain the working power adjustment value corresponding to the charging demand candidate set. The adjustment is as follows: the working power corresponding to the first-level charging demand candidate set B1 is multiplied by the adjustment coefficient d1; the working power corresponding to the second-level charging demand candidate set B2 is multiplied by the adjustment coefficient d2; the working power corresponding to the third-level charging demand candidate set B3 is multiplied by the adjustment coefficient d3; and the working power corresponding to the fourth-level charging demand candidate set B4 is multiplied by the adjustment coefficient d4. When the charging status value is greater than the preset gradient reference threshold Yu4, an abnormal signal of excessive working current and voltage of the charging pile is generated and sent to the charging pile human-machine interaction platform with the text "The charging pile is in an abnormal state of excessive working current and voltage. Please have staff check the line fault".

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