An intelligent integrated management system and method for transportation energy based on artificial intelligence

Abstract

The present invention relates to the technical field of traffic energy management, and particularly to an intelligent integrated management system and method for traffic energy based on artificial intelligence. The present invention outputs the recommended alternative charging areas to be selected according to the calculated coincidence degree and the function values of each charging area, and can allocate them according to the mobility of electric vehicles, reducing the peak load of the distribution network; and further analyzes and calculates the standby power demand of the target vehicle, calculates the recommended charging duration of the target vehicle according to the standby power demand, and pushes it to the user side, so as to plan the charging duration of the vehicle, shorten the coincidence duration between the charging duration of the vehicle and the peak power consumption period, thereby realizing the load dispatching of the distribution network, and also reducing the charging cost of users. It can reduce the charging duration during peak hours, increase the charging duration during low peak hours, and reduce the charging cost.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic energy management, and particularly to an intelligent fusion management system and method for traffic energy based on artificial intelligence. Background Art

[0002] With the popularization of electric vehicles, their charging demand will significantly increase the load of the distribution network. In order to alleviate the impact of electric vehicles on the distribution network and give full play to the potential of electric vehicles, the industry has proposed an interactive mode between electric vehicles and the distribution network. For example, the "vehicle-pile-network" interactive mode realizes functions such as peak shaving and valley filling, and new energy consumption through the mobile energy storage characteristics of electric vehicles, thereby reducing the peak-valley difference of the distribution network load, improving the new energy consumption capacity, and reducing the need for capacity expansion and transformation of the distribution network.

[0003] However, there is a situation of disordered charging for electric vehicles. For example, when an electric vehicle suddenly needs to be charged during use, and the charging address is also randomly selected, this will cause a significant increase in the peak load of the distribution network. The disordered charging behavior of electric vehicles may further exacerbate the peak-valley difference of the grid load, making the dispatching and operation management of the distribution network more complex and difficult. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent fusion management system and method for traffic energy based on artificial intelligence to solve the problems proposed in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent fusion management method for traffic energy based on artificial intelligence, the management method includes the following steps:

[0006] Step S1: Obtain the target charging area marked by the target vehicle, predict the arrival time and charging time period of the target vehicle, obtain the peak power consumption time period of the target charging area, determine whether there is an overlapping time period between the charging duration of the target vehicle and the peak power consumption time period of the target charging area, and calculate the overlap degree;

[0007] Step S2: If the overlap degree is greater than the overlap degree threshold, obtain the passing areas of the target vehicle's passing route, extract the charging areas with idle charging piles in the passing areas as alternative charging areas, calculate the function values of the alternative charging areas, and output the recommended alternative charging areas to be replaced according to the function values;

[0008] Step S3: After the target vehicle is connected to the charging pile, obtain the charging area where the connected charging pile is located, determine the peak power consumption time period of the distribution area where the charging area is located, and calculate the coverage value between the charging time period of the current vehicle and the peak power consumption time period of the distribution area;

[0009] Step S4: If the coverage value is greater than the coverage value threshold, obtain the historical charging data of the target vehicle, extract the historical charging records of the target vehicle, calculate the charging frequency of the target vehicle, analyze to obtain the standby power demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the standby power demand, and push it to the user terminal.

[0010] Further, the step S1 includes:

[0011] Step S101: Select the target charging area, obtain the historical power consumption data of the target charging area, analyze the peak power consumption time period of the target charging area according to the historical power consumption data, and record the peak power consumption time period as [t1, t2], where t1 represents the start time point of the peak power consumption time period, and t2 represents the end time point of the peak power consumption time period;

[0012] Step S102: Record the time point when the target vehicle arrives at the target charging area as t3, obtain the charging records of the target vehicle, extract the records with the charge reaching full charge in the charging records as the target charging record data, obtain the charging record duration corresponding to each target charging record data, input the charge amount and the charging record duration in each target charging record data into the training model to obtain the charging duration prediction model, input the target charge amount of the target vehicle, and output the predicted charging duration TD of the target vehicle;

[0013] Step S103: Take the time point when the target vehicle arrives at the target charging area as the start time of charging. According to the predicted charging duration TD, obtain the charging time period of the target vehicle as [t3, t4], where t3 represents the start time point of the charging time period, and t4 represents the end time point of the charging time period. Establish a time axis, mark the peak power consumption time period and the charging time period on the time axis, mark the overlapping time period as [t5, t6], and calculate the overlap degree C1. The calculation formula is C1 = (t6 - t5) / (t3 - t4), where (t3 - t4) represents the charging duration of the charging time period [t3, t4], and (t6 - t5) represents the overlapping duration of the overlapping time period [t5, t6].

[0014] Further, the step S2 includes:

[0015] Step S201: Set the overlap degree threshold as C 阈 , if C1 > C 阈When it is, the driving route of the target vehicle to the target charging area is obtained, the areas passed by the driving route are marked as passed areas, all charging areas in the passed areas are extracted, the peak power consumption time periods of each charging area are marked on the time axis, the time point t9 when the target vehicle arrives at the specified charging area is predicted, the time point when the target vehicle arrives at the specified charging area is used as the charging start time, the charging time period of the target vehicle is predicted as [t9, t10], the time period when the charging time period coincides with the peak power consumption time period of the specified charging area is marked as [t7, t8], the coincidence degree C2 between the target vehicle and the corresponding charging area is calculated, and its calculation formula is C2 = (t8 - t7) / (t10 - t9), where (t10 - t9) represents the charging duration of the charging time period [t9, t10], and (t8 - t7) represents the coincidence duration of the coincidence time period [t7, t8], and the charging area where C2 < C1 is used as the alternative charging area;

[0016] Step S202: Eliminate the charging areas in the alternative charging areas where all the charging piles are occupied, obtain all the charging pile types in the charging areas with idle charging piles, divide the charging piles according to the fast charging type and the slow charging type, obtain the number of idle charging piles and the types of idle charging piles in each alternative charging area, record the number of fast charging type charging piles as D1, record the number of slow charging type charging piles as D2, and calculate the slow charging ratio q = D2 / (D1 + D2);

[0017] Step S203: Obtain the charging records of each idle charging pile in the alternative charging areas, mark the records of unfinished charging orders in the charging records as abnormal charging records, obtain the end times of the charging orders corresponding to all the abnormal charging records, extract the start time of the next charging record recorded by the current charging pile after the abnormal charging record, calculate the time difference T1 between the two records, if T1 < T1 阈值 then judge that the abnormal charging record is the target abnormal data, extract all the target abnormal data, and calculate the abnormal charging rate Q of the charging pile i = n / m, where n is the number of all target abnormal data of a single charging pile, m is the number of all abnormal charging records in the alternative charging area, and Q i is the abnormal charging rate of the i-th charging pile;

[0018] Step S204: Calculate the function value K of each alternative charging area, and its calculation formula is K = α1*∑Q i + α2*q, where i = 1, 2, 3,......, i, α1 and α2 represent weight values, * represents the product of two numerical values, and the charging area with the smallest function value is output as the recommended alternative charging area.

[0019] Further, the step S3 includes:

[0020] Step S301, obtain the divided distribution network area, mark the charging pile connected to the target vehicle, obtain the distribution network area where the charging pile is located, extract the peak power consumption time period [tR1, tR2] of the distribution network area, where tR1 represents the starting time point of the peak power consumption time period of the distribution network area, tR2 represents the end time point of the peak power consumption time period of the distribution network area, obtain the current estimated charging time period [tR3, tR4] of the target vehicle, where tR3 represents the estimated charging time period. The starting time point of the interval, tR4 represents the expected end time point of the charging time period, the overlapping time period of the expected charging time period and the peak power consumption time period is obtained and marked as [tR5, tR6], and the coverage value S is calculated. The calculation formula is S=(tR6-tR5) / (tR4-tR3), where (tR6-tR5) represents the overlapping duration of the overlapping time period [tR5, tR6], and (tR4-tR3) represents the charging duration of the expected charging time period [tR3, tR4].

[0021] Further, the step S4 includes:

[0022] Step S401: Set the coverage threshold to S 阈 , if S>S 阈 , then the historical charging data of the target vehicle is obtained, the historical charging record of the target vehicle is extracted, and the record of the target vehicle completing a charging order is marked as a charging number;

[0023] Step S402, calculate the charging frequency F of the target vehicle, the calculation formula is F=the number of charging times of the target vehicle / the total usage time of the target vehicle, obtain the daily usage of the target vehicle, extract the urban area where the target vehicle travels every day, mark the urban area where the target vehicle appears most times as the frequently used urban area, obtain the daily power consumption of the target vehicle in the frequently used urban area to form a line graph, calculate the daily average power consumption P1 of the target vehicle in the frequently used urban area, and obtain the maximum value P of the daily power consumption man ;

[0024] Step S403, extracting the target vehicle's single-day driving route in the commonly used urban area, recording the target vehicle's one-day driving route in the commonly used urban area as a target event, obtaining the driving routes in two adjacent target events, eliminating the same sections in the two driving routes corresponding to the two target events, retaining different sections as target sections, calculating the power consumption used by the target vehicle on the target section, marking it as reserved power consumption, obtaining all reserved power consumption, generating a line graph in units of days, and calculating the reserved power consumption average value P2 of the reserved power consumption;

[0025] Step S404. Calculate the backup power demand based on the daily average power consumption and the reserved power average value. The calculation formula is: W = P1 + P2 * β1 + P man * β2, where β1 and β1 are weight values. According to the current remaining power of the target vehicle, predict the charging duration when reaching the backup power demand W, obtain the backup charging time period as [tR7, tR8], and obtain the end time point of the backup charging time period as tR8. The end time point of the peak power consumption time period is tR2. According to the time sequence, if tR8 < tR2, then mark the duration between the time point tR8 and the time point tR2 as T2. Calculate TP = (T2 / (tR8 - tR7)) + b according to the formula, where TP represents the waiting duration ratio, (tR8 - tR7) represents the charging duration of the backup charging time period, and b is a fixed value. If TP > T2 阈 , then output the recommended charging duration as [tR7, tR8]; if tR8 ≥ tR2 or TP ≤ T2 阈 , then output the recommended power as full charge.

[0026] An intelligent traffic energy integration management system based on artificial intelligence, which is applied to an intelligent traffic energy integration management method based on artificial intelligence. The management system includes a power peak prediction module, a charging area selection module, and a power demand analysis module; the power peak prediction module is used to predict the power peak time periods of each charging area and each distribution network area, and calculate the coincidence degree between the charging time period of the target vehicle and the power peak time period; the charging area selection module is used to output the recommended alternative charging areas to be selected according to the calculated coincidence degree and the function values of each charging area; the power demand analysis module is used to analyze and calculate the backup power demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the backup power demand, and push it to the user terminal.

[0027] The power peak prediction module includes a charging area unit, a distribution area unit, and a target vehicle unit. The charging area unit is used to obtain the power peak time period of the target charging area, and the distribution area unit is used to obtain the power peak time period of the divided distribution network area; the target vehicle unit is used to obtain the charging time period of the target vehicle.

[0028] The charging area selection module includes a charging pile analysis unit and a region selection unit. The charging pile analysis unit is used to calculate the abnormal probability of each charging pile and the fast charging ratio of the charging area corresponding to the charging pile; the region selection unit calculates the function value of the region according to the abnormal charging rate and the fast charging ratio of the charging area, and outputs the recommended alternative charging area to be replaced according to the function value.

[0029] The electricity demand analysis module includes an electricity quantity statistics unit, a redundancy unit, and an output unit. The electricity quantity statistics unit is used to obtain the daily electricity consumption of the target vehicle and calculate the average daily electricity consumption of the target vehicle. The redundancy unit is used to calculate the average reserved electricity consumption of the target vehicle. The output unit is used to calculate the backup electricity demand based on the daily electricity consumption and the average reserved electricity consumption, and output the recommended charging duration according to the backup electricity demand.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] 1. By analyzing the charging time period of the target vehicle with the peak electricity consumption time periods of each charging area and the peak electricity consumption time period of the distribution network area, calculating the coincidence degree between the charging time period of the target vehicle and the peak electricity consumption time period, as well as the function values of each charging area, the present invention outputs the alternative charging areas recommended for selection according to the calculated coincidence degree and the function values of each charging area. It can be allocated according to the mobility of electric vehicles, reducing the peak load of the distribution network. Furthermore, by analyzing and calculating the backup electricity demand of the target vehicle, calculating the recommended charging duration of the target vehicle according to the backup electricity demand, and pushing it to the user terminal, the charging duration of the vehicle can be planned, shortening the coincidence duration between the charging duration of the vehicle and the peak electricity consumption time period. Thus, the load dispatching of the distribution network can be realized, and the charging cost of users can also be reduced. It can reduce the charging duration during peak hours and increase the charging duration during low peak hours, reducing the charging cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flowchart of a traffic energy intelligent fusion management method based on artificial intelligence of the present invention;

[0033] Figure 2 It is a schematic structural diagram of a traffic energy intelligent fusion management system based on artificial intelligence of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] Embodiment 1: As Figure 1 shown, the present invention provides a traffic energy intelligent fusion management method based on artificial intelligence. The management method includes the following steps:

[0036] Step S1: Obtain the target charging area marked by the target vehicle, predict the arrival time and charging time period of the target vehicle, obtain the peak power consumption time period of the target charging area, determine whether there is an overlapping time period between the charging duration of the target vehicle and the peak power consumption time period of the target charging area, and calculate the overlap degree;

[0037] The said step S1 includes:

[0038] Step S101: Select the target charging area, obtain the historical power consumption data of the target charging area, analyze the peak power consumption time period of the target charging area based on the historical power consumption data, and record the peak power consumption time period as [t1, t2], where t1 represents the start time point of the peak power consumption time period and t2 represents the end time point of the peak power consumption time period;

[0039] Step S102: Record the time point when the target vehicle arrives at the target charging area as t3, obtain the charging records of the target vehicle, extract the records with the charging amount reaching full charge in the charging records as the target charging record data, obtain the charging record duration corresponding to each target charging record data, input the charging amount and charging record duration in each target charging record data into the training model to obtain the charging duration prediction model, input the target charging amount of the target vehicle, and output the predicted charging duration TD of the target vehicle;

[0040] Step S103: Take the time point when the target vehicle arrives at the target charging area as the start time of charging. According to the predicted charging duration TD, obtain the charging time period of the target vehicle as [t3, t4], where t3 represents the start time point of the charging time period and t4 represents the end time point of the charging time period. Establish a time axis, mark the peak power consumption time period and the charging time period on the time axis, mark the overlapping time period between the two as [t5, t6], and calculate the overlap degree C1. The calculation formula is C1 = (t6 - t5) / (t3 - t4), where (t3 - t4) represents the charging duration of the charging time period [t3, t4], and (t6 - t5) represents the overlapping duration of the overlapping time period [t5, t6].

[0041] For example, the time when the target vehicle arrives at the target charging area is 19:00, the peak power consumption time period of the target charging area is [17:00, 21:00], and the predicted duration of the target vehicle is 1h. Then the charging time period of the target vehicle is [19:00, 20:00]. According to the time axis, the overlapping time period between the peak power consumption time period and the charging time period can be obtained as [19:00, 20:00], where the overlap degree C1 = 1h / 1h = 1.

[0042] Step S2: If the coincidence degree is greater than the coincidence degree threshold, obtain the area passed by the target vehicle's travel route, extract the charging areas with idle charging piles in the passed area as alternative charging areas, calculate the function values of the alternative charging areas, and output the recommended alternative charging areas to be replaced according to the function values;

[0043] The said Step S2 includes:

[0044] Step S201: Set the coincidence degree threshold as C 阈 , if C1 > C 阈 , then obtain the travel route of the target vehicle to the target charging area, mark the area passed by the travel route as the passed area, extract all the charging areas in the passed area, mark the peak power consumption time periods of each charging area on the time axis, predict the time point t9 when the target vehicle arrives at the specified charging area, take the time point when the target vehicle arrives at the specified charging area as the charging start time, predict the charging time period of the target vehicle as [t9, t10], mark the time period when the charging time period coincides with the peak power consumption time period of the specified charging area as [t7, t8], calculate the coincidence degree C2 between the target vehicle and the corresponding charging area, and its calculation formula is C2 = (t8 - t7) / (t10 - t9), where (t10 - t9) represents the charging duration of the charging time period [t9, t10], and (t8 - t7) represents the coincidence duration of the coincidence time period [t7, t8], and take the charging areas with C2 < C1 as alternative charging areas;

[0045] Step S202: Exclude the charging areas in the alternative charging areas where all the charging piles are occupied, obtain all the charging pile types in the charging areas with idle charging piles, divide the charging piles according to fast charging types and slow charging types, obtain the number of idle charging piles and the types of idle charging piles in each alternative charging area, record the number of fast charging type charging piles as D1, record the number of slow charging type charging piles as D2, and calculate the slow charging ratio q = D2 / (D1 + D2);

[0046] Step S203: Obtain the charging records of each idle charging pile in the alternative charging areas, mark the records of uncompleted charging orders in the charging records as abnormal charging records, obtain the end times of the charging orders corresponding to all the abnormal charging records, extract the start time of the next charging record recorded by the current charging pile after the abnormal charging record, calculate the time difference T1 between the two records, if T1 < T1 阈值 , then determine that this abnormal charging record is the target abnormal data, extract all the target abnormal data, and calculate the abnormal charging rate Q i = n / m, where n is the number of all target abnormal data of a single charging pile, and m is the number of all abnormal charging records in this alternative charging area, Q iis the abnormal charging rate of the i-th charging pile;

[0047] Step S204: Calculate the function value K of each alternative charging area, and its calculation formula is K = α1 * ∑Q i + α2 * q, where i = 1, 2, 3,......, i, α1 and α2 represent weight values, * represents the product of two numerical values, and the charging area with the smallest function value is output as the recommended alternative charging area.

[0048] For example, set C 阈 = 0.2. Since C1 = 1 is greater than C2 = 0.2, use the method in Step S1 to calculate the value of C2. The process is as follows: Obtain the charging time period [19:30, 20:30] for reaching the specified charging area. The peak electricity consumption time period of the specified charging area is [17:00, 20:00]. The overlapping time period between the charging time period and the specified charging area is [19:30, 20:00]. Then C2 = 0.5h / 1h = 0.5. Since C2 is less than C1, C2 is used as the alternative charging area;

[0049] The number of available fast-charging type charging piles in this alternative charging area is 5, and the number of available slow-charging type charging piles is 3. Then the fast-charging ratio is q = 3 / (5 + 3) = 0.375;

[0050] Extract the abnormal charging records of the charging piles in the alternative charging area. Record the time difference between a certain abnormal charging record and the next charging record as T1. If T1 is less than T1 阈值 , it means that the target vehicle recharges again in a very short time period, indicating that this abnormal record is determined to be caused by abnormal charging, rather than the situation where the charging order is completed or the charging vehicle actively stops charging;

[0051] If the number of abnormal charging data of this charging pile is 20, and the total abnormal charging data of the charging area where this charging pile is located is 110, then calculate the charging abnormal rate Q of this charging pile = 20 / 110 = 0.18. Add up the charging abnormal rates of all idle charging piles to get ∑Q i = 1.2;

[0052] Set α1 and α2 as fixed values, and the sum of α1 and α2 is 1. Generally, α1 takes 0.6 and α2 takes 0.4. Calculate K = 0.6 * 1.2 + 0.4 * 0.375 = 0.87. The larger the calculated K value, the more likely it is that there are charging abnormalities in this charging area or it is not easy to use fast-charging type charging piles in this charging area. The occurrence of the above two situations will affect the charging efficiency. Therefore, the larger the K value, the less recommended it is to choose this charging area. Finally, the charging area corresponding to the smallest K value is output as the recommended alternative charging area.

[0053] Step S3: After the target vehicle is connected to the charging pile, obtain the charging area where the connected charging pile is located, determine the peak power consumption time period of the power distribution area where the charging area is located, and calculate the coverage value between the current vehicle's charging time period and the peak power consumption time period of the power distribution area;

[0054] The said step S3 includes:

[0055] Step S301: Obtain the divided power grid area, mark the charging pile connected by the target vehicle, obtain the power grid area where the charging pile is located, extract the peak power consumption time period [tR1, tR2] of this power grid area, where tR1 represents the start time point of the peak power consumption time period of this power grid area, tR2 represents the end time point of the peak power consumption time period of this power grid area, obtain the currently estimated charging time period [tR3, tR4] of the target vehicle, where tR3 represents the start time point of the estimated charging time period, tR4 represents the end time point of the estimated charging time period, obtain the overlapping time period between the estimated charging time period and the peak power consumption time period and mark it as [tR5, tR6], and calculate the coverage value S. Its calculation formula is S = (tR6 - tR5) / (tR4 - tR3), where (tR6 - tR5) represents the overlapping duration of the overlapping time period [tR5, tR6], and (tR4 - tR3) represents the charging duration of the estimated charging time period [tR3, tR4].

[0056] For example, if the peak power consumption time period of the power grid area is [15:30, 21:30], and the estimated charging time period is [19:50, 21:20], then the overlapping time period between the estimated charging time period and the peak power consumption time period is [19:50, 21:20], and S = 1.5h / 1.5 = 1

[0057] Step S4: If the coverage value is greater than the coverage value threshold, obtain the historical charging data of the target vehicle, extract the historical charging records of the target vehicle, calculate the charging frequency of the target vehicle, analyze to obtain the standby power demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the standby power demand, and push it to the user terminal.

[0058] The said step S4 includes:

[0059] Step S401: Set the coverage value threshold as S 阈 , if S > S 阈 , then obtain the historical charging data of the target vehicle, extract the historical charging records of the target vehicle, and mark the record of the target vehicle completing a charging order as one charging time;

[0060] Step S402, calculate the charging frequency F of the target vehicle, the calculation formula is F=the number of charging times of the target vehicle / the total usage time of the target vehicle, obtain the daily usage of the target vehicle, extract the urban area where the target vehicle travels every day, mark the urban area where the target vehicle appears most times as the frequently used urban area, obtain the daily power consumption of the target vehicle in the frequently used urban area to form a line graph, calculate the daily average power consumption P1 of the target vehicle in the frequently used urban area, and obtain the maximum value P of the daily power consumption man ;

[0061] Step S403, extracting the target vehicle's single-day driving route in a commonly used urban area, recording the target vehicle's one-day driving route in a commonly used urban area as a target event, obtaining the driving routes in two adjacent target events, eliminating the same sections in the two driving routes corresponding to the two target events, retaining different sections as target sections, calculating the power consumption used by the target vehicle on the target section, marking it as reserved power consumption, obtaining all reserved power consumption, generating a line graph in units of days, and calculating the reserved power consumption average value P2 of the reserved power consumption; wherein, calculating the power consumption of different sections as the reserved power consumption is to avoid insufficient power consumption caused by unexpected situations when the user uses the vehicle.

[0062] Step S404: Calculate the standby power demand based on the daily average power consumption and the average reserved power consumption. The calculation formula is: W = P1 + P2 * β1 + P man *β2, where β1 and β2 are weight values. According to the current remaining power of the target vehicle, the charging time when the standby power demand W is reached is predicted, and the standby charging time period is obtained as [tR7, tR8]. The end time point of the standby charging time period is obtained as tR8, and the end time point of the peak power consumption time period is tR2. According to the time sequence, if tR8<tR2, the time between tR8 and tR2 is marked as T2. According to the formula, TP=(T2 / (tR8-tR7)+b, where TP represents the waiting time ratio, (tR8-tR7) represents the charging time of the standby charging time period, and b is a fixed value. If TP>T2 阈 , then the recommended charging time is [tR7, tR8]; if tR8 ≥ tR2 or TP ≤ T2 阈 , the recommended output power is full charge.

[0063] Among them, if the end time of the backup charging time period is less than the end time of the peak power consumption time period, it means that after the backup charging is completed, it is still in the peak power consumption time period of the area. At this time, the end time of the backup charging time period and the end time of the peak power consumption time period are obtained to obtain the remaining time for the peak to end, and calculate TP=0.5h*2h+3=4. If TP>T2 阈, it indicates that there is still a long time until the end of the peak electricity consumption period. At this time, it is not recommended to continue charging, but to output a recommendation to stop charging after reaching the standby power demand; if TP ≤ T2 阈 , it indicates that the time point of the end of the peak electricity consumption period is very close at this time. You can continue to wait for the end of the peak electricity consumption period. Therefore, a recommendation to fully charge can be output. If the end time point of the standby charging period is greater than the peak electricity consumption period, that is, the end time point of the charging completion is after the peak electricity consumption period, it means that there will be no peak-valley load here. Therefore, it can be directly fully charged and the recommended battery level is full.

[0064] Embodiment 2: As Figure 2 shown, an intelligent integrated management system for transportation energy based on artificial intelligence is applied to an intelligent integrated management method for transportation energy based on artificial intelligence. The management system includes a peak electricity consumption prediction module, a charging area selection module, and a power demand analysis module; the peak electricity consumption prediction module is used to predict the peak electricity consumption time periods of each charging area and each power distribution network area, and calculate the coincidence degree between the charging time period of the target vehicle and the peak electricity consumption time period; the charging area selection module is used to output the recommended alternative charging areas to be selected according to the calculated coincidence degree and the function values of each charging area; the power demand analysis module is used to analyze and calculate the standby power demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the standby power demand, and push it to the user terminal.

[0065] The peak electricity consumption prediction module includes a charging area unit, a power distribution area unit, and a target vehicle unit. The charging area unit is used to obtain the peak electricity consumption time period of the target charging area, and the power distribution area unit is used to obtain the peak electricity consumption time period of the divided power distribution network area; the target vehicle unit is used to obtain the charging time period of the target vehicle.

[0066] The charging area selection module includes a charging pile analysis unit and a region selection unit. The charging pile analysis unit is used to calculate the abnormal probability of each charging pile and the fast charging ratio of the charging area corresponding to the charging pile; the region selection unit calculates the function value of the region according to the abnormal charging rate and fast charging ratio of the charging area, and outputs the recommended alternative charging area to be replaced according to the function value.

[0067] The power demand analysis module includes a power consumption statistics unit, a redundancy unit, and an output unit. The power consumption statistics unit is used to obtain the daily power consumption of the target vehicle and calculate the average daily power consumption of the target vehicle; the redundancy unit is used to calculate the average reserved power consumption of the target vehicle; the output unit is used to calculate the standby power demand according to the daily power consumption and the average reserved power consumption, and output the recommended charging duration according to the standby power demand.

[0068] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A traffic energy intelligent fusion management method based on artificial intelligence, characterized in that: The management method includes the following steps: Step S1: Obtain the target charging area marked by the target vehicle, predict the arrival time and charging time period of the target vehicle, obtain the peak power consumption time period of the target charging area, determine whether there is an overlapping time period between the charging duration of the target vehicle and the peak power consumption time period of the target charging area, and calculate the overlap degree; Step S2: If the overlap degree is greater than the overlap degree threshold, obtain the areas passed by the target vehicle's travel route, extract the charging areas with idle charging piles in the passed areas as alternative charging areas, calculate the function values of the alternative charging areas, and output the recommended alternative charging areas to be replaced according to the function values; Step S3: After the target vehicle is connected to the charging pile, obtain the charging area where the connected charging pile is located, determine the peak power consumption time period of the distribution area where the charging area is located, and calculate the coverage value between the current vehicle's charging time period and the peak power consumption time period of the distribution area; Step S4: If the coverage value is greater than the coverage value threshold, obtain the historical charging data of the target vehicle, extract the historical charging records of the target vehicle, calculate the charging frequency of the target vehicle, analyze and obtain the standby power consumption demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the standby power consumption demand, and push it to the user terminal; The said Step S2 includes: Step S201: Set the overlap threshold as C 阈 , if C1 > C 阈 , then obtain the travel route for the target vehicle to the target charging area, mark the areas passed by the travel route as passed areas, extract all the charging areas in the passed areas, obtain the peak power consumption time periods of each charging area and mark them on the time axis, predict the time point t9 when the target vehicle arrives at the designated charging area, take the time point when the target vehicle arrives at the designated charging area as the charging start time, predict the charging time period of the target vehicle as [t9, t10], mark the time period when the charging time period overlaps with the peak power consumption time period of the designated charging area as [t7, t8], calculate the overlap degree C2 between the target vehicle and the corresponding charging area, and its calculation formula is C2 = (t8 - t7) / (t10 - t9), where (t10 - t9) represents the charging duration of the charging time period [t9, t10], and (t8 - t7) represents the overlap duration of the overlap time period [t7, t8], and take the charging area with C2 < C1 as the alternative charging area; Step S202: Eliminate the charging areas in the alternative charging areas where all the charging piles are occupied, obtain all the charging pile types in the charging areas with idle charging piles, divide the charging piles according to fast charging types and slow charging types, obtain the number of idle charging piles and the types of idle charging piles in each alternative charging area, record the number of fast charging type charging piles as D1, record the number of slow charging type charging piles as D2, and calculate the slow charging ratio q = D2 / (D1 + D2); Step S203: Obtain the charging records of each idle charging pile in the alternative charging area, mark the records of uncompleted charging orders in the charging records as abnormal charging records, obtain the end time of the charging orders corresponding to all abnormal charging records, extract the start time of the next charging record recorded by the current charging pile after the abnormal charging record, and calculate the time difference T1 between the two records. If T1 < T1 阈值 , then determine that this abnormal charging record is the target abnormal data, extract all target abnormal data, and calculate the abnormal charging rate Q of this charging pile i = n / m, where n is the number of all target abnormal data of a single charging pile, and m is the number of all abnormal charging records in this alternative charging area. Q i is the abnormal charging rate of the i-th charging pile; Step S204: Calculate the function value K of each alternative charging area, and its calculation formula is K = α1 * ∑Q i + α2 * q, where i = 1, 2, 3,......, i, α1 and α2 represent weight values, * represents the product of two numerical values, and output the charging area with the smallest function value as the recommended alternative charging area; The said Step S3 includes: Step S301: Obtain the divided power distribution network area, mark the charging pile connected by the target vehicle, obtain the power distribution area where the charging pile is located, extract the peak power consumption time period [tR1, tR2] of the power distribution area, where tR1 represents the start time point of the peak power consumption time period of the power distribution area, tR2 represents the end time point of the peak power consumption time period of the power distribution area, obtain the current estimated charging time period [tR3, tR4] of the target vehicle, where tR3 represents the start time point of the estimated charging time period, tR4 represents the end time point of the estimated charging time period, obtain the overlapping time period between the estimated charging time period and the peak power consumption time period and mark it as [tR5, tR6], and calculate the coverage value S, and its calculation formula is S = (tR6 - tR5) / (tR4 - tR3), where (tR6 - tR5) represents the overlapping duration of the overlapping time period [tR5, tR6], and (tR4 - tR3) represents the charging duration of the estimated charging time period [tR3, tR4]; The said Step S4 includes: Step S401: Set the coverage value threshold to S 阈 , if S > S 阈 , then obtain the historical charging data of the target vehicle, extract the historical charging records of the target vehicle, and mark the record of the target vehicle completing a charging order as one charging time; Step S402: Calculate the charging frequency F of the target vehicle. The calculation formula is F = the number of charging times of the target vehicle / the total usage duration of the target vehicle. Obtain the daily usage situation of the target vehicle, extract the urban areas where the target vehicle travels daily, mark the urban area with the most occurrences of the target vehicle as the frequently used urban area, obtain the daily power consumption of the target vehicle in the frequently used urban area to form a line chart, calculate the single-day average power consumption P1 of the target vehicle in the frequently used urban area, and obtain the maximum value P of the single-day power consumption man ; Step S403: Extract the single-day driving route of the target vehicle within the common urban area. Record the driving route of the target vehicle within the common urban area in one day as one target event. Obtain the driving routes in two adjacent target events. Exclude the same road sections in the two driving routes corresponding to the two target events, and retain the different road sections as the target road sections. Calculate the electricity consumption used by the target vehicle on the target road sections, mark it as the retained electricity consumption. Obtain all the retained electricity consumptions, generate a line chart on a daily basis, and calculate the average retained electricity consumption P2 of the retained electricity consumptions. Step S404: Calculate the backup power demand based on the daily average power consumption and the reserved power average value. The calculation formula is: W = P1 + P2 * β1 + P man * β2, where β1 and β1 are weight values. According to the current remaining power of the target vehicle, predict the charging duration when reaching the backup power demand W, and obtain the backup charging time period as [tR7, tR8]. Obtain the end time point of the backup charging time period as tR8, and the end time point of the peak power consumption time period as tR2. According to the time sequence, if tR8 < tR2, then mark the duration between the time point tR8 and the time point tR2 as T2. Calculate TP = (T2 / (tR8 - tR7)) + b according to the formula, where TP represents the waiting duration ratio, (tR8 - tR7) represents the charging duration of the backup charging time period, and b is a fixed value. If TP > T2 阈 , then output the recommended charging duration as [tR7, tR8]; if tR8 ≥ tR2 or TP ≤ T2 阈 , then output the recommended power as full charge.

2. The intelligent fusion management method of transportation energy based on artificial intelligence according to claim 1, characterized in that: The said step S1 includes: Step S101: Select the target charging area, obtain the historical electricity consumption data of the target charging area, analyze the electricity consumption peak time period of the target charging area based on the historical electricity consumption data, and record the electricity consumption peak time period as [t1, t2], where t1 represents the start time point of the electricity consumption peak time period, and t2 represents the end time point of the electricity consumption peak time period. Step S102: Record the time point when the target vehicle arrives at the target charging area as t3, obtain the charging records of the target vehicle, extract the records with the charging amount reaching full charge in the charging records as the target charging record data, obtain the charging record duration corresponding to each target charging record data, input the charging amount and the charging record duration in each target charging record data into the training model to obtain the charging duration prediction model, and input the target charging amount of the target vehicle to output the predicted charging duration TD of the target vehicle. Step S103: Use the time point when the target vehicle arrives at the target charging area as the charging start time. According to the predicted charging duration TD, obtain the charging time period of the target vehicle as [t3, t4], where t3 represents the start time point of the charging time period, and t4 represents the end time point of the charging time period. Establish a time axis, mark the electricity consumption peak time period and the charging time period on the time axis, mark the overlapping time period as [t5, t6], and calculate the overlapping degree C1. The calculation formula is C1 = (t6 - t5) / (t3 - t4), where (t3 - t4) represents the charging duration of the charging time period [t3, t4], and (t6 - t5) represents the overlapping duration of the overlapping time period [t5, t6].

3. A traffic energy intelligent fusion management system based on artificial intelligence, which is applied to the traffic energy intelligent fusion management method according to any one of claims 1-2, and is characterized in that: The said management system includes an electricity consumption peak prediction module, a charging area selection module, and an electricity demand analysis module; the electricity consumption peak prediction module is used to predict the electricity consumption peak time periods of each charging area and each power distribution network area, and calculate the overlapping degree between the charging time period of the target vehicle and the electricity consumption peak time period. The charging area selection module is used to output the recommended alternative charging areas to be selected according to the calculated overlapping degree and the function values of each charging area; the electricity demand analysis module is used to analyze and calculate the standby electricity demand of the target vehicle, calculate the recommended charging duration of the target vehicle according to the standby electricity demand, and push it to the user terminal.

4. The intelligent fusion management system for transportation energy based on artificial intelligence according to claim 3, wherein: The electricity peak prediction module includes a charging area unit, a power distribution area unit, and a target vehicle unit. The charging area unit is used to obtain the electricity peak time period of the target charging area, and the power distribution area unit is used to obtain the electricity peak time period of the divided power grid area; the target vehicle unit is used to obtain the charging time period of the target vehicle.

5. The intelligent fusion management system for transportation energy based on artificial intelligence according to claim 3, characterized in that: The charging area selection module includes a charging pile analysis unit and an area selection unit. The charging pile analysis unit is used to calculate the abnormal probability of each charging pile and the fast charging ratio of the charging area corresponding to the charging pile; the area selection unit calculates the function value of the area according to the abnormal charging rate and fast charging ratio of the charging area, and outputs the alternative charging area recommended for replacement according to the function value.

6. The intelligent fusion management system for transportation energy based on artificial intelligence according to claim 3, characterized in that: The electricity demand analysis module includes an electricity consumption statistics unit, a redundancy unit, and an output unit. The electricity consumption statistics unit is used to obtain the daily electricity consumption of the target vehicle and calculate the average daily electricity consumption of the target vehicle; the redundancy unit is used to calculate the average reserved electricity consumption of the target vehicle; the output unit is used to calculate the standby electricity demand according to the daily electricity consumption and the average reserved electricity consumption, and output the recommended charging duration according to the standby electricity demand.

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