Charging strategy optimization method and system based on big data

Abstract

The invention discloses a charging strategy optimization method and system based on big data. The method comprises the following steps: dividing a vehicle parking time interval into a plurality of first time intervals based on a preset time interval; analyzing according to the charging request, and formulating a charging scheme of the to-be-charged vehicle; when the charging scheme of the to-be-charged vehicle does not meet the vehicle charging condition, in a slow charging mode, multiple pieces of power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the multiple pieces of power adjustment data are analyzed to determine a charging adjustment scheme; in the fast charging mode, calculating a third adjustment score of each first time interval, analyzing each first time interval in sequence according to a descending order of the third adjustment scores, and determining a charging adjustment scheme; and updating the residual power supply power of the power grid. Through reasonable arrangement of the charging time period, the charging load is optimized, the power grid pressure is reduced, and the stability and safety of power grid operation are improved.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle charging technology, and more specifically, to a charging strategy optimization method and system based on big data. Background Art

[0002] With the rapid development of the electric vehicle industry, the demand for charging has increased dramatically, which has put forward higher requirements for the efficient operation of charging facilities and the stable operation of the power grid. Traditional charging strategies are often based on static planning, which is difficult to adapt to the changing charging environment, user needs and power grid load. They lack real-time, flexibility and intelligence, resulting in low utilization of charging facilities, large fluctuations in power grid load, and poor user experience.

[0003] Therefore, the prior art has defects and is in urgent need of improvement. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a charging strategy optimization method and system based on big data, which can reasonably arrange the charging period, optimize the charging load, reduce the pressure of the power grid during peak hours, and improve the stability and safety of the power grid operation.

[0005] A first aspect of the present invention provides a charging strategy optimization method based on big data, comprising: Obtaining charging request data of the vehicle to be charged; the charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; Dividing the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; Analyze the charging request, formulate a charging plan for the vehicle to be charged, and charge the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; When the charging scheme of the vehicle to be charged does not meet the vehicle charging condition, an analysis is performed according to the charging mode selected by the user. When the user selects the slow charging mode, a plurality of power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the charging adjustment scheme is determined by analyzing the plurality of power adjustment data; When the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine a charging adjustment plan; The charging schemes of the vehicle to be charged and the vehicle being charged are adjusted according to the charging adjustment scheme, and the remaining power supply power of the power grid corresponding to the first time interval is updated.

[0006] In this solution, the analysis is performed according to the charging request to formulate a charging plan for the vehicle to be charged, including: When the user selects the slow charging mode, it is determined whether the vehicle to be charged meets the slow charging conditions according to the required power of the vehicle to be charged, the vehicle parking time interval and the rated slow charging power; If so, the slow charging time interval is determined according to the remaining power supply of the power grid and the parking time interval of the vehicle, and a slow charging plan is formulated in combination with the rated slow charging power; If not, the user is asked whether to perform fast charging; When the user selects the fast charging mode, the power supply score of each first time interval is calculated according to the remaining power supply power of the power grid, the maximum fast charging power of the vehicle to be charged and the electricity price; One or more first time intervals are determined as fast charging time intervals for the vehicle to be charged in descending order of the power supply scores, and the fast charging power of each fast charging time interval is determined according to the remaining power of the vehicle at the start time of the fast charging time interval, and a fast charging plan is formulated.

[0007] In this solution, the slow charging time interval is determined according to the remaining power supply of the power grid and the parking time interval of the vehicle, including: Filtering the first time intervals in which the remaining power supply power of the power grid is less than the rated slow charging power of the vehicle to be charged, and determining one or more sets of first time intervals according to the continuous first time intervals; Dividing the one or more sets of first time intervals according to the electricity price adjustment time to determine one or more sets of second time intervals; Calculate a first charging score for each second time interval set based on the time length and electricity price of the time interval set; Determine the required charging time for the vehicle based on the ratio of the required power of the vehicle and the rated slow charging power; The time lengths of the second time interval set are accumulated in descending order of the first charging scores. When the accumulated time length is greater than or equal to the charging time required for the vehicle, the second time interval set last accumulated is intercepted according to the first time difference between the accumulated time length and the charging time required for the vehicle, and the first time interval in the accumulated second time interval set is determined as the slow charging time interval.

[0008] In this solution, one or more first time intervals are determined as the fast charging time intervals of the vehicle to be charged in descending order of the power supply scores, and the fast charging power of each fast charging time interval is determined according to the remaining power of the vehicle at the start time of the fast charging time interval, and a fast charging plan is formulated, including: Filtering the first time interval in which the power supply score is less than a preset score threshold; Determine the first time interval as the second time interval in chronological order; Determine the minimum power between the remaining power supply power of the power grid in the second time interval and the maximum fast charging power of the vehicle to be charged as the first charging power in the second time interval; Calculating the charge amount in the second time interval, and when the charge amount in the second time interval is greater than or equal to the power required by the vehicle, determining the second time interval as a fast charging time interval, and determining the first charging power as the fast charging power corresponding to the fast charging time interval; When the charge amount in the second time interval is less than the amount of electricity required by the vehicle, predicting the battery temperature data at the end time of the second time interval according to the first charging power, time length and the vehicle battery soc value at the start time of the second time interval; When the battery temperature data is less than a first preset battery temperature threshold, determining the next first time interval as the next second time interval in chronological order, and determining the first charging power of the next second time interval; When the battery temperature data is greater than or equal to a first preset battery temperature threshold, predicting a cooling time interval in which the battery temperature data is reduced to a second preset battery temperature threshold according to the external environment data, determining the first first time interval after the cooling time interval as the next second time interval, and determining the first charging power for the next second time interval; The charging amounts of all the second time intervals are accumulated. When the accumulated charging amounts are greater than or equal to the amount of electricity required by the vehicle, all the second time intervals are determined as fast charging time intervals, and the first charging power of each second time interval is determined as the fast charging power of the corresponding fast charging time interval.

[0009] In this solution, when the user selects the slow charging mode, multiple power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the multiple power adjustment data are analyzed to determine the charging adjustment solution, including: Calculate a first adjustment score for each first time interval according to the rated slow charging power of the vehicle to be charged, the power of the remaining power of the power grid, and the electricity price; Determine a first time interval in which the first adjustment score is greater than a preset adjustment score threshold as a third time interval; Determine a plurality of power adjustment data according to the number of vehicles being charged in each third time interval; each power adjustment data corresponds to a slow charging time interval and a rated slow charging power of a vehicle being charged; Calculating a second adjustment score for each power adjustment data; Select the power adjustment data in descending order of the second adjustment scores for analysis, and obtain the time position of the slow charging time interval of the vehicle being charged corresponding to the power adjustment data in the corresponding first time interval set; When the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is at both ends of the corresponding first time interval set, the power adjustment data meets the adjustment condition; On the contrary, the corresponding first time interval set is divided according to the slow charging time interval of the vehicle being charged corresponding to the power adjustment data, and it is determined whether the time lengths of the two sub-time interval sets obtained after the division are both greater than the preset time length threshold; If yes, the power adjustment data meets the adjustment condition; if no, the adjustment condition is not met, and the power adjustment data is filtered; When the power adjustment data meets the adjustment conditions, the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is adjusted to the first time interval connected to the end of the corresponding first time interval set, and the third time interval corresponding to the power adjustment data is determined as the slow charging time interval of the vehicle to be charged.

[0010] This plan also includes: Second adjustment score m 2 The calculation method is expressed as: ; Among them, m 1 is the first adjusted score, p L1 The power adjustment data corresponds to the rated slow charging power of the vehicle being charged, p 0 Preset supplementary power for the system, p RP is the remaining power supply of the power grid, k 1 The adjustment factor for the second adjustment score.

[0011] In this solution, when the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine the charging adjustment solution, including: Calculate a third adjustment score for each first time interval according to the minimum fast charging power of the vehicle to be charged, the remaining power supply power of the power grid in the first time interval, and the minimum time interval between the first time interval and the occupied time interval; determining the first time interval with the largest third adjustment score as the fourth time interval; Calculating a second time difference between the fourth time interval and a previous occupied time interval of the vehicle to be charged, and calculating a third time difference between the fourth time interval and a next occupied time interval of the vehicle to be charged; Predicting the battery temperature data at the start time of the fourth time interval based on the battery temperature data at the end time of the previous occupied time interval, the external environment data, and the second time difference; Calculate the maximum battery temperature data at the start time of the next occupied time interval according to the first preset battery temperature threshold and the fast charging power of the next occupied time interval, and predict the maximum battery temperature data at the end time of the fourth time interval in combination with the third time difference; Determine a first predicted charging power for the vehicle to be charged in the fourth time interval by analyzing the battery temperature data at the start time of the fourth time interval and the maximum battery temperature data at the end time; When the first predicted charging power is greater than the minimum fast charging power of the vehicle to be charged and less than the sum of the remaining power supply of the power grid and the maximum fast charging power of the vehicle being charged in the fourth time interval, the fourth time interval meets the adjustment condition; Determine the charging vehicle corresponding to the minimum fast charging power value that is greater than the difference between the first predicted charging power and the remaining power supply power of the power grid as an adjustment vehicle; The fast charging time interval of the adjusted vehicle is adjusted according to the remaining fast charging power of the power grid in other first time intervals; the fourth time interval is determined as the fast charging power interval of the vehicle to be charged, and the first predicted charging power is determined as the corresponding fast charging power; When the fourth time interval does not meet the adjustment condition, the next first time interval is determined as the fourth time interval in descending order of the third adjustment scores, and the adjustment verification is performed again.

[0012] This plan also includes: When the power adjustment condition of the fast charging mode is still not met after all the first time intervals are analyzed, a fourth adjustment score for each fast charging time interval is calculated according to the maximum fast charging power of the vehicle to be charged, the fast charging power of the fast charging time interval, and the remaining power supply power of the power grid; The fast charging time interval with the largest fourth adjustment score is determined as the fifth time interval; Predicting the maximum battery temperature data at the end time of the fifth time interval according to the fourth time difference between the fifth time interval and the next fast charging time interval of the vehicle to be charged and the maximum battery temperature data at the start time of the next fast charging time interval; Analyze the battery temperature data at the start time of the fifth time interval and the maximum battery temperature data at the end time to determine a second predicted charging power for the vehicle to be charged in the fifth time interval; Determine the vehicle being charged, which corresponds to the minimum fast charging power value greater than the difference between the second predicted charging power and the fast charging power of the vehicle to be charged in the fifth time interval, as an adjustment vehicle; The fast charging power of the vehicle to be charged in the fifth time interval is updated according to the second predicted charging power.

[0013] This plan also includes: The required power and parking time interval of the vehicle being charged are input into a preset vehicle charging cost prediction model, and the required power and parking time interval of other vehicles being charged are analyzed to determine the maximum charging cost of the vehicle being charged; Calculate the predicted charging cost of the vehicle being charged according to the charging time interval, charging power and electricity price of the vehicle being charged; When the predicted charging fee is greater than the maximum charging fee, a prohibition adjustment mark is set for the charging vehicle.

[0014] A second aspect of the present invention provides a charging strategy optimization system based on big data, comprising: A data acquisition module is used to acquire charging request data of the vehicle to be charged; the charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; A time interval division module, used to divide the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; A charging plan formulation module, used to analyze the charging request, formulate a charging plan for the vehicle to be charged, and charge the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; A charging scheme adjustment module is used to analyze the charging mode selected by the user when the charging scheme of the vehicle to be charged does not meet the vehicle charging conditions. When the user selects the slow charging mode, multiple power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the charging adjustment scheme is determined by analyzing the multiple power adjustment data; when the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine the charging adjustment scheme; The grid surplus power monitoring module is used to adjust the charging schemes of the vehicle to be charged and the vehicle being charged according to the charging adjustment scheme, and to update the grid surplus power corresponding to the first time interval.

[0015] The present invention discloses a charging strategy optimization method and system based on big data, the method comprising: dividing a vehicle parking time interval into multiple first time intervals based on a preset time interval; analyzing according to a charging request, formulating a charging plan for a vehicle to be charged; when the charging plan for a vehicle to be charged does not meet the vehicle charging conditions, in a slow charging mode, determining multiple power adjustment data according to the number of vehicles being charged in each first time interval, analyzing the multiple power adjustment data to determine a charging adjustment plan; in a fast charging mode, calculating a third adjustment score for each first time interval, analyzing each first time interval in order from large to small according to the third adjustment score, determining a charging adjustment plan; and updating the remaining power supply power of the power grid. The present invention optimizes the charging load, reduces the pressure on the power grid, and improves the stability and safety of the power grid operation by reasonably arranging the charging period. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a charging strategy optimization method based on big data provided by the present invention is shown; Figure 2 A block diagram of a charging strategy optimization system based on big data provided by the present invention is shown. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flow chart of a charging strategy optimization method based on big data provided by the present invention is shown.

[0020] like Figure 1 As shown, the present invention discloses a charging strategy optimization method based on big data, comprising: S102, obtaining charging request data of the vehicle to be charged; the charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; S104, dividing the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; S106, analyzing the charging request, formulating a charging plan for the vehicle to be charged, and charging the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; S108, when the charging scheme of the vehicle to be charged does not meet the vehicle charging condition, analyzing according to the charging mode selected by the user, when the user selects the slow charging mode, determining multiple power adjustment data according to the number of vehicles being charged in each first time interval, and analyzing the multiple power adjustment data to determine the charging adjustment scheme; S110, when the user selects the fast charging mode, calculating the third adjustment score of each first time interval, analyzing each first time interval in descending order of the third adjustment score, and determining a charging adjustment plan; S112, adjusting the charging plans of the vehicle to be charged and the vehicle being charged according to the charging adjustment plan, and updating the remaining power supply power of the power grid corresponding to the first time interval.

[0021] According to an embodiment of the present invention, the vehicle parking time interval is first divided into a plurality of first time intervals by a preset time interval (such as taking the hour as the division standard), wherein the preset time interval is set by those skilled in the art according to actual needs.

[0022] When the vehicle to be charged is charged, if the user selects the slow charging mode as the charging mode, first determine whether the slow charging conditions are met. If so, determine one or more sets of second time intervals according to the remaining power supply of the power grid, the rated slow charging power of the vehicle to be charged and the electricity price, select the first time interval in one or more sets of second time intervals as the slow charging time interval according to the first charging score and the required power of the vehicle, and formulate a slow charging plan for the vehicle to be charged. When the user selects the fast charging mode, determine the first charging power of the second time interval according to the remaining power supply of the power grid and the maximum fast charging power of the vehicle to be charged, and determine the next second time interval according to the battery temperature data at the end of the second time interval. When the accumulated charging amount of the second time interval is greater than or equal to the required power of the vehicle, determine all the second time intervals as fast charging time intervals, determine the first charging power of each second time interval as the fast charging power of the corresponding fast charging time interval, and determine the fast charging plan for the vehicle to be charged.

[0023] When the charging scheme of the vehicle to be charged does not meet the vehicle charging conditions, that is, the charging amount of the charging time interval (including slow charging time and fast charging time) is less than the amount of electricity required by the vehicle, the charging time interval of the vehicle being charged is adjusted. When the user selects the slow charging mode, the second adjustment score of each power adjustment data is calculated, and the power adjustment data are selected in order from large to small in the second adjustment score for analysis to verify whether the power adjustment data meets the adjustment conditions, and the slow charging time interval of the vehicle being charged corresponding to the power adjustment data that meets the adjustment conditions is adjusted. When the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and the first time interval with the largest third adjustment score is determined as the fourth time interval. The first predicted charging power of the vehicle to be charged in the fourth time interval is calculated according to the second time difference and the third time difference between the fourth time interval and the adjacent occupied time interval of the vehicle to be charged, and the fast charging time interval of the vehicle being charged corresponding to the minimum fast charging power in the fourth time interval that is greater than the difference between the first predicted charging power and the remaining power supply power of the power grid is adjusted. When the power adjustment conditions of the fast charging mode are still not met after the analysis of all the first time intervals is completed, the fast charging power of the fast charging time interval occupied by the charging vehicle is adjusted. If the power adjustment conditions of the fast charging mode are still not met after the adjustment, the user is reminded through a mobile phone or other device terminal.

[0024] At the same time, the corresponding remaining power supply power of the power grid is updated in real time according to the charging power of the vehicle to be charged and the vehicle being charged in each first time interval after each power distribution or power adjustment.

[0025] In addition, the power grid can be supplemented with auxiliary power by the wind and solar power generation devices, and the sum of the average power in each first time interval and the remaining power supply power of the power grid is determined as the final remaining power supply power of the power grid.

[0026] According to an embodiment of the present invention, analyzing the charging request and formulating a charging plan for the vehicle to be charged includes: When the user selects the slow charging mode, it is determined whether the vehicle to be charged meets the slow charging conditions according to the required power of the vehicle to be charged, the vehicle parking time interval and the rated slow charging power; If so, the slow charging time interval is determined according to the remaining power supply of the power grid and the parking time interval of the vehicle, and a slow charging plan is formulated in combination with the rated slow charging power; If not, the user is asked whether to perform fast charging; When the user selects the fast charging mode, the power supply score for each first time interval is calculated based on the remaining power supply of the power grid, the maximum fast charging power of the vehicle to be charged, and the electricity price; One or more first time intervals are determined as fast charging time intervals for the vehicle to be charged in descending order of the power supply scores, and the fast charging power of each fast charging time interval is determined according to the remaining power of the vehicle at the start time of the fast charging time interval, and a fast charging plan is formulated.

[0027] It should be noted that when the user selects the slow charging mode, it is determined whether the product of the length of the vehicle parking time interval, the rated slow charging power and the system setting influence coefficient is greater than the required power of the vehicle, and whether the vehicle to be charged meets the slow charging conditions. When the product is greater than the required power of the vehicle, the vehicle to be charged meets the slow charging conditions. According to the relationship between the remaining power supply power of the power grid in each first time interval within the vehicle parking time interval and the rated slow charging power of the vehicle to be charged, one or more first time intervals are selected as the slow charging time intervals of the vehicle to be charged. When the user selects fast charging, the slow charging mode is switched to the fast charging mode.

[0028] When the user selects the fast charging mode, the power difference between the remaining power supply of the power grid and the maximum fast charging power of the vehicle to be charged in each first time interval is calculated, and the power difference and electricity price of each first time interval are multiplied by the corresponding influence weights, and the calculation results are accumulated to determine the power supply score of each first time interval. Among them, the influence weights of the power difference and electricity price of the first time interval are set by the system, and the sum of the influence weights of the power difference and electricity price of the first time interval is 1. Analyze each first time interval in order from large to small according to the power supply score, and determine the first time interval in which the remaining power supply of the power grid is greater than the minimum fast charging power of the vehicle to be charged as the fast charging time interval of the vehicle to be charged. Finally, the maximum fast charging power corresponding to the remaining power of the vehicle and the remaining power supply of the power grid are determined as the fast charging power of the current fast charging time interval.

[0029] According to an embodiment of the present invention, determining a slow charging time interval according to the remaining power supply of the power grid and the parking time interval of the vehicle includes: Filtering the first time intervals in which the remaining power supply of the power grid is less than the rated slow charging power of the vehicle to be charged, and determining one or more sets of first time intervals according to the continuous first time intervals; Divide one or more sets of first time intervals according to the electricity price adjustment time to determine one or more sets of second time intervals; Calculate a first charging score for each second time interval set based on the time length and electricity price of the time interval set; Determine the required charging time for the vehicle based on the ratio of the required power of the vehicle and the rated slow charging power; The time lengths of the second time interval set are accumulated in descending order of the first charging scores. When the accumulated time length is greater than or equal to the charging time required for the vehicle, the second time interval set last accumulated is intercepted according to the first time difference between the accumulated time length and the charging time required for the vehicle, and the first time interval in the accumulated second time interval set is determined as the slow charging time interval.

[0030] It should be noted that the battery temperature change of the vehicle battery under slow charging is low, which has little impact on the battery health. Therefore, when the vehicle is slow charged, it is preferred to select a time interval set with a longer time length for charging. The time length and electricity price of the second time interval set are multiplied by the corresponding impact weights, and the calculation results are accumulated to determine the first charging score of each second time interval set.

[0031] Among them, the time length of the second time interval set and the impact weight of the electricity price are both set by the system. The sum of the time length of the second time interval set and the impact weight of the electricity price is 1. In order to reduce the user's charging costs, the impact weight of the electricity price is greater than the impact weight of the time length.

[0032] According to an embodiment of the present invention, one or more first time intervals are determined as fast charging time intervals for the vehicle to be charged in descending order of the power supply scores, and the fast charging power of each fast charging time interval is determined according to the remaining power of the vehicle at the start time of the fast charging time interval, and a fast charging plan is formulated, including: Filtering the first time interval in which the power supply score is less than a preset score threshold; Determine the first time interval as the second time interval in chronological order; Determine the minimum power between the remaining power supply power of the power grid in the second time interval and the maximum fast charging power of the vehicle to be charged as the first charging power in the second time interval; Calculate the charge amount in the second time interval, and when the charge amount in the second time interval is greater than or equal to the amount of electricity required by the vehicle, determine the second time interval as a fast charging time interval, and determine the first charging power as the fast charging power corresponding to the fast charging time interval; When the charge amount in the second time interval is less than the amount of electricity required by the vehicle, predicting the battery temperature data at the end time of the second time interval according to the first charging power, time length and vehicle battery soc value at the start time of the second time interval; When the battery temperature data is less than a first preset battery temperature threshold, determining the next first time interval as the next second time interval in chronological order, and determining the first charging power for the next second time interval; When the battery temperature data is greater than or equal to a first preset battery temperature threshold, a cooling time interval in which the battery temperature data is reduced to a second preset battery temperature threshold is predicted according to the external environment data, the first first time interval after the cooling time interval is determined as the next second time interval, and the first charging power for the next second time interval is determined; The charging amounts of all the second time intervals are accumulated. When the accumulated charging amounts are greater than or equal to the amount of electricity required by the vehicle, all the second time intervals are determined as fast charging time intervals, and the first charging power of each second time interval is determined as the fast charging power of the corresponding fast charging time interval.

[0033] It should be noted that the first time interval is screened based on the power supply score, and the first time interval corresponding to the peak electricity price is filtered by the preset score threshold. When the parking time of the vehicle to be charged includes three electricity price periods: peak, flat, and valley, the first time interval is analyzed in the order of valley period, flat period, and peak period by setting two preset score thresholds. Among them, the preset score threshold is set and adjusted by the system according to the electricity price.

[0034] The charging amount of the second time interval is determined by multiplying the time length of the second time interval by the first charging power. After the second time interval is determined as the fast charging time interval, the remaining power supply power of the power grid in the second time interval is updated according to the difference between the remaining power supply power of the power grid and the first charging power.

[0035] Select the second time intervals for analysis in sequence until the sum of the charging amounts of all the fast-charging time intervals is greater than the required power of the vehicle to be charged, and determine all the fast-charging time intervals and the corresponding fast-charging power of the vehicle to be charged. When the charging amount of the second time interval is less than the required power of the vehicle, analyze it through the preset vehicle battery charging temperature prediction model to determine the battery temperature data at the end of the second time interval. When the battery temperature data is less than the first preset battery temperature threshold, determine the next first time interval as the next second time interval in chronological order, and determine the fast-charging power of the second time interval; otherwise, wait for the vehicle battery temperature to drop to the second preset battery temperature threshold before selecting the next second time interval for analysis.

[0036] Among them, the preset vehicle battery charging temperature is obtained by training various charging data generated during the vehicle charging process (including charging time, charging power, battery temperature and external environment data, etc.), and the first preset battery temperature threshold and the second preset battery temperature threshold are set by technical personnel in this field according to actual needs. The first preset battery temperature threshold is greater than the second preset battery temperature threshold.

[0037] In addition, when there is no first time interval with a power supply score greater than a preset score threshold, a first time interval with a maximum power supply score is selected from the filtered first time intervals and determined as a second time interval for further analysis.

[0038] According to an embodiment of the present invention, when a user selects a slow charging mode, a plurality of power adjustment data are determined according to the number of vehicles being charged in each first time interval, and a charging adjustment scheme is determined by analyzing the plurality of power adjustment data, including: Calculate a first adjustment score for each first time interval according to the rated slow charging power of the vehicle to be charged, the power of the remaining power of the power grid, and the electricity price; Determine a first time interval in which the first adjustment score is greater than a preset adjustment score threshold as a third time interval; Determine a plurality of power adjustment data according to the number of vehicles being charged in each third time interval; each power adjustment data corresponds to a slow charging time interval and a rated slow charging power of a vehicle being charged; Calculating a second adjustment score for each power adjustment data; Select the power adjustment data in descending order of the second adjustment scores for analysis, and obtain the time position of the slow charging time interval of the vehicle being charged corresponding to the power adjustment data in the corresponding first time interval set; When the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is at both ends of the corresponding first time interval set, the power adjustment data meets the adjustment condition; On the contrary, the corresponding first time interval set is divided according to the slow charging time interval of the vehicle being charged corresponding to the power adjustment data, and it is determined whether the time lengths of the two sub-time interval sets obtained after the division are both greater than the preset time length threshold; If yes, the power adjustment data meets the adjustment condition; if no, the adjustment condition is not met and the power adjustment data is filtered; When the power adjustment data meets the adjustment conditions, the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is adjusted to the first time interval connected to the end of the corresponding first time interval set, and the third time interval corresponding to the power adjustment data is determined as the slow charging time interval of the vehicle to be charged.

[0039] It should be noted that the first adjustment score m in the first time interval 1 The calculation method is expressed as ; Among them, p a(L1) is the rated slow charging power of the vehicle to be charged, p RP is the remaining power supply of the power grid, q EP is the electricity price, k a and k bare all influence weights, and the influence weight k a and k b The value of is set by the system, k a +k b =1.

[0040] The preset adjustment score threshold and the preset time length threshold are set by those skilled in the art according to actual needs.

[0041] In addition, when the remaining power supply power of the first time interval connected to the end of the corresponding first time interval set is less than the slow charging power of the slow charging time interval, the first time interval connected to the end of each first time interval set is judged in turn for analysis, and the first time interval whether the remaining power supply power of the power grid is greater than the slow charging power of the slow charging time interval is determined as the slow charging time interval after the vehicle is adjusted in the power adjustment data.

[0042] According to an embodiment of the present invention, it also includes: Second adjustment score m 2 The calculation method is expressed as: ; Among them, m 1 is the first adjusted score, p b(L1) The power adjustment data corresponds to the rated slow charging power of the vehicle being charged, p 0 Preset supplementary power for the system, p RP is the remaining power supply power of the power grid in the first time interval, k 1 The adjustment factor for the second adjustment score.

[0043] It should be noted that the adjustment coefficient k of the second adjustment score is 1 The system is set to avoid that the rated slow charging power of the charging vehicle in the current first time interval is adjusted so that the remaining power supply of the power grid does not meet the rated slow charging power of the vehicle to be charged, that is, the sum of the rated slow charging power of the charging vehicle and the remaining power supply of the power grid is less than the rated slow charging power of the vehicle to be charged. 2 The system preset supplementary power p is added during the process 0 .

[0044] According to an embodiment of the present invention, when a user selects a fast charging mode, a third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine a charging adjustment plan, including: Calculate a third adjustment score for each first time interval according to the minimum fast charging power of the vehicle to be charged, the remaining power supply power of the power grid in the first time interval, and the minimum time interval between the first time interval and the occupied time interval; determining the first time interval with the largest third adjustment score as the fourth time interval; Calculate a second time difference between the fourth time interval and the last occupied time interval of the vehicle to be charged, and calculate a third time difference between the fourth time interval and the next occupied time interval of the vehicle to be charged; Predicting the battery temperature data at the start time of the fourth time interval based on the battery temperature data at the end time of the previous occupied time interval, the external environment data, and the second time difference; Calculate the maximum battery temperature data at the start time of the next occupied time interval according to the first preset battery temperature threshold and the fast charging power of the next occupied time interval, and predict the maximum battery temperature data at the end time of the fourth time interval in combination with the third time difference; Determine a first predicted charging power for the vehicle to be charged in the fourth time interval by analyzing the battery temperature data at the start time of the fourth time interval and the maximum battery temperature data at the end time; When the first predicted charging power is greater than the minimum fast charging power of the vehicle to be charged and less than the sum of the remaining power supply of the power grid and the maximum fast charging power of the vehicle being charged in the fourth time interval, the fourth time interval meets the adjustment condition; Determine the charging vehicle corresponding to the minimum fast charging power value that is greater than the difference between the first predicted charging power and the remaining power supply power of the power grid as an adjustment vehicle; The fast charging time interval of the vehicle is adjusted according to the remaining fast charging power of the power grid in other first time intervals; the fourth time interval is determined as the fast charging power interval of the vehicle to be charged, and the first predicted charging power is determined as the corresponding fast charging power; When the fourth time interval does not meet the adjustment condition, the next first time interval is determined as the fourth time interval in descending order of the third adjustment scores, and the adjustment verification is performed again.

[0045] It should be noted that the third adjustment score m of the first time interval 3 The calculation method is expressed as ; Among them, p a(L2)min is the minimum fast charging power of the vehicle to be charged, p RP is the remaining power supply power of the power grid in the first time interval, T a is the minimum time interval between the first time interval and the occupied time interval, k c and k d are all influence weights, and the influence weight k c and k d The value of is set by the system, k c +k d =1.

[0046] The second time difference is the time difference between the start time of the fourth time interval and the end time of the last occupied time interval of the vehicle to be charged, and the third time difference is the time difference between the end time of the fourth time interval and the start time of the next occupied time interval of the vehicle to be charged. The battery temperature data at the start time of the fourth time interval and the maximum battery temperature at the end time are calculated by the preset vehicle battery charging temperature prediction model, and the first predicted charging power of the vehicle to be charged in the fourth time interval is determined according to the time length of the fourth time interval. The fast charging power of each charging vehicle in the fourth time interval is analyzed, and the charging vehicle corresponding to the minimum fast charging power greater than the difference between the first predicted charging power and the remaining power supply power of the power grid is determined as the adjustment vehicle, and the fast charging scheme of the adjustment vehicle in the fourth time interval is adjusted to other first time intervals where the remaining power supply power of the power grid is greater than the fast charging power of the adjustment vehicle in the fourth time interval.

[0047] According to an embodiment of the present invention, it also includes: When the power adjustment condition of the fast charging mode is still not met after all the first time intervals are analyzed, a fourth adjustment score for each fast charging time interval is calculated according to the maximum fast charging power of the vehicle to be charged, the fast charging power of the fast charging time interval, and the remaining power supply power of the power grid; The fast charging time interval with the largest fourth adjustment score is determined as the fifth time interval; Predicting the maximum battery temperature data at the end time of the fifth time interval according to the fourth time difference between the fifth time interval and the next fast charging time interval of the vehicle to be charged and the maximum battery temperature data at the start time of the next fast charging time interval; Analyze the battery temperature data at the start time of the fifth time interval and the maximum battery temperature data at the end time to determine a second predicted charging power for the vehicle to be charged in the fifth time interval; Determine the vehicle being charged corresponding to the minimum fast charging power value that is greater than the difference between the second predicted charging power and the fast charging power of the vehicle to be charged in the fifth time interval as the adjustment vehicle; The fast charging power of the vehicle to be charged in the fifth time interval is updated according to the second predicted charging power.

[0048] It should be noted that, after all the first time intervals are analyzed, if the charging amount of the fast charging time interval of the vehicle to be charged is still less than the vehicle power required by the vehicle to be charged, it is determined that the power adjustment condition of the fast charging mode is still not met, and the fourth adjustment score m of the fast charging time interval n is calculated. 4(n) . ; Among them, p a(L2)max is the maximum fast charging power of the vehicle to be charged, p a(L2)nis the fast charging power of the vehicle to be charged in the fast charging time interval n, p RP(n) is the remaining power supply of the power grid in the fast charging time interval n, k e and k f are all influence weights, and the influence weight k e and k f The value of is set by the system, k e +k f =1.

[0049] By analyzing the preset vehicle battery charging temperature prediction model, the battery temperature data at the start time of the fifth time interval and the maximum battery temperature data at the end time are calculated, and the second predicted charging power of the vehicle to be charged in the fifth time interval is determined. When the second predicted charging power is greater than the maximum fast charging power of the vehicle to be charged, the maximum fast charging power of the vehicle to be charged is determined as the second predicted charging power. After determining the adjusted vehicle, the fast charging scheme of the adjusted vehicle in the fifth time interval is adjusted to other first time intervals where the remaining power supply power of other power grids is greater than the fast charging power of the adjusted vehicle in the fifth time interval.

[0050] According to an embodiment of the present invention, it also includes: The required power and parking time interval of the vehicle being charged are input into a preset vehicle charging cost prediction model, and the required power and parking time interval of other vehicles being charged are analyzed to determine the maximum charging cost of the vehicle being charged; Calculate the predicted charging cost of the vehicle being charged according to the charging time interval, charging power and electricity price of the vehicle being charged; When the predicted charging fee is greater than the maximum charging fee, a prohibition adjustment mark is set for the charging vehicle.

[0051] It should be noted that when a vehicle chooses to charge, the vehicle power required and the parking time interval of the vehicle being charged are analyzed through the preset vehicle charging cost prediction model, and the vehicle power required and the parking time interval of other vehicles being charged are analyzed to determine the maximum and minimum charging amount of the vehicle being charged in the three peak, flat and valley electricity price periods, and the maximum charging cost of the vehicle being charged is calculated in combination with the electricity prices of the three peak, flat and valley electricity price periods. The predicted charging cost of each vehicle being charged is calculated in real time. When the predicted charging cost is greater than the corresponding maximum charging cost, the vehicle being charged is marked as prohibited from adjustment and will no longer participate in the subsequent charging time interval adjustment.

[0052] The preset vehicle charging cost prediction model is trained based on parameters such as the parking time interval, charging amount, charging cost, average number of vehicles charged at the charging station per day, average charging cost and average charging unit price of each vehicle in the vehicle's historical charging data.

[0053] Figure 2 A block diagram of a charging strategy optimization system based on big data provided by the present invention is shown.

[0054] like Figure 2 As shown, the second aspect of the present invention provides a charging strategy optimization system based on big data, comprising: A data acquisition module is used to acquire charging request data of the vehicle to be charged; the charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; A time interval division module, used to divide the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; A charging plan formulation module is used to analyze the charging request, formulate a charging plan for the vehicle to be charged, and charge the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; A charging scheme adjustment module is used to analyze the charging mode selected by the user when the charging scheme of the vehicle to be charged does not meet the vehicle charging conditions. When the user selects the slow charging mode, multiple power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the charging adjustment scheme is determined by analyzing the multiple power adjustment data; when the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine the charging adjustment scheme; The grid surplus power monitoring module is used to adjust the charging plans of the vehicles to be charged and the vehicles being charged according to the charging adjustment plan, and to update the grid surplus power corresponding to the first time interval.

[0055] The information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, the "charging request data of vehicles to be charged" and "remaining power supply of the power grid" involved in this disclosure are all obtained with full authorization.

[0056] The present invention discloses a charging strategy optimization method and system based on big data, the method comprising: dividing a vehicle parking time interval into multiple first time intervals based on a preset time interval; analyzing according to a charging request, formulating a charging plan for a vehicle to be charged; when the charging plan for a vehicle to be charged does not meet the vehicle charging conditions, in a slow charging mode, determining multiple power adjustment data according to the number of vehicles being charged in each first time interval, analyzing the multiple power adjustment data to determine a charging adjustment plan; in a fast charging mode, calculating a third adjustment score for each first time interval, analyzing each first time interval in order from large to small according to the third adjustment score, determining a charging adjustment plan; and updating the remaining power supply power of the power grid. The present invention optimizes the charging load, reduces the pressure on the power grid, and improves the stability and safety of the power grid operation by reasonably arranging the charging period.

[0057] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0058] In the various embodiments of the present invention, all functional units may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0060] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A charging strategy optimization method based on big data, characterized in that: include: Obtaining charging request data of the vehicle to be charged; the charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; Dividing the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; Analyze the charging request, formulate a charging plan for the vehicle to be charged, and charge the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; When the charging scheme of the vehicle to be charged does not meet the vehicle charging condition, an analysis is performed according to the charging mode selected by the user. When the user selects the slow charging mode, a plurality of power adjustment data are determined according to the number of vehicles being charged in each first time interval, and the charging adjustment scheme is determined by analyzing the plurality of power adjustment data; When the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine a charging adjustment plan; The charging schemes of the vehicle to be charged and the vehicle being charged are adjusted according to the charging adjustment scheme, and the remaining power supply power of the power grid corresponding to the first time interval is updated.

2. The charging strategy optimization method based on big data according to claim 1 is characterized in that: The step of analyzing the charging request and formulating a charging plan for the vehicle to be charged includes: When the user selects the slow charging mode, it is determined whether the vehicle to be charged meets the slow charging conditions according to the required power of the vehicle to be charged, the vehicle parking time interval and the rated slow charging power; If so, the slow charging time interval is determined according to the remaining power supply of the power grid and the parking time interval of the vehicle, and a slow charging plan is formulated in combination with the rated slow charging power; If not, the user is asked whether to perform fast charging; When the user selects the fast charging mode, the power supply score of each first time interval is calculated according to the remaining power supply power of the power grid, the maximum fast charging power of the vehicle to be charged and the electricity price; One or more first time intervals are determined as fast charging time intervals for the vehicle to be charged in descending order of the power supply scores, and the fast charging power of each fast charging time interval is determined according to the remaining power of the vehicle at the start time of the fast charging time interval, and a fast charging plan is formulated.

3. The charging strategy optimization method based on big data according to claim 2 is characterized in that: The determining of the slow charging time interval according to the remaining power supply of the power grid and the parking time interval of the vehicle includes: Filtering the first time intervals in which the remaining power supply power of the power grid is less than the rated slow charging power of the vehicle to be charged, and determining one or more sets of first time intervals according to the continuous first time intervals; Dividing the one or more sets of first time intervals according to the electricity price adjustment time to determine one or more sets of second time intervals; Calculate a first charging score for each second time interval set based on the time length and electricity price of the time interval set; Determine the required charging time for the vehicle based on the ratio of the required power of the vehicle and the rated slow charging power; The time lengths of the second time interval set are accumulated in descending order of the first charging scores. When the accumulated time length is greater than or equal to the charging time required for the vehicle, the second time interval set last accumulated is intercepted according to the first time difference between the accumulated time length and the charging time required for the vehicle, and the first time interval in the accumulated second time interval set is determined as the slow charging time interval.

4. The charging strategy optimization method based on big data according to claim 2 is characterized in that: The method of determining one or more first time intervals as the fast charging time intervals of the vehicle to be charged in descending order of the power supply scores, and determining the fast charging power of each fast charging time interval according to the remaining power of the vehicle at the start time of the fast charging time interval, and formulating a fast charging plan includes: Filtering the first time interval in which the power supply score is less than a preset score threshold; Determine the first time interval as the second time interval in chronological order; Determine the minimum power between the remaining power supply power of the power grid in the second time interval and the maximum fast charging power of the vehicle to be charged as the first charging power in the second time interval; Calculating the charge amount in the second time interval, and when the charge amount in the second time interval is greater than or equal to the power required by the vehicle, determining the second time interval as a fast charging time interval, and determining the first charging power as the fast charging power corresponding to the fast charging time interval; When the charge amount in the second time interval is less than the amount of electricity required by the vehicle, predicting the battery temperature data at the end time of the second time interval according to the first charging power, time length and the vehicle battery soc value at the start time of the second time interval; When the battery temperature data is less than a first preset battery temperature threshold, determining the next first time interval as the next second time interval in chronological order, and determining the first charging power of the next second time interval; When the battery temperature data is greater than or equal to a first preset battery temperature threshold, predicting a cooling time interval in which the battery temperature data is reduced to a second preset battery temperature threshold according to the external environment data, determining the first first time interval after the cooling time interval as the next second time interval, and determining the first charging power for the next second time interval; The charging amounts of all the second time intervals are accumulated. When the accumulated charging amounts are greater than or equal to the amount of electricity required by the vehicle, all the second time intervals are determined as fast charging time intervals, and the first charging power of each second time interval is determined as the fast charging power of the corresponding fast charging time interval.

5. The charging strategy optimization method based on big data according to claim 1 is characterized in that: When the user selects the slow charging mode, a plurality of power adjustment data are determined according to the number of vehicles being charged in each first time interval, and a charging adjustment scheme is determined by analyzing the plurality of power adjustment data, including: Calculate a first adjustment score for each first time interval according to the rated slow charging power of the vehicle to be charged, the power of the remaining power of the power grid, and the electricity price; Determine a first time interval in which the first adjustment score is greater than a preset adjustment score threshold as a third time interval; Determine a plurality of power adjustment data according to the number of vehicles being charged in each third time interval; each power adjustment data corresponds to a slow charging time interval and a rated slow charging power of a vehicle being charged; Calculating a second adjustment score for each power adjustment data; Select the power adjustment data in descending order of the second adjustment scores for analysis, and obtain the time position of the slow charging time interval of the vehicle being charged corresponding to the power adjustment data in the corresponding first time interval set; When the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is at both ends of the corresponding first time interval set, the power adjustment data meets the adjustment condition; On the contrary, the corresponding first time interval set is divided according to the slow charging time interval of the vehicle being charged corresponding to the power adjustment data, and it is determined whether the time lengths of the two sub-time interval sets obtained after the division are both greater than the preset time length threshold; If yes, the power adjustment data meets the adjustment condition; if no, the adjustment condition is not met, and the power adjustment data is filtered; When the power adjustment data meets the adjustment conditions, the slow charging time interval of the vehicle being charged corresponding to the power adjustment data is adjusted to the first time interval connected to the end of the corresponding first time interval set, and the third time interval corresponding to the power adjustment data is determined as the slow charging time interval of the vehicle to be charged.

6. The charging strategy optimization method based on big data according to claim 5 is characterized in that: Also includes: The calculation method of the second adjustment score m2 is expressed by the formula: ; Among them, m1 is the first adjustment score, p L1 The power adjustment data corresponds to the rated slow charging power of the vehicle being charged, p0 is the system preset supplementary power, and p RP is the remaining power supply power of the power grid, and k1 is the adjustment coefficient of the second adjustment score.

7. The charging strategy optimization method based on big data according to claim 5 is characterized in that: When the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine the charging adjustment plan, including: Calculate a third adjustment score for each first time interval according to the minimum fast charging power of the vehicle to be charged, the remaining power supply power of the power grid in the first time interval, and the minimum time interval between the first time interval and the occupied time interval; determining the first time interval with the largest third adjustment score as the fourth time interval; Calculating a second time difference between the fourth time interval and a previous occupied time interval of the vehicle to be charged, and calculating a third time difference between the fourth time interval and a next occupied time interval of the vehicle to be charged; Predicting the battery temperature data at the start time of the fourth time interval based on the battery temperature data at the end time of the previous occupied time interval, the external environment data, and the second time difference; Calculate the maximum battery temperature data at the start time of the next occupied time interval according to the first preset battery temperature threshold and the fast charging power of the next occupied time interval, and predict the maximum battery temperature data at the end time of the fourth time interval in combination with the third time difference; Determine a first predicted charging power for the vehicle to be charged in the fourth time interval by analyzing the battery temperature data at the start time of the fourth time interval and the maximum battery temperature data at the end time; When the first predicted charging power is greater than the minimum fast charging power of the vehicle to be charged and less than the sum of the remaining power supply of the power grid and the maximum fast charging power of the vehicle being charged in the fourth time interval, the fourth time interval meets the adjustment condition; Determine the charging vehicle corresponding to the minimum fast charging power value that is greater than the difference between the first predicted charging power and the remaining power supply power of the power grid as an adjustment vehicle; The fast charging time interval of the adjusted vehicle is adjusted according to the remaining fast charging power of the power grid in other first time intervals; the fourth time interval is determined as the fast charging power interval of the vehicle to be charged, and the first predicted charging power is determined as the corresponding fast charging power; When the fourth time interval does not meet the adjustment condition, the next first time interval is determined as the fourth time interval in descending order of the third adjustment scores, and the adjustment verification is performed again.

8. The charging strategy optimization method based on big data according to claim 7 is characterized in that: Also includes: When the power adjustment condition of the fast charging mode is still not met after all the first time intervals are analyzed, a fourth adjustment score for each fast charging time interval is calculated according to the maximum fast charging power of the vehicle to be charged, the fast charging power of the fast charging time interval, and the remaining power supply power of the power grid; The fast charging time interval with the largest fourth adjustment score is determined as the fifth time interval; Predicting the maximum battery temperature data at the end time of the fifth time interval according to the fourth time difference between the fifth time interval and the next fast charging time interval of the vehicle to be charged and the maximum battery temperature data at the start time of the next fast charging time interval; Analyze the battery temperature data at the start time of the fifth time interval and the maximum battery temperature data at the end time to determine a second predicted charging power for the vehicle to be charged in the fifth time interval; Determine the vehicle being charged, which corresponds to the minimum fast charging power value greater than the difference between the second predicted charging power and the fast charging power of the vehicle to be charged in the fifth time interval, as an adjustment vehicle; The fast charging power of the vehicle to be charged in the fifth time interval is updated according to the second predicted charging power.

9. The charging strategy optimization method based on big data according to claim 1 is characterized in that: Also includes: The required power and parking time interval of the vehicle being charged are input into a preset vehicle charging cost prediction model, and the required power and parking time interval of other vehicles being charged are analyzed to determine the maximum charging cost of the vehicle being charged; Calculate the predicted charging cost of the vehicle being charged according to the charging time interval, charging power and electricity price of the vehicle being charged; When the predicted charging fee is greater than the maximum charging fee, a prohibition adjustment mark is set for the charging vehicle.

10. A charging strategy optimization system based on big data, used to implement the charging strategy optimization method based on big data as described in any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to acquire charging request data of a vehicle to be charged; The charging request data includes the charging mode selected by the user, the required power of the vehicle to be charged, the vehicle parking time interval, the rated fast charging power interval and the rated slow charging power; the charging mode includes a slow charging mode and a fast charging mode; A time interval division module, used to divide the vehicle parking time interval into a plurality of first time intervals based on a preset time interval; A charging plan formulation module, used to analyze the charging request, formulate a charging plan for the vehicle to be charged, and charge the vehicle to be charged according to the charging plan; the charging plan includes a fast charging plan and a slow charging plan; A charging scheme adjustment module, configured to analyze the charging mode selected by the user when the charging scheme of the vehicle to be charged does not meet the vehicle charging conditions, and when the user selects the slow charging mode, determine a plurality of power adjustment data according to the number of vehicles being charged in each first time interval, and analyze the plurality of power adjustment data to determine the charging adjustment scheme; When the user selects the fast charging mode, the third adjustment score of each first time interval is calculated, and each first time interval is analyzed in descending order of the third adjustment score to determine a charging adjustment plan; The grid surplus power monitoring module is used to adjust the charging schemes of the vehicle to be charged and the vehicle being charged according to the charging adjustment scheme, and to update the grid surplus power corresponding to the first time interval.

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