Vehicle charging management method and system based on charging terminal data processing

Abstract

The invention provides a vehicle charging management method and system based on charging terminal data processing, and the method comprises the steps: obtaining the charging demand information of a vehicle, and searching a charging station list meeting a condition from a preset charging station database; the charging stations in the charging station list are intelligently sorted, the sorted charging station list is recommended to the user, and confirmation selection of the user is received; sending a reservation request to the selected charging station according to confirmation selection of the user; the confirmation response of the charging station is received, the charging pile is allocated for the vehicle according to the confirmation response, the efficiency of the charging station selection and charging process can be remarkably improved through an intelligent sorting and reservation mechanism, the charging experience is improved, the intelligent sorting and reservation mechanism is beneficial to reasonable allocation of charging pile resources, excessive congestion of the charging station is avoided, and the charging efficiency is improved. And the efficiency of the whole charging network is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a vehicle charging management method and system based on charging terminal data processing. Background Art

[0002] In the current field of electric vehicle charging management, with the popularization of electric vehicles and the development of technology, the demand for efficient charging station management and optimized charging experience is growing. However, existing charging management methods face many challenges, especially when it comes to the intelligent selection and optimization of charging stations. On the one hand, most existing charging management systems only consider a single factor when recommending charging stations, such as the shortest distance or the lowest price. Such an approach often fails to fully consider the personalized needs of users and the actual conditions of charging stations. On the other hand, due to the lack of an effective sorting and recommendation mechanism, users often need to spend a long time looking for a suitable charging station, which not only wastes time but also leads to a decline in user experience. In addition, even if the user successfully finds a suitable charging station, he will face the problem of mismatching charging interface types, resulting in failure to charge or extra time to find other charging stations, which causes great inconvenience to the user's travel plan. Furthermore, the existing technology does not take into account the actual waiting time of the charging station when recommending the charging station, resulting in the recommendation of a charging station with a short distance, and the user may have to find a new charging station due to the long waiting time. Therefore, the existing charging management method cannot effectively solve the above problems, and there is an urgent need for a new method that can comprehensively consider various factors, intelligently recommend charging stations and optimize the charging experience. Summary of the invention

[0003] The embodiments of the present invention provide a vehicle charging management method and system based on charging terminal data processing, which can at least solve the technical problem in the prior art that the actual waiting time of the charging station is not taken into account when recommending a charging station, resulting in the recommendation of a charging station even if it is close.

[0004] A first aspect of an embodiment of the present invention provides a vehicle charging management method based on charging terminal data processing, comprising:

[0005] Obtaining charging requirement information of the vehicle, wherein the charging requirement information includes the current power status of the vehicle, the estimated driving distance, the vehicle type, and the charging preference;

[0006] According to the charging demand information, a list of charging stations that meet the conditions is retrieved from a preset charging station database;

[0007] Intelligently sort the charging stations in the charging station list, where the sorting criteria include but are not limited to distance, number of idle charging piles, charging price, and user evaluation;

[0008] Recommending the sorted list of charging stations to the user and receiving the user's confirmation selection;

[0009] According to the user's confirmation selection, a reservation request is sent to the selected charging station;

[0010] receiving a confirmation response from the charging station, and assigning a charging pile to the vehicle according to the confirmation response;

[0011] After the vehicle arrives at the charging station, it automatically starts the charging process and monitors the charging progress;

[0012] After charging is completed, the fee is automatically settled and deducted through the user's payment method.

[0013] Furthermore, the retrieving a list of charging stations that meet the conditions from a preset charging station database according to the charging demand information includes:

[0014] Calculate the ratio between the vehicle's current state of charge and the estimated driving distance Where S current Indicates the current power status, S expected Indicates the estimated driving distance;

[0015] If R<θ (where θ is a preset threshold), then find the nearest charging station based on the vehicle's current location information;

[0016] If R ≥ θ, the optimal charging station is retrieved based on the charging preference and the expected driving path.

[0017] Furthermore, the intelligently sorting the charging stations in the charging station list includes:

[0018] Use historical data to predict the waiting time T of each charging station i ;

[0019] Establish a comprehensive scoring model for charging stations F i =w1·D i +w2·C i +w3·P i +w4·U i , where D i Indicates distance, C i Indicates the number of idle charging piles, P i Indicates the charging price, U i represents user evaluation, w1, w2, w3, w4 are the corresponding weights;

[0020] The time penalty factor α and the distance penalty factor β are introduced to adjust the comprehensive score. i ′=(F i -α·T i )-β·log(D i) Adjust the overall score;

[0021] According to the adjusted comprehensive score F i ’ to sort.

[0022] Furthermore, the expression of weight is as follows:

[0023] For distance D i The weight w1 is based on the vehicle type V t and the estimated driving distance S e Calculated as Where λ1 is the distance weight coefficient;

[0024] For the number of idle charging piles C i The weight w2 is based on the charging pile utilization rate U c Calculated as w2 = λ2·(1-U c ), where λ2 is the weight coefficient of the idle charging pile;

[0025] For charging price P i The weight w3 is based on the average charging price P a Calculated as Where λ3 is the price weight coefficient;

[0026] For user evaluation i The weight w4 is based on the user evaluation score S u And the evaluation weight coefficient λ4 is calculated as w4 = λ4·S u ;

[0027] Among them, the distance weight coefficient λ1, the idle charging pile weight coefficient λ2, the price weight coefficient λ3, the evaluation weight coefficient λ4, the time penalty factor α and the distance penalty factor β are obtained through machine learning model training.

[0028] Furthermore, the sending of the reservation request to the selected charging station according to the user's confirmation selection also includes:

[0029] Perform secondary confirmation based on the compatibility of the vehicle type with the charging interface type supported by the charging station;

[0030] If there is no match, an alternative charging station is recommended to the user;

[0031] The second confirmation includes:

[0032] According to vehicle type V t And the charging station supports the charging interface type C p Calculate the matching degree where δ ij is a Boolean value. If the i-th type of vehicle is compatible with the j-th charging interface, then δ ij =1, otherwise δij =0, N is the total number of charging interface types;

[0033] If the matching degree M is lower than the preset threshold τ, it is judged as mismatch and an alternative charging station is recommended;

[0034] The recommendations for alternative charging stations are based on:

[0035] Calculate all matching charging stations C match Overall score F i ′;

[0036] According to the comprehensive score F i ′For all matching charging stations C match Sort by

[0037] Select the charging station with the highest overall score as the recommended charging station;

[0038] Among them, the comprehensive score F i ' is calculated as follows:

[0039] For each charging station C i , calculate its comprehensive score F i =w1·D i +w2·C i +w3·P i +w4·U i ;

[0040] Adjust the overall score F i ′=(F i -α·T i )-β·log(D i ), wherein w1, w2, w3, w4, α, β are weights and penalty factors calculated according to the method described in claim 4.

[0041] Furthermore, the automatic starting of the charging process and monitoring of the charging progress also includes:

[0042] Real-time monitoring of vehicle charging status, including charging current I, voltage V and other parameters;

[0043] Using dynamic models To predict the remaining charging time, where E(t) is the remaining charging energy, E0 is the initial charging energy, t0 is the start charging time, and t is the current time;

[0044] If an abnormal situation is detected, charging will be automatically suspended and the user will be notified.

[0045] Furthermore, the automatic settlement of fees and deduction of fees through the user's payment method also includes:

[0046] The fee is calculated using a weighted average algorithm, where w i is the weight, p i It is an expense item;

[0047] Send payment method based on user payment history behavior.

[0048] In a second aspect, an embodiment of the present invention provides a vehicle charging management system based on charging terminal data processing, including:

[0049] An acquisition module is used to acquire charging demand information of the vehicle, wherein the charging demand information includes the current power state of the vehicle, the estimated driving distance, the vehicle type and the charging preference;

[0050] A retrieval module, configured to retrieve a list of charging stations that meet the conditions from a preset charging station database according to the charging demand information;

[0051] A sorting module, used to intelligently sort the charging stations in the charging station list, where the sorting criteria include but are not limited to distance, number of idle charging piles, charging price and user evaluation;

[0052] A recommendation module, used to recommend a sorted list of charging stations to the user and receive the user's confirmation selection;

[0053] A request module, used to send a reservation request to the selected charging station according to the user's confirmation selection;

[0054] A response module, used to receive a confirmation response from the charging station and allocate a charging pile to the vehicle according to the confirmation response;

[0055] A start module is used to automatically start the charging process and monitor the charging progress after the vehicle arrives at the charging station;

[0056] The deduction module is used to automatically settle the fees and deduct the money through the user's payment method after charging is completed.

[0057] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle charging management based on charging terminal data processing as described in any one of the first aspects.

[0058] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium, wherein a computer program is stored in the readable storage medium, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes any of the vehicle charging management based on charging terminal data processing described in the first aspect.

[0059] The embodiment of the present invention obtains the charging demand information of the vehicle, wherein the charging demand information includes the current power state of the vehicle, the expected driving distance, the vehicle type and the charging preference; retrieves a list of qualified charging stations from a preset charging station database according to the charging demand information; intelligently sorts the charging stations in the charging station list, and the sorting basis includes but is not limited to the distance, the number of idle charging piles, the charging price and the user evaluation; recommends the sorted charging station list to the user and receives the user's confirmation selection; sends a reservation request to the selected charging station according to the user's confirmation selection; receives the confirmation response of the charging station, and allocates the charging pile to the vehicle according to the confirmation response; after the vehicle arrives at the charging station, automatically starts the charging process and monitors the charging progress; after the charging is completed, automatically settles the fee and deducts the money through the user's payment method. Through the intelligent sorting and reservation mechanism, the efficiency of the charging station selection and the charging process can be significantly improved, and the waiting time of the user can be reduced. The user can select the most suitable charging station according to his or her preferences and the comprehensive score of the charging station, which improves the charging experience. The intelligent sorting and reservation mechanism helps to reasonably allocate charging pile resources, avoid overcrowding of charging stations, and improve the efficiency of the entire charging network.

[0060] The beneficial effects of the embodiments of the present invention can refer to the technical effects corresponding to the technical features in the specific implementation methods, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a vehicle charging management process based on charging terminal data processing provided by an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a vehicle charging management system module based on charging terminal data processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0065] First, an embodiment of the present invention provides a vehicle charging management method based on charging terminal data processing, such as Figure 1 As shown, including:

[0066] Step S1, obtaining charging demand information of the vehicle, wherein the charging demand information includes the current power status of the vehicle, the estimated driving distance, the vehicle type and the charging preference;

[0067] In this embodiment, the system needs to obtain the vehicle's charging demand information, which includes the vehicle's current charge status, expected driving distance, vehicle type (such as electric vehicle or plug-in hybrid vehicle), and user's charging preference (such as fast charging or slow charging).

[0068] Example: Assume that the current state of charge of an electric vehicle is 20%, the estimated driving distance is 100 kilometers, the vehicle type is a pure electric vehicle, and the user's charging preference is fast charging.

[0069] Step S2, according to the charging demand information, retrieving a list of charging stations that meet the conditions from a preset charging station database;

[0070] The embodiment of the present invention retrieves a list of charging stations that meet the conditions in a preset charging station database based on the acquired charging demand information. These conditions may include the type of charging station (fast charging or slow charging), distance, number of idle charging piles, etc.

[0071] Example: The system found 5 nearby charging stations in the database, 3 of which support fast charging, are within 5 kilometers, and have idle charging piles.

[0072] Step S3, intelligently sorting the charging stations in the charging station list, wherein the sorting criteria include but are not limited to distance, number of idle charging piles, charging price and user evaluation;

[0073] The embodiment of the present invention intelligently sorts the retrieved charging stations based on factors including distance, number of idle charging piles, charging price, and user evaluation.

[0074] Example: The system ranks charging stations based on comprehensive factors such as distance, number of idle charging piles, charging price and user evaluation. Charging station A ranks first because it is closest, has the most idle charging piles, has a moderate price and has a high user evaluation.

[0075] Step S4, recommending the sorted charging station list to the user and receiving the user's confirmation selection;

[0076] The embodiment of the present invention recommends a list of charging stations that has been intelligently sorted to the user, and receives a confirmation selection from the user.

[0077] Example: The system recommends a ranked list of charging stations to the user, and the user selects charging station A which ranks first.

[0078] Step S5, sending a reservation request to the selected charging station according to the user's confirmation selection;

[0079] The embodiment of the present invention sends a reservation request to the selected charging station according to the user's confirmation selection, and the reservation request includes information such as the estimated arrival time and the required charging power.

[0080] Example: The user is expected to arrive at charging station A within 1 hour and the required charging power is 100kW. The system sends a reservation request to charging station A, including this information.

[0081] Step S6, receiving a confirmation response from the charging station, and allocating a charging pile to the vehicle according to the confirmation response;

[0082] The embodiment of the present invention receives a confirmation response from the charging station and allocates a charging pile to the vehicle according to the confirmation response.

[0083] Example: Charging station A confirms the reservation request and reserves a charging pile with a power of 100 kW for the user.

[0084] Step S7, after the vehicle arrives at the charging station, automatically start the charging process and monitor the charging progress;

[0085] When the vehicle arrives at the charging station, the system automatically starts the charging process and monitors the charging progress in real time.

[0086] Example: After the user arrives at charging station A, the system automatically starts the charging process and continuously monitors the charging progress.

[0087] Step S8: After charging is completed, the fee is automatically settled and deducted through the user's payment method.

[0088] After charging is completed, the system automatically settles the fee and deducts the payment through the user's payment method.

[0089] Example: After charging is completed, the system calculates the cost based on the charging time and power, and deducts the money through the user's mobile payment method.

[0090] In summary, the beneficial effects achieved by the embodiments of the present invention are as follows:

[0091] Improve efficiency: Through intelligent sorting and reservation mechanisms, the efficiency of charging station selection and charging process can be significantly improved, reducing users' waiting time.

[0092] Improve user experience: Users can choose the most suitable charging station based on their preferences and the comprehensive ratings of the charging stations, which improves the charging experience.

[0093] Optimize resource allocation: Intelligent sorting and reservation mechanisms help to reasonably allocate charging pile resources, avoid overcrowding at charging stations, and improve the efficiency of the entire charging network.

[0094] Enhanced safety: The system automatically monitors the charging progress, which can detect abnormal situations in time and ensure the safety of the charging process.

[0095] In another embodiment of the present invention, according to the charging demand information, retrieving a list of charging stations that meet the conditions from a preset charging station database includes:

[0096] Step S21, calculating the ratio between the vehicle's current power state and the estimated driving distance Where S current Indicates the current power status, S expected Indicates the estimated driving distance;

[0097] Step S211, if R<θ (where θ is a preset threshold), then search for the nearest charging station based on the current location information of the vehicle;

[0098] Step S212: If R ≥ θ, then the optimal charging station is retrieved according to the charging preference and the expected driving route. In the embodiment of the present invention, the ratio between the current state of charge of the vehicle and the expected driving distance is calculated. To decide whether immediate charging is needed. If R < θ (where θ is a preset threshold, such as 0.2), the nearest charging station is searched based on the vehicle's current location information; if R ≥ θ, the optimal charging station is retrieved based on the charging preference and the expected driving path.

[0099] Assume that the current state of charge of an electric car is 20% (S current = 20%), the estimated driving distance is 100 km (S expected =100km), then If the preset threshold θ=0.2, the system will search for the nearest charging station.

[0100] In another embodiment of the present invention, the intelligently sorting the charging stations in the charging station list includes:

[0101] Step S31: Use historical data to predict the waiting time T of each charging station. i ;

[0102] Step S32: Establishing a comprehensive scoring model F for charging stations i =w1·D i +w2·C i +w3·P i +w4·U i , where D i Indicates distance, C iIndicates the number of idle charging piles, P i Indicates the charging price, U i represents user evaluation, w1, w2, w3, w4 are the corresponding weights;

[0103] Step S33, introduce the time penalty factor α and the distance penalty factor β to adjust the comprehensive score, i ′=(F i -α·T i )-β·log(D i ) Adjust the overall score;

[0104] Step S34: according to the adjusted comprehensive score F i ’ to sort.

[0105] In the embodiment of the present invention, historical data is used to predict the waiting time T of each charging station. i , and through the comprehensive scoring model F i =w1·D i +w2·C i +w3·P i +w4·U i Sort the charging stations. i Indicates distance, C i Indicates the number of idle charging piles, P i Indicates the charging price, U i represents user evaluation. The system introduces time penalty factor α and distance penalty factor β to adjust the comprehensive score. i ′=(F i -α·T i )-β·log(D i ) to adjust the overall score.

[0106] Example: The system predicts that the waiting time for charging station A is 10 minutes (T i = 10min), the distance is 5 km (D i =5km), the number of idle charging piles is 5 (C i =5), the charging price is 0.2 yuan per kWh (P i =0.2 yuan / kWh), and the user evaluation score is 4.5 (U i =4.5). Assume that the weights are w1=0.3, w2=0.2, w3=0.2, w4=0.3, the time penalty factor α=0.05, and the distance penalty factor β=0.1. The comprehensive score is F i =0.3·5+0.2·5+0.2·0.2+0.3·4.5=1.5+1+0.04+1.35=3.89. The adjusted comprehensive score F i′=3.89-0.05·10-0.1·log(5)≈3.39.

[0107] The embodiment of the present invention not only considers factors such as distance, number of idle charging piles, charging price and user evaluation, but also uses historical data to predict the waiting time of each charging station, and uses a comprehensive scoring model F i =w1·D i +w2·C i +w3·P i +w4·U i Sort the charging stations. Among them, D i Indicates distance, C i Indicates the number of idle charging piles, P i Indicates the charging price, U i represents the user evaluation. The system also introduces the time penalty factor α and the distance penalty factor β to adjust the comprehensive score. i ′=(F i -α·T i )-β·log(D i ) to adjust the overall score.

[0108] Specific embodiment: The embodiment of the present invention considers three charging stations A, B and C, and the specific data are as follows:

[0109]

[0110] Assume that the weights are w1=0.2, w2=0.3, w3=0.2, w4=0.3, the time penalty factor α=0.05, and the distance penalty factor β=0.1.

[0111] 1. Calculate the original comprehensive score F i

[0112] For charging station A:

[0113] F A =0.2·3+0.3·4+0.2·1.2+0.3·4.5=0.6+1.2+0.24+1.35=3.39

[0114] For charging station B:

[0115] F B =0.2·5+0.3·3+0.2·1.0+0.3·4.0=1.0+0.9+0.2+1.2=3.3

[0116] For Charging Station C:

[0117] F C=0.2·8+0.3·5+0.2·1.1+0.3·4.7=1.6+1.5+0.22+1.41=4.73

[0118] 2. Introduce time penalty factor α and distance penalty factor β to adjust the comprehensive score F i '

[0119] For charging station A:

[0120] F A ′=(3.39-0.05·10)-0.1·log(3)≈3.39-0.5-0.1·0.477=2.8423

[0121] For charging station B:

[0122] F B ′=(3.3-0.05·5)-0.1·log(5)≈3.3-0.25-0.1·0.699=2.9801

[0123] For Charging Station C:

[0124] F C ′=(4.73-0.05·15)-0.1·log(8)≈4.73-0.75-0.1·0.903=3.8767

[0125] 3. According to the adjusted comprehensive score F i ' to sort

[0126] After adjustment, Charging Station C had the highest overall score, followed by Charging Station B, and finally Charging Station A.

[0127] The embodiment of the present invention adjusts the comprehensive score by introducing a time penalty factor and a distance penalty factor, which can significantly improve the efficiency of charging station selection and the charging process, reduce the user's waiting time, and the user can select the most suitable charging station according to the comprehensive score, thereby improving the charging experience.

[0128] In another embodiment provided by the present invention, the expression of weight is as follows:

[0129] Step S41, for distance D i The weight w1 is based on the vehicle type V t and the estimated driving distance S e Calculated as Where λ1 is the distance weight coefficient;

[0130] Step S42: for the number of idle charging piles C i The weight w2 is based on the charging pile utilization rate U c Calculated as w2 = λ2·(1-Uc ), where λ2 is the weight coefficient of the idle charging pile;

[0131] Step S41: For the charging price P i The weight w3 is based on the average charging price P a Calculated as Where λ3 is the price weight coefficient;

[0132] Step S43: User evaluation U i The weight w4 is based on the user evaluation score S u And the evaluation weight coefficient λ4 is calculated as w4 = λ4·S u ;

[0133] Among them, the distance weight coefficient λ1, the idle charging pile weight coefficient λ2, the price weight coefficient λ3, the evaluation weight coefficient λ4, the time penalty factor α and the distance penalty factor β are obtained through machine learning model training.

[0134] The embodiment of the present invention not only considers the impact of vehicle type and expected driving distance on distance weight, but also considers the impact of charging pile utilization and average charging price on the number of idle charging piles and charging price weight. In addition, user evaluation scores are also included in the calculation. By introducing a machine learning model to train these weight coefficients and penalty factors, the actual situation can be more accurately reflected, thereby improving the rationality and accuracy of charging station sorting.

[0135] In actual application, suppose there are three charging stations A, B, and C. The specific data are as follows:

[0136]

[0137] Assume that the coefficients obtained by training the machine learning model are λ1=0.02, λ2=0.3, λ3=0.2, λ4=0.5, the time penalty factor α=0.05, and the distance penalty factor β=0.1.

[0138] 1. Calculate weights

[0139] For charging station A:

[0140]

[0141] w2=0.3·(1-0.6)=0.3·0.4=0.12

[0142]

[0143] w4=0.5·4.5=2.25

[0144] For charging station B:

[0145]

[0146] w2=0.3·(1-0.7)=0.3·0.3=0.09

[0147]

[0148] w4=0.5·4.0=2.0

[0149] For Charging Station C:

[0150]

[0151] w2=0.3·(1-0.5)=0.3·0.5=0.15

[0152]

[0153] w4=0.5·4.7=2.35

[0154] 2. Calculate the original comprehensive score F i

[0155] For charging station A:

[0156] F A =1.33·3+0.12·4+0·1.2+2.25·4.5=3.99+0.48+0+10.125=14.595

[0157] For charging station B:

[0158] F B =1.33·5+0.09·3+0.018·1.0+2.0·4.0=6.65+0.27+0.018+8.0=14.938

[0159] For Charging Station C:

[0160] F C =1.33·8+0.15·5+0·1.1+2.35·4.7=10.64+0.75+0+11.045=22.435

[0161] 3. Introduce time penalty factor α and distance penalty factor β to adjust the comprehensive score F i '

[0162] For charging station A:

[0163] F A ′=(14.595-0.05·10)-0.1·log(3)≈14.595-0.5-0.1·0.477=13.9473

[0164] For charging station B:

[0165] F B ′=(14.938-0.05·5)-0.1·log(5)≈14.938-0.25-0.1·0.699=14.5881

[0166] For Charging Station C:

[0167] F C ′=(22.435-0.05·15)-0.1·log(8)≈22.435-0.75-0.1·0.903=21.5917

[0168] 4. Based on the adjusted comprehensive score F i ' to sort

[0169] After the adjustment, charging station C has the highest comprehensive score, followed by charging station B, and finally charging station A. The system recommends a sorted list of charging stations to the user, and the user chooses charging station C with the highest comprehensive score. The user is expected to arrive at charging station C within 1 hour, and the required charging power is 100kW. The system sends a reservation request to charging station C, which confirms the reservation request and reserves a charging pile with a power of 100kW for the user. After the user arrives at charging station C, the system automatically starts the charging process and continuously monitors the charging progress.

[0170] The embodiment of the present invention can more accurately match the actual needs of users by incorporating factors such as vehicle type, expected driving distance, charging pile utilization and average charging price into weight calculation. By introducing time penalty factor and distance penalty factor to adjust the comprehensive score, decisions can be made more efficiently and user waiting time can be reduced. The weights can be adjusted according to user evaluation scores so that the recommended charging stations are more in line with user preferences and more personalized services are provided.

[0171] In another embodiment provided by the present invention, according to the user's confirmation selection, a reservation request is sent to the selected charging station. In addition, a second confirmation is required based on the matching of the vehicle type and the charging interface type supported by the charging station. If the matching degree is lower than a preset threshold, the system will recommend an alternative charging station to the user. Based on this, according to the user's confirmation selection, the reservation request is sent to the selected charging station, and further includes:

[0172] Step S51, performing secondary confirmation based on the compatibility between the vehicle type and the charging interface type supported by the charging station;

[0173] Step S52: If there is no match, recommend an alternative charging station to the user;

[0174] The second confirmation includes:

[0175] According to vehicle type V t And the charging station supports the charging interface type C p Calculate the matching degree where δ ij is a Boolean value. If the i-th type of vehicle is compatible with the j-th charging interface, then δ ij =1, otherwise δ ij =0, N is the total number of charging interface types;

[0176] Step S53: if the matching degree M is lower than the preset threshold τ, it is determined as mismatch and an alternative charging station is recommended;

[0177] The recommendations for alternative charging stations are based on:

[0178] Calculate all matching charging stations C match Overall score F i ′;

[0179] According to the comprehensive score F i ′For all matching charging stations C match Sort by

[0180] Step S54, selecting the charging station with the highest comprehensive score as the recommended charging station;

[0181] Among them, the comprehensive score F i ' is calculated as follows:

[0182] For each charging station C i , calculate its comprehensive score F i =w1·D i +w2·C i +w3·P i +w4·U i ;

[0183] Adjust the overall score F i ′=(F i -α·T i )-β·log(D i ), where w1, w2, w3, w4, α, β are weights and penalty factors.

[0184] In practical applications, it is assumed that the system considers two charging stations A and B, and the specific data are as follows:

[0185]

[0186] Assume that the vehicle type is EV (electric vehicle), and supports two charging interface types: CC and CHAdeMO. The preset threshold τ=2.

[0187] 1. Calculate the matching degree

[0188] For charging station A:

[0189]

[0190] For charging station B:

[0191]

[0192] 2. Calculate the overall score

[0193] Assume that the coefficients obtained by training the machine learning model are λ1=0.02, λ2=0.3, λ3=0.2, λ4=0.5, the time penalty factor α=0.05, and the distance penalty factor β=0.1.

[0194] For charging station A:

[0195]

[0196] F A =1.33·3+0.12·4+0·1.2+2.25·4.5=3.99+0.48+0+10.125=14.595

[0197] F A ′=(14.595-0.05·10)-0.1·log(3)≈14.595-0.5-0.1·0.477=13.9473

[0198] For charging station B:

[0199]

[0200] w2=0.3·(1-0.7)=0.09

[0201]

[0202] w4=0.5·4.0=2.0

[0203] F B =1.33·5+0.09·3+0.018·1.0+2.0·4.0=6.65+0.27+0.018+8.0=14.938

[0204] F B ′=(14.938-0.05·5)-0.1·log(5)≈14.938-0.25-0.1·0.699=14.5881

[0205] 3. Matching Degree Determination

[0206] For charging station A:

[0207] M A =2≥τ

[0208] Therefore, charging station A matches.

[0209] For charging station B:

[0210] M B =1<τ

[0211] Therefore, charging station B is not a match.

[0212] 4. Recommended charging stations

[0213] Since charging station B does not match, the system recommends charging station A to the user.

[0214] The embodiment of the present invention ensures the compatibility of the charging interface types between the vehicle and the charging station by calculating the matching degree, thereby avoiding the situation where charging cannot be performed due to incompatibility. By ensuring the compatibility of the charging interface types, users can avoid wasting time at incompatible charging stations, thereby improving charging efficiency. Then, by recommending the charging station with the highest matching degree, it is ensured that users can successfully complete the charging process, thereby improving the overall charging experience.

[0215] In another embodiment of the present invention, the automatically starting the charging process and monitoring the charging progress further includes:

[0216] Step S61, real-time monitoring of the vehicle charging status, including parameters such as charging current I and voltage V;

[0217] Step S62, using dynamic model To predict the remaining charging time, where E(t) is the remaining charging energy, E0 is the initial charging energy, t0 is the start charging time, and t is the current time;

[0218] Step S63: If an abnormal situation is detected, charging is automatically suspended and the user is notified.

[0219] In this step, not only the charging process is automatically started, but also the charging progress is monitored in real time, including monitoring the vehicle charging status, using dynamic models to predict the remaining charging time, and automatically pausing charging and notifying the user when an abnormal situation is detected.

[0220] Real-time monitoring of vehicle charging status:

[0221] Monitoring parameters: including charging current I, voltage V and other parameters.

[0222] Example: Assume the charging current is 100A and the voltage is 400V.

[0223] Use dynamic models to predict remaining charging time:

[0224] Dynamic Models: Using Integration To predict the remaining charging time, where E(t) is the remaining charging energy, E0 is the initial charging energy, t0 is the start charging time, and t is the current time.

[0225] Example: Assume that the initial charging energy is 50 kWh, the charging start time is 10:00, the current time is 10:15, the charging current remains stable at 100 A, and the voltage is 400 V.

[0226] Detecting anomalies:

[0227] Abnormal conditions: including but not limited to sudden changes in charging current or voltage, interruption of charging process, etc. Example: Assume that during charging, the charging current suddenly drops from 100A to 50A.

[0228] 1. Calculate the remaining charging time

[0229] Calculate the initial charge energy:

[0230] E0=V·Q

[0231] Where V is the voltage and Q is the battery capacity. Assuming the battery capacity is 125Ah, then

[0232] E0=400V·125Ah=50kWh

[0233] Calculate the charged energy:

[0234]

[0235] Assuming the charging current remains stable at 100A for 15 minutes from 10:00 to 10:15, then

[0236]

[0237] Calculate the remaining charging energy:

[0238] E(t)=E0-ΔE=50kWh-25kWh=25kWh

[0239] Calculate the remaining charging time:

[0240] Assume that the charging power remains stable, charging power P = V·I. If the charging current suddenly drops from 100A to 50A, then

[0241] P=400V·50A=20kW

[0242] Remaining charging time t r It can be calculated by dividing the remaining energy by the charging power:

[0243]

[0244] 2. Detect abnormal situations and take action:

[0245] A sudden drop in charging current is detected:

[0246] I new =50A

[0247] Measures to be taken:

[0248] The system automatically pauses charging and sends a notification of the abnormal situation to the user.

[0249] The embodiments of the present invention can improve the efficiency of the charging process by real-time monitoring of the charging status and predicting the remaining charging time, so that users can plan subsequent trips more accurately. By updating the remaining charging time in real time, users can understand the charging progress at any time and improve the charging experience. By automatically detecting abnormal situations and taking timely measures, the safety of the charging process can be ensured and potential safety risks can be avoided.

[0250] In another embodiment of the present invention, the automatic settlement of fees and deduction of fees through the user's payment method further include:

[0251] Step S71, using a weighted average algorithm to calculate the cost, where w i is the weight, p i It is an expense item;

[0252] Step S72, sending the payment method according to the user's payment history behavior.

[0253] In order to better understand the vehicle charging management method based on charging terminal data processing proposed in claim 7, we can explain each step in detail through a specific implementation example, give specific examples, and finally analyze the beneficial effects of the method.

[0254] In this step, not only are the fees automatically settled and deducted through the user's payment method, but the fees are also calculated using a weighted average algorithm, and a payment method is recommended based on the user's payment history.

[0255] The fee is calculated using a weighted average algorithm:

[0256] Calculation method: The fee is calculated using a weighted average algorithm, where w i is the weight, p i It is an expense item.

[0257] Example: Assume that the cost items include basic electricity charges, service charges, and parking fees, which are denoted as p1, p2, and p3 respectively, and the corresponding weights are w1, w2, and w3.

[0258] Recommend payment methods based on user payment history:

[0259] Recommended method: Intelligently recommend payment methods based on the user's payment history, including but not limited to credit cards, mobile payments, points redemption, etc.

[0260] Example: Suppose a user's payment history shows that he prefers to use mobile payments.

[0261] 1. Calculate the cost

[0262] Cost items and weights:

[0263] -Basic electricity fee p1 = 20 yuan, weight w1 = 0.5

[0264] -Service fee p2 = 5 yuan, weight w2 = 0.3

[0265] -Parking fee p3 = 10 yuan, weight w3 = 0.2

[0266] Calculate total cost:

[0267]

[0268] 2. Recommended payment method

[0269] Analyze user payment history behavior:

[0270] -Users' payment records in the past year show that the proportion of mobile payment is the highest, reaching 70%.

[0271] Recommended payment methods:

[0272] -Based on the user's payment history, the system recommends using mobile payment to complete this fee settlement.

[0273] Based on the above calculation results, the system automatically settled the fee of 13.5 yuan and completed the deduction through the user's mobile payment method.

[0274] By using a weighted average algorithm to calculate fees, the total fee can be calculated quickly and accurately, improving settlement efficiency. By intelligently recommending payment methods based on the user's payment history behavior, the convenience of payment can be improved and the user experience can be enhanced.

[0275] In another embodiment provided by the present invention, a vehicle charging management system based on charging terminal data processing is provided, such as Figure 2 As shown, including:

[0276] An acquisition module is used to acquire charging demand information of the vehicle, wherein the charging demand information includes the current power state of the vehicle, the estimated driving distance, the vehicle type and the charging preference;

[0277] A retrieval module, configured to retrieve a list of charging stations that meet the conditions from a preset charging station database according to the charging demand information;

[0278] A sorting module, used to intelligently sort the charging stations in the charging station list, where the sorting criteria include but are not limited to distance, number of idle charging piles, charging price and user evaluation;

[0279] A recommendation module, used to recommend a sorted list of charging stations to the user and receive the user's confirmation selection;

[0280] A request module, used to send a reservation request to the selected charging station according to the user's confirmation selection;

[0281] A response module, used to receive a confirmation response from the charging station and allocate a charging pile to the vehicle according to the confirmation response;

[0282] A start module is used to automatically start the charging process and monitor the charging progress after the vehicle arrives at the charging station;

[0283] The deduction module is used to automatically settle the fees and deduct the money through the user's payment method after charging is completed.

[0284] An embodiment of the present invention further provides an electronic device, including:

[0285] processor;

[0286] a memory for storing processor-executable instructions;

[0287] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0288] A fourth aspect of the embodiments of the present invention is:

[0289] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0290] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0291] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle charging management method based on charging terminal data processing, characterized in that: include: Obtaining charging requirement information of the vehicle, wherein the charging requirement information includes the current power status of the vehicle, the estimated driving distance, the vehicle type, and the charging preference; According to the charging demand information, a list of charging stations that meet the conditions is retrieved from a preset charging station database; Intelligently sort the charging stations in the charging station list, where the sorting criteria include but are not limited to distance, number of idle charging piles, charging price, and user evaluation; Recommending the sorted list of charging stations to the user and receiving the user's confirmation selection; According to the user's confirmation selection, a reservation request is sent to the selected charging station; receiving a confirmation response from the charging station, and assigning a charging pile to the vehicle according to the confirmation response; After the vehicle arrives at the charging station, it automatically starts the charging process and monitors the charging progress; After charging is completed, the fee is automatically settled and deducted through the user's payment method.

2. The method according to claim 1, characterized in that The retrieving a list of charging stations that meet the conditions from a preset charging station database according to the charging demand information includes: Calculate the ratio between the vehicle's current state of charge and the estimated driving distance Where S current Indicates the current power status, S expected Indicates the estimated driving distance; If R<θ (where θ is a preset threshold), then find the nearest charging station based on the vehicle's current location information; If R ≥ θ, the optimal charging station is retrieved based on the charging preference and the expected driving path.

3. The method according to claim 1, characterized in that The intelligently sorting the charging stations in the charging station list includes: Use historical data to predict the waiting time T of each charging station i ; Establish a comprehensive scoring model for charging stations F i =w1·D i +w2·C i +w3·P i +w4·U i , where D i Indicates distance, C i Indicates the number of idle charging piles, P i Indicates the charging price, U i represents user evaluation, w1, w2, w3, w4 are the corresponding weights; The time penalty factor α and the distance penalty factor β are introduced to adjust the comprehensive score. i ′=(F i -α·T i )-β·log(D i ) Adjust the overall score; According to the adjusted comprehensive score F i ’ to sort.

4. The method according to claim 3, characterized in that The expression of weight is as follows: For distance D i The weight w1 is based on the vehicle type V t and the estimated driving distance S e Calculated as Where λ1 is the distance weight coefficient; For the number of idle charging piles C i The weight w2 is based on the charging pile utilization rate U c Calculated as w2 = λ2·(1-U c ), where λ2 is the weight coefficient of the idle charging pile; For charging price P i The weight w3 is based on the average charging price P a Calculated as Where λ3 is the price weight coefficient; For user evaluation i The weight w4 is based on the user evaluation score S u And the evaluation weight coefficient λ4 is calculated as w4 = λ4·S u ; Among them, the distance weight coefficient λ1, the idle charging pile weight coefficient λ2, the price weight coefficient λ3, the evaluation weight coefficient λ4, the time penalty factor α and the distance penalty factor β are obtained through machine learning model training.

5. The method according to claim 4, characterized in that The method of sending a reservation request to the selected charging station according to the user's confirmation selection also includes: Perform secondary confirmation based on the compatibility of the vehicle type with the charging interface type supported by the charging station; If there is no match, an alternative charging station is recommended to the user; The second confirmation includes: According to vehicle type V t And the charging station supports the charging interface type C p Calculate the matching degree where δ ij is a Boolean value. If the i-th type of vehicle is compatible with the j-th charging interface, then δ ij =1, otherwise δ ij =0, N is the total number of charging interface types; If the matching degree M is lower than the preset threshold τ, it is judged as mismatch and an alternative charging station is recommended; The recommendations for alternative charging stations are based on: Calculate all matching charging stations C match Overall score F i ′; According to the comprehensive score F i ′For all matching charging stations C match Sort by Select the charging station with the highest overall score as the recommended charging station; Among them, the comprehensive score F i ' is calculated as follows: For each charging station C i , calculate its comprehensive score F i =w1·D i +w2·C i +w3·P i +w4·U i ; Adjust the overall score F i ′=(F i -α·T i )-β·log(D i ), wherein w1, w2, w3, w4, α, β are weights and penalty factors calculated according to the method described in claim 4.

6. The method according to claim 1, characterized in that The method of automatically starting the charging process and monitoring the charging progress also includes: Real-time monitoring of vehicle charging status, including charging current I, voltage V and other parameters; Using dynamic models To predict the remaining charging time, where E(t) is the remaining charging energy, E0 is the initial charging energy, t0 is the start charging time, and t is the current time; If an abnormal situation is detected, charging will be automatically suspended and the user will be notified.

7. The method according to claim 1, characterized in that The automatic settlement of fees and deduction of the payment method by the user also include: The fee is calculated using a weighted average algorithm, where w i is the weight, p i It is an expense item; Send payment method based on user payment history behavior.

8. A vehicle charging management system based on charging terminal data processing, characterized in that: include: An acquisition module is used to acquire charging demand information of the vehicle, wherein the charging demand information includes the current power state of the vehicle, the estimated driving distance, the vehicle type and the charging preference; A retrieval module, configured to retrieve a list of charging stations that meet the conditions from a preset charging station database according to the charging demand information; A sorting module, used to intelligently sort the charging stations in the charging station list, where the sorting criteria include but are not limited to distance, number of idle charging piles, charging price and user evaluation; A recommendation module, used to recommend a sorted list of charging stations to the user and receive the user's confirmation selection; A request module, used to send a reservation request to the selected charging station according to the user's confirmation selection; A response module, used to receive a confirmation response from the charging station and allocate a charging pile to the vehicle according to the confirmation response; A start module is used to automatically start the charging process and monitor the charging progress after the vehicle arrives at the charging station; The deduction module is used to automatically settle the fees and deduct the money through the user's payment method after charging is completed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the vehicle charging management method based on charging terminal data processing as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: A computer program is stored in the readable storage medium, and the computer program includes a program code for controlling a process to execute a process, and the process includes a vehicle charging management method based on charging terminal data processing according to any one of claims 1 to 7.

Images