A method and system for intelligently recommending automobile charging piles based on multi-dimensional analysis

Abstract

The present invention discloses a method and system for intelligently recommending automobile charging piles based on multi-dimensional analysis, which relates to the field of automobile charging technology. By calculating the multi-dimensional fitting of the charging urgency index CEI and multiple traffic factors, the charging pile area recommendation index CR is further optimized to ensure that the recommendation result not only considers the availability of the charging pile, but also fully reflects the close connection between traffic conditions and vehicle charging needs. Delays or charging difficulties caused by factors such as the location of the charging pile or traffic peaks in traditional methods are avoided, thereby greatly improving the accuracy of charging pile recommendations and user experience, and realizing a more intelligent recommendation logic. This method not only provides car owners with a more personalized recommendation solution, but also avoids recommending to high traffic flow areas during peak congestion periods, reduces the waiting time for vehicle charging, and improves the fluidity and efficiency of the entire electric vehicle charging system.

Description

Technical Field

[0001] The present invention relates to the field of automobile charging technology, and in particular to a method and system for intelligently recommending automobile charging piles based on multi-dimensional analysis. Background Art

[0002] In the invention of Chinese patent application No. CN202410637067.X, a charging pile intelligent recommendation method and system based on vehicle data analysis is disclosed, which relates to the field of automobile charging technology. The method includes: collecting the charging specification data, current location and current time of the vehicle, performing mobile prediction analysis, and obtaining the predicted location and predicted urgency; dividing the distribution area and collecting multiple charging piles therein; obtaining the corrected weight distribution according to the historical charging pile selection data of the vehicle; predicting multiple congestion levels of multiple charging piles according to the current time, combining the charging pile distribution information, charging pile specification information, charging specification data, current location, predicted location, and selecting weight distribution, performing optimization recommendation analysis of multiple charging piles, obtaining the optimal charging pile, and making recommendations. The present invention achieves the technical effect of improving the degree of adaptation of charging pile recommendations to the personalized needs of vehicles and the comprehensiveness of charging station optimization recommendations.

[0003] It can be seen that most of the current charging pile recommendation systems rely on static information, such as the location of the charging pile, the load and availability of the charging pile. This information usually does not reflect the traffic flow and congestion in real time, which leads to the fact that car owners often cannot effectively avoid traffic peaks and congested areas when choosing charging piles. For example, during peak hours, charging piles located on busy roads may cause car owners to waste a lot of time due to traffic congestion or queuing, or even miss the best charging time. In addition, the existing system does not fully consider the real-time changes of traffic signals and the complex traffic environment, which greatly affects the charging experience of car owners. The disadvantage of this traditional method is the singleness of its recommendation logic, the lack of dynamic adjustment ability, and the inability to adapt to the changing traffic conditions. Car owners can only make choices based on static geographic information and charging pile status, but do not get intelligent guidance based on real-time traffic flow and peak forecasts. This inflexible recommendation system often causes car owners to spend more time looking for idle charging piles during peak travel periods, and may even miss the best charging time, reducing the overall charging efficiency and car owners' experience. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligently recommending automobile charging piles based on multi-dimensional analysis, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligently recommending automobile charging piles based on multi-dimensional analysis, comprising the following steps:

[0006] S1. Obtain traffic flow information, traffic signal information and intersection prediction information at intersections in the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV;

[0007] S2. Preprocess the acquired regional traffic feature vector TBV to obtain the traffic flow index TFI of the charging pile area, evaluate the traffic status of the charging pile area, and generate the charging pile area congestion index CU to reflect the congestion status of the charging pile area;

[0008] S3. By analyzing the vehicle status, the charging urgency index CEI of the vehicle charging demand is obtained, and multi-dimensional fitting is performed with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR;

[0009] S4. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and generate a recommendation effect index set REs according to the recommendation effect index RE, and prompt the vehicle to go to the charging pile area through the recommendation effect index set REs.

[0010] Preferably, said S1 includes S11, S12 and S13;

[0011] S11, through the vehicle's on-board map and vehicle networking function, connect to the intelligent transportation system to obtain real-time traffic flow information at the intersection of the i-th charging pile area in the 1 to n charging pile areas within a fixed range from the vehicle, reflect the traffic flow information at the intersection of the charging pile area within a fixed period, and obtain the total traffic flow TF(t) at time t;

[0012] The total vehicle flow TF(t) is obtained by the following calculation formula:

[0013] ;

[0014] Where M represents the total number of roads at the intersection of the i-th charging pile area, and V(j, t) represents the traffic volume of the j-th road at time t.

[0015] Preferably, S12, interacting with the intelligent transportation network through the Internet of Vehicles system, obtaining the traffic signal information and intersection prediction information of the i-th charging pile area in real time, and obtaining the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t by quantifying the traffic signal information and intersection prediction information;

[0016] The traffic signal information includes different stages of green light, yellow light and red light, as well as the duration of each stage. At different times, different intersection lights will be in different states. The traffic signal state TS(t) at time t is obtained by quantifying the traffic signal information at time t.

[0017] The traffic signal state TS(t) at time t is obtained by the following quantization method:

[0018] ;

[0019] When the traffic light status Status is 'Green', the traffic signal status TS(t) at time t has an amplitude of 1;

[0020] When the traffic light status Status is 'Red', the amplitude of the traffic signal status TS(t) at time t is 0;

[0021] The intersection prediction information is obtained through the interaction between the Internet of Vehicles and the traffic flow monitoring platform. By obtaining the traffic signal status TS(t) of the i-th charging pile area at time t in real time, the traffic flow V(j, t) information on the j-th road at time t is predicted, and the road condition prediction TP(t) at time t is obtained to reflect the congestion situation in the future.

[0022] The traffic condition prediction TP(t) at time t is obtained by the following calculation formula:

[0023] ;

[0024] In the formula, △V(j) represents the predicted traffic flow change of the j-th road, and F(j, t) represents the congestion coefficient of the j-th road at time t;

[0025] S13, by integrating the total vehicle flow TF(t) at time t, the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t, a regional traffic feature vector TBV of the i-th charging pile area intersection is formed.

[0026] Preferably, the predicted traffic flow change ΔV(j) on the j-th road is obtained by the following calculation formula:

[0027] ;

[0028] Where Vpred(j, t) represents the predicted traffic flow of the jth road at time t;

[0029] The congestion coefficient F(j, t) of the j-th road at time t is obtained by the following calculation formula:

[0030] ;

[0031] Where min represents the minimization function, D(j, t) represents the traffic density of the j-th road at time t, which is obtained by counting the number of vehicles passing through in a fixed unit time, C(j) represents the maximum capacity of the j-th road, which specifically represents the maximum number of vehicles that can be accommodated in a fixed unit time, and SCF represents the traffic signal correction factor.

[0032] Preferably, S2 includes S21 and S22;

[0033] S21, preprocessing the regional traffic characteristic vector TBV of the i-th charging pile area intersection, including data standardization processing and conversion into a unified dimension, and then substituting the standard characteristic vector BTBV into the established traffic flow analysis formula to calculate the traffic flow index TFI of the charging pile area, and evaluating the traffic status of the charging pile area according to the obtained traffic flow index TFI;

[0034] Among them, the standard feature vector BTBV includes the standardized total vehicle flow BTF (t), the standardized traffic signal status BTS (t) and the standardized road condition prediction BTP (t);

[0035] The traffic flow index TFI is obtained by the following calculation formula:

[0036] ;

[0037] In the formula, ln represents the logarithmic function;

[0038] The traffic status is obtained by the following evaluation method:

[0039] When the traffic flow index TFI ≥ 0.8, the traffic status of the charging pile area is congested, and the traffic status mark JTzt = 9 is obtained;

[0040] When the traffic flow index TFI ≥ 0.5, the traffic state in the charging pile area is slow, and the traffic state flag JTzt = 3 is obtained;

[0041] When the traffic flow index TFI is less than 0.5, the traffic state of the charging pile area is obtained as a green wave state, and the traffic state flag JTzt=1 is obtained.

[0042] Preferably, S22, by fitting the traffic flow index TFI and the traffic signal correction factor SCF, the flow density of the charging pile area and the control effect of the traffic signal are obtained, and the charging pile area congestion index CU is generated to reflect the congestion state of the charging pile area.

[0043] The charging pile area congestion index CU is obtained by the following calculation formula:

[0044] ;

[0045] Wherein, α represents the traffic state influence coefficient, which is specifically used to adjust the influence of the traffic state mark JTz on the congestion index CU of the charging pile area; β represents the traffic signal factor weight coefficient, which is specifically used to adjust the influence of the traffic signal correction factor SCF on the congestion index CU of the charging pile area.

[0046] Preferably, said S3 includes S31 and S32;

[0047] S31, acquiring a charging urgency index CEI of the vehicle charging demand by analyzing the vehicle status, wherein the vehicle status includes a maximum battery charge Emax, a maximum mileage Dmax, a remaining mileage Drem, a remaining charge Erem, and a driving mode Mmake;

[0048] The charging urgency index CEI is obtained by the following calculation formula:

[0049] ;

[0050] Wherein, ln represents the logarithmic function, λ represents the correction factor of the driving mode Mmake, and e represents the exponential function.

[0051] Preferably, S32, performing multi-dimensional fitting on the charging emergency index CEI, the acquired traffic flow index TFI and the charging pile area congestion index CU, to obtain a charging pile area recommendation index CR;

[0052] The charging pile area recommendation index CR is obtained by the following calculation formula:

[0053] ;

[0054] Where exp represents the exponential function, c1, c2 and c3 represent the influence coefficients of the charging urgency index CEI, the traffic flow index TFI and the charging pile area congestion index CU on the charging pile area recommendation index CR, respectively, and c1+c2+c3=1. The specific value is set by the user.

[0055] Preferably, the S4 includes S41;

[0056] S41. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and simultaneously obtain the recommendation effect index set REs={RE(1), RE(2), ..., RE(n)} of 1 to n charging pile areas within the fixed range of the vehicle, and synchronously perform bubble sorting on the recommendation effect index set REs. According to the bubble sorted recommendation effect index set REs, output the charging pile area with a subscript value equal to 1, and display it to the user through a visual interface, and simultaneously marginalize the charging pile areas with subscript values ​​equal to 2 and subscript values ​​equal to 3 to display to the user as alternatives;

[0057] The recommendation effect index RE is obtained by the following calculation formula:

[0058] ;

[0059] Where AR represents the proportion of available charging piles in the charging pile area, r1, r2 and r3 represent the influence coefficients of the charging pile area recommendation index CR, the proportion of available charging piles AR and the traffic flow index TFI on the recommendation effect index RE, and r1+r2+r3=1. The specific value is set by the user.

[0060] An intelligent recommendation system for automobile charging piles based on multi-dimensional analysis, including a traffic data collection module, a processing analysis and congestion assessment module, a vehicle analysis and recommendation module, and a recommendation result assessment module;

[0061] The traffic data acquisition module obtains traffic flow information, traffic signal information and intersection prediction information at the intersection of the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV;

[0062] The processing analysis and congestion assessment module pre-processes the acquired regional traffic feature vector TBV, obtains the traffic flow index TFI of the charging pile area, assesses the traffic status of the charging pile area, and generates the charging pile area congestion index CU to reflect the congestion status of the charging pile area;

[0063] The vehicle analysis and recommendation module obtains the charging urgency index CEI of the vehicle charging demand by analyzing the vehicle status, and performs multi-dimensional fitting with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR;

[0064] The recommendation result evaluation module evaluates the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtains the recommendation effect index RE, and generates a recommendation effect index set REs according to the recommendation effect index RE, and prompts the vehicle to go to the charging pile area through the recommendation effect index set REs.

[0065] The present invention provides a method and system for intelligently recommending automobile charging piles based on multi-dimensional analysis, which has the following beneficial effects:

[0066] (1) By calculating the multi-dimensional fitting of the charging urgency index CEI and multiple traffic factors, the charging pile area recommendation index CR is further optimized to ensure that the recommendation results not only consider the availability of charging piles, but also fully reflect the close connection between traffic conditions and vehicle charging needs. This avoids delays or charging difficulties caused by factors such as the location of charging piles or traffic peaks in traditional methods, thereby greatly improving the accuracy of charging pile recommendations and user experience. Compared with the existing technology, this method solves the shortcomings of traditional charging pile recommendation systems that ignore traffic peaks and the urgency of charging needs, and breaks through the single recommendation mode that only relies on the health status of charging piles. A more intelligent recommendation logic is achieved. This method not only provides car owners with more personalized recommendation solutions, but also avoids recommending to high traffic flow areas during peak congestion hours, reduces the charging waiting time of vehicles, and improves the fluidity and efficiency of the entire electric vehicle charging system.

[0067] (2) By fitting the traffic signal correction factor SCF and the traffic flow index TFI, the congestion index CU of the charging pile area is obtained, which effectively reflects the congestion persistence of the charging pile area. This method combines multiple factors such as the traffic conditions, traffic signals and traffic density in the charging pile area to provide a dynamic and flexible evaluation mechanism, thereby avoiding overly congested areas when recommending charging piles, reducing the charging waiting time caused by traffic problems, and improving the user's charging experience. Compared with traditional methods, charging pile recommendations are not limited to static area selection, but can also be dynamically adjusted according to real-time traffic flow changes and signal status. This real-time optimization can effectively avoid vehicles staying in high traffic pressure areas, improve the mobility and availability of charging piles, and enhance the charging convenience of users.

[0068] (3) Through multi-dimensional fitting of the charging urgency index CEI, the traffic flow index TFI and the charging pile area congestion index CU, the charging pile area recommendation index CR is obtained, which provides a more comprehensive reference for the decision-making of recommending charging piles. The introduction of the recommendation effect index RE allows the charging pile recommendation to not only consider the traffic conditions of the area, but also integrate the availability of charging piles. This makes the charging pile recommendation more in line with the actual needs of users by evaluating the proportion of available charging piles in the charging pile area AR. When the proportion of available charging piles in the charging pile area is higher, the recommendation effect of the area is better, ensuring that users can find idle charging piles faster and more conveniently. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1This is a schematic diagram of the steps of a method for intelligently recommending automobile charging piles based on multi-dimensional analysis of the present invention;

[0070] Figure 2 The present invention is a schematic diagram of a block diagram of an intelligent recommendation system for automobile charging piles based on multi-dimensional analysis. DETAILED DESCRIPTION

[0071] The technical solutions 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0072] Example 1

[0073] The present invention provides a method for intelligently recommending automobile charging piles based on multi-dimensional analysis. Figure 1 , including the following steps:

[0074] S1. Obtain traffic flow information, traffic signal information and intersection prediction information at intersections in the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV;

[0075] S2. Preprocess the acquired regional traffic feature vector TBV to obtain the traffic flow index TFI of the charging pile area, evaluate the traffic status of the charging pile area, and generate the charging pile area congestion index CU to reflect the congestion status of the charging pile area;

[0076] S3. By analyzing the vehicle status, the charging urgency index CEI of the vehicle charging demand is obtained, and multi-dimensional fitting is performed with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR;

[0077] S4. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and generate a recommendation effect index set REs according to the recommendation effect index RE, and prompt the vehicle to go to the charging pile area through the recommendation effect index set REs.

[0078] In this embodiment, through the collection and analysis of the regional traffic feature vector TBV, the traffic flow index TFI and congestion index CU of each charging pile area are accurately reflected, and the traffic status of different areas is accurately evaluated, and personalized charging pile recommendations are provided for vehicles based on this information. At the same time, by calculating the multi-dimensional fitting of the charging urgency index CEI and multiple traffic factors, the charging pile area recommendation index CR is further optimized to ensure that the recommendation result not only considers the availability of the charging pile, but also fully reflects the close connection between the traffic conditions and the charging needs of the vehicle. Finally, by evaluating the recommendation effect index RE, this method can screen out the best choice from multiple charging pile areas, avoiding delays or charging difficulties caused by factors such as the location of the charging pile or traffic peak in the traditional method, thereby greatly improving the accuracy of the charging pile recommendation and user experience. Compared with the prior art, this method solves the shortcomings of the traditional charging pile recommendation system that ignores the traffic peak and the urgency of charging demand, and breaks through the single recommendation mode that only relies on the health status of the charging pile. By combining the traffic flow index TFI, the charging pile area congestion index CU and the charging urgency index CEI into the system, a more intelligent recommendation logic is achieved. This method not only provides car owners with more personalized recommendation options, but also avoids recommending to high traffic areas during peak congestion hours, reduces vehicle charging waiting time, and improves the fluidity and efficiency of the entire electric vehicle charging system.

[0079] Example 2

[0080] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11, S12 and S13;

[0081] S11, through the vehicle's on-board map and vehicle networking function, connect to the intelligent transportation system to obtain real-time traffic flow information at the intersection of the i-th charging pile area in the 1 to n charging pile areas within a fixed range from the vehicle, reflect the traffic flow information at the intersection of the charging pile area within a fixed period, and obtain the total traffic flow TF(t) at time t;

[0082] The total vehicle flow TF(t) is obtained by the following calculation formula:

[0083] ;

[0084] Where M represents the total number of roads at the intersection of the i-th charging pile area, and V(j, t) represents the traffic volume of the j-th road at time t, specifically the number of vehicles passing through at time t.

[0085] S12, then interacting with the intelligent transportation network through the Internet of Vehicles system, obtaining the traffic signal information and intersection prediction information of the i-th charging pile area in real time, and obtaining the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t by quantifying the traffic signal information and intersection prediction information;

[0086] The traffic signal information includes different stages of green light, yellow light and red light, as well as the duration of each stage. At different times, different intersection lights will be in different states. The traffic signal state TS(t) at time t is obtained by quantifying the traffic signal information at time t.

[0087] The traffic signal state TS(t) at time t is obtained by the following quantization method:

[0088] ;

[0089] When the traffic light status Status is 'Green', the traffic signal status TS(t) at time t has an amplitude of 1;

[0090] When the traffic light status Status is 'Red', the amplitude of the traffic signal status TS(t) at time t is 0;

[0091] The intersection prediction information is obtained through the interaction between the Internet of Vehicles and the traffic flow monitoring platform. By obtaining the traffic signal status TS(t) of the i-th charging pile area at time t in real time, the traffic flow V(j, t) information on the j-th road at time t is predicted, and the road condition prediction TP(t) at time t is obtained to reflect the congestion situation in the future.

[0092] The traffic condition prediction TP(t) at time t is obtained by the following calculation formula:

[0093] ;

[0094] In the formula, △V(j) represents the predicted traffic flow change of the j-th road, and F(j, t) represents the congestion coefficient of the j-th road at time t;

[0095] S13, by integrating the total vehicle flow TF(t) at time t, the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t, a regional traffic feature vector TBV of the i-th charging pile area intersection is formed.

[0096] The predicted traffic flow change △V(j) on the j-th road is obtained by the following calculation formula:

[0097] ;

[0098] Where Vpred(j, t) represents the predicted traffic flow of the jth road at time t, which is obtained by obtaining the average traffic flow at time t through historical traffic data. This formula is used to quantify the difference between the predicted traffic flow and the actual traffic flow, which can help the system identify potential traffic flow changes and perform optimization processing, such as avoiding recommending charging piles in congested areas during peak hours;

[0099] The congestion coefficient F(j, t) of the j-th road at time t is obtained by the following calculation formula:

[0100] ;

[0101] Wherein, min represents the minimization function, D(j, t) represents the flow density of the jth road at time t, which is obtained by counting the number of vehicles passing through in a fixed unit time, C(j) represents the maximum capacity of the jth road, which specifically represents the maximum number of vehicles that can be accommodated in a fixed unit time, which is determined by the design speed, number of lanes and road type of the road at the initial stage of road setting, SCF represents the traffic signal correction factor, which reflects the impact of traffic signals on flow. Under different signal conditions, the capacity will be different. For example, when the light is green, the vehicle's capacity is higher and the traffic signal correction factor SCF value is smaller; when the light is red, the vehicle cannot pass and the traffic signal correction factor SCF value is larger. This formula is used to reflect the congestion situation of the jth road. The closer the congestion coefficient is to 1, the greater the traffic pressure on the road. This value can be used to determine whether the road section should be avoided for charging pile recommendation.

[0102] In this embodiment, based on the vehicle's on-board map and the Internet of Vehicles function, the system obtains the total traffic flow TF(t) at the intersection of the charging pile area, the traffic signal status TS(t) and the road condition prediction TP(t) in real time. Through this information, the traffic status of the charging pile area can be accurately evaluated at time t, and the recommendation can be further optimized to avoid the peak of charging pile usage caused by traffic congestion, thereby improving the charging efficiency of the vehicle. Especially in periods of large traffic flow fluctuations, the real-time adjustment of traffic signals and the introduction of road condition predictions make the charging pile recommendation not only limited to the geographical location, but also take into account the timeliness and flow fluctuations during the charging process, effectively avoiding the impact of peak hours on the selection of charging pile locations. Compared with traditional charging pile recommendation systems, this method pays more attention to changes in real-time traffic conditions and vehicle dynamic needs, so that it can provide vehicles with more intelligent and accurate charging pile selection in complex traffic environments. It can dynamically adjust the charging pile recommendation plan to avoid recommending to areas with greater traffic pressure and reduce the waiting time of vehicles in high-traffic areas. At the same time, taking into account the impact of different traffic light states and congestion coefficients, the choice of charging piles will be optimized as traffic conditions change, so that vehicles can obtain the most suitable charging pile recommendations, thereby maximizing charging efficiency, avoiding unnecessary traffic delays, and further improving the user's charging experience and the overall efficiency of the system.

[0103] Example 3

[0104] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0105] S21, preprocessing the regional traffic characteristic vector TBV of the i-th charging pile area intersection, including data standardization processing and conversion into a unified dimension, and then substituting the standard characteristic vector BTBV into the established traffic flow analysis formula to calculate the traffic flow index TFI of the charging pile area, and evaluating the traffic status of the charging pile area according to the obtained traffic flow index TFI;

[0106] Among them, the standard feature vector BTBV includes the standardized total vehicle flow BTF (t), the standardized traffic signal status BTS (t) and the standardized road condition prediction BTP (t);

[0107] The traffic flow index TFI is obtained by the following calculation formula:

[0108] ;

[0109] In the formula, ln represents a logarithmic function. The traffic flow index TFI value calculated by this formula will tend to 1, indicating that the traffic congestion in the area is serious and the charging piles in this area should be avoided. This reflects that the traffic volume is small, the traffic signal is good, and the road condition forecast is good. On the contrary, if the traffic volume is small, the traffic signal is good, and the road condition forecast is good, the traffic flow index TFI will tend to 0, indicating that the traffic in the area is very smooth and it is suitable to choose charging piles.

[0110] The traffic status is obtained by the following evaluation method:

[0111] When the traffic flow index TFI ≥ 0.8, the traffic status of the charging pile area is congested, and the traffic status mark JTzt = 9 is obtained;

[0112] When the traffic flow index TFI ≥ 0.5, the traffic state in the charging pile area is slow, and the traffic state flag JTzt = 3 is obtained;

[0113] When the traffic flow index TFI is less than 0.5, the traffic state of the charging pile area is obtained as a green wave state, and the traffic state flag JTzt=1 is obtained.

[0114] S22, by fitting the traffic flow index TFI and the traffic signal correction factor SCF, the flow density of the charging pile area and the control effect of the traffic signal are obtained, and the charging pile area congestion index CU is generated to reflect the congestion status of the charging pile area.

[0115] The charging pile area congestion index CU is obtained by the following calculation formula:

[0116] ;

[0117] Wherein, α represents the traffic state influence coefficient, which is specifically used to adjust the influence of the traffic state mark JTz on the congestion index CU of the charging pile area; β represents the traffic signal factor weight coefficient, which is specifically used to adjust the influence of the traffic signal correction factor SCF on the congestion index CU of the charging pile area. This formula reflects the congestion degree of the charging pile area. The range of the congestion index CU value of the charging pile area is limited to between 0 and 1. The closer the congestion index CU value of the charging pile area is to 1, the more serious the congestion in the charging pile area is; the closer the congestion index CU of the charging pile area is to 0, the smoother the traffic in the current charging pile area is.

[0118] In this embodiment, the obtained regional traffic feature vector is standardized to ensure that data of different dimensions can be compared and calculated under a unified dimension. Through this standardized feature vector, combined with the traffic flow analysis formula, the traffic flow index TFI of the charging pile area is accurately calculated, thereby providing a basis for evaluating the traffic status. Furthermore, by fitting the traffic signal correction factor SCF and the traffic flow index TFI, the congestion index CU of the charging pile area is obtained, which effectively reflects the congestion persistence of the charging pile area. This method combines multiple factors such as the traffic conditions, traffic signals and traffic density of the charging pile area to provide a dynamic and flexible evaluation mechanism, thereby avoiding overly congested areas when recommending charging piles, reducing the charging waiting time caused by traffic problems, and improving the user's charging experience. Compared with traditional methods, this solution has significant advantages, especially in the evaluation of the congestion situation and traffic flow persistence of the charging pile area. Through the comprehensive analysis of the traffic signal correction factor SCF and the traffic flow index TFI, the charging pile recommendation is not limited to static area selection, but can also be dynamically adjusted according to real-time traffic flow changes and signal status. This real-time optimization can effectively prevent vehicles from staying in high-traffic areas, improve the mobility and availability of charging piles, and enhance the convenience of charging for users. Ultimately, the charging pile area congestion index CU provides more accurate decision support for charging pile recommendations, ensuring that the recommended charging pile area not only meets the charging needs of the vehicle, but also makes the best choice in terms of traffic conditions, thereby improving the overall charging efficiency and user experience.

[0119] Example 4

[0120] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0121] S31. Analyze the vehicle status, including the maximum battery power Emax, the maximum mileage Dmax, the remaining mileage Drem, the remaining power Erem, and the driving mode Mmake, for example, the economic mode (driving mode Mmake=0.5), the standard mode (driving mode Mmake=1), and the sports mode (driving mode Mmake=1.5), to obtain the charging urgency index CEI of the vehicle charging demand;

[0122] The charging urgency index CEI is obtained by the following calculation formula:

[0123] ;

[0124] Wherein, ln represents the logarithmic function, λ represents the correction factor of the driving mode Mmake, and e represents the exponential function.

[0125] S32, performing multi-dimensional fitting on the charging emergency index CEI, the acquired traffic flow index TFI and the charging pile area congestion index CU, to obtain a charging pile area recommendation index CR;

[0126] The charging pile area recommendation index CR is obtained by the following calculation formula:

[0127] ;

[0128] Where exp represents the exponential function, c1, c2 and c3 represent the influence coefficients of the charging urgency index CEI, the traffic flow index TFI and the charging pile area congestion index CU on the charging pile area recommendation index CR, respectively, and c1+c2+c3=1. The specific value is set by the user.

[0129] The S4 includes S41;

[0130] S41. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and simultaneously obtain the recommendation effect index set REs={RE(1), RE(2), ..., RE(n)} of 1 to n charging pile areas within the fixed range of the vehicle, and synchronously perform bubble sorting on the recommendation effect index set REs. According to the bubble sorted recommendation effect index set REs, output the charging pile area with a subscript value equal to 1, and display it to the user through a visual interface, and simultaneously marginalize the charging pile areas with subscript values ​​equal to 2 and subscript values ​​equal to 3 to display to the user as alternatives;

[0131] The recommendation effect index RE is obtained by the following calculation formula:

[0132] ;

[0133] In the formula, AR represents the proportion of available charging piles in the charging pile area. Specifically, the proportion of available charging piles AR=0 means that no charging piles are available, and the proportion of available charging piles AR=1 means that all charging piles are available. This value affects the recommendation effect. The higher the proportion of available charging piles AR, the more sufficient the charging piles in the area are, and the better the recommendation effect is. r1, r2 and r3 respectively represent the influence coefficients of the charging pile area recommendation index CR, the proportion of available charging piles AR and the traffic flow index TFI on the recommendation effect index RE, and r1+r2+r3=1. The specific value is set by the user.

[0134] In this example, in steps S3 and S4, the charging urgency index CEI of the vehicle is calculated based on multiple factors of the vehicle, which ensures that the charging needs of each vehicle are accurately evaluated. In particular, different driving modes will produce different correction effects on the charging urgency index, further enhancing the personalized analysis of vehicle needs. Next, through the multi-dimensional fitting of the charging urgency index CEI, the traffic flow index TFI and the charging pile area congestion index CU, the charging pile area recommendation index CR is obtained, which provides a more comprehensive reference for the decision-making of recommending charging piles. The introduction of the recommendation effect index RE makes the charging pile recommendation not only consider the traffic conditions of the area, but also integrates the availability of charging piles. This makes the charging pile recommendation more in line with the actual needs of users by evaluating the proportion of available charging piles AR in the charging pile area. When the proportion of available charging piles in the charging pile area is high, the recommendation effect of the area is better, ensuring that users can find idle charging piles faster and more conveniently. In addition, by bubble sorting the recommendation effect index set REs, the system can accurately give priority recommendations for charging pile areas based on the recommendation effects of different areas. At the same time, the alternative areas are also clearly displayed to ensure that users have more choices. Compared with traditional methods, this solution provides dynamic and personalized charging pile recommendations by accurately analyzing vehicle charging needs and real-time traffic conditions. It not only considers the availability of charging piles, but also reflects traffic conditions and congestion, greatly improving the user's charging experience. The recommendation system can flexibly provide users with the best charging pile selection based on multi-dimensional data on charging needs, traffic flow, and congestion, thereby optimizing the use efficiency of charging piles, reducing charging waiting time, and improving user satisfaction.

[0135] Example 5

[0136] An intelligent recommendation system for car charging piles based on multi-dimensional analysis, please refer to Figure 2 ,Specifically: including traffic data collection module, processing analysis and congestion assessment module, vehicle analysis and recommendation module and recommendation result evaluation module;

[0137] The traffic data acquisition module obtains traffic flow information, traffic signal information and intersection prediction information at the intersection of the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV;

[0138] The processing analysis and congestion assessment module pre-processes the acquired regional traffic feature vector TBV, obtains the traffic flow index TFI of the charging pile area, assesses the traffic status of the charging pile area, and generates the charging pile area congestion index CU to reflect the congestion status of the charging pile area;

[0139] The vehicle analysis and recommendation module obtains the charging urgency index CEI of the vehicle charging demand by analyzing the vehicle status, and performs multi-dimensional fitting with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR;

[0140] The recommendation result evaluation module evaluates the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtains the recommendation effect index RE, and generates a recommendation effect index set REs according to the recommendation effect index RE, and prompts the vehicle to go to the charging pile area through the recommendation effect index set REs.

[0141] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently recommending automobile charging piles based on multi-dimensional analysis, characterized in that: The following steps are involved: S1. Obtain traffic flow information, traffic signal information and intersection prediction information at intersections in the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV; S2. Preprocess the acquired regional traffic feature vector TBV to obtain the traffic flow index TFI of the charging pile area, evaluate the traffic status of the charging pile area, and generate the charging pile area congestion index CU to reflect the congestion status of the charging pile area; The S2 includes S21 and S22; S21, preprocessing the regional traffic characteristic vector TBV of the i-th charging pile area intersection, including data standardization processing and conversion into a unified dimension, and then substituting the standard characteristic vector BTBV into the established traffic flow analysis formula to calculate the traffic flow index TFI of the charging pile area, and evaluating the traffic status of the charging pile area according to the obtained traffic flow index TFI; Among them, the standard feature vector BTBV includes the standardized total vehicle flow BTF (t), the standardized traffic signal status BTS (t) and the standardized road condition prediction BTP (t); The traffic flow index TFI is obtained by the following calculation formula: ； In the formula, ln represents the logarithmic function; The traffic status is obtained by the following evaluation method: When the traffic flow index TFI ≥ 0.8, the traffic status of the charging pile area is congested, and the traffic status mark JTzt = 9 is obtained; When the traffic flow index TFI ≥ 0.5, the traffic state in the charging pile area is slow, and the traffic state flag JTzt = 3 is obtained; When the traffic flow index TFI is less than 0.5, the traffic state of the charging pile area is obtained as a green wave state, and the traffic state flag JTzt=1 is obtained; S22, by fitting the traffic flow index TFI and the traffic signal correction factor SCF, the flow density of the charging pile area and the control effect of the traffic signal are obtained, and the charging pile area congestion index CU is generated to reflect the congestion status of the charging pile area. The charging pile area congestion index CU is obtained by the following calculation formula: ； In the formula, α represents the traffic state influence coefficient, which is specifically used to adjust the influence of the traffic state mark JTz on the congestion index CU of the charging pile area; β represents the traffic signal factor weight coefficient, which is specifically used to adjust the influence of the traffic signal correction factor SCF on the congestion index CU of the charging pile area; S3. By analyzing the vehicle status, the charging urgency index CEI of the vehicle charging demand is obtained, and multi-dimensional fitting is performed with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR; S4. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and generate a recommendation effect index set REs according to the recommendation effect index RE, and prompt the vehicle to go to the charging pile area through the recommendation effect index set REs.

2. According to claim 1, a method for intelligently recommending automobile charging piles based on multi-dimensional analysis is characterized in that: Said S1 includes S11, S12 and S13; S11, through the vehicle's on-board map and vehicle networking function, connect to the intelligent transportation system to obtain real-time traffic flow information at the intersection of the i-th charging pile area in the 1 to n charging pile areas within a fixed range from the vehicle, reflect the traffic flow information at the intersection of the charging pile area within a fixed period, and obtain the total traffic flow TF(t) at time t; The total vehicle flow TF(t) is obtained by the following calculation formula: ； Where M represents the total number of roads at the intersection of the i-th charging pile area, and V(j, t) represents the traffic volume of the j-th road at time t.

3. The method for intelligently recommending automobile charging piles based on multi-dimensional analysis according to claim 2 is characterized in that: S12, then interacting with the intelligent transportation network through the Internet of Vehicles system, obtaining the traffic signal information and intersection prediction information of the i-th charging pile area in real time, and obtaining the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t by quantifying the traffic signal information and intersection prediction information; The traffic signal information includes different stages of green light, yellow light and red light, as well as the duration of each stage. At different times, different intersection lights will be in different states. The traffic signal state TS(t) at time t is obtained by quantifying the traffic signal information at time t. The traffic signal state TS(t) at time t is obtained by the following quantization method: ； When the traffic light status Status is 'Green', the traffic signal status TS(t) at time t has an amplitude of 1; When the traffic light status Status is 'Red', the amplitude of the traffic signal status TS(t) at time t is 0; The intersection prediction information is obtained through the interaction between the Internet of Vehicles and the traffic flow monitoring platform. By obtaining the traffic signal status TS(t) of the i-th charging pile area at time t in real time, the traffic flow V(j, t) information on the j-th road at time t is predicted, and the road condition prediction TP(t) at time t is obtained to reflect the congestion situation in the future. The traffic condition prediction TP(t) at time t is obtained by the following calculation formula: ； In the formula, △V(j) represents the predicted traffic flow change of the j-th road, and F(j, t) represents the congestion coefficient of the j-th road at time t; S13, by integrating the total vehicle flow TF(t) at time t, the traffic signal state TS(t) at time t and the road condition prediction TP(t) at time t, a regional traffic feature vector TBV of the i-th charging pile area intersection is formed.

4. The method for intelligently recommending automobile charging piles based on multi-dimensional analysis according to claim 3 is characterized in that: The predicted traffic flow change △V(j) on the j-th road is obtained by the following calculation formula: ； Where Vpred(j, t) represents the predicted traffic flow of the jth road at time t; The congestion coefficient F(j, t) of the j-th road at time t is obtained by the following calculation formula: ； Where min represents the minimization function, D(j, t) represents the traffic density of the j-th road at time t, which is obtained by counting the number of vehicles passing through in a fixed unit time, C(j) represents the maximum capacity of the j-th road, which specifically represents the maximum number of vehicles that can be accommodated in a fixed unit time, and SCF represents the traffic signal correction factor.

5. The method for intelligently recommending automobile charging piles based on multi-dimensional analysis according to claim 1 is characterized in that: The S3 includes S31 and S32; S31, acquiring a charging urgency index CEI of the vehicle charging demand by analyzing the vehicle status, wherein the vehicle status includes a maximum battery charge Emax, a maximum mileage Dmax, a remaining mileage Drem, a remaining charge Erem, and a driving mode Mmake; The charging urgency index CEI is obtained by the following calculation formula: ； Wherein, ln represents the logarithmic function, λ represents the correction factor of the driving mode Mmake, and e represents the exponential function.

6. The method for intelligently recommending automobile charging piles based on multi-dimensional analysis according to claim 5 is characterized in that: S32, performing multi-dimensional fitting on the charging emergency index CEI, the acquired traffic flow index TFI and the charging pile area congestion index CU, to obtain a charging pile area recommendation index CR; The charging pile area recommendation index CR is obtained by the following calculation formula: ； Where exp represents the exponential function, c1, c2 and c3 represent the influence coefficients of the charging urgency index CEI, the traffic flow index TFI and the charging pile area congestion index CU on the charging pile area recommendation index CR, respectively, and c1+c2+c3=1. The specific value is set by the user.

7. The method for intelligently recommending automobile charging piles based on multi-dimensional analysis according to claim 1 is characterized in that: The S4 includes S41; S41. Evaluate the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtain the recommendation effect index RE, and simultaneously obtain the recommendation effect index set REs={RE(1), RE(2), ..., RE(n)} of 1 to n charging pile areas within the fixed range of the vehicle, and synchronously perform bubble sorting on the recommendation effect index set REs. According to the bubble sorted recommendation effect index set REs, output the charging pile area with a subscript value equal to 1, and display it to the user through a visual interface, and simultaneously marginalize the charging pile areas with subscript values ​​equal to 2 and subscript values ​​equal to 3 to display to the user as alternatives; The recommendation effect index RE is obtained by the following calculation formula: ； Where AR represents the proportion of available charging piles in the charging pile area, r1, r2 and r3 represent the influence coefficients of the charging pile area recommendation index CR, the proportion of available charging piles AR and the traffic flow index TFI on the recommendation effect index RE, and r1+r2+r3=1. The specific value is set by the user.

8. An intelligent recommendation system for automobile charging piles based on multi-dimensional analysis, applied to an intelligent recommendation method for automobile charging piles based on multi-dimensional analysis according to any one of claims 1 to 7, characterized in that: It includes traffic data collection module, processing analysis and congestion assessment module, vehicle analysis and recommendation module and recommendation result assessment module; The traffic data acquisition module obtains traffic flow information, traffic signal information and intersection prediction information at the intersection of the charging pile area through the intelligent transportation network to form a regional traffic feature vector TBV; The processing analysis and congestion assessment module pre-processes the acquired regional traffic feature vector TBV, obtains the traffic flow index TFI of the charging pile area, assesses the traffic status of the charging pile area, and generates the charging pile area congestion index CU to reflect the congestion status of the charging pile area; The vehicle analysis and recommendation module obtains the charging urgency index CEI of the vehicle charging demand by analyzing the vehicle status, and performs multi-dimensional fitting with the obtained traffic flow index TFI and the charging pile area congestion index CU to obtain the charging pile area recommendation index CR; The recommendation result evaluation module evaluates the charging pile recommendation result according to the obtained charging pile area recommendation index CR, obtains the recommendation effect index RE, and generates a recommendation effect index set REs according to the recommendation effect index RE, and prompts the vehicle to go to the charging pile area through the recommendation effect index set REs.

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