Internet of Vehicles platform system and method integrating charging project management and vehicle-network interaction

Abstract

The present invention discloses an Internet of Vehicles platform system and method that integrates charging project management and vehicle-network interaction, and relates to the technical field of Internet of Vehicles. The present invention includes: S10: analyzing and predicting the congestion index of each public station and the flow index between each public station; S20: predicting the feasibility coefficient of the implementation of the charging project; S30: integrating the acquired data and building an operation information database; S40: the vehicle-network interaction platform generates a charging guidance trajectory based on the target charging station and feeds it back to the user's smart terminal; S50: the user chooses whether to perform the charging operation based on the charging guidance trajectory. The present invention realizes the selection of charging stations based on the flow of people and vehicles, which not only improves the frequency of use of charging stations, but also improves the intelligent management and control effect of the vehicle-network interaction platform on charging vehicles, and realizes the management of the entire life cycle of charging projects from planning and construction to operation and maintenance, thereby improving the integrated management effect of charging projects.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking technology, and specifically to a vehicle networking platform system and method that integrates charging project management and vehicle-network interaction. Background Art

[0002] The Internet of Vehicles uses sensing technology to perceive vehicle status information, and relies on wireless communication networks and modern intelligent information processing technologies to achieve intelligent traffic management, intelligent decision-making of traffic information services, and intelligent control of vehicles.

[0003] At present, the full life cycle management of charging projects is used to achieve the operation and maintenance capability management of charging piles during their life cycle. The construction and implementation process of charging projects is not within the scope of management. During the construction of charging piles, only the impact of traffic flow on the construction location of charging piles is currently considered. Since charging stations are usually built in areas with high pedestrian flow, the density of pedestrian flow will also affect the charging waiting time of vehicles to be charged. At the same time, the existing vehicle-grid interactive platform only selects charging stations for vehicles to be charged based on the principle of closest distance. It cannot provide accurate reference for the health status of the selected charging stations and the actual traffic conditions of the vehicles to be charged on the route to the selected charging stations, and thus cannot achieve intelligent control of the vehicles to be charged. Summary of the Invention

[0004] The purpose of the present invention is to provide a vehicle network platform system and method that integrates charging project management and vehicle-network interaction to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a vehicle networking platform method that integrates charging project management and vehicle-network interaction, the method comprising:

[0006] S10: Based on the declared charging projects, obtain the location distribution data of each public station within the construction area, analyze and predict the congestion index of each public station, and the flow index between public stations;

[0007] S20: Based on the declared charging project, the construction location coordinates of the charging station to be constructed within the construction area are obtained. Combined with the congestion index of the target public station and the flow index between the target public station and other public stations, the feasibility coefficient of the charging project is predicted;

[0008] S30: After the declared charging project is implemented, obtain monitoring data, fault alarm data, and distribution facility operation data of the charging stations constructed within the construction area, integrate the acquired data, and build an operation information database;

[0009] S40: The user views the operation information database through the vehicle-network interaction platform and determines the target charging station. The vehicle-network interaction platform generates a charging guidance trajectory based on the target charging station and feeds it back to the user's smart terminal;

[0010] S50: The user selects whether to perform charging according to the charging guidance trajectory.

[0011] Realize a full-cycle management system for charging projects from planning and construction to operation and maintenance.

[0012] Furthermore, the S10 includes:

[0013] S101: Determine the construction area of ​​the charging project based on the declared charging project, construct a two-dimensional coordinate system on the plane where the construction area is located, and obtain the regional distribution position of the public station i built within the construction area in the two-dimensional coordinate system, where i=1,2,…,m represents the corresponding number of each public station built within the construction area, and m represents the total number of public stations built within the construction area;

[0014] S102: Obtain a historical passenger flow heat map of the area where the public station i is located, and identify target change areas and concentrated areas in each historical passenger flow heat map;

[0015] S103: Obtain the intersection area of ​​each concentrated area identified in step S102, and calculate the intersection area W of the intersection area. i , and the block area H of the red block in the intersection area i and the block area V of the orange block i To collect, according to D i =a×(H i / W i )+b×(V i / W i ) Congestion coefficient D of public station i i Calculate and calculate D i With 1-exp(-W i ) is calculated by multiplying the two to get the congestion index F of public station i i , where a and b represent proportional coefficients, exp() represents an exponential function with base e and e=2.73;

[0016] S104: Acquire the collective area of ​​each target change area identified in step S102, and search for the associated public stations of public station i. Based on the search result, predict the flow index between public station i and each associated public station.

[0017] Furthermore, the specific method of identifying the target change area and the concentrated area in each historical pedestrian flow heat map in S102 is as follows:

[0018] The concentrated area refers to the combined area of ​​the red blocks and orange blocks in the historical pedestrian flow heat map;

[0019] The method for identifying the target change area is as follows: randomly select a historical pedestrian flow heat map, divide the boundary of the selected historical pedestrian flow heat map according to the color rendering situation, and obtain several boundary division areas. Each boundary division area is rendered by only one color, and use straight lines to connect the center of the collection area with the center of each boundary division area to obtain several gradient trajectories. The gradient trajectory points from the collection area to the boundary division area and the color depth of the gradient trajectory is from dark to shallow along the pointing direction. The number of color type changes of each gradient trajectory is obtained along the pointing direction of the gradient trajectory. The reciprocal of the number of color type changes of each gradient trajectory is used as the base, and the color depth sorting number of the color rendered by the boundary division area passed by each gradient trajectory is used as the exponent to construct an exponential function. The value coefficient of each gradient trajectory is calculated, and the gradient trajectory corresponding to the maximum value of the calculated value coefficient is used as the target gradient trajectory, and the boundary division area passed by the target gradient trajectory is used as the target change area.

[0020] Furthermore, the specific method of predicting the flow index between the public station i and each associated public station in S104 is:

[0021] The collection area of ​​each target change area identified by public station i is obtained. Each independent area in the collection area has a target gradient trajectory. The associated public stations of public station i are searched, and the associated public stations found are numbered. The numbering result is: j=1,2,…n; n represents the total number of associated public stations found, and the associated public station j of public station i is recorded as the associated public station i j , if the target gradient trajectory of public station i exists with the associated public station i j The target gradient trajectory points in the opposite direction and the public station i is associated with the public station i j If no other public stations are built between them, it is considered that the associated public station i j Associate public station for target i´ j Otherwise, it is not considered to be associated with the public station i j Associate a public station for the target;

[0022] According to the determination method of the target associated public station, the target associated public station i´ j The flow trajectory A between public station i ij Determine and record the flow trajectory A ij The two trajectory endpoints are trajectory endpoint i and trajectory endpoint i´ j , the color depth sorting number of the color rendered by the trajectory endpoint i is the same as the trajectory endpoint i' jThe sum of the color depth sorting numbers of the rendered colors X ij Calculate according to Q ij =ζ×[1-exp(-|X ij |)] for public station i and target associated public station i´ j The flow index between the prediction, Q ij Indicates that public station i is associated with target public station i´ j The flow index between two random public stations is concentrated and diverged at the boundary of the target change area. The flow index between two random public stations predicted based on the color depth of the color rendered at the endpoints of the flow trajectory can more accurately reflect the flow of people along the flow trajectory.

[0023] Furthermore, the S20 includes:

[0024] S201: Based on the declared charging project, the construction center coordinates (x p ,y p ), where p = 1, 2, ..., q represents the number of each charging station built within the construction area, and q represents the total number of charging stations built within the construction area;

[0025] S202: Determine the construction center coordinates (x p ,y p ) Is it in flow trajectory A ij If not, then the charging station pile p is considered to be located at the flow trajectory A. ij The vertical distance L ijp Calculate, if in, then L ijp =0;

[0026] According to the distance formula between two points, the distance U between the charging station pile p and the center of the public station i collection area is considered. ip Perform calculations;

[0027] S203: Q ij is the base, L ijp Construct an exponential function for the index, and consider the effect of building a charging station pile p on the flow trajectory A of the flow of people. ij Flow influence coefficient B ijp Perform calculations;

[0028] U ip Ratio of building safety distance U´ ip Calculate, if U´ ip >1, then for U´ ip The reciprocal of F iThe product between them is calculated to obtain the construction influence coefficient P of public station i on the construction of charging station pile p. ip , if U´ ip ≤1, then P ip =F i ;

[0029] According to G p =min{1-(f1×B 1jp +f2×P 1p ),…,1-(f1×B ijp +f2×P ip )} Calculate the construction index of the charging station pile p to be built, where G p It represents the construction index of the charging station p to be built, f1 and f2 represent the relationship coefficients, and min represents the minimum value symbol;

[0030] S204: For all G from p=1 to p=q p A summation process is performed, and the ratio between the summation process result and the value q is calculated to obtain the implementation feasibility coefficient E of the charging project.

[0031] Furthermore, the S30 includes:

[0032] S301: When E>the set threshold, the declared charging project is implemented; otherwise, the declared charging project is not implemented;

[0033] When the declared charging project is implemented, obtain the historical monitoring data, historical fault alarm signal duration, and historical distribution facility operation data of the charging piles p built within the construction area;

[0034] S302: Randomly select a historical fault alarm signal duration period [t, η], intercept the historical distribution facility operation data within the time period [t, η], and obtain the abnormal operation characteristics γ of the distribution facility of the charging station p within the time period [t, η] based on the changes in the intercepted historical distribution facility operation data. p(t→η) The specific method is: according to the collection interval z of historical monitoring data, the monitoring data collection time points in the time period [t,η] are numbered, and the numbering results are: d=1,2,…,k; k represents the total number, and in the time period [t,t+z], max{M pt ,M p(t+z)} and min{M pt ,M p(t+z) The difference between pt→p(t+z) Calculate and calculate M pt→p(t+z) The voltage variation coefficient T of the power distribution facilities of the charging station pile p in the time period [t, t+z] is calculated by comparing the voltage of the power distribution facilities of the charging station pile p with the voltage of the power distribution facilities of the charging station pile p in the time period [t, t+z]. p(t→t+z), γ p(t→η) ={T p(t→t+z) ,T p(t+z→t+z×2) ,…,T p(t+η-z→t+η)}, where max represents the maximum value symbol, N1 represents the average working voltage of the power distribution facilities of the charging station P during normal operation, and M pt represents the operating voltage of the power distribution facilities of the charging station p at time t;

[0035] S303: In the time period [t, η], the historical monitoring data of the charging station pile p is intercepted, and the abnormal operation characteristics ψ of the charging station pile p in the time period [t, η] are obtained according to the changes in the intercepted historical monitoring data. p(t→η) , ψ p(t→η) ={Y p(t→t+z) ,Y p(t+z→t+z×2) ,…,Y p(t+η-z→t+η)}, where Y p(t→t+z) represents the voltage variation coefficient of the charging station pile p in the time period [t, t+z];

[0036] S304: Build a real-time operation information database for the charging project.

[0037] Furthermore, the specific method of constructing the real-time operation information database of the charging project in S304 is as follows:

[0038] The abnormal operation characteristics of the distribution facilities γ p(t→η) 、Abnormal operation characteristics ψ p(t→η) , and the charging station pile operation and maintenance personnel integrate the fault diagnosis results, fault maintenance plan and fault maintenance time of the charging station pile p to obtain a fault record, and merge and store all the obtained fault records to obtain a charging station pile fault database;

[0039] Repeat the operations of steps S301 to S303 to determine the real-time abnormal operation characteristics and real-time abnormal operation characteristics of the power distribution facilities of the charging station pile p, calculate the similarity of the change trends between the determined real-time abnormal operation characteristics and real-time abnormal operation characteristics and the abnormal operation characteristics of the power distribution facilities recorded in each fault record stored in the charging station pile fault database, and calculate the average of the two calculated similarities to obtain the fault matching coefficient;

[0040] If the fault matching coefficient is greater than or equal to the fault threshold, the fault maintenance time g stored in the fault record corresponding to the maximum value of the fault matching coefficient is obtained, and the operation information of the charging station p in the time period [l, l+g] is that the charging station p stops operating;

[0041] If the fault matching coefficients are all less than the fault threshold, there is no need to obtain the fault maintenance time stored in the fault record corresponding to the maximum fault matching coefficient. The operation information of the charging station pile p in the time period [l, l+z] includes the normal operation of the charging station pile p, the average traffic coefficient X of the charging station pile p in the time period [l, l+z], and the average traffic coefficient X of the charging station pile p in the time period [l, l+z]. p(l→l+z) , the remaining charging time I of the charging station pile p at time l pl , X p(l→l+z) = E p(l-z→l) , where E p(l-z→l) represents the implementation feasibility coefficient of the charging station pile p in the time period [lz,l];

[0042] Traverse all charging stations and build a real-time operation information database for charging projects.

[0043] By comparing the similarity between the real-time abnormal operation characteristics and the abnormal operation characteristics recorded in each fault record stored in the charging station pile fault database in terms of change direction and change speed in time series, the health status of the charging station pile and the distribution facilities of the charging station pile is evaluated in real time, with high evaluation efficiency and accurate evaluation results.

[0044] Furthermore, the S40 includes:

[0045] S401: A three-dimensional map of the construction area is obtained. The vehicle-network interactive platform marks each constructed charging station on the three-dimensional map and adds a marker index to each marked position. The marker index is the operating information of the charging station. By displaying the operating information stored in the operation information database on the three-dimensional map, users can intuitively view the real-time operating status of each charging station.

[0046] S402: The user enters the vehicle-network interaction platform through the smart terminal. The vehicle-network interaction platform generates the user's driving trajectory π based on the positioning information of the smart terminal and the user's target location information;

[0047] Based on the three-dimensional map, a charging station that is in normal operation is randomly selected. The shortest distance J between the selected charging station and the driving trajectory π is calculated. If 0 < J < o, the selected charging station is screened and retained. If J ≥ o, the selected charging station is screened and eliminated. Based on the selected charging stations, a set of charging stations to be selected is obtained, where o represents the maximum offset distance of the driving trajectory that the user can accept.

[0048] S403: Randomly select a charging station from the selected charging station set, and calculate the matching coefficient between the selected charging station and the user. The specific calculation formula is: θ=exp(-|I´-τ| / I´)×X´ (l1→l1+τ), where θ represents the matching coefficient between the selected charging station and the user, and τ represents the time required for the user's vehicle to be charged to travel from the positioning position to the construction location of the selected charging station;

[0049] Traverse the charging stations in the selected charging station set and select the charging station with the maximum matching coefficient as the target station;

[0050] S404: A charging guidance trajectory is generated on a three-dimensional map, starting from the user's smart terminal's location and ending at the target charging station's construction location. The vehicle-grid interactive platform then feeds the generated charging guidance trajectory back to the user's smart terminal. By leveraging the real-time operation status of the charging station, the user's distance from the charging station, and the charging station's real-time traffic coefficient, staggered charging of vehicles to be charged is achieved. This staggered charging includes both staggered charging station usage and staggered charging station traffic. Compared to existing staggered charging solutions, this takes into account congestion at nearby public stations and pedestrian flow, thereby improving the time required for vehicles to reach the charging station and improving charging efficiency.

[0051] An IoV platform system integrating charging project management and vehicle-network interaction, comprising a congestion index prediction module, a flow index prediction module, a charging project implementation feasibility prediction module, an operation information database construction module, a charging guidance trajectory generation module, and a charging selection module;

[0052] The congestion index prediction module is used to analyze and predict the congestion index of each public station;

[0053] The flow index prediction module is used to analyze and predict the flow index between public stations;

[0054] The charging project implementation feasibility prediction module is used to obtain the construction location coordinates of the charging station to be constructed within the construction area based on the declared charging project, and to predict the implementation feasibility coefficient of the charging project based on the congestion index of the target public station and the flow index between the target public station and other public stations;

[0055] The operation information database construction module is used to integrate the monitoring data of the charging station piles, fault alarm data and distribution facility operation data and build an operation information database;

[0056] The charging guide trajectory generation module is used to generate a charging guide trajectory;

[0057] The charging selection module is used to select whether to perform a charging operation according to the user's selection of the charging guidance trajectory.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention predicts the feasibility coefficient of the implementation of the charging project through the congestion index of each public station and the flow index between each public station. After the implementation of the charging project, the real-time health status of the charging station pile is evaluated by constructing an operation information database of the charging station pile, and the operation data stored in the operation information database is visualized in a three-dimensional map to perform a preliminary screening of the charging station pile. Finally, according to the driving time of the vehicle to be charged to reach the selected charging station pile, the remaining charging time of the selected charging station pile, and the traffic coefficient of the vehicle to be charged on the route to the selected charging station pile, the charging guidance trajectory of the vehicle to be charged is generated to realize the charging management of the vehicle to be charged. The present invention realizes the management of the entire life cycle of the charging project from planning and construction to operation and maintenance, and improves the integrated management effect of the charging project.

[0060] 2. Based on the historical passenger flow heat map of each public station, the present invention predicts the impact of the flow of people at the public station and between adjacent public stations on the construction of charging stations, and the feasibility coefficient of the implementation of the charging project. The present invention selects charging stations based on the flow of people and vehicles, which not only increases the frequency of use of charging stations, but also improves the intelligent management and control effect of the vehicle-network interactive platform on charging vehicles. The change in vehicle flow is reflected based on the travel time of the vehicle to be charged to the selected charging station.

[0061] 3. The present invention generates several fault records based on the historical fault data of the charging station piles, and realizes the assessment of the real-time health status of the charging station piles based on the similarity of the changing trends between each fault record and the real-time abnormal operation characteristics of the power distribution facilities of the charging station piles or the abnormal operation characteristics of the charging station piles.

[0062] 4. The present invention can realize staggered charging of vehicles to be charged. Staggered charging includes staggered use of charging stations and staggered traffic to charging stations. Compared with existing staggered charging solutions, the time required for charging vehicles to travel to charging stations is taken into account, taking into account the congestion of public stations near charging stations and the flow of people, thereby improving the charging efficiency of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of the workflow of the vehicle networking platform method for integrating charging project management and vehicle-network interaction of the present invention;

[0064] Figure 2 This is a schematic diagram of the working principle structure of the Internet of Vehicles platform system that integrates charging project management and vehicle-network interaction in the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0066] like Figure 1-Figure 2 As shown, the present invention provides a vehicle networking platform system and method technical solution that integrates charging project management and vehicle-network interaction, and a vehicle networking platform method that integrates charging project management and vehicle-network interaction, the method comprising:

[0067] S10: Based on the declared charging projects, obtain the location distribution data of each public station within the construction area, analyze and predict the congestion index of each public station, and the flow index between public stations. Public stations include bus stops and hailing stations.

[0068] The S10 includes:

[0069] S101: Determine the construction area of ​​the charging project based on the declared charging project, construct a two-dimensional coordinate system on the plane where the construction area is located, and obtain the regional distribution position of the public station i built within the construction area in the two-dimensional coordinate system, where i=1,2,…,m represents the corresponding number of each public station built within the construction area, and m represents the total number of public stations built within the construction area;

[0070] S102: Obtain a historical pedestrian flow heat map of the area where public station i is located. The principle of obtaining the pedestrian flow heat map is as follows: by collecting and analyzing the positioning data of mobile phone users, the number of users in each area is determined, and the map color is rendered according to the number of users to achieve real-time display of the population density in the area. The darker the map color, the greater the population density. The map colors are red, orange, yellow, green, cyan, and purple in descending order of color depth. The corresponding sequence numbers of red, orange, yellow, green, cyan, and purple are 1, 2, 3, 4, 5, and 6 respectively. The positioning data is obtained in accordance with the regulations. The target change area and concentrated area in each historical pedestrian flow heat map are identified;

[0071] The concentrated area refers to the combined area of ​​red blocks and orange blocks in the historical pedestrian flow heat map. Red and orange indicate high pedestrian density;

[0072] The identification method of the target change area is as follows: randomly select a historical pedestrian flow heat map, divide the boundary of the selected historical pedestrian flow heat map according to the color rendering situation, and obtain several boundary division areas, each boundary division area is rendered by only one color and there is a boundary line of the historical pedestrian flow heat map in each boundary division area, use straight lines to connect the center of the collection area with the center of each boundary division area respectively, and obtain several gradient trajectories, the gradient trajectory points from the collection area to the boundary division area and the color depth of the gradient trajectory is from dark to shallow along the pointing direction, and the number of color type changes of each gradient trajectory is obtained along the pointing direction of the gradient trajectory, with the reciprocal of the number of color type changes of each gradient trajectory as the base, and the color depth sorting number of the color rendered by the boundary division area passed by each gradient trajectory as the exponent to construct an exponential function, calculate the value coefficient of each gradient trajectory, and take the gradient trajectory corresponding to the maximum value coefficient as the target gradient trajectory, and take the boundary division area passed by the target gradient trajectory as the target change area;

[0073] S103: Obtain the intersection area of ​​each concentrated area identified in step S102, and calculate the intersection area W of the intersection area. i , and the block area H of the red block in the intersection area i and the block area V of the orange block i To collect, according to D i =a×(H i / W i )+b×(V i / W i ) Congestion coefficient D of public station i i Calculate and calculate D i With 1-exp(-W i ) is calculated by multiplying the two to get the congestion index F of public station i i , where a and b represent proportional coefficients, exp() represents an exponential function with base e and e=2.73;

[0074] S104: Acquire the collection area of ​​each target change area identified in step S102. Each independent area in the collection area has a target gradient trajectory. Search for the associated public stations of public station i. Number the found associated public stations. The numbering result is: j=1, 2, ... n; n represents the total number of found associated public stations. The associated public station j of public station i is recorded as the associated public station i. j , if the target gradient trajectory of public station i exists with the associated public station i j The target gradient trajectory points in the opposite direction and the public station i is associated with the public station i j If no other public stations are built between them, it is considered that the associated public station ij Associate public station for target i´ j Otherwise, it is not considered to be associated with the public station i j Associate a public station for the target;

[0075] According to the determination method of the target associated public station, the target associated public station i´ j The flow trajectory A between public station i ij To determine, the two target gradient trajectories are extended along their respective pointing directions until the extension points coincide. The extended line segments are the flow trajectories of the two target gradient trajectories, and the flow trajectory A is recorded. ij The two trajectory endpoints are trajectory endpoint i and trajectory endpoint i´ j , the color depth sorting number of the color rendered by the trajectory endpoint i is the same as the trajectory endpoint i' j The sum of the color depth sorting numbers of the rendered colors X ij Calculate according to Q ij =ζ×[1-exp(-|X ij |)] for public station i and target associated public station i´ j The flow index between the prediction, Q ij Indicates that public station i is associated with target public station i´ j The flow index between ζ and ζ represents the error coefficient;

[0076] S20: Based on the declared charging project, the construction location coordinates of the charging station to be constructed within the construction area are obtained. Combined with the congestion index of the target public station and the flow index between the target public station and other public stations, the feasibility coefficient of the charging project is predicted;

[0077] The S20 includes:

[0078] S201: Based on the declared charging project, the construction center coordinates (x p ,y p ), where p = 1, 2, ..., q represents the number of each charging station built within the construction area, and q represents the total number of charging stations built within the construction area;

[0079] S202: Determine the construction center coordinates (x p ,y p ) Is it in flow trajectory A ij If not, then the charging station pile p is considered to be located at the flow trajectory A. ij The vertical distance L ijp Calculate, if in, then L ijp =0;

[0080] According to the distance formula between two points, the distance U between the charging station pile p and the center of the public station i collection area is considered. ip Perform calculations;

[0081] S203: Q ij is the base, L ijp Construct an exponential function for the index, and consider the effect of building a charging station pile p on the flow trajectory A of the flow of people. ij Flow influence coefficient B ijp Perform calculations;

[0082] U ip Ratio of building safety distance U´ ip Calculate, if U´ ip >1, then for U´ ip The reciprocal of F i The product between them is calculated to obtain the construction influence coefficient P of public station i on the construction of charging station pile p. ip , if U´ ip ≤1, then P ip =F i , construction safety distance>5 meters;

[0083] According to G p =min{1-(f1×B 1jp +f2×P 1p ),…,1-(f1×B ijp +f2×P ip )} Calculate the construction index of the charging station pile p to be built, where G p It represents the construction index of the charging station p to be built, f1 and f2 represent the relationship coefficients, and min represents the minimum value symbol;

[0084] S204: For all G from p=1 to p=q p Perform a summation process, calculate the ratio between the summation result and the value q, and obtain the implementation feasibility coefficient E of the charging project;

[0085] S30: After the declared charging project is implemented, obtain monitoring data, fault alarm data, and distribution facility operation data of the charging stations constructed within the construction area, integrate the acquired data, and build an operation information database;

[0086] The S30 includes:

[0087] S301: When E>the set threshold, the declared charging project is implemented; otherwise, the declared charging project is not implemented;

[0088] When the declared charging project is implemented, obtain the historical monitoring data, historical fault alarm signal duration, and historical distribution facility operation data of the charging piles p built within the construction area;

[0089] S302: Randomly select a historical fault alarm signal duration period [t, η], intercept the historical distribution facility operation data within the time period [t, η], and obtain the abnormal operation characteristics γ of the distribution facility of the charging station p within the time period [t, η] based on the changes in the intercepted historical distribution facility operation data. p(t→η) The specific method is: according to the collection interval z of historical monitoring data, the monitoring data collection time points in the time period [t,η] are numbered, and the numbering results are: d=1,2,…,k; k represents the total number, and in the time period [t,t+z], max{M pt ,M p(t+z)} and min{M pt ,M p(t+z) The difference between pt→p(t+z) Calculate and calculate M pt→p(t+z) The voltage variation coefficient T of the power distribution facilities of the charging station pile p in the time period [t, t+z] is calculated by comparing the voltage of the power distribution facilities of the charging station pile p with the voltage of the power distribution facilities of the charging station pile p in the time period [t, t+z]. p(t→t+z) , γ p(t→η) ={T p(t→t+z) ,T p(t+z→t+z×2) ,…,T p(t+η-z→t+η)}, where max represents the maximum value symbol, N1 represents the average working voltage of the power distribution facilities of the charging station P during normal operation, and M pt represents the operating voltage of the power distribution facilities of the charging station pile p at time t, M p(t+z) represents the operating voltage of the power distribution facilities of charging station p at time t+z;

[0090] The abnormal operation characteristics of the power distribution facilities refer to the set of voltage variation coefficients of the power distribution facilities of the charging station p within the time period [t,η];

[0091] S303: In the time period [t, η], the historical monitoring data of the charging station pile p is intercepted, and the abnormal operation characteristics ψ of the charging station pile p in the time period [t, η] are obtained according to the changes in the intercepted historical monitoring data. p(t→η) , the specific method is: in the [t, t+z] time period, max{S pt ,S p(t+z)} and min{S pt ,S p(t+z) The difference S between pt→p(t+z) Calculate and calculate S pt→p(t+z) The ratio between the value of N2 and N2 is calculated to obtain the voltage variation coefficient Y of the charging station pile p in the time period [t, t+z].p(t→t+z) , ψ p(t→η) ={Y p(t→t+z) ,Y p(t+z→t+z×2) ,…,Y p(t+η-z→t+η)}, where N2 represents the average operating voltage of the charging station pile p during normal operation, S pt represents the operating voltage of the charging station pile p at time t, S p(t+z) represents the operating voltage of the charging station pile p at time t+z;

[0092] S304: The obtained abnormal operation characteristics of the power distribution facilities γ p(t→η) 、Abnormal operation characteristics ψ p(t→η) , and the charging station pile operation and maintenance personnel integrate the fault diagnosis results, fault maintenance plan and fault maintenance time of the charging station pile p to obtain a fault record, and merge and store all the obtained fault records to obtain a charging station pile fault database;

[0093] Repeat the operations of steps S301 to S303 to determine the abnormal operation characteristics and abnormal operation characteristics of the power distribution facilities of the charging station pile p in the time period [lz, l]. Calculate the similarity of the change trends between the abnormal operation characteristics and real-time abnormal operation characteristics of the power distribution facilities determined above and the abnormal operation characteristics and abnormal operation characteristics recorded in each fault record stored in the charging station pile fault database. Calculate the average of the two calculated similarities between the change trends to obtain the fault matching coefficient.

[0094] If the fault matching coefficient is greater than or equal to the fault threshold, the fault maintenance time g stored in the fault record corresponding to the maximum fault matching coefficient is obtained. The operation information of the charging station p in the time period [l, l+g] is that the charging station p stops operating, where l represents the real time.

[0095] If the fault matching coefficients are all less than the fault threshold, there is no need to obtain the fault maintenance time stored in the fault record corresponding to the maximum fault matching coefficient. The operation information of the charging station pile p in the time period [l, l+z] includes the normal operation of the charging station pile p, the average traffic coefficient X of the charging station pile p in the time period [l, l+z], and the average traffic coefficient X of the charging station pile p in the time period [l, l+z]. p(l→l+z) , the remaining charging time I of the charging station pile p at time l pl , X p(l→l+z) = E p(l-z→l) , where E p(l-z→l) represents the implementation feasibility coefficient of charging station p within the time period [lz, l]. The similarity of change trends refers to the similarity between the abnormal operation characteristics of two distribution facilities or the abnormal operation characteristics in terms of change direction and change speed in the time series. The remaining charging time of the charging station = the charging time selected by the user - the charging time of the vehicle at the charging station.

[0096] Traverse charging stations and build a real-time operation information database for charging projects;

[0097] S40: The user views the operation information database through the vehicle-network interaction platform and determines the target charging station. The vehicle-network interaction platform generates a charging guidance trajectory based on the target charging station and feeds it back to the user's smart terminal;

[0098] S40 includes:

[0099] S401: A three-dimensional map of the construction area is obtained. The vehicle-network interactive platform marks each charging station to be constructed in the three-dimensional map and adds a marking index to each marked position. The marking index is the operation information of the charging station.

[0100] S402: The user accesses the vehicle-network interaction platform through the smart terminal. The vehicle-network interaction platform generates a driving trajectory π of the user based on the positioning information of the smart terminal and the user's target location information. The positioning information and the target location information are obtained after the user's authorization. The driving trajectory π is a default driving route generated by the navigation software based on the positioning location (the positioning location is the starting location) and the target location (the target location is the end location).

[0101] Based on the three-dimensional map, a charging station that is in normal operation is randomly selected. The shortest distance J between the selected charging station and the driving trajectory π is calculated. If 0 < J < o, the selected charging station is screened and retained. If J ≥ o, the selected charging station is screened and eliminated. Based on the selected charging stations, a set of charging stations to be selected is obtained, where o represents the maximum offset distance of the driving trajectory that the user can accept.

[0102] S403: Randomly select a charging station from the selected charging station set, and calculate the matching coefficient between the selected charging station and the user. The specific calculation formula is: θ=exp(-|I´-τ| / I´)×X´ (l1→l1+τ) , where θ represents the matching coefficient between the selected charging station and the user, τ represents the time required for the user's vehicle to be charged to travel from the positioning position to the construction location of the selected charging station, τ is estimated by the navigation system, I' represents the remaining charging time of the selected charging station, and X' (l1→l1+τ) It represents the average traffic coefficient of the selected charging station in the time period [l1, l1+τ]. The average traffic coefficient is the implementation feasibility coefficient of the selected charging station in the time period [l1, l1+τ]. l1 represents the real-time time value corresponding to the location of the vehicle to be charged.

[0103] Traverse the charging stations in the selected charging station set and select the charging station with the maximum matching coefficient as the target station;

[0104] S404: A charging guidance trajectory is generated on a three-dimensional map, starting from the positioning location of the user's smart terminal and ending at the construction location of the target charging pile. The vehicle-network interactive platform feeds the generated charging guidance trajectory back to the user's smart terminal. The charging guidance trajectory is a default driving route generated by the navigation software based on the positioning location and the construction location of the target charging pile.

[0105] S50: The user selects whether to perform charging according to the charging guidance trajectory.

[0106] The IoV platform system integrates charging project management and vehicle-network interaction. The system includes a congestion index prediction module, a flow index prediction module, a charging project implementation feasibility prediction module, an operation information database construction module, a charging guidance trajectory generation module, and a charging selection module.

[0107] The congestion index prediction module is used to analyze and predict the congestion index of each public station;

[0108] The flow index prediction module is used to analyze and predict the flow index between public stations;

[0109] The charging project implementation feasibility prediction module is used to obtain the construction location coordinates of the charging station piles to be built within the construction area based on the declared charging project. It combines the congestion index of the target public station and the flow index between the target public station and other public stations to predict the implementation feasibility coefficient of the charging project.

[0110] The operation information database construction module is used to integrate the monitoring data of charging station piles, fault alarm data and distribution facility operation data and build an operation information database;

[0111] The charging guide trajectory generation module is used to generate a charging guide trajectory;

[0112] The charging selection module is used to select whether to perform a charging operation according to the user's selection of the charging guidance trajectory.

[0113] Example 1: Assume that the shortest distance between the selected charging station and the driving trajectory π is J=5 kilometers, and the maximum deviation distance of the driving trajectory that the user can accept is o=10 kilometers;

[0114] Since 0<J=5km<o=5km, the charging station will be selected for screening and retention operation;

[0115] Traverse the charging stations that can operate normally, and obtain a set of charging stations to be selected based on the retained charging stations;

[0116] Assume that the time required for the user's vehicle to be charged to travel from the positioning position to the selected charging station is τ = 5 minutes, the remaining charging time of the charging station when the vehicle to be charged is located at the positioning position is I' = 4 minutes, and the average traffic coefficient of the charging station in the time period [l1, l1+τ] is X'. (l1→l1+τ) =0.8, then the matching coefficient between the charging station and the user is:

[0117] θ=exp(-|I´-τ| / I´)×X´ (l1→l1+τ) =exp[-0.25]×0.8=0.62;

[0118] That is, the matching coefficient between the charging station and the user is selected as 0.62;

[0119] Traverse the charging stations in the selected charging station set and select the charging station with the maximum matching coefficient as the target station;

[0120] Taking the positioning position of the user's smart terminal as the starting point and the construction position of the target station as the end point, a charging guidance trajectory is generated in the three-dimensional map, and the vehicle-network interactive platform feeds back the generated charging guidance trajectory to the user's smart terminal.

[0121] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A vehicle networking platform method that integrates charging project management and vehicle-network interaction, characterized by: The method comprises: S10: Based on the declared charging projects, obtain the location distribution data of each public station within the construction area, analyze and predict the congestion index of each public station, and the flow index between public stations; The S10 includes: S101: Determine the construction area of ​​the charging project based on the declared charging project, construct a two-dimensional coordinate system on the plane where the construction area is located, and obtain the regional distribution position of the public station i built within the construction area in the two-dimensional coordinate system, where i=1,2,…,m represents the corresponding number of each public station built within the construction area, and m represents the total number of public stations built within the construction area; S102: Obtain a historical passenger flow heat map of the area where the public station i is located, and identify target change areas and concentrated areas in each historical passenger flow heat map; S103: Obtain the intersection area of ​​each concentrated area identified in step S102, and calculate the intersection area W of the intersection area. i , and the block area H of the red block in the intersection area i and the block area V of the orange block i To collect, according to D i =a×(H i / W i )+b×(V i / W i ) Congestion coefficient D of public station i i Calculate and calculate D i With 1-exp(-W i ) is calculated by multiplying the two to get the congestion index F of public station i i , where a and b represent proportional coefficients, exp() represents an exponential function with base e and e=2.73; S104: Acquire the collective area of ​​each target change area identified in step S102, and search for the associated public stations of public station i. Based on the search results, predict the flow index between public station i and each associated public station. The specific method is as follows: The collection area of ​​each target change area identified by public station i is obtained. Each independent area in the collection area has a target gradient trajectory. The associated public stations of public station i are searched, and the associated public stations found are numbered. The numbering result is: j=1,2,…n; n represents the total number of associated public stations found, and the associated public station j of public station i is recorded as the associated public station i j , if the target gradient trajectory of public station i exists with the associated public station i j The target gradient trajectory points in the opposite direction and the public station i is associated with the public station i j If no other public stations are built between them, it is considered that the associated public station i j Associate public station for target i´ j Otherwise, it is not considered to be associated with the public station i j Associate a public station for the target; According to the determination method of the target associated public station, the target associated public station i´ j The flow trajectory A between public station i ij Determine and record the flow trajectory A ij The two trajectory endpoints are trajectory endpoint i and trajectory endpoint i´ j , the color depth sorting number of the color rendered by the trajectory endpoint i is the same as the trajectory endpoint i' j The sum of the color depth sorting numbers of the rendered colors X ij Calculate according to Q ij =ζ×[1-exp(-|X ij |)] for public station i and target associated public station i´ j The flow index between the prediction, Q ij Indicates that public station i is associated with target public station i´ j The flow index between ζ and ζ represents the error coefficient; S20: Based on the declared charging project, the construction location coordinates of the charging station to be constructed within the construction area are obtained. Combined with the congestion index of the target public station and the flow index between the target public station and other public stations, the feasibility coefficient of the charging project is predicted; The S20 includes: S201: Based on the declared charging project, the construction center coordinates (x p ,y p ), where p = 1, 2, ..., q represents the number of each charging station built within the construction area, and q represents the total number of charging stations built within the construction area; S202: Determine the construction center coordinates (x p ,y p ) Is it in flow trajectory A ij If not, then the charging station pile p is considered to be located at the flow trajectory A. ij The vertical distance L ijp Calculate, if in, then L ijp =0; According to the distance formula between two points, the distance U between the charging station pile p and the center of the public station i collection area is considered. ip Perform calculations; S203: Q ij is the base, L ijp Construct an exponential function for the index, and consider the effect of building a charging station pile p on the flow trajectory A of the flow of people. ij Flow influence coefficient B ijp Perform calculations; U ip Ratio of building safety distance U´ ip Calculate, if U´ ip >1, then for U´ ip The reciprocal of F i The product between them is calculated to obtain the construction influence coefficient P of public station i on the construction of charging station pile p. ip , if U´ ip ≤1, then P ip =F i ; According to G p =min{1-(f1×B 1jp +f2×P 1p ),…,1-(f1×B ijp +f2×P ip )} Calculate the construction index of the charging station pile p to be built, where G p It represents the construction index of the charging station p to be built, f1 and f2 represent the relationship coefficients, and min represents the minimum value symbol; S204: For all G from p=1 to p=q p Perform a summation process, calculate the ratio between the summation result and the value q, and obtain the implementation feasibility coefficient E of the charging project; S30: After the declared charging project is implemented, obtain monitoring data, fault alarm data, and distribution facility operation data of the charging stations constructed within the construction area, integrate the acquired data, and build an operation information database; S40: The user views the operation information database through the vehicle-network interaction platform and determines the target charging station. The vehicle-network interaction platform generates a charging guidance trajectory based on the target charging station and feeds it back to the user's smart terminal; S50: The user selects whether to perform charging according to the charging guidance trajectory.

2. The vehicle networking platform method for integrating charging project management and vehicle-network interaction according to claim 1 is characterized by: The specific method of S102 for identifying the target change area and the concentrated area in each historical pedestrian flow heat map is: The concentrated area refers to the combined area of ​​the red blocks and orange blocks in the historical pedestrian flow heat map; The method for identifying the target change area is as follows: randomly select a historical pedestrian flow heat map, divide the boundary of the selected historical pedestrian flow heat map according to the color rendering situation, and obtain several boundary division areas. Each boundary division area is rendered by only one color, and use straight lines to connect the center of the collection area with the center of each boundary division area to obtain several gradient trajectories. The gradient trajectory points from the collection area to the boundary division area and the color depth of the gradient trajectory is from dark to shallow along the pointing direction. The number of color type changes of each gradient trajectory is obtained along the pointing direction of the gradient trajectory. The reciprocal of the number of color type changes of each gradient trajectory is used as the base, and the color depth sorting number of the color rendered by the boundary division area passed by each gradient trajectory is used as the exponent to construct an exponential function. The value coefficient of each gradient trajectory is calculated, and the gradient trajectory corresponding to the maximum value of the calculated value coefficient is used as the target gradient trajectory, and the boundary division area passed by the target gradient trajectory is used as the target change area.

3. The vehicle networking platform method for integrating charging project management and vehicle-network interaction according to claim 2 is characterized by: The S30 includes: S301: When E>the set threshold, the declared charging project is implemented; otherwise, the declared charging project is not implemented; When the declared charging project is implemented, obtain the historical monitoring data, historical fault alarm signal duration, and historical distribution facility operation data of the charging piles p built within the construction area; S302: Randomly select a historical fault alarm signal duration period [t, η], intercept the historical distribution facility operation data within the time period [t, η], and obtain the abnormal operation characteristics γ of the distribution facility of the charging station p within the time period [t, η] based on the changes in the intercepted historical distribution facility operation data. p(t→η) The specific method is: according to the collection interval z of historical monitoring data, the monitoring data collection time points in the time period [t,η] are numbered, and the numbering results are: d=1,2,…,k; k represents the total number, and in the time period [t,t+z], max{M pt ,M p(t+z) } and min{M pt ,M p(t+z) The difference between pt→p(t+z) Calculate and calculate M pt→p(t+z) The voltage variation coefficient T of the power distribution facilities of the charging station pile p in the time period [t, t+z] is calculated by comparing the voltage of the power distribution facilities of the charging station pile p with the voltage of the power distribution facilities of the charging station pile p in the time period [t, t+z]. p(t→t+z) , γ p(t→η) ={T p(t→t+z) ,T p(t+z→t+z×2) ,…,T p(t+η-z→t+η) }, where max represents the maximum value symbol, N1 represents the average working voltage of the power distribution facilities of the charging station P during normal operation, and M pt represents the operating voltage of the power distribution facilities of the charging station p at time t; S303: In the time period [t, η], the historical monitoring data of the charging station pile p is intercepted, and the abnormal operation characteristics ψ of the charging station pile p in the time period [t, η] are obtained according to the changes in the intercepted historical monitoring data. p(t→η) , ψ p(t→η) ={Y p(t→t+z) ,Y p(t+z→t+z×2) ,…,Y p(t+η-z→t+η) }, where Y p(t→t+z) represents the voltage variation coefficient of the charging station pile p in the time period [t, t+z]; S304: Build a real-time operation information database for the charging project.

4. The vehicle networking platform method for integrating charging project management and vehicle-network interaction according to claim 3 is characterized by: The specific method of constructing the real-time operation information database of the charging project in S304 is: The abnormal operation characteristics of the distribution facilities γ p(t→η) 、Abnormal operation characteristics ψ p(t→η) , and the charging station pile operation and maintenance personnel integrate the fault diagnosis results, fault maintenance plan and fault maintenance time of the charging station pile p to obtain a fault record, and merge and store all the obtained fault records to obtain a charging station pile fault database; Repeat the operations of steps S301 to S303 to determine the real-time abnormal operation characteristics and real-time abnormal operation characteristics of the power distribution facilities of the charging station pile p, calculate the similarity of the change trends between the determined real-time abnormal operation characteristics and real-time abnormal operation characteristics and the abnormal operation characteristics of the power distribution facilities recorded in each fault record stored in the charging station pile fault database, and calculate the average of the two calculated similarities to obtain the fault matching coefficient; If the fault matching coefficient is greater than or equal to the fault threshold, the fault maintenance time g stored in the fault record corresponding to the maximum value of the fault matching coefficient is obtained, and the operation information of the charging station p in the time period [l, l+g] is that the charging station p stops operating; If the fault matching coefficients are all less than the fault threshold, there is no need to obtain the fault maintenance time stored in the fault record corresponding to the maximum fault matching coefficient. The operation information of the charging station pile p in the time period [l, l+z] includes the normal operation of the charging station pile p, the average traffic coefficient X of the charging station pile p in the time period [l, l+z], and the average traffic coefficient X of the charging station pile p in the time period [l, l+z]. p(l→l+z) , the remaining charging time I of the charging station pile p at time l pl , X p(l→l+z) = E p(l-z→l) , where E p(l-z→l) represents the implementation feasibility coefficient of the charging station pile p in the time period [lz,l]; Traverse all charging stations and build a real-time operation information database for charging projects.

5. The vehicle networking platform method for integrating charging project management and vehicle-network interaction according to claim 4 is characterized by: The S40 includes: S401: A three-dimensional map of the construction area is obtained. The vehicle-network interactive platform marks each charging station to be constructed in the three-dimensional map and adds a marking index to each marked position. The marking index is the operation information of the charging station. S402: The user enters the vehicle-network interaction platform through the smart terminal. The vehicle-network interaction platform generates the user's driving trajectory π based on the positioning information of the smart terminal and the user's target location information; Based on the three-dimensional map, a charging station that is in normal operation is randomly selected. The shortest distance J between the selected charging station and the driving trajectory π is calculated. If 0 < J < o, the selected charging station is screened and retained. If J ≥ o, the selected charging station is screened and eliminated. Based on the selected charging stations, a set of charging stations to be selected is obtained, where o represents the maximum offset distance of the driving trajectory that the user can accept. S403: Randomly select a charging station from the selected charging station set, and calculate the matching coefficient between the selected charging station and the user. The specific calculation formula is: θ=exp(-|I´-τ| / I´)×X´ (l1→l1+τ) , where θ represents the matching coefficient between the selected charging station and the user, τ represents the time required for the user's vehicle to be charged to travel from the positioning position to the construction location of the selected charging station, I' represents the remaining charging time of the selected charging station, and X' (l1→l1+τ) represents the average traffic coefficient of the selected charging station in the time period [l1,l1+τ], where l1 represents the real-time time value corresponding to the location of the vehicle to be charged; Traverse the charging stations in the selected charging station set and select the charging station with the maximum matching coefficient as the target station; S404: With the positioning position of the user's smart terminal as the starting point and the construction position of the target station as the end point, a charging guidance trajectory is generated in the three-dimensional map, and the vehicle-network interaction platform feeds back the generated charging guidance trajectory to the user's smart terminal.

6. A vehicle networking platform system for integrated charging project management and vehicle-network interaction for implementing the vehicle networking platform method for integrated charging project management and vehicle-network interaction as described in any one of claims 1 to 5, characterized in that: The system includes a congestion index prediction module, a flow index prediction module, a charging project implementation feasibility prediction module, an operation information database construction module, a charging guidance trajectory generation module and a charging selection module; The congestion index prediction module is used to analyze and predict the congestion index of each public station; The flow index prediction module is used to analyze and predict the flow index between public stations; The charging project implementation feasibility prediction module is used to obtain the construction location coordinates of the charging station to be constructed within the construction area based on the declared charging project, and to predict the implementation feasibility coefficient of the charging project based on the congestion index of the target public station and the flow index between the target public station and other public stations; The operation information database construction module is used to integrate the monitoring data of the charging station piles, fault alarm data and distribution facility operation data and build an operation information database; The charging guide trajectory generation module is used to generate a charging guide trajectory; The charging selection module is used to select whether to perform a charging operation according to the user's selection of the charging guidance trajectory.

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