Charging field intelligent guiding system and method based on new energy automobile

Abstract

The invention relates to the technical field of charging field management and detection, in particular to a charging field intelligent guiding system and method based on a new energy automobile, and the system comprises a management layer, an analysis layer and an interaction layer. The user identity information is uploaded through the management layer and stored in the management layer, the management layer synchronously evaluates the parking charging risk of the user based on the user identity information, and the analysis layer receives the user parking charging risk evaluation result in the management layer in real time, analyzes the matching degree of each vacant parking space in a charging field and the user based on the evaluation result, and sends the result to the management layer. According to the invention, the parking charging risk of the user is evaluated by uploading the related parameters of the user, the adaptive matching of the charging parking space and the user is further realized based on the state and complexity analysis of the charging parking space in the charging field, the guiding service is provided for the parking charging of the user based on the configuration, the parking charging risk of the user is reduced, and the user experience is improved. And the friendly degree of the charging field to a new driver is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging field management and detection, and in particular to a charging field intelligent guidance system and method based on new energy vehicles. Background Art

[0002] New energy charging stations are places that provide charging services for new energy vehicles. They are generally equipped with multiple charging piles, which can charge multiple new energy vehicles at the same time. These charging piles are of different types, such as fast charging piles and slow charging piles, to meet the charging needs of different vehicles and the user's schedule. Their construction is of great significance to promoting the popularization and development of new energy vehicles, reducing carbon emissions, and promoting sustainable transportation.

[0003] The invention patent with application number 202011467101.1 discloses a method for guiding charging of new energy vehicles on highways, which is characterized by comprising the following steps: obtaining the current position and remaining mileage of the new energy vehicle; calculating the reachable charging service area based on the remaining mileage and the current position; when the new energy vehicle can only reach a preset number of service areas with its power, guiding the new energy vehicle to charge at the charging service area with the lowest discount coefficient and idle charging piles. When the remaining power of the new energy vehicle can only reach a preset number of service areas, a preset discount coefficient is assigned to each service area that can be reached by different new energy vehicles in real time, and the discount coefficients of each service area for the same new energy vehicle are different. The charging fee paid by the user is the product of the electricity fee calculated by the charging pile and the discount coefficient. The lowest discount coefficient is assigned to the service area where new energy vehicles are most expected to charge, and the highest discount coefficient is assigned to the service area where new energy vehicles are least expected to charge.

[0004] The application aims to solve the problem that "most current methods use the method of lowering nighttime electricity prices to guide new energy vehicles to charge at night to achieve the purpose of peak shaving and valley filling. This method of guiding new energy vehicles to charge is too simple and crude, and cannot achieve effective load scheduling and balancing."

[0005] Since new energy vehicles are easier to drive than manual fuel transmission vehicles, a large number of users with driver's licenses who have not purchased a car have flocked to the role of driver. However, their driving skills are often weaker than those of drivers with longer driving experience, which makes the parking and charging scenes in new energy charging fields more complicated and the process of parking and charging in charging fields more dangerous.

[0006] To this end, we proposed an intelligent guidance system for charging fields based on new energy vehicles. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an intelligent guidance system and method for a charging field based on new energy vehicles, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In the first aspect, an intelligent guidance system for charging field based on new energy vehicles includes: a management layer, an analysis layer and an interaction layer;

[0010] The user identity information is uploaded through the management layer and stored in the management layer. The management layer simultaneously evaluates the user's parking and charging risk based on the user identity information. The analysis layer receives the user's parking and charging risk assessment results in real time from the management layer, analyzes the matching degree between each idle parking space in the charging field and the user based on the assessment results, and decides on the idle parking space configured for the user based on the matching degree analysis results between each idle parking space and the user. The interaction layer further receives the idle parking space configured for the user in the analysis layer, and plans a parking and charging path for the user using the current user location information and the idle parking space location information, and then feeds back the planned parking and charging path to the user.

[0011] The analysis layer includes a receiving module, an analyzing module and a decision module. The receiving module is used to manage the user parking charging risk assessment results in the management layer. The analyzing module is used to obtain the user parking charging risk assessment results received by the receiving module, and analyze the matching degree between each free parking space in the charging field and the user based on the assessment results. The decision module is used to receive the analysis results of the matching degree between the free parking space and the user in the analysis module, and decide on the free parking space finally configured for the user through the analysis results.

[0012] The analysis logic of the matching degree between each vacant parking space in the charging field and the user in the analysis module is expressed as follows:

[0013]

[0014] Where: f(a) is the reference value of the matching degree between the free parking space a and the user; d(p a ,p0) is the shortest path distance between the position of the free parking space a and the current position of the user's car based on the parking lot plane model; k is the risk value of the user's parking and charging; θ(a) is the complexity of the free parking space a; τ1 and τ2 are normalization factors;

[0015] Among them, the larger the f(a) value is, the lower the matching degree between the vacant parking space and the user is. Conversely, the smaller the f(a) value is, the higher the matching degree between the vacant parking space and the user is. The normalization factors τ1 and τ2 are customized by the system end user, and the normalization factors τ1 and τ2 are both greater than 1, and τ1 < τ2, τ1 + τ2 = 3, θ(a) = Q × L central , the free parking space finally allocated to the user in the decision module is the free parking space with the highest matching degree with the user;

[0016] Q represents the occupancy coverage rate of adjacent parking spaces around the vacant parking spaces, L centralIndicates the center rate of the free parking spaces based on the channel model in the parking lot plane model;

[0017] The occupancy coverage rate Q of the adjacent parking spaces around the vacant parking space is: x represents the number of occupied adjacent parking spaces in a 3*3 range with an empty parking space as the center; the empty parking space is based on the center rate L of the channel model in the parking lot plane model. central It is: the edge channel nodes in the channel model are adjacent and connected to each other to form a closed graph. Taking the closed graph as a reference, the center rate of the idle parking spaces.

[0018] Furthermore, the management layer includes an upload module, a storage module and an evaluation module, the upload module is used to upload user identity information, the storage module is used to receive the user identity information uploaded in the upload module and store the user identity information, and the evaluation module is used to traverse the user identity information stored in the storage module and use the user identity information to evaluate the user's parking and charging risk;

[0019] Among them, the operation of uploading user identity information in the upload module, that is, the user connects to the network to access the system through a mobile device, and manually enters the identity information in the upload module of the system. The user identity information includes: user name, contact information, driver's license information surface image, and vehicle registration certificate information surface image. When the storage module stores the user identity information, the user identity information is distinguished and stored based on the user name in the user identity information.

[0020] Furthermore, before assessing the risk of parking and charging for a user, the assessment module extracts the following information from the driver's license information surface image and vehicle registration license information surface image of the user's identity information based on the OCR recognition algorithm: the date of first receipt of the user's driver's license, the type of vehicle permitted to be driven, and the user's date of birth; the registration date of the user's vehicle registration license, the city of receipt, and the vehicle inspection validity period;

[0021] The user parking charging risk evaluated by the evaluation module is sent to the storage module in real time and placed in the storage location corresponding to the user identity information in the storage module.

[0022] Furthermore, the user parking charging risk assessment logic in the assessment module is expressed as:

[0023]

[0024] Where: k is the risk value of the user parking for charging; q is the cumulative number of times the user drives into the charging station; t enrol The registration time of the user's driving license; t fitst The time when the user first received the driver's license; t now is the current time; t eff is the vehicle inspection validity period; γ is the impact factor;

[0025] Among them, the smaller the user parking and charging risk value k is, the lower the user parking and charging risk is, and vice versa, the higher the user parking and charging risk is. A continuous operation cycle is set in the evaluation module. The evaluation module continuously evaluates the user parking and charging risk based on the continuous operation cycle, and iterates the user parking and charging risk corresponding to the user identity information storage position in the storage module with the latest evaluated user parking and charging risk in real time. The continuous operation cycle set in the evaluation module is customized by the system user, and the continuous operation cycle is set with the number of times the user drives the vehicle into the charging field as the continuous operation cycle measurement unit.

[0026] Furthermore, the value of the impact factor γ is subject to:

[0027]

[0028] Where: ε is the reference value; m is the total number of types of vehicles that the user's driver's license allows; d(p1,p2) is the distance between the city where the user's driver's license is obtained, p1, and the city where the parking lot is located, p2; ω is the weight;

[0029] Among them, weight ω∈(0,1), weight ω is set based on user age, the closer the user age is to the interval of 25-35, the closer the user age is to any set of end values ​​in the interval, the larger the weight value is, conversely, the smaller the weight value is, the user age is in the interval of 25-35, the more the user age is in the middle position in the interval, the larger the weight value is, conversely, the smaller the value is, the maximum weight in the interval of 25-35 is greater than the maximum weight outside the interval, the minimum weight outside the interval of 25-35 is less than the minimum weight in the interval, d(p1,p2)≥1, when the city where the user receives the driving license and the city where the parking lot is located are the same city, d(p1,p2)=1, influence factor γ∈(0,1), influence factor γ is inversely proportional to the reference value ε, the larger the reference value ε, the smaller the influence factor γ.

[0030] Furthermore, the analysis module stores a parking lot plane model, which is integrated by a parking space model and a passage model in the parking lot. The parking space model is represented based on blocks, and the passage model is represented based on a combination of polylines. A group of distance measuring sensors are deployed at the center of each parking space in the parking lot. When the distance measurement result of the distance measuring sensor is less than one meter, the parking space where the distance measuring sensor is located is determined to be an occupied parking space. Otherwise, it is determined to be an empty parking space, that is, an unoccupied parking space.

[0031] The distance measurement sensor sends the distance measurement result to the parking lot plane model stored in the analysis module in real time. The blocks representing the parking space model in the parking lot plane model are rendered in two different colors to distinguish the parking space occupancy status. The vacant parking spaces determined based on the colors of the blocks representing the parking space model in the parking lot plane model are used for the analysis of the matching degree between the vacant parking spaces and the users in the analysis module.

[0032] When a user drives a car through a parking lot gate, the car's onboard navigation system will provide real-time feedback of the car's current location information to the analysis module.

[0033] Furthermore, the interaction layer includes a planning module, a transmission module and an iteration module. The planning module is used to receive the vacant parking spaces configured for the user in the analysis layer, and plan the parking and charging path based on the location information of the vacant parking spaces and the current location information of the user's car. The transmission module is used to receive the parking and charging path planned in the planning module, and feed back the parking and charging path to the control panel of the user's car. The iteration module is used to set the iteration frequency, and control the transmission module to run continuously based on the iteration frequency.

[0034] Among them, after receiving the parking charging path fed back by the transmission module, the control panel of the user's car displays the parking charging path, and marks the direction of the user's car driving in the parking charging path. The direction of the user's car driving is determined based on the two latest sets of continuous car position information. The iteration frequency set in the iteration module is customized by the system user, and the iteration frequency is not less than 60 times / min. The transmission module is in a continuous operating state based on the iteration frequency, and each operation feeds back a new set of parking charging paths to the control panel of the user's car.

[0035] Furthermore, when the parking charging path in the planning module performs the planning operation, the channel model where the free parking space location information and the current location information of the user's car are located is identified in the channel model of the parking lot plane model. When the channel model is unique, the identified channel model is the parking charging path planned by the planning module. When the channel model is not unique, the channel model is selected based on the following formula, and the selected channel model is used as the parking charging path;

[0036] The choice of the channel model is subject to:

[0037]

[0038] Where: χ is the selection tendency value of the channel model; d min is the length of the shortest section among the sections divided based on the turning points in the channel model; d is the total length of the channel model; u is the total amount of parking on both sides of the channel model; t v is the parking time of the vth vehicle;

[0039] Among them, based on the above formula, the selection tendency value of each channel model is calculated, and a group of channel models with the largest tendency value is selected as the final selection result of the planning module. Table pair Seeking average.

[0040] Furthermore, the receiving module is interactively connected to the evaluation module via a wireless network, the evaluation module is interactively connected to the storage module and the upload module via a wireless network, the receiving module is interactively connected to the analysis module and the decision module via a wireless network, the decision module is interactively connected to the planning module via a wireless network, the planning module is interactively connected to the transmission module and the iteration module via a wireless network, and the iteration module is interactively connected to the transmission module via a wireless network.

[0041] In a second aspect, a charging field intelligent guidance method based on a new energy vehicle comprises the following steps:

[0042] Step 1: Upload user identity information, store the user identity information, and simultaneously assess the user's parking and charging risk based on the user identity information;

[0043] Step 11: Setting and applying the user parking charging risk assessment logic;

[0044] Step 2: Obtain the user's parking charging risk assessment results, build a charging field plane model, and identify idle parking spaces and non-idle parking spaces based on the charging field plane model;

[0045] Step 3: Analyze the matching degree between the user and each vacant parking space according to the user parking and charging risk assessment result, and select the vacant parking space configuration and user with the highest matching degree;

[0046] Step 31: Setting and applying the logic of analyzing the matching degree between users and available parking spaces;

[0047] Step 4: Obtain a parking charging path in the charging field plane model according to the selected vacant parking space and the current location information of the user's car;

[0048] Step 5: Setting and applying the parking charging path selection logic;

[0049] Step 6: Apply the parking charging path selection logic to select the parking charging path, and interact with the control panel of the user's car in real time to display the parking charging path on the control panel.

[0050] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0051] Beneficial effects:

[0052] The present invention provides an intelligent guidance system for charging fields based on new energy vehicles. During operation, the system evaluates the risk of users parking for charging by uploading relevant parameters of users, and further achieves adaptive matching between charging spaces and users based on the analysis of the status and complexity of charging spaces in the charging field. Based on this configuration, guidance services are provided for users parking for charging, thereby reducing the risk of users parking for charging, and further improving the "friendliness" of charging fields for new drivers. In addition, the system provides management effects for charging piles deployed in charging spaces in charging fields to a certain extent, and at the same time, based on the execution of an intelligent guidance method for charging fields based on new energy vehicles, further operation logic support is provided for the above system, thereby ensuring a more stable application of the technical solution composed of the above system and method. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 It is a structural schematic diagram of an intelligent guidance system for charging field based on new energy vehicles;

[0055] Figure 2 A schematic diagram of a flow chart of an intelligent guidance method for a charging field based on new energy vehicles;

[0056] Figure 3 It is a schematic diagram of the plane model of the charging field in the present invention;

[0057] Figure 4 is the occupancy coverage rate Q of the adjacent parking spaces around the vacant parking spaces in the present invention, and the center rate L of the vacant parking spaces based on the channel model in the parking lot plane model central Example diagram of value-taking logic;

[0058] The numbers in the figure represent: 1. Examples of free parking spaces matching the user. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] The present invention will be further described below in conjunction with the embodiments.

[0061] Embodiment 1:

[0062] The present embodiment is a charging field intelligent guidance system based on new energy vehicles, such as Figure 1 As shown, it includes: management layer, analysis layer and interaction layer;

[0063] The user identity information is uploaded through the management layer and stored in the management layer. The management layer simultaneously evaluates the user's parking and charging risk based on the user identity information. The analysis layer receives the user's parking and charging risk assessment results in real time from the management layer, analyzes the matching degree between each idle parking space in the charging field and the user based on the assessment results, and decides on the idle parking space configured for the user based on the matching degree analysis results between each idle parking space and the user. The interaction layer further receives the idle parking space configured for the user in the analysis layer, and plans a parking and charging path for the user using the current user location information and the idle parking space location information, and then feeds back the planned parking and charging path to the user.

[0064] The management layer includes an upload module, a storage module and an evaluation module. The upload module is used to upload user identity information. The storage module is used to receive the user identity information uploaded in the upload module and store the user identity information. The evaluation module is used to traverse the user identity information stored in the storage module and use the user identity information to evaluate the user's parking and charging risk.

[0065] Among them, the operation of uploading user identity information in the upload module, that is, the user connects to the network to access the system through a mobile device, and manually enters the identity information in the upload module of the system. The user identity information includes: user name, contact information, driver's license information surface image, and vehicle registration certificate information surface image. When the storage module stores the user identity information, the user identity information is distinguished and stored based on the user name in the user identity information;

[0066] The analysis layer includes a receiving module, an analysis module and a decision module. The receiving module is used to manage the user parking charging risk assessment results in the management layer. The analysis module is used to obtain the user parking charging risk assessment results received by the receiving module, and analyze the matching degree between each free parking space in the charging field and the user based on the assessment results. The decision module is used to receive the analysis results of the matching degree between the free parking space and the user in the analysis module, and decide on the free parking space finally configured for the user through the analysis results.

[0067] The analysis logic of the matching degree between each vacant parking space in the charging field and the user in the analysis module is expressed as:

[0068]

[0069] Where: f(a) is the reference value of the matching degree between the free parking space a and the user; d(p a,p0) is the shortest path distance between the position of the free parking space a and the current position of the user's car based on the parking lot plane model; k is the risk value of the user's parking and charging; θ(a) is the complexity of the free parking space a; τ1 and τ2 are normalization factors;

[0070] Among them, the larger the f(a) value is, the lower the matching degree between the vacant parking space and the user is. Conversely, the smaller the f(a) value is, the higher the matching degree between the vacant parking space and the user is. The normalization factors τ1 and τ2 are customized by the system end user, and the normalization factors τ1 and τ2 are both greater than 1, and τ1 < τ2, τ1 + τ2 = 3, θ(a) = Q × L central , the free parking space finally allocated to the user in the decision module is the free parking space with the highest matching degree with the user;

[0071] Q represents the occupancy coverage rate of adjacent parking spaces around vacant parking spaces, L central Indicates the center rate of the free parking spaces based on the channel model in the parking lot plane model;

[0072] The occupancy coverage rate Q of adjacent parking spaces around the vacant parking spaces is: x represents the number of occupied adjacent parking spaces in a 3*3 range with an empty parking space as the center; the empty parking space is based on the center rate L of the channel model in the parking lot plane model. central The edge channel nodes in the channel model are adjacent and connected to form a closed graph. The closed graph is used as a reference to the center rate of the free parking spaces.

[0073] The interaction layer includes a planning module, a transmission module and an iteration module. The planning module is used to receive the idle parking spaces configured for the user in the analysis layer, and plan the parking and charging path based on the location information of the idle parking spaces and the current location information of the user's car. The transmission module is used to receive the parking and charging path planned in the planning module, and feed back the parking and charging path to the control panel of the user's car. The iteration module is used to set the iteration frequency, and control the transmission module to run continuously based on the iteration frequency.

[0074] Among them, after receiving the parking charging path fed back by the transmission module, the control panel of the user's car displays the parking charging path, and marks the direction of the user's car in the parking charging path. The direction of the user's car is determined based on the latest two sets of continuous car position information. The iteration frequency set in the iteration module is customized by the system end user, and the iteration frequency is not less than 60 times / min. The transmission module is in a continuous operation state based on the iteration frequency, and each operation feeds back a new set of parking charging paths to the control panel of the user's car.

[0075] The receiving module is interactively connected to the evaluation module through a wireless network, the evaluation module is interactively connected to the storage module and the upload module through a wireless network, the receiving module is interactively connected to the analysis module and the decision module through a wireless network, the decision module is interactively connected to the planning module through a wireless network, the planning module is interactively connected to the transmission module and the iteration module through a wireless network, and the iteration module is interactively connected to the transmission module through a wireless network.

[0076] In this embodiment, the upload module is executed to upload user identity information, the storage module synchronously receives the user identity information uploaded in the upload module, and stores the user identity information. The evaluation module is post-operated to traverse the user identity information stored in the storage module, and the user identity information is used to evaluate the user's parking and charging risk. The receiving module further manages the user's parking and charging risk assessment results in the management layer, and then the analysis module obtains the user's parking and charging risk assessment results received by the receiving module, and analyzes the matching degree between each idle parking space in the charging field and the user based on the assessment result. The decision module synchronously receives the analysis result of the matching degree between the idle parking space and the user in the analysis module, and decides the idle parking space finally configured for the user through the analysis result. The planning module further receives the idle parking space configured for the user in the analysis layer, and plans the parking and charging path based on the idle parking space location information and the current location information of the user's driving car. The transmission module receives the parking and charging path planned in the planning module in real time, and feeds back the parking and charging path to the control panel of the user's driving car. Finally, the iteration frequency is set by the iteration module, and the transmission module is controlled to run continuously based on the iteration frequency.

[0077] Through the operation of the system in the above embodiment, an effective and reliable guidance effect is provided for users with charging needs after they drive into the charging field, ensuring that the charging parking spaces in the charging field can better adapt to the driving level of the driving users, making the charging field more "friendly" to novices.

[0078] See also Figure 4 As shown in the figure Figure 3 The figure is intercepted and rendered with filled and unfilled to distinguish between vacant parking spaces and occupied parking spaces. Based on the map (a), the application parameters of the occupancy coverage rate Q of adjacent parking spaces around the vacant parking spaces are shown in determining the value. Based on the map (b) and the arrows therein, the logic of determining the value of the vacant parking spaces based on the center rate of the channel model in the parking lot plane model is shown.

[0079] Embodiment 2:

[0080] In terms of specific implementation, based on the first embodiment, this embodiment refers to Figure 1 The intelligent guidance system for charging field based on new energy vehicles in Example 1 is further described in detail:

[0081] Before assessing the risk of parking and charging, the assessment module extracts the following information from the driver's license information surface image and vehicle registration license information surface image of the user's identity information based on the OCR recognition algorithm: the date of first receipt of the user's driver's license, the type of vehicle allowed to be driven, the user's date of birth; the registration date of the user's vehicle registration license, the city of receipt, and the vehicle inspection validity period;

[0082] The user parking charging risk evaluated by the evaluation module is sent to the storage module in real time and placed in the storage location corresponding to the user identity information in the storage module;

[0083] The user parking charging risk assessment logic in the assessment module is expressed as:

[0084]

[0085] Where: k is the risk value of the user parking for charging; q is the cumulative number of times the user drives into the charging station; t enrol The registration time of the user's driving license; t fitst The time when the user first received the driver's license; t now is the current time; t eff is the vehicle inspection validity period; γ is the impact factor;

[0086] The smaller the user parking charging risk value k is, the lower the user parking charging risk is, and vice versa. The evaluation module is provided with a continuous operation cycle. The evaluation module continuously evaluates the user parking charging risk based on the continuous operation cycle, and iterates the user parking charging risk corresponding to the user identity information storage position in the storage module with the latest evaluated user parking charging risk in real time. The continuous operation cycle set in the evaluation module is customized by the system end user, and the continuous operation cycle is set with the number of times the user drives the vehicle into the charging field as the continuous operation cycle measurement unit;

[0087] The value of the impact factor γ follows:

[0088]

[0089] Where: ε is the reference value; m is the total number of types of vehicles that the user's driver's license allows; d(p1,p2) is the distance between the city where the user's driver's license is obtained, p1, and the city where the parking lot is located, p2; ω is the weight;

[0090] Among them, weight ω∈(0,1), weight ω is set based on user age, the closer the user age is to the interval of 25-35, the closer the user age is to any set of end values ​​in the interval, the larger the weight value is, conversely, the smaller the weight value is, the user age is in the interval of 25-35, the more the user age is in the middle position in the interval, the larger the weight value is, conversely, the smaller the value is, the maximum weight in the interval of 25-35 is greater than the maximum weight outside the interval, the minimum weight outside the interval of 25-35 is less than the minimum weight in the interval, d(p1,p2)≥1, when the city where the user receives the driving license and the city where the parking lot is located are the same city, d(p1,p2)=1, influence factor γ∈(0,1), influence factor γ is inversely proportional to the reference value ε, the larger the reference value ε, the smaller the influence factor γ.

[0091] In this embodiment, through the above settings, further operation data support is provided for the system in Example 1, and at the same time, the user parking charging risk assessment logic in the assessment module is further digitally limited, providing further data reference for system background users.

[0092] like Figure 1 As shown, the analysis module stores a parking lot plane model, which is integrated by a parking space model and a passage model in the parking lot. The parking space model is represented based on blocks, and the passage model is represented based on a combination of polylines. A group of distance measuring sensors are deployed at the center of each parking space in the parking lot. When the distance measurement result of the distance measuring sensor is less than one meter, the parking space where the distance measuring sensor is located is determined to be an occupied parking space. Otherwise, it is determined to be an empty parking space, that is, an unoccupied parking space.

[0093] The distance measurement sensor sends the distance measurement result to the parking lot plane model stored in the analysis module in real time. The blocks representing the parking space model in the parking lot plane model are rendered in two different colors to distinguish the parking space occupancy status. The vacant parking spaces determined based on the colors of the blocks representing the parking space model in the parking lot plane model are used for the analysis of the matching degree between the vacant parking spaces and the users in the analysis module.

[0094] When a user drives a car through a parking lot gate, the car's onboard navigation system will provide real-time feedback of the car's current location information to the analysis module.

[0095] Through the above settings, a specified construction logic is provided for the parking lot plane model applied when the analysis module is run.

[0096] like Figure 1As shown, when the parking charging path in the planning module performs the planning operation, the channel model where the free parking space location information and the current location information of the user's car are located is identified in the channel model of the parking lot plane model. When the channel model is unique, the identified channel model is the parking charging path planned by the planning module. When the channel model is not unique, the channel model is selected based on the following formula, and the selected channel model is used as the parking charging path;

[0097] The choice of channel model is subject to:

[0098]

[0099] Where: χ is the selection tendency value of the channel model; d min is the length of the shortest section among the sections divided based on the turning points in the channel model; d is the total length of the channel model; u is the total amount of parking on both sides of the channel model; t v is the parking time of the vth vehicle;

[0100] Among them, based on the above formula, the selection tendency value of each channel model is calculated, and a group of channel models with the largest tendency value is selected as the final selection result of the planning module. Table pair Seeking average.

[0101] Through the above logic formula, the interaction layer of the system provides necessary selection logic support for the selection of parking charging path.

[0102] Embodiment three:

[0103] In terms of specific implementation, based on the first embodiment, this embodiment refers to Figure 2 The intelligent guidance system for charging field based on new energy vehicles in the first embodiment is further described in detail:

[0104] A charging field intelligent guidance method based on new energy vehicles comprises the following steps:

[0105] Step 1: Upload user identity information, store the user identity information, and simultaneously assess the user's parking and charging risk based on the user identity information;

[0106] Step 11: Setting and applying the user parking charging risk assessment logic;

[0107] Step 2: Obtain the user's parking charging risk assessment results, build a charging field plane model, and identify idle parking spaces and non-idle parking spaces based on the charging field plane model;

[0108] Step 3: Analyze the matching degree between the user and each vacant parking space according to the user parking and charging risk assessment result, and select the vacant parking space configuration and user with the highest matching degree;

[0109] Step 31: Setting and applying the logic of analyzing the matching degree between users and available parking spaces;

[0110] Step 4: Obtain a parking charging path in the charging field plane model according to the selected vacant parking space and the current location information of the user's car;

[0111] Step 5: Setting and applying the parking charging path selection logic;

[0112] Step 6: Apply the parking charging path selection logic to select the parking charging path, and interact with the control panel of the user's car in real time to display the parking charging path on the control panel.

[0113] In summary, during operation, the system in the above embodiment can evaluate the risk of user parking and charging by uploading relevant parameters of the user, and further achieve adaptive matching between the charging spaces and the users based on the status and complexity analysis of the charging spaces in the charging field. Based on this configuration, guidance services are provided to users for parking and charging, thereby reducing the risk of user parking and charging, and further improving the "friendliness" of the charging field for new drivers. In addition, the system can provide management effects for the charging piles deployed in the charging spaces in the charging field to a certain extent. At the same time, based on the execution of the method, further operation logic support is provided for the above system, thereby ensuring a more stable application of the technical solution composed of the above system and method.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent guidance system for charging field based on new energy vehicles, characterized in that: include: Management, analysis and interaction layers; The user identity information is uploaded through the management layer and stored in the management layer. The management layer simultaneously evaluates the user's parking and charging risk based on the user identity information. The analysis layer receives the user's parking and charging risk assessment results in real time from the management layer, analyzes the matching degree between each idle parking space in the charging field and the user based on the assessment results, and decides on the idle parking space configured for the user based on the matching degree analysis results between each idle parking space and the user. The interaction layer further receives the idle parking space configured for the user in the analysis layer, and plans a parking and charging path for the user using the current user location information and the idle parking space location information, and then feeds back the planned parking and charging path to the user. The analysis layer includes a receiving module, an analyzing module and a decision module. The receiving module is used to manage the user parking charging risk assessment results in the management layer. The analyzing module is used to obtain the user parking charging risk assessment results received by the receiving module, and analyze the matching degree between each free parking space in the charging field and the user based on the assessment results. The decision module is used to receive the analysis results of the matching degree between the free parking space and the user in the analysis module, and decide on the free parking space finally configured for the user through the analysis results. The analysis logic of the matching degree between each vacant parking space in the charging field and the user in the analysis module is expressed as follows: Where: f(a) is the reference value of the matching degree between the free parking space a and the user; d(p a ,p0) is the shortest path distance between the position of the free parking space a and the current position of the user's car based on the parking lot plane model; k is the risk value of the user's parking and charging; θ(a) is the complexity of the free parking space a; τ1 and τ2 are normalization factors; Among them, the larger the f(a) value is, the lower the matching degree between the vacant parking space and the user is. Conversely, the smaller the f(a) value is, the higher the matching degree between the vacant parking space and the user is. The normalization factors τ1 and τ2 are customized by the system end user, and the normalization factors τ1 and τ2 are both greater than 1, and τ1 < τ2, τ1 + τ2 = 3, θ(a) = Q × L central , the free parking space finally allocated to the user in the decision module is the free parking space with the highest matching degree with the user; Q represents the occupancy coverage rate of adjacent parking spaces around the vacant parking spaces, L central Indicates the center rate of the free parking spaces based on the channel model in the parking lot plane model; The occupancy coverage rate Q of the adjacent parking spaces around the vacant parking space is: x represents the number of occupied adjacent parking spaces in a 3*3 range with an empty parking space as the center; the empty parking space is based on the center rate L of the channel model in the parking lot plane model. central It is: the edge channel nodes in the channel model are adjacent and connected to each other to form a closed graph. Taking the closed graph as a reference, the center rate of the idle parking spaces.

2. According to claim 1, a charging field intelligent guidance system based on new energy vehicles is characterized in that: The management layer includes an upload module, a storage module and an evaluation module. The upload module is used to upload user identity information. The storage module is used to receive the user identity information uploaded by the upload module and store the user identity information. The evaluation module is used to traverse the user identity information stored in the storage module and use the user identity information to evaluate the user's parking and charging risk. Among them, the operation of uploading user identity information in the upload module, that is, the user connects to the network to access the system through a mobile device, and manually enters the identity information in the upload module of the system. The user identity information includes: user name, contact information, driver's license information surface image, and vehicle registration certificate information surface image. When the storage module stores the user identity information, the user identity information is distinguished and stored based on the user name in the user identity information.

3. According to claim 2, a charging field intelligent guidance system based on new energy vehicles is characterized in that: Before assessing the risk of parking and charging for a user, the assessment module extracts the following information from the driver's license information surface image and vehicle registration license information surface image of the user's identity information based on the OCR recognition algorithm: the date of first receipt of the user's driver's license, the type of vehicle permitted to be driven, and the user's date of birth; the registration date of the user's vehicle registration license, the city of receipt, and the vehicle inspection validity period; The user parking charging risk evaluated by the evaluation module is sent to the storage module in real time and placed in the storage location corresponding to the user identity information in the storage module.

4. According to claim 2, a charging field intelligent guidance system based on new energy vehicles is characterized in that: The user parking charging risk assessment logic in the assessment module is expressed as: Where: k is the risk value of the user parking for charging; q is the cumulative number of times the user drives into the charging station; t enrol The registration time of the user's driving license; t fitst The time when the user first received the driver's license; t now is the current time; t eff The validity period of the vehicle inspection; γ is the impact factor; Among them, the smaller the user parking and charging risk value k is, the lower the user parking and charging risk is, and vice versa, the higher the user parking and charging risk is. A continuous operation cycle is set in the evaluation module. The evaluation module continuously evaluates the user parking and charging risk based on the continuous operation cycle, and iterates the user parking and charging risk corresponding to the user identity information storage position in the storage module with the latest evaluated user parking and charging risk in real time. The continuous operation cycle set in the evaluation module is customized by the system user, and the continuous operation cycle is set with the number of times the user drives the vehicle into the charging field as the continuous operation cycle measurement unit.

5. According to claim 4, a charging field intelligent guidance system based on new energy vehicles is characterized in that: The value of the impact factor γ is subject to: Where: ε is the reference value; m is the total number of types of vehicles that the user's driver's license allows; d(p1,p2) is the distance between the city where the user's driver's license is obtained, p1, and the city where the parking lot is located, p2; ω is the weight; Among them, weight ω∈(0,1), weight ω is set based on user age, the closer the user age is to the interval of 25-35, the closer the user age is to any set of end values ​​in the interval, the larger the weight value is, conversely, the smaller the weight value is, the user age is in the interval of 25-35, the more the user age is in the middle position in the interval, the larger the weight value is, conversely, the smaller the value is, the maximum weight in the interval of 25-35 is greater than the maximum weight outside the interval, the minimum weight outside the interval of 25-35 is less than the minimum weight in the interval, d(p1,p2)≥1, when the city where the user receives the driving license and the city where the parking lot is located are the same city, d(p1,p2)=1, influence factor γ∈(0,1), influence factor γ is inversely proportional to the reference value ε, the larger the reference value ε, the smaller the influence factor γ.

6. According to claim 1, a charging field intelligent guidance system based on new energy vehicles is characterized in that: The analysis module stores a parking lot plane model, which is integrated by a parking space model and a passage model in the parking lot. The parking space model is represented based on blocks, and the passage model is represented based on a combination of polylines. A group of distance measuring sensors are deployed at the center of each parking space in the parking lot. When the distance measurement result of the distance measuring sensor is less than one meter, the parking space where the distance measuring sensor is located is determined to be an occupied parking space. Otherwise, it is determined to be an empty parking space, that is, an unoccupied parking space. The distance measurement sensor sends the distance measurement result to the parking lot plane model stored in the analysis module in real time. The blocks representing the parking space model in the parking lot plane model are rendered in two different colors to distinguish the parking space occupancy status. The vacant parking spaces determined based on the colors of the blocks representing the parking space model in the parking lot plane model are used for the analysis of the matching degree between the vacant parking spaces and the users in the analysis module. When a user drives a car through a parking lot gate, the car's onboard navigation system will provide real-time feedback of the car's current location information to the analysis module.

7. According to claim 1, a charging field intelligent guidance system based on new energy vehicles is characterized in that: The interaction layer includes a planning module, a transmission module and an iteration module. The planning module is used to receive the idle parking spaces configured for the user in the analysis layer, and plan the parking and charging path based on the location information of the idle parking spaces and the current location information of the user's car. The transmission module is used to receive the parking and charging path planned in the planning module, and feed back the parking and charging path to the control panel of the user's car. The iteration module is used to set the iteration frequency, and control the transmission module to run continuously based on the iteration frequency. Among them, after receiving the parking charging path fed back by the transmission module, the control panel of the user's car displays the parking charging path, and marks the direction of the user's car driving in the parking charging path. The direction of the user's car driving is determined based on the two latest sets of continuous car position information. The iteration frequency set in the iteration module is customized by the system user, and the iteration frequency is not less than 60 times / min. The transmission module is in a continuous operating state based on the iteration frequency, and each operation feeds back a new set of parking charging paths to the control panel of the user's car.

8. The intelligent guidance system for charging field based on new energy vehicles according to claim 7 is characterized in that: When the parking charging path in the planning module performs the planning operation, the channel model where the free parking space location information and the current location information of the user's car are located is identified in the channel model of the parking lot plane model. When the channel model is unique, the identified channel model is the parking charging path planned by the planning module. When the channel model is not unique, the channel model is selected based on the following formula, and the selected channel model is used as the parking charging path; The choice of the channel model is subject to: Where: χ is the selection tendency value of the channel model; d min is the length of the shortest section among the sections divided based on the turning points in the channel model; d is the total length of the channel model; u is the total amount of parking on both sides of the channel model; t v is the parking time of the vth vehicle; Among them, based on the above formula, the selection tendency value of each channel model is calculated, and a group of channel models with the largest tendency value is selected as the final selection result of the planning module. Table pair Seeking the average.

9. The intelligent guidance system for charging field based on new energy vehicles according to claim 1 is characterized in that: The receiving module is interactively connected to the evaluation module via a wireless network, the evaluation module is interactively connected to the storage module and the upload module via a wireless network, the receiving module is interactively connected to the analysis module and the decision module via a wireless network, the decision module is interactively connected to the planning module via a wireless network, the planning module is interactively connected to the transmission module and the iteration module via a wireless network, and the iteration module is interactively connected to the transmission module via a wireless network.

10. A method for intelligent guidance of a charging field based on a new energy vehicle, the method being an implementation method of an intelligent guidance system for a charging field based on a new energy vehicle as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Upload user identity information, store the user identity information, and simultaneously assess the user's parking and charging risk based on the user identity information; Step 11: Setting and applying the user parking charging risk assessment logic; Step 2: Obtain the user's parking charging risk assessment results, build a charging field plane model, and identify idle parking spaces and non-idle parking spaces based on the charging field plane model; Step 3: Analyze the matching degree between the user and each vacant parking space according to the user parking and charging risk assessment result, and select the vacant parking space configuration and user with the highest matching degree; Step 31: Setting and applying the logic of analyzing the matching degree between users and available parking spaces; Step 4: Obtain a parking charging path in the charging field plane model according to the selected vacant parking space and the current location information of the user's car; Step 5: Setting and applying the parking charging path selection logic; Step 6: Apply the parking charging path selection logic to select the parking charging path, and interact with the control panel of the user's car in real time to display the parking charging path on the control panel.

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