Electric vehicle charging station ordered charging method based on intelligent queuing

Abstract

The invention discloses an electric vehicle charging station ordered charging method based on intelligent queuing, which comprises the following steps: acquiring a remote charging queue adding request, and extracting a vehicle identifier and current position information; a sliding window mechanism is adopted, a fixed time interval is set, charging station traffic flow and charging pile state change data are collected, and the estimated waiting time is used for updating queuing information; according to the distance between the vehicle and the charging station, the priority queue algorithm is adopted to allocate the charging pile position in combination with the predicted waiting time, and it is ensured that the closer vehicle preferentially obtains the charging opportunity; acquiring position information of a plurality of vehicles entering the range of the charging station at the same time, processing the requests by using a timestamp sorting algorithm based on vehicle entering time, and determining a guiding sequence of each vehicle; and generating a navigation path from the current position to the specified charging pile position according to the guiding sequence and the distributed charging pile position, and sending path information to the vehicle-mounted navigation system. The charging efficiency can be remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent charging, and in particular relates to an orderly charging method for an electric vehicle charging station based on intelligent queuing. Background Art

[0002] In the intelligent queuing system of electric vehicle charging stations, there is a technical contradiction: how to find a balance between ensuring the convenience of car owners to join the charging queue remotely and the accuracy of real-time updated queue information. After the car owner remotely joins the charging queue through the mobile phone app, the system needs to update the number of people in the queue and the estimated waiting time at the current station in real time so that the car owner can plan the charging plan in advance. However, since the traffic volume and charging pile status of the charging station may change at any time, the system needs to update this information frequently to ensure that the data obtained by the car owner is accurate. This raises a question: how can the system update information frequently while avoiding data delays or system overload caused by too frequent data updates.

[0003] In addition, when the system automatically identifies the vehicle distance and assigns the charging station, it needs to ensure that the on-board navigation can trigger the guidance program in time to guide the vehicle to the designated charging station. However, when multiple vehicles enter the charging station range at the same time, the system needs to process these requests quickly and ensure that each vehicle can be accurately guided to the corresponding charging station. This involves another problem: how can the system ensure that the guidance process of each vehicle proceeds smoothly without guidance errors or delays while processing multiple vehicle requests?

[0004] In the prior art, the above problems have not been solved. Summary of the invention

[0005] The present invention proposes an orderly charging method for an electric vehicle charging station based on intelligent queuing to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides an orderly charging method for electric vehicle charging stations based on intelligent queuing, comprising the following steps:

[0007] Get a remote request to join the charging queue and extract the vehicle identifier and current location information;

[0008] Calculate the estimated waiting time based on the number of people currently queuing at the charging station, the status of the charging pile and the current location information, and store the estimated waiting time and vehicle identifier;

[0009] According to the preset time interval, a sliding window mechanism is used to collect the charging station traffic flow and charging pile status change data, and update the estimated waiting time;

[0010] Based on the distance between the vehicle and the charging station and the expected waiting time, a priority queue algorithm is used to allocate charging piles, so that vehicles that are closer have priority in getting charging opportunities.

[0011] When several vehicles enter the charging station range at the same time, these requests are processed using a timestamp sorting algorithm based on the vehicle entry time to determine the booting order for each vehicle;

[0012] Based on the guidance sequence and the assigned charging pile positions, a navigation path from the current position to the designated charging pile position is generated, and the path information is sent to the on-board navigation system.

[0013] Preferably, the method further includes updating the charging pile status:

[0014] When queuing, if the status of the charging pile changes, the update mechanism is immediately triggered to update, and the number of queue members and the estimated waiting time are recalculated using the updated queue information, and the updated information is pushed to the relevant car owners;

[0015] When the vehicle approaches a charging pile, the real-time status of the assigned charging pile is obtained. If the status of the charging pile changes, the charging pile position is reallocated and the navigation path is updated.

[0016] Preferably, obtaining a remote charging queue joining request and extracting a vehicle identifier and current location information includes:

[0017] The car owner submits a request to remotely join the charging queue through the mobile app, and extracts the vehicle identification information and current location data from the request;

[0018] Querying relevant attributes of the vehicle in a pre-established vehicle database according to the vehicle identification information;

[0019] Determining whether the vehicle is within a preset charging station service range according to the current location data;

[0020] If the vehicle is within the service range, the vehicle identification information and location data are added to the charging queue.

[0021] Preferably, storing the estimated waiting time and the vehicle identifier comprises:

[0022] The current number of people queuing at the charging station and the usage status of the charging piles are obtained, and the estimated waiting time for each vehicle is calculated using a preset algorithm in combination with the vehicle identifier and current location information; the calculated estimated waiting time is associated with the vehicle identifier and stored in the database to form a vehicle waiting time record.

[0023] Preferably, the update estimated waiting time includes:

[0024] The preset time interval value in the sliding window mechanism is obtained, and data is collected on the change value of vehicle flow at the charging station and the status value of the charging pile. The time series analysis method is used to preprocess the data of the change value of vehicle flow and the status value of the charging pile to obtain standardized data. Based on the standardized data, the regression model is used to calculate the expected waiting value of each vehicle to generate the waiting time prediction result.

[0025] Preferably, the method of allocating charging pile positions using a priority queue algorithm includes:

[0026] Get the distance between the vehicle and the charging station and the current estimated waiting time;

[0027] The priority queue algorithm is used to calculate the allocation value of the charging pile by combining the distance value and the expected waiting time value;

[0028] According to the assigned value, determine which vehicles obtain the charging opportunity value;

[0029] If the vehicle obtains a charging opportunity value, the charging pile status value of the vehicle is updated;

[0030] Use the updated charging pile status value to recalculate the estimated waiting time value;

[0031] Adjust the sorting value of the priority queue according to the recalculated estimated waiting time value;

[0032] Get the adjusted priority queue sort value and update the allocation value of the charging pile.

[0033] Preferably, determining the guidance order of each vehicle comprises:

[0034] Sensors are used to obtain the location information and timestamps of several vehicles entering the charging station range; the entry time of the vehicle is calculated through a preset timestamp sorting algorithm to obtain the vehicle sorting value; the vehicle guidance sequence value is determined according to the sorting value, and the vehicle guidance queue is generated; for the vehicles in the guidance queue, the real-time location value of each vehicle is obtained, and the distance value from the charging pile is calculated; a priority algorithm based on the distance value is used, combined with the guidance sequence value, to generate the vehicle's charging allocation value; according to the charging allocation value, the status value of the charging pile is updated to generate the latest charging pile usage information; through the latest charging pile usage information, the waiting time value of the vehicle is recalculated, and the sorting value of the guidance queue is adjusted.

[0035] Preferably, generating a navigation path from the current position to the designated charging station position and sending the path information to the vehicle navigation system includes:

[0036] Through a preset path planning algorithm, combined with the current position of the vehicle and the designated charging station, a navigation path from the current position to the designated charging station is generated; the generated navigation path information is sent to the on-board navigation system for reference by the vehicle driver; based on the navigation path information, the position change of the vehicle is monitored in real time to determine whether the vehicle deviates from the preset path; if the vehicle deviates from the preset path, the navigation path from the current position to the designated charging station is replanned and the navigation path information is updated; the updated navigation path information is received through the on-board navigation system to adjust the vehicle's driving direction.

[0037] The present invention also proposes a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0038] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0039] Compared with the prior art, the present invention has the following advantages and technical effects:

[0040] The present invention discloses an orderly charging method for an electric vehicle charging station based on intelligent queuing. The method receives a request from a car owner to remotely join a charging queue through a mobile phone App, and obtains a vehicle identifier and location information. According to the current status of the charging station, the estimated waiting time is calculated and stored in association with the vehicle. The queuing information is updated in real time using a sliding window mechanism, and the update is triggered immediately when the status of the charging pile changes. The present invention uses a priority queue algorithm to allocate charging pile positions in combination with vehicle distance and waiting time. For multiple vehicles entering the charging station at the same time, timestamp sorting is used to determine the guidance order. According to the allocation result, a navigation path is generated and sent to the vehicle-mounted system. When approaching a charging pile, the present invention will also make dynamic adjustments according to the real-time status to ensure that the vehicle reaches an available charging pile smoothly. This intelligent scheduling scheme significantly improves the charging efficiency, reduces the waiting time of car owners, and provides a convenient and efficient solution for charging electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] Embodiment 1

[0046] like Figure 1 As shown, this embodiment provides an orderly charging method for an electric vehicle charging station based on intelligent queuing, comprising the following steps:

[0047] Get a remote request to join the charging queue and extract the vehicle identifier and current location information;

[0048] Calculate the estimated waiting time based on the number of people currently queuing at the charging station, the status of the charging pile and the current location information, and store the estimated waiting time and vehicle identifier;

[0049] According to the preset time interval, a sliding window mechanism is used to collect the charging station traffic flow and charging pile status change data, and update the estimated waiting time;

[0050] Based on the distance between the vehicle and the charging station and the expected waiting time, a priority queue algorithm is used to allocate charging piles, so that vehicles that are closer have priority in getting charging opportunities.

[0051] When several vehicles enter the charging station range at the same time, these requests are processed using a timestamp sorting algorithm based on the vehicle entry time to determine the booting order for each vehicle;

[0052] Based on the guidance sequence and the assigned charging pile positions, a navigation path from the current position to the designated charging pile position is generated, and the path information is sent to the on-board navigation system.

[0053] Furthermore, the method also includes updating the charging pile status in real time, and the updating method is as follows:

[0054] When queuing, if the status of the charging pile changes, the update mechanism is immediately triggered to update, and the number of queue members and the estimated waiting time are recalculated using the updated queue information, and the updated information is pushed to the relevant car owners;

[0055] When the vehicle approaches a charging pile, the real-time status of the assigned charging pile is obtained. If the status of the charging pile changes, the charging pile position is reallocated and the navigation path is updated.

[0056] The above content specifically includes the following steps:

[0057] S101. Use a mobile phone App to obtain a remote request to join a charging queue submitted by a car owner, and extract the vehicle identification information and current location data from the request. Query the relevant attributes of the vehicle in a pre-established vehicle database based on the vehicle identification information. Determine whether the vehicle is within the preset charging station service range based on the current location data. If the vehicle is within the service range, add the vehicle identification information and location data to the charging queue.

[0058] Specifically, the mobile application needs to design a simple user interface so that car owners can easily submit remote charging requests. For example, after the user opens the application, the interface directly displays the distribution map of nearby charging stations. The user only needs to click on the target charging station to initiate the request. The application will automatically collect vehicle location information. For example, when a car owner initiates a charging request in the East Business District, the system immediately obtains its latitude and longitude coordinates. Vehicle identification information usually includes license plate number, vehicle brand and model, etc. For example, the new energy vehicle license plate "Beijing New Energy" has a corresponding vehicle database that stores the vehicle's charging interface type, battery capacity and other technical parameters. This information is very important for subsequent charging scheduling and can estimate the charging time. The service range of the charging station is an important parameter, and the service radius is usually defined with the charging station as the center. If a charging station is located in a commercial area, the service radius is set to five kilometers, and vehicles outside the range will not be able to join the queue. This ensures that the owner arrives at the charging station within a reasonable time to avoid waiting too long. The charging queue needs to be dynamically managed based on multiple factors. The system records information such as the power status and arrival time of each vehicle. For example, if a vehicle has 10% remaining power, the system will increase its priority. If there are users who have made reservations in the queue, they will also be given a higher priority. The calculation of the estimated charging time takes into account factors such as the vehicle's battery capacity, current power, and charging power. For example, for a vehicle with a range of 400 kilometers and 20% remaining power, it is estimated that it will take 40 minutes to complete the charging using a DC fast charging pile. This time estimate is very helpful for car owners to plan their trips. The scheduling algorithm needs to balance multiple goals, including charging efficiency, waiting time, user experience, etc. A scoring mechanism can be used, where vehicles with too low power will score higher, and users who have been waiting for a long time will also receive additional points. The system sorts according to the total score and dynamically adjusts the charging order.

[0059] S102: Obtain the current number of people queuing at the charging station and the usage status of the charging pile, and calculate the estimated waiting time for each vehicle using a preset algorithm in combination with the vehicle identifier and current location information. The calculated estimated waiting time is associated with the vehicle identifier and stored in a database to form a vehicle waiting time record.

[0060] Specifically, the charging station queue management system mainly optimizes scheduling by real-time monitoring of the use of charging piles. Take a large charging station in a certain city as an example. The station is equipped with ten fast charging piles, each of which can charge one car at the same time. The system records basic vehicle information, such as charging demand, vehicle model, etc., through the vehicle identifier, and obtains the real-time location of the vehicle. The location information can be used to determine whether the vehicle is within the service range. Usually, a five-kilometer service radius is defined with the charging station as the center. The calculation of the expected waiting time needs to consider multiple factors. For example, an electric car is three kilometers away from the charging station, and the estimated driving time is ten minutes. There are currently eight charging piles in use at the charging station, two of which are expected to be fully charged within fifteen minutes. The system combines the driving time with the estimated idle time of the charging pile to deduce that the vehicle is expected to wait for five minutes. This information will be stored in the database to form a dynamically updated waiting time record.

[0061] S103, obtaining the preset time interval value in the sliding window mechanism, and collecting data on the change value of vehicle flow at the charging station and the status value of the charging pile. Using the time series analysis method, preprocess the data of the change value of vehicle flow and the status value of the charging pile to obtain standardized data. Based on the standardized data, the regression model is used to calculate the expected waiting value of each vehicle to generate the waiting time prediction result.

[0062] Specifically, the sliding window mechanism observes the dynamic changes of charging stations by setting a fixed time interval value, such as setting data collection every ten minutes. In actual applications, the traffic flow of charging stations often shows obvious time characteristics, such as large traffic flow during the morning peak period and small traffic flow during the late night period. The status values ​​of charging piles include idle, in use, faulty and other situations, and these states will change dynamically over time. Time series analysis methods can effectively process such dynamic data. For changes in traffic flow, the original data can be converted into standardized data. Assuming that a charging station has an average of 20 vehicles arriving per hour during the morning peak period, with a standard deviation of five vehicles, then when 25 vehicles arrive at a certain moment, its standardized value is positive one. The status values ​​of charging piles also need to be standardized to map different states to a unified interval. The calculation of the expected waiting time uses a regression model, and the model input includes factors such as current traffic flow, the number of available charging piles, and historical service time. For example, a charging station has ten charging piles, eight of which are currently in use, and there are five waiting vehicles. According to historical data, the average charging time for each vehicle is 40 minutes, so the expected waiting time for newly arrived vehicles may take about 30 minutes.

[0063] S104. Obtain the charging pile state change value and trigger the update mechanism. Use the update mechanism to recalculate the queue value. Calculate the estimated waiting time value based on the queue value. Determine the updated queue value and estimated waiting time value as the information value. If the information value is generated, push it to the relevant car owner. Update the charging pile state change value based on the push result. Use the updated charging pile state change value to re-trigger the update mechanism.

[0064] Specifically, for example, the charging pile state change value is reflected in the change of the working state of the charging pile, including idle, occupied, faulty and other states. For example, a charging station has ten charging piles, eight of which are in use, one is idle, and one is faulty. When a car completes charging and leaves, the charging pile state changes from occupied to idle, triggering a state change. The update mechanism adopts a real-time monitoring method, and immediately starts the queue calculation when a state change is detected. For example, there were originally fifteen cars in the queue. After a charging pile is released, the system immediately re-counts and the number of people in the queue is updated to fourteen. This instant update ensures the accuracy of the data and allows car owners to understand the actual waiting situation in a timely manner. The estimated waiting time is calculated based on multiple factors, including the current number of vehicles in the queue, the charging demand of each vehicle, and the charging rate of the charging pile. Assuming that the average charging time for each car is forty minutes, the owner of the fifth place is expected to wait for about two hundred minutes. This prediction allows car owners to arrange their time reasonably. The generation of information values ​​adopts a standardized format and contains specific numerical information. For example, information is pushed to car owners: There are currently fourteen people in the queue, and the expected waiting time is one hundred and sixty minutes. This clear way of displaying information makes it easier for car owners to understand and make decisions. Push notifications are delivered through multiple channels, including mobile app push notifications and SMS notifications, to ensure that information is delivered in a timely manner. The system tracks the delivery of information based on the push status and updates the status record of the charging pile accordingly. If the car owner cancels the queue after receiving the information, the system will immediately update the status and recalculate the waiting time for subsequent vehicles. For example, if a car owner chooses to leave because the waiting time is too long, the queue system will be adjusted immediately, and the waiting time for subsequent vehicles will be shortened accordingly. The continuity of status changes is reflected in the cyclic update of the system. When a charging pile becomes idle after completing charging, the system automatically notifies the next owner in line and recalculates the waiting time for the remaining vehicles. This dynamic update mechanism ensures the continuity and efficiency of the charging station operation. The entire process forms a closed loop, from the change of the charging pile status to the information push, and then to the status update, each link is interrelated. For example, during the morning peak period, a charging station can timely discover the surge in charging demand through continuous status monitoring and information updates, helping the station to adjust its operation strategy in time and improve service efficiency. This dynamic management mode can optimize the allocation of charging resources and improve user experience.

[0065] S105. Obtain the distance value between the vehicle and the charging station and the current estimated waiting time value. Use the priority queue algorithm to calculate the allocation value of the charging pile in combination with the distance value and the estimated waiting time value. Determine which vehicles obtain the charging opportunity value based on the allocation value. If the vehicle obtains the charging opportunity value, update the charging pile status value of the vehicle. Use the updated charging pile status value to recalculate the estimated waiting time value. Adjust the ranking value of the priority queue based on the recalculated estimated waiting time value. Obtain the adjusted priority queue ranking value and update the allocation value of the charging pile.

[0066] Specifically, obtaining the distance between the vehicle and the charging station is the basic link of the entire dispatching system. The real-time position coordinates of the vehicle are obtained through the positioning system, and the straight-line distance between the two points is calculated in combination with the known coordinates of the charging station. For example, car owner A is located in Wudaokou, Dongcheng District, about three kilometers away from the Haidianmen West Charging Station and five kilometers away from the Chaoyangmen Charging Station. At this time, the system knows through intelligent calculation that the estimated waiting time for the Haidianmen West Charging Station is thirty minutes, and the estimated waiting time for the Chaoyangmen Charging Station is twenty minutes. On this basis, the priority queue algorithm comprehensively considers the distance factor and the waiting time, and intelligently allocates the charging demand. For example, the distance weight is set to 0.4, and the waiting time weight is set to 0.6, and the allocation priority is obtained through weighted calculation. For car owner A, the comprehensive score for choosing the Haidianmen West Charging Station is: the distance score is ten points multiplied by zero point four, plus the waiting time score is seven points multiplied by zero point six, and the final score is eight points two. The comprehensive score for choosing the Chaoyangmen Charging Station is: the distance score is six points multiplied by zero point four, plus the waiting time score is eight points multiplied by zero point six, and the final score is seven points two. Therefore, the system recommends that car owner A go to the Haidianmenxi charging station. When the vehicle gets a chance to charge, the status of the charging pile needs to be updated in time. For example, after car owner A starts charging, the status of the charging pile changes from idle to occupied, and the estimated charging time is forty minutes. The system immediately recalculates the waiting time of other charging piles at the charging station. Assuming that there are three vehicles in the queue, and the estimated charging time for each vehicle is thirty minutes, the estimated waiting time for the last vehicle is ninety minutes. The order of the priority queue will be dynamically adjusted as various parameters change. When a car owner cancels the queue or the charging time ends earlier than expected, the system will recalculate various indicators. For example, if car owner A completes charging ten minutes ahead of schedule, the estimated waiting time of the queued vehicle will be reduced by ten minutes accordingly, and the priority of subsequent vehicles in the queue will be adjusted accordingly to ensure that charging resources are optimally configured. The update of the allocation value reflects the real-time and intelligent nature of the system. When new charging needs arise, the system will re-evaluate the existing queue and dynamically adjust it according to the latest distance value and waiting time. For example, when car owner B has a new charging demand, the system will compare its priority with the existing vehicles in the queue. If its comprehensive score is higher, it may get a higher priority charging opportunity, which reflects the fairness and efficiency of the system scheduling.

[0067] S106. Use sensors to obtain the location information and timestamps of multiple vehicles entering the charging station. Use a preset timestamp sorting algorithm to calculate the entry time of the vehicle to obtain the vehicle's sorting value. Determine the vehicle's guidance sequence value based on the sorting value and generate a vehicle guidance queue. For the vehicles in the guidance queue, obtain the real-time location value of each vehicle and calculate its distance value from the charging pile. Use a priority algorithm based on the distance value and combine it with the guidance sequence value to generate the vehicle's charging allocation value. Update the status value of the charging pile based on the charging allocation value and generate the latest charging pile usage information. Recalculate the waiting time value of the vehicle based on the latest charging pile usage information and adjust the sorting value of the guidance queue.

[0068] Specifically, the charging station uses multiple position sensors to monitor the information of vehicle entry range. The sensors are installed at the entrance of the charging station and the main road nodes. Taking the charging station with ten charging piles as an example, the sensors can detect the location coordinates and entry time of the vehicle. When multiple vehicles enter one after another, the system will generate a data record containing a timestamp, such as a vehicle entering the east gate at 9:05 am and another vehicle entering the south gate at 9:08 am. The timestamp sorting algorithm arranges the vehicles in the order of entry, giving priority to vehicles that enter earlier. This sorting method reflects the principle of first-come, first-served fairness and avoids late-arriving vehicles from cutting in line. The sorted guidance order can ensure that vehicles enter the charging area in an orderly manner and reduce congestion. For example, vehicles at the east gate are guided to charging area No. 1 in turn, and vehicles at the south gate are guided to charging area No. 2. The system obtains the location information of each vehicle in real time and calculates the actual distance between the vehicle and each charging pile through the positioning module. Assume that there are five charging piles in charging area No. 1, the distance between the south side vehicle and charging pile No. 1 is ten meters, and the distance between the north side vehicle and charging pile No. 5 is twenty meters. Based on these distance data, the priority algorithm will analyze and generate a charging allocation plan. The calculation of the charging allocation value takes into account the distance factor and the guidance order, and allocates the vehicle to the most suitable charging pile. For example, a vehicle that is closer to the No. 1 charging pile gets a higher allocation priority, but if the vehicle arrives later, its priority will be appropriately reduced. This balancing mechanism ensures both charging efficiency and basic queue order. When the status of a charging pile changes, such as when a charging pile starts or ends charging, the system will update the usage information. This information includes the working status of the charging pile, the remaining charging time, etc. Suppose the No. 2 charging pile is charging a car, and it is expected to take another 30 minutes. The system will record this status and use it for subsequent scheduling. Based on the latest charging pile usage information, the system re-estimates the waiting time. If the No. 3 charging pile is expected to be idle in five minutes, and the No. 4 charging pile will take 40 minutes to be idle, the system will give priority to guiding the vehicle to the No. 3 charging pile. This dynamic adjustment mechanism can improve the overall operating efficiency of the charging station, reduce the waiting time of vehicles, and improve user satisfaction. By recalculating the waiting time, the order of the guidance queue will be adjusted accordingly. This dynamic adjustment ensures the optimal allocation of charging resources, making the entire charging process smoother and more efficient. For example, when a charging pile is found to be faulty, the system will immediately adjust the guidance queue and reallocate the vehicles originally planned to use the charging pile to other available charging piles.

[0069] S107. Obtain the guidance sequence of the vehicle and the assigned charging pile position, and determine the current position of the vehicle and the designated charging pile position. Generate a navigation path from the current position to the designated charging pile position through a preset path planning algorithm, combined with the current position of the vehicle and the designated charging pile position. Send the generated navigation path information to the vehicle navigation system for the driver's reference. Based on the navigation path information, monitor the position changes of the vehicle in real time to determine whether the vehicle deviates from the preset path. If the vehicle deviates from the preset path, re-plan the navigation path from the current position to the designated charging pile position and update the navigation path information. Receive the updated navigation path information through the vehicle navigation system and adjust the driving direction of the vehicle. According to the real-time position of the vehicle and the navigation path information, predict the arrival time of the vehicle and update the usage status of the charging pile position.

[0070] Specifically, the charging pile guidance system will first obtain the charging pile location information and current location of each vehicle. Taking a large charging station as an example, there are ten charging piles in the charging station, distributed in different areas, and each charging pile has a unique number and coordinate information. When a new energy vehicle enters the range of the charging station, the system will obtain its real-time location coordinates through the on-board positioning module, and start path planning in combination with the assigned charging pile position information. The path planning algorithm adopts an improved shortest path algorithm, taking into account practical factors such as the road structure and turning radius in the charging station. In the charging station, the path planning of the vehicle needs to comprehensively consider a variety of practical factors to ensure that the vehicle can reach the charging pile efficiently and safely. Although the traditional shortest path algorithm (such as the Dijkstra algorithm) can calculate the shortest distance between two points, in the complex environment of the charging station, the simple distance calculation cannot meet the actual needs. Therefore, the improved path planning algorithm came into being, which combines the actual constraints such as the road structure and turning radius in the charging station to optimize the efficiency and practicality of path planning. According to the "Electric Vehicle Charging Station Design Specifications", the design parameters such as road width, slope and turning radius in the charging station have a direct impact on the vehicle's driving path. For example, the width of a single lane should not be less than 3.5 meters, the width of a double lane should not be less than 6 meters, the road slope should not be greater than 6%, and the turning radius should not be less than 9 meters. These design specifications not only ensure the driving safety of the vehicle, but also provide important constraints for the path planning algorithm. The improved path planning algorithm needs to take these constraints into consideration and avoid planning untravelable paths by dynamically adjusting the path weights. In addition, the improved path planning algorithm can use heuristic search methods, such as the A* algorithm, combined with the jump point search technology to further optimize the search efficiency. By introducing the heuristic function, the algorithm can quickly find the optimal path from the entrance to the charging pile, and dynamically adjust the path planning to adapt to the real-time road conditions. For example, when the vehicle flow in the charging station is large, the algorithm can dynamically adjust the path according to the real-time data to avoid congestion and ensure that the vehicle can quickly reach the charging pile. In practical applications, the improved path planning algorithm can not only improve the operating efficiency of the charging station, but also improve the user's charging experience. By comprehensively considering factors such as road structure, turning radius, and real-time road conditions, the algorithm can plan a driving path for the vehicle that is both in line with design specifications and efficient, ensuring that the vehicle can travel smoothly in the charging station and reducing unnecessary waiting time. This improved path planning method provides strong support for the intelligent management of charging stations and lays the foundation for the development of future intelligent transportation systems.

[0071] For example, when a vehicle is assigned to the charging pile numbered five, the system will plan the optimal driving route based on the vehicle's current location coordinates and the charging pile's location coordinates, combined with traffic rules such as one-way traffic and no U-turns in the station. The planned path will avoid areas with dense traffic and reduce the possibility of congestion. After the navigation path is generated, it will be sent to the vehicle navigation system in real time. Assuming that the planned path contains three key turning points: turn right at the entrance, turn left on the middle road, and go straight into the end, the system will provide the driver with clear guidance information through voice prompts and map display. At the same time, the system continuously monitors the actual driving trajectory of the vehicle, and determines whether re-planning is required by comparing the deviation value between the preset path and the actual trajectory. When the vehicle deviates from the preset path, the system will immediately start the re-planning mechanism. For example, if a vehicle originally planned to turn right but missed the turning intersection, the system will recalculate the path based on the current position and may plan a new route that passes the next intersection to turn around. The new navigation path will be pushed to the vehicle system in real time to ensure that the driver can adjust the driving direction in time. The system will also dynamically predict the arrival time based on the vehicle's driving speed and remaining distance. For example, if a vehicle is 200 meters away from a designated charging station and is currently traveling at a speed of 20 kilometers per hour, the system will estimate an arrival time of about one minute, taking into account possible deceleration and turns along the way. This predicted time will be used to update the usage status of the charging station so that other waiting vehicles can understand the waiting time more accurately. When the vehicle approaches the target charging station, the system will further refine the guidance information. The specific location of the charging station and the parking auxiliary line are displayed on the on-board display to help the driver park accurately. During the entire guidance process, the system continuously optimizes the path information to ensure that the vehicle can reach the designated charging location safely and efficiently, thereby improving the operating efficiency of the charging station.

[0072] S108. Obtain the real-time status of the charging pile and determine whether the currently allocated charging pile position is available. If the charging pile status is unavailable, call the allocation algorithm to reselect an available charging pile position. Update the navigation path information based on the reallocated charging pile position and send it to the vehicle system. Predict the vehicle arrival time and update the charging pile usage status by real-time monitoring of the vehicle position and navigation path.

[0073] Specifically, the intelligent dispatching system dynamically manages the use status of charging piles. When it is detected that the vehicle is about to arrive, the charging pile is preheated in advance to optimize the charging efficiency. The system realizes charging reservation and status synchronization through two-way communication between the on-board terminal and the charging pile. This intelligent dispatching mechanism can significantly improve the operating efficiency of charging stations and reduce vehicle waiting time. For example, an electric car sets out from the city center to a suburban charging station and encounters traffic control on the way. The system automatically postpones the reservation time by 30 minutes to avoid affecting other users. The entire navigation and dispatching process forms a closed-loop control, and the service quality is ensured through real-time monitoring and dynamic adjustment. The system continuously monitors the vehicle's operating status, including speed, power and location information, and provides data support for dispatching decisions. This intelligent dispatching solution can effectively improve the utilization rate of charging facilities, reduce user waiting time, and achieve optimal allocation of charging resources.

[0074] This embodiment further proposes a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0075] This embodiment also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0076] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An orderly charging method for electric vehicle charging stations based on intelligent queuing, characterized in that: The following steps are involved: Get a remote request to join the charging queue and extract the vehicle identifier and current location information; Calculate the estimated waiting time based on the number of people currently queuing at the charging station, the status of the charging pile and the current location information, and store the estimated waiting time and vehicle identifier; According to the preset time interval, a sliding window mechanism is used to collect the charging station traffic flow and charging pile status change data, and update the estimated waiting time; Based on the distance between the vehicle and the charging station and the expected waiting time, a priority queue algorithm is used to allocate charging piles, so that vehicles that are closer have priority in getting charging opportunities. When several vehicles enter the charging station range at the same time, these requests are processed using a timestamp sorting algorithm based on the vehicle entry time to determine the booting order for each vehicle; Based on the guidance sequence and the assigned charging pile positions, a navigation path from the current position to the designated charging pile position is generated, and the path information is sent to the on-board navigation system.

2. The method according to claim 1, characterized in that: The method also includes updating the charging pile status: When queuing, if the status of the charging pile changes, the update mechanism is immediately triggered to update, and the number of queue members and the estimated waiting time are recalculated using the updated queue information, and the updated information is pushed to the relevant car owners; When the vehicle approaches a charging pile, the real-time status of the assigned charging pile is obtained. If the status of the charging pile changes, the charging pile position is reallocated and the navigation path is updated.

3. The method according to claim 1, characterized in that Get a remote request to join the charging queue, extract the vehicle identifier and current location information including: The car owner submits a request to remotely join the charging queue through the mobile app, and extracts the vehicle identification information and current location data from the request; Querying relevant attributes of the vehicle in a pre-established vehicle database according to the vehicle identification information; Determining whether the vehicle is within a preset charging station service range according to the current location data; If the vehicle is within the service range, the vehicle identification information and location data are added to the charging queue.

4. The method according to claim 1, characterized in that The storage of estimated waiting time and vehicle identifier includes: The current number of people queuing at the charging station and the usage status of the charging piles are obtained, and the estimated waiting time for each vehicle is calculated using a preset algorithm in combination with the vehicle identifier and current location information; the calculated estimated waiting time is associated with the vehicle identifier and stored in the database to form a vehicle waiting time record.

5. The method according to claim 1, characterized in that The estimated waiting times for the updates include: The preset time interval value in the sliding window mechanism is obtained, and data is collected on the change value of vehicle flow at the charging station and the status value of the charging pile. The time series analysis method is used to preprocess the data of the change value of vehicle flow and the status value of the charging pile to obtain standardized data. Based on the standardized data, the regression model is used to calculate the expected waiting value of each vehicle to generate the waiting time prediction result.

6. The method according to claim 1, characterized in that The use of a priority queue algorithm to allocate charging pile positions includes: Get the distance between the vehicle and the charging station and the current estimated waiting time; The priority queue algorithm is used to calculate the allocation value of the charging pile by combining the distance value and the expected waiting time value; According to the assigned value, determine which vehicles obtain the charging opportunity value; If the vehicle obtains a charging opportunity value, the charging pile status value of the vehicle is updated; Use the updated charging pile status value to recalculate the estimated waiting time value; Adjust the sorting value of the priority queue according to the recalculated estimated waiting time value; Get the adjusted priority queue sort value and update the allocation value of the charging pile.

7. The method according to claim 1, characterized in that Determining the guidance order of each vehicle includes: Sensors are used to obtain the location information and timestamps of several vehicles entering the charging station range; the entry time of the vehicle is calculated through a preset timestamp sorting algorithm to obtain the vehicle sorting value; the vehicle guidance sequence value is determined according to the sorting value, and the vehicle guidance queue is generated; for the vehicles in the guidance queue, the real-time location value of each vehicle is obtained, and the distance value from the charging pile is calculated; a priority algorithm based on the distance value is used, combined with the guidance sequence value, to generate the vehicle's charging allocation value; according to the charging allocation value, the status value of the charging pile is updated to generate the latest charging pile usage information; through the latest charging pile usage information, the waiting time value of the vehicle is recalculated, and the sorting value of the guidance queue is adjusted.

8. The method according to claim 1, characterized in that The generating of a navigation path from the current position to the designated charging pile position and sending the path information to the vehicle navigation system comprises: Through a preset path planning algorithm, combined with the current position of the vehicle and the designated charging station, a navigation path from the current position to the designated charging station is generated; the generated navigation path information is sent to the on-board navigation system for reference by the vehicle driver; based on the navigation path information, the position change of the vehicle is monitored in real time to determine whether the vehicle deviates from the preset path; if the vehicle deviates from the preset path, the navigation path from the current position to the designated charging station is replanned and the navigation path information is updated; the updated navigation path information is received through the on-board navigation system to adjust the vehicle's driving direction.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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