Intelligent management method for new energy vehicle charging pile based on internet of things

Abstract

The application discloses a new energy vehicle charging pile intelligent management method based on an Internet of Things, and particularly relates to the technical field of the Internet of Things; the vehicle identification, the current position and the residual power of a vehicle entering a charging area are acquired; it is judged whether there is a critical state that the vehicle is insufficient to reach any idle charging pile, and the vehicle is marked as a high-risk vehicle; the residual endurance time of the vehicle is predicted through historical charging behaviors, and a scheduling priority weight is constructed; a scheduling request is sent to each charging pile based on a sorting result, a minimum disturbance queuing reconstruction model is constructed, and it is judged whether a waiting time fault tolerance value meets the requirements; if the power load of a target charging pile is out of limit, the target charging pile is switched to an auxiliary charging pile and the scheduling is reordered; the application improves the access efficiency of the high-risk vehicle, reduces the extreme stagnation risk, and has strong real-time performance.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically to an intelligent management method for new energy vehicle charging piles based on IoT. Background Technology

[0002] With the widespread application of new energy vehicles, the density of charging piles in cities and highway service areas is constantly increasing. However, most current charging pile management systems are based solely on queuing strategies under fixed power conditions, lacking the ability to dynamically adjust the priority of vehicles with extremely low battery levels. Especially when multiple vehicles enter a low-battery state almost simultaneously, traditional systems struggle to identify extreme cases where a vehicle's remaining range is less than 2 kilometers. This can easily cause low-battery vehicles to run out of power in the queue and be forced to remain there, leading to road congestion and charging pile congestion, severely impacting traffic and user experience.

[0003] Furthermore, in charging hotspots such as highway service areas or large parking lots, the high concentration of vehicles, complex user intentions, and susceptibility to communication signal interference cause traditional scheduling methods to experience large response delays and unstable scheduling during execution. They are unable to obtain data such as vehicle battery level, location, and historical charging behavior in real time and accurately, thus missing the best scheduling opportunity. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent management method for new energy vehicle charging piles based on the Internet of Things, so as to solve the shortcomings of the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management method for new energy vehicle charging piles based on the Internet of Things, comprising:

[0006] S100: Obtain the unique identifier ID, current location coordinates, and remaining battery power E0 of the new energy vehicles waiting to be charged within the area;

[0007] S200. Compare the remaining battery power E0 with the distance to various charging piles in the area and the queuing status of the charging piles to determine whether there is a critical situation where the vehicle's remaining range is insufficient to reach any available charging pile. If so, mark the vehicle as a high-risk vehicle.

[0008] S300. For vehicles marked as high-risk, calculate their average energy consumption rate R per unit time based on their historical charging records, and predict their remaining driving time T′ by combining the remaining energy E0.

[0009] S400: Add vehicles with remaining range T′ less than the current shortest queuing time to the emergency dispatch queue, and sort all vehicles waiting to be charged according to the reciprocal of their remaining range T′ as weight W′.

[0010] S500: Based on the sorting results, send a scheduling request to the charging pile and perform minimum disturbance queuing reconstruction calculation for the current user of the charging pile to determine the adjusted waiting time fault tolerance value ΔW.

[0011] S600 If the waiting time tolerance value ΔW is less than the set threshold ε, then execute the scheduling replacement operation, notify the charging pile to release one seat in the current queue, and arrange for high-risk vehicles to access charging.

[0012] S700: If the current charging pile power load exceeds the safety limit, the scheduling target will be transferred to the auxiliary charging pile, the scheduling requests will be reordered according to the weight W′, and the process will return to S500.

[0013] Preferably, S500 includes:

[0014] Based on the scheduling priority and the scheduling weight of the vehicles ranked first, scheduling request information with vehicle identification, estimated access time and remaining range is sent to each charging pile within the reachable range in sequence.

[0015] Receive the current queuing structure data returned by the target charging station, including the remaining charging time and historical average charging time of the charging vehicle and the queuing vehicle.

[0016] Based on the expected charging time of the vehicles requesting scheduling and the target queuing structure, a minimum disturbance queuing reconstruction model is constructed, which prioritizes attempting to insert into the position that minimizes the increase in waiting time for other vehicles.

[0017] Calculate the difference in waiting time for all vehicles before and after the reconstruction, and define the total waiting time increment as the adjusted waiting time tolerance value ΔW. If ΔW is lower than the preset threshold, the queue reconstruction operation is performed.

[0018] Preferably, the construction of the minimum disturbance queuing reconstruction model includes:

[0019] Obtain the estimated charging time of the vehicle requesting the dispatch, and combine the remaining charging time of each vehicle in the current queue of the target charging pile with the historical average charging cycle to construct the time axis of the current queue sequence.

[0020] The incremental waiting time of each vehicle in the original queue is accumulated after the vehicle with the scheduling request is inserted into different positions, forming a mapping relationship between the insertion position and the total waiting increment;

[0021] The optimal position corresponding to the insertion point is selected as the adjustment position based on the principle of minimum total waiting time increment.

[0022] Based on the adjusted position, generate new queuing structure candidate schemes and calculate the adjusted waiting time tolerance value ΔW.

[0023] Preferably, the marked vehicle is a high-risk vehicle, including:

[0024] If no charging pile Pi simultaneously satisfies the following conditions: the straight-line distance Di from the current location to the charging pile is less than or equal to Lmax, where Lmax is the maximum driving distance of the vehicle; and the estimated waiting time Wi is less than or equal to Tmax, where Tmax is the maximum waiting time of the vehicle from the current location.

[0025] Then it is determined that the current vehicle is in a critical state where it cannot reach any charging station.

[0026] For vehicles that meet the criteria for being unable to reach any charging station, a status label of "high risk" is assigned.

[0027] Preferably, predicting the remaining battery life T′ includes: dividing the remaining battery power E0 by the average battery consumption rate R per unit time to obtain the remaining battery life T′.

[0028] Preferably, the process of sorting the scheduling priorities of all vehicles to be charged includes: generating a sorted global scheduling queue, denoted as Qall, in which vehicles in Qall will initiate scheduling requests in descending order of priority.

[0029] Preferably, if the waiting time tolerance value ΔW is less than the set threshold ε, a scheduling replacement operation is performed, which specifically includes: updating the current charging pile queue structure according to the selected adjustment position, inserting high-priority vehicles into the designated position; extending the estimated access time of all vehicles after the insertion position by the length of the estimated charging time of the newly added vehicle; and synchronously updating the estimated start charging time and completion charging time of all queued vehicles.

[0030] Preferably, the auxiliary charging pile set is used to transfer the scheduling request, including: the current power load limit is not exceeded; the current queue length does not exceed the regional average queue length; and the spatial distance from the current location of the vehicle requesting the scheduling is less than 1.5 times the initial target charging pile.

[0031] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0032] 1. This invention constructs an intelligent scheduling mechanism based on an Internet of Things platform, combining vehicle remaining power, range, queuing status and charging pile load data, to achieve the identification and priority access control of high-risk new energy vehicles. It effectively solves the problem in traditional charging management methods that cannot identify the critical state of "insufficient range to reach any charging pile", and significantly improves the scheduling agility and security of the system in the context of multiple vehicles operating concurrently and limited resources.

[0033] 2. This invention ensures that while guaranteeing access for high-priority vehicles, the impact on other queuing vehicles is kept within an acceptable range through a minimum disturbance queuing reconstruction model and a waiting time fault tolerance judgment mechanism; and when the power load of the charging pile exceeds the limit, the scheduling target is dynamically redirected to the auxiliary charging pile, thus realizing efficient resource reallocation and fault-tolerant scheduling capabilities. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0035] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] For examples, please refer to Figure 1 As shown in this embodiment, the intelligent management method for new energy vehicle charging piles based on the Internet of Things includes:

[0038] S100: Obtain the unique identifier ID, current location coordinates, and remaining battery power E0 of the new energy vehicles waiting to be charged within the area.

[0039] First, obtain the unique identifier ID for each new energy vehicle waiting to be charged within the area. This includes obtaining the vehicle's license plate number as the main vehicle identifier; obtaining the vehicle's current location coordinates; and reading the vehicle's current remaining battery power E0 in real time, in kWh.

[0040] S200. The remaining battery power E0 is compared with the distance to various charging piles in the area and the queuing status of the charging piles to determine whether there is a critical situation where the vehicle's remaining range is insufficient to reach any available charging pile. If so, the vehicle is marked as a high-risk vehicle.

[0041] After collecting the vehicle's unique identifier, current location coordinates, and remaining battery power, an accessibility analysis is performed to determine whether the vehicle can safely reach the charging station. This is to identify any critical situations where the vehicle cannot reach any available charging station due to low remaining battery power, and vehicles meeting these conditions are marked as high-risk.

[0042] First, based on the current location coordinates of the vehicle to be charged, construct an Euclidean spatial distance matrix from the current location to all charging piles in the area. The specific method is as follows: record the geographical coordinates of each charging pile in the area, labeled as P1, P2, ..., Pn, where n is the total number of charging piles in the area; denote the current location of the vehicle as L0, and calculate the spatial straight-line distance Di from L0 to each Pi (i=1 to n), in meters.

[0043] For each charging station, a current queuing state model is constructed to predict the shortest waiting time Wi required from now until a vehicle actually connects to the charging station. The specific construction method includes:

[0044] Get the number of vehicles Mi currently charging at each charging station;

[0045] Get the average charging time Tavg of vehicles in the current queue, in minutes;

[0046] The estimated waiting time Wi for the vehicle if it joins the end of the current queue is calculated by multiplying Mi by Tavg to obtain the estimated queuing time in minutes.

[0047] Based on the unit energy consumption rate R (unit: kWh / km) and remaining energy E0 (unit: kWh) under standard driving conditions, the maximum driving distance Lmax is calculated by dividing the remaining energy E0 by R and multiplying by 1000, which is then converted to meters. For example, if the vehicle has 5 kWh of remaining energy and the energy consumption is 0.2 kWh per kilometer, then the maximum driving distance is 5 divided by 0.2, which equals 25 kilometers, or 25,000 meters.

[0048] The minimum physical reachability condition for a charging station is set as follows: the distance Di from L0 to a certain charging station Pi is less than or equal to the vehicle's maximum driving distance Lmax, and the estimated queuing time Wi of the charging station is not greater than Tmax, where Tmax is the longest waiting time the vehicle can wait from its current location.

[0049] If no charging station Pi simultaneously satisfies the following two conditions:

[0050] First, the straight-line distance Di from the current location to the charging station is less than or equal to Lmax;

[0051] Second, the expected waiting time Wi is less than or equal to Tmax (Tmax can be taken as the maximum time that the vehicle's remaining battery power can support, in minutes).

[0052] Then it is determined that the current vehicle is in a critical state where it cannot reach any charging station.

[0053] For vehicles that meet the above criteria for not being able to reach any charging station, their status label is assigned as high risk.

[0054] S300: For vehicles marked as high-risk, calculate their average energy consumption rate R per unit time based on their historical charging records, and predict their remaining driving time T′ by combining the remaining energy E0.

[0055] After marking high-risk vehicles, the remaining driving time of these marked new energy vehicles is predicted to quantify their remaining operational time, providing a timescale basis for subsequent scheduling priority calculations. This step is based on historical charging behavior data of the vehicles and combined with the current remaining battery power for calculation, specifically including:

[0056] First, the historical charging records of the target high-risk vehicle within a preset time window are retrieved from the vehicle historical data storage unit of the IoT platform. This time window contains charging behavior data from the most recent 90 days to ensure data representativeness and timeliness. Each historical charging record includes at least the following fields: start time of a single charge; end time of a single charge; battery level before charging; and battery level after charging.

[0057] The acquired historical charging records are filtered for validity, and the following abnormal records are removed: records with a charging duration of less than 5 minutes; records with interruptions or fault markers during charging; records with zero or negative changes in power level. After filtering, a set of valid historical charging records is obtained, denoted as H, and the number of records is denoted as k, where k is a natural number greater than or equal to 3.

[0058] Based on the effective historical charging record set H, a calculation model for the vehicle's energy consumption rate per unit time is constructed to reflect the energy consumption characteristics of the vehicle under normal operating conditions.

[0059] For each historical record in set H, the average power consumption rate corresponding to that charging session is calculated as follows:

[0060] The difference in battery level before and after the charge is taken as the battery consumption value during the trip.

[0061] The time difference between the start and end times of this charging session is taken as the corresponding runtime.

[0062] Divide the power consumption value by the running time to obtain the power consumption rate per unit time for that record.

[0063] The arithmetic mean of the power consumption rates per unit time corresponding to the above k historical records is used to obtain the average power consumption rate R of the target vehicle per unit time. The physical meaning of R is the average power consumption of the vehicle per unit time, expressed in kilowatt-hours per minute.

[0064] After obtaining the average power consumption rate R per unit time, the vehicle's current remaining power E0 obtained in step S100 is used as the input parameter to construct a remaining driving time prediction model.

[0065] The remaining driving range prediction model is constructed as follows: Using the vehicle's current remaining battery power as the total amount that can be consumed, and the average battery consumption rate per unit time as the consumption rate, the model predicts the length of time the vehicle can operate continuously without charging by using the ratio between the two. Specifically, the prediction process is as follows: Divide the remaining battery power E0 by the average battery consumption rate R per unit time to obtain the remaining driving range T′, where T′ is in minutes. For example, when the vehicle's remaining battery power E0 is 4 kilowatt-hours and the average battery consumption rate R is 0.04 kilowatt-hours per minute, the predicted remaining driving range T′ is 100 minutes.

[0066] Through the above steps, a quantitative prediction of the remaining operating time of high-risk vehicles was achieved, providing a clear and calculable time basis for subsequent charging pile scheduling and priority ranking.

[0067] S400: Add vehicles with remaining range T′ less than the current shortest queuing time to the emergency dispatch queue, and sort all vehicles waiting to be charged according to the reciprocal of their remaining range T′ as weight W′.

[0068] First, for all charging piles in operation within the area, obtain their current queue information, including: the number of vehicles currently charging; the number of vehicles registered to queue for charging; and the historical average charging time for each vehicle, in minutes.

[0069] Based on the above information, the shortest waiting time for each charging station to provide the next available charging spot from the current moment is predicted and denoted as W1, W2...Wn, where n is the total number of charging stations.

[0070] By taking the minimum value of all W1 to Wn, the shortest queuing time for any vehicle in the current area to access a charging station is obtained, denoted as Wmin, in minutes.

[0071] The remaining driving time T′ of each high-risk vehicle predicted in step S300 is compared with Wmin.

[0072] Vehicles meeting the following conditions are considered to be unable to connect to any available charging station before their range expires without adjusting the current queuing structure, posing a risk of being stranded, and must be added to the emergency dispatch queue:

[0073] The remaining battery life T′ is less than the current shortest queuing time Wmin.

[0074] Vehicle information that meets the above conditions is recorded in the emergency dispatch queue, which is denoted as Qemergency and is used to assign higher dispatch priority in subsequent sorting.

[0075] To quantify the scheduling priority of each vehicle waiting to be charged, a scheduling priority weight W′ is constructed. This weight is based on the vehicle's remaining driving time, and its reciprocal is used to reflect the degree of urgency. The shorter the remaining driving time, the higher the weight. The weight W′ is calculated as follows: take the vehicle's remaining driving time T′ as the denominator and take its reciprocal, with the unit being a weight coefficient per minute. For example, when a vehicle has a remaining driving time of 20 minutes, the corresponding weight W′ is 1 divided by 20, which equals 0.05; if another vehicle has T′ of 10 minutes, then W′ is 0.1, with a higher weight. To prevent the weight from approaching infinity due to T′ being too small, a lower limit threshold Tmin is set, with the unit being minutes. When T′ is less than Tmin, W′ is uniformly assigned the value of 1 divided by Tmin to maintain the stability of the weight calculation. The default value of Tmin is 3 minutes.

[0076] All vehicles awaiting charging (including high-risk vehicles and ordinary vehicles) are sorted in descending order according to their scheduling weight W′, with higher weights indicating higher priority.

[0077] A sorted global scheduling queue, denoted as Qall, is generated. Vehicles in Qall will initiate scheduling requests in descending order of priority.

[0078] This step implements a scheduling and sorting mechanism based on remaining operational capacity, effectively guaranteeing the priority access rights of high-risk vehicles in resource-constrained scenarios and reducing the risk of traffic congestion events caused by energy depletion.

[0079] S500: Based on the sorting results, send a scheduling request to the charging pile, and perform minimum disturbance queuing reconstruction calculation for the current user of the charging pile to determine the adjusted waiting time tolerance value ΔW.

[0080] After obtaining the scheduling queue of all vehicles waiting to be charged, sorted by scheduling weight, scheduling requests are initiated sequentially to each charging station within the reach of each vehicle, starting with the vehicle at the top of the queue according to scheduling priority. A minimum disturbance queuing reconfiguration model is then constructed to control the impact range of the queuing reconfiguration. This process specifically includes:

[0081] For each high-priority vehicle waiting to be charged, the set of charging stations within its reach is obtained, and a scheduling request is sent to each target charging station. The scheduling request information includes the vehicle's unique identifier, current time, predicted access time, estimated charging duration, and remaining range.

[0082] Upon receiving a scheduling request, the target charging station returns its current queuing structure data. The queuing structure data includes:

[0083] The vehicle number currently being charged and its remaining charging time;

[0084] The estimated charging time for each vehicle waiting to be charged in the queue;

[0085] The historical average charging cycle data for each vehicle is used to help calculate the future charging completion time.

[0086] After receiving the queuing structure data of the target charging station, a queuing timeline is constructed based on the current moment. This timeline is a linear timeline that sequentially arranges the estimated charging completion time of all vehicles (including vehicles currently charging and those in the queue) from the current moment.

[0087] The time point is calculated by adding the current remaining charging time to the vehicle's historical average charging cycle. If a vehicle currently charging has 30 minutes remaining and the average charging time for the next vehicle is 40 minutes, then the vehicle's end time is 70 minutes from the current moment.

[0088] The estimated charging time of the dispatched vehicle is paired with the time axis to construct multiple reconstruction schemes corresponding to the insertion positions, which are used to evaluate the impact of insertion at different times.

[0089] The vehicle requesting the dispatch is virtually inserted into all possible positions in the current queuing timeline. Each insertion recalculates the waiting time of all other vehicles in the current queuing structure and calculates the difference between their waiting times and those in the original structure.

[0090] The waiting time differences corresponding to all insertion positions are accumulated to obtain the total waiting time increment for all queuing vehicles at each insertion position. This process forms a mapping table between insertion positions and total waiting time increments, which is used for subsequent optimal position selection.

[0091] In the insertion mapping table, the insertion position with the smallest corresponding total waiting time increment is selected as the optimal adjustment position for the vehicle at the target charging station. The queuing structure corresponding to this insertion point is the candidate reconstruction scheme.

[0092] If multiple insertion points produce the same minimum increment, the insertion point that is closest to the current time is selected to reduce the waiting risk for high-weight vehicles.

[0093] The candidate reconfiguration scheme is compared with the original queuing structure. The change in waiting time for all vehicles before and after reconfiguration is statistically analyzed, and the total waiting time increment is calculated and denoted as the adjusted waiting time tolerance value ΔW, in minutes. The preset threshold Wthreshold is the maximum allowable total waiting time increment. The value range of Wthreshold is dynamically set according to the congestion situation of the charging station, with a default initial value of 15 minutes.

[0094] If ΔW is less than or equal to Wthreshold, the queuing structure adjustment is considered to be within the tolerable range, the queuing queue is restructured, and the vehicle requesting dispatch is inserted into the optimal position; if ΔW is greater than Wthreshold, the current insertion attempt is abandoned, the vehicle requesting dispatch enters the next available charging station, and the dispatch evaluation is re-performed.

[0095] Through the above steps, a minimal disruption reconstruction mechanism for the vehicle access process of dispatch requests is realized, ensuring that the overall dispatch efficiency and the probability of timely access for high-risk vehicles are improved without significantly increasing the waiting time of other vehicles.

[0096] S600 If the waiting time tolerance value ΔW is less than the set threshold ε, then the scheduling replacement operation is executed, and the charging pile is notified to release one seat in the current queue and arrange for high-risk vehicles to access the charging.

[0097] After completing the construction of the minimum disturbance queuing reconstruction model and calculating the waiting time tolerance value ΔW of the candidate queuing structure, in order to determine whether the queuing structure is acceptable, ΔW needs to be compared with the set tolerance threshold ε to decide whether to perform the scheduling replacement operation.

[0098] The fault tolerance threshold ε is used to limit the degree of disturbance to the overall queue structure caused by the insertion of high-priority vehicles, measured in minutes. This threshold represents the maximum cumulative waiting time allowed in the entire charging queue due to replacement operations. ε is dynamically adjusted based on the charging station's operating status. The initial recommended value is 15 minutes. When the number of vehicles in the queue exceeds a preset saturation threshold (e.g., 10 vehicles), ε can be automatically reduced to 10 minutes to reduce the sensitivity to queue reconfiguration under high-volume conditions.

[0099] The total waiting time tolerance value ΔW calculated in step S500 is compared with the currently set threshold ε:

[0100] If ΔW is less than or equal to ε, it means that the scheduling insertion operation is within the tolerance range of the existing queuing structure and meets the execution conditions.

[0101] If ΔW is greater than ε, it is considered to be too much of a disturbance to the current queuing structure. The current scheduling attempt is cancelled, and the vehicle is turned to the next available charging station for re-evaluation.

[0102] If the condition ΔW is less than or equal to ε is met, a scheduling replacement operation is performed, which specifically includes:

[0103] Based on the adjustment position selected in step S500, update the current queuing structure of the charging pile and insert high-priority vehicles into the designated positions;

[0104] The estimated access time for all vehicles after this insertion position will be extended by the same amount of time as the estimated charging time for the new vehicle.

[0105] The estimated start and end times of charging for all queued vehicles are updated synchronously for subsequent scheduling predictions and display interfaces.

[0106] After the queue reconstruction is completed, a queue structure update command is sent to the target charging pile control unit through the vehicle-to-pile communication interface, instructing it to perform access and charging scheduling according to the new queue order.

[0107] At the same time, status update information is pushed to the following two types of vehicles respectively:

[0108] The high-priority vehicle that is inserted receives a successful access instruction and learns its expected access time and the number of the charging pile it is connected to.

[0109] All vehicles whose queue positions have changed receive a waiting time update notification, which is displayed on the user terminal and used for further dispatching feedback.

[0110] The above methods enable the priority dispatching of high-risk vehicles within a controllable range, effectively improving the real-time performance and security of dispatching responses while minimizing the impact on the queuing experience of other users.

[0111] S700: If the current charging pile power load exceeds the safety limit, the scheduling target will be transferred to the auxiliary charging pile, the scheduling requests will be reordered according to the weight W′, and the process will return to S500.

[0112] After completing the optimization and reconstruction of the queuing structure and the fault tolerance judgment of the waiting time, in order to ensure the safe and stable operation of the charging pile, the power load status of the target charging pile must be detected before the scheduling is executed. If its current load power exceeds the preset safety limit, the current scheduling plan must be stopped immediately and the scheduling request transferred to the auxiliary charging pile for reprocessing.

[0113] After receiving a scheduling request, the target charging pile reports its current operating status data in real time, including: the current charging power output in kilowatts; the number of vehicles connected in parallel within the same time period; the current charging rate of each vehicle; and the internal temperature rise and protection trigger counter values.

[0114] The total output power of the current charging pile is compared with its set safe power limit. The safe power limit is the maximum rated continuous output power set at the factory, in kilowatts, denoted as Pmax.

[0115] If the current total output power is greater than Pmax, or the difference between it and Pmax is less than the set safety buffer threshold ΔPmin, then the charging pile is considered to be in an overload or near-overload state, and new vehicles are prohibited from connecting to prevent overheating, tripping, or equipment damage.

[0116] If the target charging station is overloaded, select a set of auxiliary charging stations from other charging stations in the area that meet the following conditions for the transfer scheduling request:

[0117] The current power load limit has not been exceeded;

[0118] The current queue length does not exceed the regional average queue length.

[0119] The spatial distance to the current location of the vehicle requesting the dispatch is less than 1.5 times the initial target charging station.

[0120] Let the set of selected auxiliary charging piles be Paux. Each charging pile in the set will be used as a new scheduling target in the subsequent scheduling process.

[0121] After the target charging station is replaced, the current scheduling request queue needs to be reordered.

[0122] The weight W′ of the scheduling priority constructed in step S400 is called, and all vehicles to be scheduled are sorted in descending order based on the reciprocal of the current remaining driving time, generating a new scheduling priority queue Q′all.

[0123] For vehicles that have sent scheduling requests but failed to connect due to the target charging pile being overloaded, scheduling requests are re-initiated to each auxiliary charging pile in Paux according to the W′ value, and their access paths and estimated access times are updated.

[0124] After the scheduling request is successfully transferred to the auxiliary charging pile, the system automatically jumps back to step S500 and restarts the process of sending the scheduling request, obtaining the queuing structure, building the minimum disturbance queuing model, and judging the fault tolerance of the waiting time.

[0125] Through the above process, the system can ensure uninterrupted scheduling and dynamic resource adaptation even in emergency situations where the original target charging pile exceeds its load limit, thereby improving the overall scheduling safety, stability, and fault tolerance.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A new energy vehicle charging pile intelligent management method based on the Internet of Things, characterized in that: The method comprises the following steps: S100, obtaining the unique identifier ID, current position coordinates and remaining power E0 of the new energy vehicle entering the area; S200, comparing the remaining power E0 with the distance of each type of charging pile in the area and the queuing state of the charging pile, judging whether there is a critical situation that the remaining endurance of the vehicle is insufficient to reach any idle charging pile, if yes, marking the vehicle as a high-risk vehicle; S300, for the vehicle marked as high-risk, calculating its average power consumption rate R per unit time according to its historical charging record, and predicting its remaining endurance time T' in combination with the remaining power E0; S400, adding the vehicle with the remaining endurance time T' less than the current shortest queuing waiting time to the emergency scheduling queue, and sorting the scheduling priority of all vehicles to be charged according to the inverse of the remaining endurance time T' as the weight W'; S500, based on the sorting result, sending a scheduling request to the charging pile, and performing a minimum disturbance queuing reconstruction calculation on the current user of the charging pile to determine the adjusted waiting time tolerance value ΔW; S600, if the waiting time tolerance value ΔW is less than the set threshold ε, performing a scheduling replacement operation, notifying the charging pile to release a seat in the current queue, and arranging the high-risk vehicle to access the charging; S700, if the current charging pile power load exceeds the safe upper limit, the scheduling target is transferred to the auxiliary charging pile, the scheduling request is reordered according to the weight W', and the process returns to S500. 2.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 1, characterized in that: The S500 comprises: According to the scheduling priority, based on the scheduling weight of the vehicle with high priority, the scheduling request information with the vehicle identifier, the estimated access time and the remaining endurance time is sent to each charging pile within the reachable range in turn; Receiving the current queuing structure data returned by the target charging pile, including the charging remaining time of the charging vehicle and the queuing vehicle and the historical average charging time; Based on the predicted charging time of the scheduling request vehicle and the target queuing structure, a minimum disturbance queuing reconstruction model is constructed, and the vehicle is preferentially inserted into the position with the minimum impact on the waiting time increment of other vehicles; The waiting time difference of all vehicles before and after reconstruction is calculated, and the total waiting time increment is defined as the adjusted waiting time tolerance value ΔW, and if ΔW is lower than the preset threshold, the queue reconstruction operation is performed. 3.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 2, characterized in that: The construction of the minimum disturbance queuing reconstruction model comprises: Obtaining the predicted charging time of the scheduling request vehicle, combining the remaining charging time of each vehicle in the current queuing queue of the target charging pile and the historical average charging period, and constructing a current queuing sequence time axis; Cumulatively accumulating the increment of the waiting time of each vehicle in the original queue after the scheduling request vehicle is inserted into different positions, forming a mapping relationship between the insertion position and the total waiting increment; Selecting the optimal position corresponding to the insertion point as the adjustment position according to the principle of minimum total waiting time increment; According to the adjustment position, a new queuing structure candidate scheme is generated, and the adjusted waiting time tolerance value ΔW is calculated. 4.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 1, characterized in that: The marking of the vehicle as a high-risk vehicle comprises: If none of the charging piles Pi satisfies the following conditions simultaneously: the straight-line distance Di from the current location to the charging pile is less than or equal to Lmax, Lmax being the maximum driving distance of the vehicle; and the predicted waiting time Wi is less than or equal to Tmax, Tmax being the maximum waiting time of the vehicle from the current location; it is determined that the current vehicle is in a critical state of being unable to reach any charging pile; for the vehicle satisfying the condition of being unable to reach any charging pile, a state label of high risk is assigned to the vehicle. 5.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 1, characterized in that: The method further includes: predicting the remaining endurance time T' of the vehicle, including: using the remaining electric quantity E0 divided by the average unit time electric quantity consumption rate R to obtain the remaining endurance time T'. 6.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 1, characterized in that: The method further includes: sorting the dispatch priorities of all the vehicles to be charged, including: generating a sorted global dispatch queue, denoted as Qall, and the vehicles in the queue will initiate dispatch applications in turn according to the priorities from high to low. 7.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 3, characterized in that: If the waiting time fault tolerance value ΔW is less than a set threshold ε, a dispatch replacement operation is performed, including: according to the selected adjustment location, updating the queuing queue structure of the current charging pile, inserting a high-priority vehicle into a specified location; the predicted access time of all the vehicles after the insertion location is delayed by the predicted charging time of the newly inserted vehicle; and the predicted start charging time and the predicted completion charging time of all the queued vehicles are synchronously updated. 8.The IoT-based intelligent management method for new energy vehicle charging piles according to claim 1, characterized in that: The auxiliary charging pile set is used to transfer the dispatch request, including: the current power load does not exceed the upper limit; the current queuing length does not exceed the average queuing length of the region; and the spatial distance from the current location of the dispatch request vehicle is less than 1.5 times the initial target charging pile.

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