Charging pile position positioning method, system and terminal for new energy vehicle

Abstract

The invention relates to a charging pile position positioning method, system and terminal for a new energy automobile, and belongs to the technical field of new energy, and the charging pile position positioning method comprises the steps: obtaining a GPS coordinate position of a user; after verifying that the GPS coordinate position is valid, calling a database API, and calculating a comprehensive score value of each charging pile position within the radius xkm; descending sorting is carried out according to the scores, an alternative charging pile position list of TopN is displayed, and the TopN represents the top N of the ranking; after a user selects a target pile position in the alternative charging pile position list, an optimal path is calculated; and generating a navigation instruction according to the optimal path. The method has the beneficial effects of improving the charging experience of the user and reducing the use cost.

Description

Technical Field

[0001] The present application relates to the technical field of new energy, and in particular to a method, system and terminal for positioning a charging pile for a new energy vehicle. Background Art

[0002] With growing environmental awareness, new energy vehicles are becoming increasingly popular, and their market share continues to grow rapidly. However, new energy vehicles face the challenge of charging during use. The number of charging stations is not keeping pace with the growth rate of new energy vehicles, and their distribution is uneven. Charging stations are scarce in remote areas, causing significant inconvenience for drivers. Therefore, efficient charging station location methods have become particularly important.

[0003] Currently, the most common charging station location method in the industry uses the user's GPS coordinates to find charging stations within a certain range and sort them based on basic information such as distance and availability. Some methods also incorporate map navigation to provide users with directions to the charging station.

[0004] However, existing positioning methods often lack consideration of the comprehensive quality of charging piles. Based solely on simple information sorting, they may recommend charging piles with quality issues, slow charging speeds, or excessively high charges to users, affecting their charging experience and usage costs. Summary of the Invention

[0005] In order to improve the user's charging experience and reduce usage costs, the present application provides a method, system and terminal for locating charging piles for new energy vehicles.

[0006] In the first aspect, the present application provides a method for locating a charging pile for a new energy vehicle, which adopts the following technical solution: A method for locating a charging pile for a new energy vehicle, comprising: Get the user's GPS coordinates; After verifying that the GPS coordinates are valid, the database API is called to calculate the comprehensive score of each charging station within a radius of x km; Sort by score in descending order and display the TopN list of alternative charging stations, where TopN represents the top N. After the user selects the target charging station from the list of candidate charging stations, the optimal path is calculated; A navigation instruction is generated according to the optimal path.

[0007] By employing the above technical solution, by obtaining the user's GPS coordinates, a charging station search can be closely centered around the user's current location, ensuring that the found charging stations are highly relevant to the user's actual location. This avoids wasting time and effort searching for charging stations in unnecessary areas, improving search efficiency and allowing users to quickly find nearby charging facilities. After verifying the validity of the GPS coordinates, a database API is called to calculate the comprehensive score of each charging station within a radius of x km. A list of the top N candidate charging stations is then sorted in descending order by score. This approach helps users filter out the best charging stations from a large pool of available options. Once the user selects a target station from the list of candidate charging stations, an optimal route is calculated and navigation instructions are generated based on this route. This significantly reduces travel time, reduces detours and unnecessary driving, and improves travel efficiency. This is especially true for new energy vehicle users with low battery levels, allowing them to reach charging stations more quickly, reducing the risk of insufficient battery power and helping to optimize the allocation of charging station resources. By showcasing charging stations with high overall scores and guiding users to prioritize these high-quality charging stations, the utilization rate of high-quality charging stations can be increased. This also encourages operators of lower-scoring charging stations to improve their service quality, thereby improving the service level and resource utilization efficiency of the entire charging station industry. Furthermore, from accurately locating charging stations to efficiently navigating to them, the entire process provides users with convenient and high-quality services, which can enhance user satisfaction with new energy vehicle charging services, reduce user concerns about the difficulty of charging new energy vehicles, and further promote the popularization and development of new energy vehicles.

[0008] Optionally, the steps of calculating the comprehensive scoring value of the charging pile location include: Obtain multi-source indicator data corresponding to the charging pile position, wherein the multi-source indicator data includes the straight-line distance between the charging pile position and the GPS coordinate position, the idle rate of the charging pile position, the charging price, the user rating, the charging power, and the parking fee; Normalizing the multi-source indicator data to the interval [0, 1] to generate standard indicator data; Based on the standard indicator data, the comprehensive score value of the corresponding charging pile position is calculated.

[0009] By employing this technical solution, we capture multi-source data, including the straight-line distance between the charging station and its GPS coordinates, availability, charging price, user ratings, charging power, and parking fees. This provides a comprehensive view of the actual charging station's performance from multiple perspectives. Straight-line distance reflects the convenience of access; availability reflects the need for waiting; charging price directly impacts the user's cost of use; user ratings reflect the actual experience of other users; charging power determines charging speed; and parking fees represent the additional cost of using the charging station. Combining these indicators avoids the one-sidedness of single-metric evaluations, providing users with a more accurate reference and making the overall rating more reliable and practical. These multi-source indicators closely align with the core needs of users when using charging stations. For new energy vehicle users, distance affects travel cost and time; availability affects charging timeliness; price is related to expenses; user ratings reflect service quality; charging power affects charging efficiency; and parking fees also increase usage costs. By comprehensively considering these indicators, we can better meet the diverse needs of users and help them choose the charging station that best suits them. Normalizing multi-source indicator data to the interval [0,1] to generate standard indicator data can eliminate the impact of dimensions and orders of magnitude between different indicators, thereby improving the accuracy and reliability of the comprehensive score.

[0010] Optionally, the step of calculating the comprehensive score of the corresponding charging pile position according to the standard indicator data includes: Obtain user preset preference information and vehicle information; Retrieve the user's historical behavior data; assigning a dynamic weight to each standard indicator in the standard indicator data according to the preference information, vehicle information and the historical behavior data; Based on various standard indicators and matching dynamic weights, the comprehensive score of the corresponding charging pile position is calculated.

[0011] By employing the above technical solution, users' preset preferences are captured, incorporating their personal preferences into the calculation of the comprehensive score. For example, some users may prioritize charging prices and be willing to travel further for lower prices; others, on the other hand, prioritize charging speed and desire a quick charge. Dynamic weightings are assigned to each standard indicator based on these preferences, allowing the comprehensive score to better meet the individual needs of different users and provide charging station recommendations that better meet their expectations. Vehicle information is also crucial for selecting a charging station. Different new energy vehicles may have different charging power requirements, with some supporting fast charging while others may only accept slower charging speeds. By considering vehicle information, the weightings can be more accurately prioritized for the standard indicators that best match the vehicle. Retrieving historical user behavior data can reveal a user's past charging station usage habits and preferences. For example, if a user frequently chooses a charging station that is close to them and has high availability, these two standard indicators may be given higher weights when assigning dynamic weights. Weighting based on historical behavior data more accurately reflects a user's actual needs and usage habits, providing charging station recommendations that are more tailored to their specific needs and improve recommendation accuracy. By comprehensively considering the user's preference information, vehicle information and historical behavior data, dynamic weights are assigned to each standard indicator in the standard indicator data, making the calculation of the comprehensive score more comprehensive and accurate. This multi-factor consideration method can avoid the limitations brought by a single factor or fixed weight, and more truly reflect the comprehensive value of each charging pile to a specific user; users can choose charging piles more specifically based on this more accurate comprehensive score value, thereby improving the efficiency and satisfaction of the selection.

[0012] Optionally, the steps of calculating the optimal path include: After the user selects the target charging station in the list of candidate charging stations, the map API is called; Calculate the initial optimal path based on the GPS coordinates and the target pile position as the starting and ending points, and in combination with real-time traffic data; Based on the vehicle's current battery level and the initial optimal path length, predict the remaining battery level required to reach the target charging station; Determine whether the predicted remaining power is lower than the safety threshold; If so, the nearest alternative pile position along the way is pushed in real time, and the initial optimal path is corrected; If not, when the target charging pile is suddenly fully loaded during navigation, the optimal path is recalculated based on the spare charging piles passed by.

[0013] By implementing the above technical solution, the system uses the map API to calculate an initial optimal route based on the user's GPS coordinates and the target charging station location as the starting and ending points, combined with real-time traffic data. This planned route fully considers current traffic conditions and avoids congested areas, significantly reducing users' travel time to charging stations and improving travel efficiency. If the target charging station suddenly reaches capacity during navigation, the system automatically maps alternate charging stations along the way and recalculates the optimal route. This real-time response mechanism promptly addresses unexpected charging issues encountered by users. Based on the vehicle's current battery level and the length of the initial optimal route, the system predicts the remaining battery life at the target charging station. This feature provides users with a clearer understanding of their vehicle's battery life and allows them to plan their battery life in advance. If the predicted remaining battery life falls below a safe threshold, the system immediately recommends the nearest alternate charging stations along the way and revises the initial optimal route. This provides an effective solution for low battery situations and ensures smooth charging. This safeguard mechanism enhances the safety and reliability of new energy vehicle use, allowing users to confidently use new energy vehicles for long-distance or daily travel. From efficient route planning to safe power management, and then to real-time response to emergencies, the entire process provides users with comprehensive protection and convenience, which can significantly enhance user satisfaction with new energy vehicle charging services and further promote the popularization and development of new energy vehicles.

[0014] Optionally, the steps before recalculating the optimal path based on the spare charging piles passed through include: Predicting the driving time from the user's current location to the target location; Retrieving information of a charging pile at the target location whose remaining charging time is less than the driving time; Analyze whether the corresponding charging pile has an idle trend based on the charging pile information; If so, keep the current navigation unchanged; If not, recalculate the optimal route based on the spare charging piles along the way.

[0015] By adopting the above technical solution, the driving time from the user's current location to the target charging station is predicted, and the information of the charging piles in the target charging station with a remaining charging time less than the driving time is retrieved, which makes it more likely that the user will directly use the idle charging piles when arriving at the target charging station. This avoids the need to wait for a long time for the charging vehicle to leave after arrival, thereby greatly saving the user's time and improving the efficiency of charging. By analyzing whether the corresponding charging pile has an idle trend, the optimal path is recalculated in time based on the spare charging piles passed by when there is no idle trend, avoiding the situation where the user arrives at the target charging station and finds that there are no available charging piles, thus wasting time and energy. The user can plan in advance to go to the location with idle charging piles to ensure the smooth charging process and improve the efficiency of the entire charging journey.

[0016] Optionally, the steps after analyzing whether the corresponding charging pile has an idle trend include: Obtain actual temperature data and ambient temperature and humidity data of charging piles with idle trends; Inputting the actual temperature data, the ambient temperature and humidity data, and the predicted driving duration into a pre-built thermal failure risk threshold model to generate a predicted temperature when arriving at the target pile position; Determining whether the predicted temperature exceeds a temperature threshold; If yes, the charging pile is marked as high risk; If not, the charging pile is marked as a spare charging pile; If the number of the backup charging piles reaches the set value, the current navigation remains unchanged; If the number of the backup charging piles does not reach the set number value, the optimal route is recalculated based on the backup charging piles along the way.

[0017] By adopting the above technical solution, by introducing a thermal failure risk threshold model and combining the real-time temperature of the charging pile with the ambient temperature and humidity, it is possible to more accurately predict the working status of the charging pile when the user arrives, effectively avoiding failures or safety accidents caused by overheating of the charging pile, and improving the safety factor of the charging process. When judging whether the charging pile is available, the system not only considers whether it is idle, but also integrates multiple dimensions such as thermal risk, environmental conditions and arrival time, making path planning more intelligent and accurate, avoiding users navigating to unavailable charging piles, thereby reducing invalid trips and improving the practicality of the navigation system and user satisfaction. By introducing multi-dimensional data fusion, thermal failure risk prediction and dynamic path optimization mechanisms, a leap from "static navigation" to "intelligent navigation + safety assurance" has been achieved.

[0018] Optionally, the steps of recalculating the optimal route based on the spare charging piles along the route include: Obtain real-time status data and locations of all spare charging stations along the way; Generate a set of alternative routes based on the user's current real-time location and the locations of each backup charging station, combined with real-time traffic data; Calculate the path score of the alternative path in the alternative path set based on the user's current vehicle remaining power, the comprehensive score of each standby charging pile, and the real-time status data corresponding to each standby charging pile; The candidate path with the highest path score is taken as the optimal path.

[0019] By adopting the above technical solution, the optimal path is dynamically recalculated based on real-time status data and traffic data, which can make charging pile resources more reasonably allocated, avoid users from concentrating on certain popular but possibly saturated charging piles, and guide users to charging piles that are idle and have better overall conditions, thereby improving the overall utilization efficiency of charging piles and reducing resource waste.

[0020] Secondly, this application provides a charging pile positioning system for new energy vehicles, which adopts the following technical solutions: A charging pile positioning system for new energy vehicles, comprising: GPS location acquisition module, used to obtain the user's GPS coordinate location; A data processing module is used to call the database API after verifying the validity of the GPS coordinate position to calculate the comprehensive score value of each charging pile position within a radius of x km; A list display module is used to display a list of top N candidate charging piles in descending order of score, where Top N represents the top N rankings; the data processing module is also used to calculate the optimal path after the user selects a target pile in the list of candidate charging piles; The instruction generation module is used to generate navigation instructions according to the optimal path.

[0021] In a third aspect, the present application provides a terminal that adopts the following technical solution: A terminal, comprising: A memory storing a charging pile location positioning program for new energy vehicles; The processor is used to execute the program stored in the memory to implement the steps of the above-mentioned method for locating the charging pile for new energy vehicles.

[0022] In summary, this application has at least the following beneficial effects: By obtaining the user's GPS coordinates, the search for charging stations can be closely centered around the user's current location, ensuring that the found charging stations are highly relevant to the user's actual location. This avoids wasting time and effort searching for charging stations in unnecessary areas, improving search efficiency and allowing users to quickly find nearby charging facilities. After verifying the validity of the GPS coordinates, a database API is called to calculate the comprehensive score of each charging station within a radius of x km. A list of the top N candidate charging stations is then sorted in descending order by score. This approach helps users filter out the most suitable charging stations from a large pool of available options. Once the user selects a station from the list of candidate charging stations, the optimal route is calculated and navigation instructions are generated based on this route. This significantly reduces travel time, reduces detours and unnecessary driving, and improves travel efficiency. This is especially true for new energy vehicle users with low battery levels, allowing them to reach charging stations more quickly, reducing the risk of insufficient battery power and helping to optimize the allocation of charging station resources. By showcasing charging stations with high overall scores and guiding users to prioritize these high-quality charging stations, the utilization rate of high-quality charging stations can be increased. This also encourages operators of lower-scoring charging stations to improve their service quality, thereby improving the service level and resource utilization efficiency of the entire charging station industry. Furthermore, from accurately locating charging stations to efficiently navigating to them, the entire process provides users with convenient and high-quality services, which can enhance user satisfaction with new energy vehicle charging services, reduce user concerns about the difficulty of charging new energy vehicles, and further promote the popularization and development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a first flow chart of an embodiment of the method of the present application; Figure 2 This is a second flow chart of the method embodiment of the present application; Figure 3 This is a third flow chart of the method embodiment of the present application; Figure 4 This is a fourth flow chart of the method embodiment of the present application; Figure 5 This is a fifth flow chart of the method embodiment of the present application; Figure 6 This is a sixth flow chart of the method embodiment of the present application; Figure 7 This is the seventh flow chart of the method embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the appended drawings of the embodiments of the present invention. Figure 1 -Attached Figure 7The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] The first embodiment of the present application discloses a method for positioning a charging pile for a new energy vehicle. Figure 1 The charging pile location method includes S110-S150: S110, obtaining the GPS coordinates of the user; S120, after verifying the validity of the GPS coordinate location, call the database API to calculate the comprehensive score value of each charging pile location within the radius of x km; S130, sorting by score in descending order, and displaying a list of TopN candidate charging piles, where TopN represents the top N. S140, after the user selects a target charging station from the list of candidate charging stations, the optimal path is calculated; S150: Generate navigation instructions based on the optimal path.

[0026] Specifically, in step S110, when obtaining the user's GPS coordinate location, it can be achieved through the charging applet on the user side: after the user opens the applet on the mobile device, the system uses the device's built-in GPS module to capture the user's geographic coordinates (such as longitude and latitude) in real time. This function is directly embedded in the "Find a pile and navigate" function on the user side, allowing the user to enter an address or automatically locate the map to ensure the accuracy and low latency of coordinate acquisition.

[0027] Reference Figure 2 In S120, the specific steps of calculating the comprehensive score value of the charging pile position include S210-S230: S210, obtaining multi-source indicator data corresponding to the charging pile location, the multi-source indicator data including the straight-line distance between the charging pile location and the GPS coordinate location, the idle rate of the charging pile location, the charging price, the user rating, the charging power, and the parking fee; S220, normalizing the multi-source indicator data to the interval [0, 1] to generate standard indicator data; S230: Calculate the comprehensive score of the corresponding charging station based on the standard indicator data.

[0028] Specifically, in step S210, when verifying the validity of GPS coordinates and calling the database API to obtain multi-source indicator data, the system first checks whether the GPS coordinates are within a reasonable range (e.g., latitude between -90 degrees and 90 degrees, longitude between -180 degrees and 180 degrees). If invalid, the user is prompted to retry. After verification, the platform access layer calls the API of a unified standard data source (supporting cloud-to-cloud or SDK docking, integrating data from third-party charging platforms) to retrieve information on all charging pile locations within a radius of x km, including straight-line distance (calculated based on the Haversine formula), idle rate (obtained from the real-time device monitoring module), charging price (linked to the rate settings in the site management), user rating (derived from order details), charging power (derived from charging pile specifications), and parking fees (linked to the distributor's site management). This data is uniformly processed by the system middle layer to ensure consistent formatting.

[0029] Subsequently, in step S220, taking into account the differences in the dimensions of different indicators, the min-max standardization method is adopted. For example, the straight-line distance is mapped to the interval [0,1] according to the principle of "the closer the distance, the higher the score" (for example, if the maximum effective distance is set to 10km, it is 1 when the distance is 0km, and 0 when it is 10km), the idle rate is directly taken as the ratio of the original value to 1 (for example, an idle rate of 80% corresponds to 0.8), the charging price and parking fee are processed according to the principle of "the lower the price, the higher the score", the user score (assuming the full score is 5 points) is converted to the score value / 5, and the charging power is standardized according to the principle of "the higher the power, the higher the score" (for example, if the maximum reference power is set to 180kW, the ratio of the actual power to 180kW is the standard value).

[0030] Reference Figure 3 , S230, based on the standard indicator data, the specific steps of calculating the comprehensive score value of the corresponding charging pile position include S310-S340: S310, obtaining user preset preference information and vehicle information; S320, retrieve the user's historical behavior data; S330, assigning a dynamic weight to each standard indicator in the standard indicator data based on the preference information, vehicle information, and historical behavior data; S340: Calculate the comprehensive score of the corresponding charging pile position based on various standard indicators and matching dynamic weights.

[0031] Specifically, in step S310, when obtaining the user's preset preference information and vehicle information, the system reads the preset data through the personal center module on the user side, such as the charging preferences and price sensitivity set by the user in the mini program, and the vehicle battery capacity or charging type in the "My Car" function. This information is stored in the user management database on the platform management side for quick retrieval.

[0032] Subsequently, in step S320, when retrieving the user's historical behavior data, the system accesses the user analysis module (a data center function) on the platform management side, extracts the user's historical charging records (such as frequently used stations, charging duration), rating behavior (through evaluation data in order details) and reservation habits (derived from reservation charging logs), and combines these data with the operational trend chart on the distributor side for aggregate analysis.

[0033] In step S330, when assigning dynamic weights to standard indicators based on preference information, vehicle information and historical behavior data, a hybrid algorithm combining the analytic hierarchy process (AHP) and a machine learning model is used: First, the standard indicator data is divided into four dimensions: basic attributes (such as charging power, interface compatibility), economy (charging unit price, promotional activities), convenience (distance, surrounding facilities), and reliability (equipment failure rate, operator response speed). Each dimension contains 3-5 sub-indicators; then, based on the user's preset preference information, the initial weight matrix is ​​calculated through AHP. For example, when the user sets "charging efficiency" to 5 stars, the initial weight of the corresponding charging power indicator is increased by 20%; combined with vehicle information, if If the user's vehicle supports 120kW fast charging and the current remaining power is less than 20%, the weight of the "charging power index" will be automatically increased by 15%. At the same time, a logistic regression model is introduced to train historical behavior data, with the user's past behavior of selecting charging piles as the dependent variable and the historical performance of various indicators as the independent variable. The weight coefficients are iteratively optimized through the gradient descent method. For example, if it is found that 80% of the user's charging choices in the past are concentrated on charging piles with a unit price of less than 1.8 yuan / kWh, the dynamic weight of the "charging unit price" indicator will be increased by 10% based on the initial value. Finally, the AHP initial weight and the logistic regression model optimization weight are weighted and fused in a ratio of 6:4 to form the final dynamic weight value of each standard indicator, ensuring that the total weight is 1.

[0034] Then, in step S340, when calculating the comprehensive score of the charging station, the system applies a weighted summation formula: comprehensive score = ∑(each standard indicator value × dynamic weight). The calculation of each charging station is batch processed in the real-time device monitoring module of the platform management end (for example, using a distributed computing framework to improve performance). For example, after standardization of the multi-source indicator data of a charging station, the distance is 0.8, the idle rate is 0.9, the price is 0.7, the score is 0.85, the power is 0.95, and the parking fee is 0.6. The corresponding weights are 0.15, 0.15, 0.25, 0.2, 0.3, and 0.05, respectively. The comprehensive score is 0.8 × 0.15 + 0.9 × 0.15 + 0.7 × 0.25 + 0.85 × 0.2 + 0.95 × 0.3 + 0.6 × 0.05 = 0.835.

[0035] Finally, in step S130, when sorting and displaying the top N candidate lists, the system sorts the comprehensive scores in descending order (using a quick sort algorithm) and dynamically displays the top N (e.g., N=5) charging pile locations on the user-side interface. For example, they are presented as a card list on the "Find a Charging Pile" page, including map location, idle status, and navigation buttons. This process directly calls the core process functions of the user side, supporting one-click navigation or charging appointments after previewing, achieving a closed-loop experience.

[0036] The entire implementation process is seamless: the user side, serving as the interactive entry point, handles S110, S310, and S130; the platform access layer and system middle layer handle data acquisition for S210 and normalization for S220; the distributor side provides operational data to support S320; and the platform management side coordinates the calculation and weighting of S330 and S340. This three-party platform integration ensures the real-time and accuracy of data sources, while the decoupling of software and hardware enhances system scalability.

[0037] Reference Figure 4 In S140, the specific steps of calculating the optimal path include S410-S460: S410: After the user selects a target charging station from the list of candidate charging stations, the map API is called; S420, using the GPS coordinates and the target pile position as the starting and ending points, and combining with real-time traffic data, calculating an initial optimal path; S430, predicting the remaining power required to reach the target charging station based on the current power level of the vehicle and the initial optimal path length; S440, determining whether the predicted remaining power is lower than a safety threshold; S450: If yes, the nearest candidate pile position along the way is pushed in real time, and the initial optimal path is corrected; If not, in step S460, when the vehicle suddenly reaches full load during navigation to the target charging station, the optimal path is recalculated based on the spare charging stations passed by.

[0038] Specifically, in S410, after the user selects the target charging station from the list of alternative charging stations through the "Find Station and Navigation" function of the charging applet, the system connects with the map service provider's API through the platform access layer, calls its path planning interface, and can access real-time traffic data.

[0039] Entering S420, the system uses the user's current GPS coordinates as the starting point and the target pile position coordinates as the end point, combines the real-time traffic data obtained from the third-party platform through API docking, and uses the built-in path algorithm of the map API (such as Dijkstra or A* algorithm) to calculate the initial optimal path.

[0040] In S430, based on the vehicle's current power data uploaded in real time by the user end (obtained through the charging applet and the vehicle's Bluetooth or vehicle-mounted system interconnection) and the distance parameters of the initial optimal path, the power prediction model of the system's middle layer is called to predict the remaining power when arriving at the target charging station; the power prediction model can be trained by combining historical charging data, vehicle energy consumption curves, and the influence coefficient of real-time road conditions on energy consumption.

[0041] The S440 compares the predicted remaining battery life with a safety threshold preset by the platform management (e.g., 20% battery capacity) to determine whether the route needs to be adjusted. If the predicted remaining battery life falls below the safety threshold, the system retrieves data on nearby alternative charging stations (including location, idle status, etc.) from the platform management's real-time device monitoring module. The system then replans the route using the map API and pushes the recommended charging stations to the user's mini-program for navigation updates.

[0042] Reference Figure 5 In S460, the steps before recalculating the optimal path based on the spare charging piles passed by include S510-S550: S510, predicting the driving time from the user's current location to the target location; S520, retrieving information of a charging pile at a target location where the remaining charging time is less than the driving time; S530: Analyze whether the corresponding charging pile has an idle trend based on the charging pile information; S540, if yes, maintain the current navigation unchanged; If not, recalculate the optimal route based on the spare charging piles along the way.

[0043] Specifically, if the remaining charge is predicted to be sufficient, the system will enter a backup plan for when the target charging station is suddenly fully loaded. At this point, based on the user's current location and real-time traffic data at the target charging station, the system estimates the driving time using the map API. At step S520, the system retrieves information about charging stations at the target charging station with a remaining charge time less than the driving time (i.e., charging stations that are expected to be available when the user arrives) from the charging station operator management module on the platform management side.

[0044] In S530, combined with the device visualization monitoring data provided by the distributor (such as the historical usage frequency of the charging piles and the current charging progress change trend), it is analyzed whether these charging piles have a clear idle trend (for example, the current charging capacity has reached 90% and the charging power has decreased).

[0045] Reference Figure 6 , S530, analyzing whether the corresponding charging pile has an idle trend, the steps after that include S610-S670: S610, obtaining actual temperature data and ambient temperature and humidity data of the charging pile with an idle trend; S620: Input the actual temperature data, the ambient temperature and humidity data, and the predicted driving time into a pre-built thermal failure risk threshold model to generate a predicted temperature when arriving at the target pile position; S630, determining whether the predicted temperature exceeds a temperature threshold; S640: If yes, mark the charging pile as high risk; S650: If not, mark the charging pile as a spare charging pile; S660: If the number of spare charging piles reaches the set number, the current navigation is maintained unchanged; S670: If the number of spare charging piles does not reach the set number value, the optimal route is recalculated based on the spare charging piles along the way.

[0046] Specifically, for charging piles that tend to be idle, S610 obtains their actual temperature data (reported in real time by the charging pile sensors) and ambient temperature and humidity data (obtained from the local regulatory platform or meteorological API) through the platform access layer. In S620, this data and the predicted driving duration are input into a pre-built thermal failure risk threshold model to generate a predicted temperature upon arrival. The thermal failure risk threshold model is trained based on charging pile hardware parameters, historical temperature data, and environmental factors, and can be called through the unified data source of the system middle layer.

[0047] S630 compares the predicted temperature with the temperature threshold (e.g., 85°C). If it exceeds the threshold, it is marked as high risk; otherwise, it is marked as a backup charging pile.

[0048] When the number of spare charging piles reaches the value set by the platform management end (such as 2), the system maintains the current navigation unchanged.

[0049] Reference Figure 7 In S670, the specific steps of recalculating the optimal path based on the spare charging piles along the way include: S710, obtaining real-time status data and locations of all spare charging piles passed by; S720, generating a set of alternative routes based on the user's current real-time location and the locations of each backup charging station, in combination with real-time traffic data; S730, calculating a path score for an alternative path in the alternative path set based on the user's current vehicle remaining power, the comprehensive score of each standby charging station, and the real-time status data corresponding to each standby charging station; S740: The candidate path with the highest path score is selected as the optimal path.

[0050] Specifically, if the number of spare charging piles does not reach the number value set by the platform management end, the real-time status data (including idle rate, charging power, fault records, reservation records, etc.) and location information of all spare charging piles passed by are obtained from the platform management end.

[0051] In S720, based on the user's current real-time location (updated through the positioning function of the mini program) and the locations of each alternative pile position, combined with real-time traffic data, the map API is used to batch generate a set of alternative routes containing different passing pile positions.

[0052] In S730, the system's middle layer integrates the user's current vehicle remaining power, the comprehensive score of each alternative charging station, and real-time status data (such as whether it has been reserved), and calculates the path score of each alternative path through a preset algorithm (such as weighted summation, where the weight can be personalized by the user or set by the system default). Factors such as power adequacy, charging station reliability, and travel time are assigned different weights.

[0053] Finally, in S740, the alternative path with the highest path score is selected as the optimal path and pushed to the user through the navigation module of the charging applet, realizing intelligent decision-making for the entire process from finding the charging pile to navigation. At the same time, the data docking, equipment monitoring and user interaction of the three-party platforms involved in the entire process are all implemented based on the "user end-distributor end-platform management end" architecture, ensuring the implementation of the technical solutions for data interconnection and unified operation and maintenance management.

[0054] Based on the above method embodiments, the second embodiment of this application discloses a charging pile location positioning system for new energy vehicles. The charging pile location positioning system for new energy vehicles in the embodiment of this application can implement any of the above methods for locating charging pile locations for new energy vehicles, and the specific working processes of each module in the charging pile location positioning system for new energy vehicles can refer to the corresponding processes in the above method embodiments.

[0055] For ease of understanding, an example is given below: A charging pile positioning system for new energy vehicles includes: GPS location acquisition module, used to obtain the user's GPS coordinate location; The data processing module is used to call the database API after verifying the validity of the GPS coordinate location to calculate the comprehensive score value of each charging pile within a radius of x km; The list display module is used to display the top N candidate charging stations in descending order of score, where Top N represents the top N. The data processing module is also used to calculate the optimal path after the user selects the target station in the list of candidate charging stations. The instruction generation module is used to generate navigation instructions based on the optimal path.

[0056] The third embodiment of the present application provides a terminal. As an implementation of the terminal, the terminal may include: a memory and a processor; wherein, The memory is used to store the charging pile positioning program for new energy vehicles; The processor is used to execute the program stored in the memory to implement the steps of the above-mentioned method for locating the charging pile for new energy vehicles.

[0057] The memory may be communicatively connected to the processor via a communication bus, and the communication bus may be an address bus, a data bus, a control bus, or the like.

[0058] In addition, the memory may include a random access memory (RAM) and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0059] The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0060] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for positioning a charging pile for a new energy vehicle, characterized in that: include: Get the user's GPS coordinates; After verifying that the GPS coordinates are valid, the database API is called to calculate the comprehensive score of each charging station within a radius of x km; Sort by score in descending order and display the TopN list of alternative charging stations, where TopN represents the top N. After the user selects the target charging station from the list of candidate charging stations, the optimal path is calculated; generating navigation instructions according to the optimal path; The steps to calculate the optimal path include: After the user selects the target charging station in the list of candidate charging stations, the map API is called; Calculate the initial optimal path based on the GPS coordinates and the target pile position as the starting and ending points, and in combination with real-time traffic data; Based on the vehicle's current battery level and the initial optimal path length, predict the remaining battery level required to reach the target charging station; Determine whether the predicted remaining power is lower than the safety threshold; If so, the nearest alternative pile position along the way is pushed in real time, and the initial optimal path is corrected; If not, when the target charging pile is suddenly fully loaded during navigation, the optimal path is recalculated based on the spare charging piles passed by.

2. A method for positioning a charging pile for a new energy vehicle according to claim 1, characterized in that: The steps for calculating the comprehensive score of the charging pile include: Obtain multi-source indicator data corresponding to the charging pile position, wherein the multi-source indicator data includes the straight-line distance between the charging pile position and the GPS coordinate position, the idle rate of the charging pile position, the charging price, the user rating, the charging power, and the parking fee; Normalizing the multi-source indicator data to the interval [0, 1] to generate standard indicator data; Based on the standard indicator data, the comprehensive score value of the corresponding charging pile position is calculated.

3. A method for positioning a charging pile for a new energy vehicle according to claim 2, characterized in that: The steps of calculating the comprehensive score of the corresponding charging pile position according to the standard indicator data include: Obtain user preset preference information and vehicle information; Retrieve the user's historical behavior data; assigning a dynamic weight to each standard indicator in the standard indicator data according to the preference information, vehicle information and the historical behavior data; Based on various standard indicators and matching dynamic weights, the comprehensive score of the corresponding charging pile position is calculated.

4. A method for positioning a charging pile for a new energy vehicle according to claim 1, characterized in that: Based on the alternate charging stations passed by, the steps before recalculating the optimal route include: Predicting the driving time from the user's current location to the target location; Retrieving information of a charging pile at the target location whose remaining charging time is less than the driving time; Analyze whether the corresponding charging pile has an idle trend based on the charging pile information; If so, keep the current navigation unchanged; If not, recalculate the optimal route based on the spare charging piles along the way.

5. A method for positioning a charging pile for a new energy vehicle according to claim 4, characterized in that: The steps after analyzing whether the corresponding charging pile has an idle trend include: Obtain actual temperature data and ambient temperature and humidity data of charging piles with idle trends; Inputting the actual temperature data, the ambient temperature and humidity data, and the predicted driving duration into a pre-built thermal failure risk threshold model to generate a predicted temperature when arriving at the target pile position; Determining whether the predicted temperature exceeds a temperature threshold; If yes, the charging pile is marked as high risk; If not, the charging pile is marked as a spare charging pile; If the number of the backup charging piles reaches the set value, the current navigation remains unchanged; If the number of the backup charging piles does not reach the set number value, the optimal route is recalculated based on the backup charging piles along the way.

6. A method for positioning a charging pile for a new energy vehicle according to claim 1, characterized in that: The steps for recalculating the optimal route based on the spare charging piles along the route include: Obtain real-time status data and locations of all spare charging stations along the way; Generate a set of alternative routes based on the user's current real-time location and the locations of each backup charging station, combined with real-time traffic data; Calculate the path score of the alternative path in the alternative path set based on the user's current vehicle remaining power, the comprehensive score of each standby charging pile, and the real-time status data corresponding to each standby charging pile; The candidate path with the highest path score is taken as the optimal path.

7. A charging pile positioning system for new energy vehicles, characterized in that: The method for locating a charging pile for a new energy vehicle according to any one of claims 1 to 6 is implemented, comprising: GPS location acquisition module, used to obtain the user's GPS coordinate location; A data processing module is used to call the database API after verifying the validity of the GPS coordinate position to calculate the comprehensive score value of each charging pile position within a radius of x km; A list display module is used to display a list of top N candidate charging piles in descending order of score, where Top N represents the top N rankings; the data processing module is also used to calculate the optimal path after the user selects a target pile in the list of candidate charging piles; The instruction generation module is used to generate navigation instructions according to the optimal path.

8. A terminal, characterized in that: include: A memory storing a charging pile location positioning program for new energy vehicles; A processor is used to execute the program stored in the memory to implement the steps of the method for locating a charging pile for a new energy vehicle as described in any one of claims 1 to 6.

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