A new energy vehicle charging pile positioning method, system and terminal

Abstract

The application relates to a new energy automobile charging pile position positioning method, a system and a terminal, and belongs to the technical field of new energy. The charging pile position positioning method comprises the following steps: acquiring a GPS coordinate position of a user; after verifying that the GPS coordinate position is valid, calling a database API, calculating a comprehensive score value of each charging pile position within a radius x km; ranking the charging pile positions in descending order of scores, and displaying a TopN candidate charging pile position list, wherein TopN represents the top N; after a user selects a target pile position in the candidate charging pile position list, an optimal path is calculated; and navigation instructions are generated according to the optimal path. The application has the beneficial effects of improving the charging experience of the user and reducing the use cost.

Description

TECHNICAL FIELD

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

[0002] With the enhancement of environmental protection consciousness, new energy vehicles are more and more favored, and the market share continues to grow rapidly. However, new energy vehicles face the problem of charging difficulty in use, and the number of charging piles lags behind the growth rate of new energy vehicles, and the distribution is uneven, and there are few charging piles in remote areas, which brings great inconvenience to the travel of vehicle owners, so an efficient charging pile position positioning method becomes particularly important.

[0003] The common charging pile position positioning method in the industry at present is based on the GPS coordinates of the user, and the charging piles in a certain range are searched, and are sorted and displayed according to some basic information such as distance, whether idle or not. Some methods also combine map navigation function to provide the user with the route to the charging pile.

[0004] However, the existing positioning method often lacks consideration of the comprehensive quality of the charging pile, and only sorts according to simple information, which may recommend some charging piles with quality problems, slow charging speed or high charging cost to the user, affecting the charging experience and use cost of the user. SUMMARY

[0005] In order to improve the charging experience of the user and reduce the use cost, the application provides a charging pile position positioning method, system and terminal for a new energy vehicle.

[0006] In a first aspect, the application provides a charging pile position positioning method for a new energy vehicle, which adopts the following technical scheme:

[0007] A charging pile position positioning method for a new energy vehicle, comprising:

[0008] Obtaining the GPS coordinate position of the user;

[0009] After verifying that the GPS coordinate position is valid, calling a database API to calculate the comprehensive score value of each charging pile position within a radius of x km;

[0010] Sorting in descending order of score, and displaying a TopN candidate charging pile position list, where TopN represents the top N;

[0011] After the user selects a target pile position in the candidate charging pile position list, calculating the optimal path;

[0012] Generating navigation instructions according to the optimal path.

[0013] By adopting the technical scheme, the GPS coordinate position of the user is acquired, the charging pile search is closely carried out around the current location of the user, the charging pile found is highly related to the actual position of the user, unnecessary areas are avoided to waste time and energy to find the charging pile, the search efficiency is improved, the user can quickly find the charging facility close to the user. After verifying the validity of the GPS coordinate position, the database API is called to calculate the comprehensive score value of each charging pile within a radius of x km, and the TopN candidate charging pile list is displayed in descending order of score, which can help the user to filter out the charging pile with good comprehensive conditions from the many charging piles. When the user selects the target pile in the candidate charging pile list, the optimal path is calculated, and the navigation instruction is generated, which greatly saves the time of the user to go to the charging pile, reduces the detour and unnecessary driving in the journey, improves the travel efficiency, especially for the new energy vehicle users with low power, which can quickly reach the charging pile for charging, reduces the risk caused by insufficient power, and helps to optimize the allocation of charging pile resources. By displaying the charging pile with high comprehensive score, the user is guided to preferentially select the high-quality charging pile, which can improve the utilization rate of the high-quality charging pile, and also promote the service quality improvement of some charging piles with low score, thereby improving the service level and resource utilization efficiency of the entire charging pile industry. In addition, from the accurate search of the charging pile to the efficient navigation to the destination, the entire process provides convenient and high-quality services for the user, which can enhance the user's satisfaction with the new energy vehicle charging service, reduce the user's concern about the new energy vehicle charging difficulty, and further promote the popularization and development of new energy vehicles.

[0014] Optionally, the step of calculating the comprehensive score value of the charging pile includes:

[0015] The multi-source index data corresponding to the charging pile is acquired, and the multi-source index data includes the straight-line distance between the charging pile and the GPS coordinate position, the idle rate of the charging pile, the charging price, the user score, the charging power and the parking fee.

[0016] The multi-source index data is normalized to the [0, 1] interval to generate standard index data.

[0017] According to the standard index data, the comprehensive score value of the corresponding charging pile is calculated.

[0018] By adopting the technical scheme, the multi-source index data including the straight-line distance of the charging pile position and the GPS coordinate position, the idle rate, the charging price, the user score, the charging power and the parking fee, etc. can be obtained, and the actual situation of the charging pile can be comprehensively reflected from multiple dimensions. The straight-line distance reflects the convenience of users going to the charging pile position; the idle rate reflects whether waiting is needed during use; the charging price is directly related to the use cost of users; the user score represents the actual experience of other users; the charging power determines the charging speed; and the parking fee is the additional cost when using the charging pile. By comprehensively considering these indexes, the one-sidedness of single index evaluation is avoided, more accurate reference is provided for users, and the comprehensive score is more credible and practical. These multi-source indexes closely surround the core needs of users in the process of using the charging pile. For new energy vehicle users, the distance far or near affects the travel cost and time; the idle rate affects the timeliness of charging; the price is related to the cost of expenditure; the user score reflects the service quality; the charging power affects the charging efficiency; and the parking fee also increases the use cost. By comprehensively considering these indexes, the diversified needs of users can be better met, and the charging pile most suitable for users can be selected. The multi-source index data is normalized to the interval [0, 1] to generate standard index data, so that the influence of the dimension and order of magnitude between different indexes can be eliminated, and the accuracy and reliability of the comprehensive score can be improved.

[0019] Optionally, the step of calculating the comprehensive score value of the corresponding charging pile position according to the standard index data comprises:

[0020] obtaining the preference information and the vehicle information preset by the user;

[0021] obtaining the historical behavior data of the user;

[0022] allocating a dynamic weight to each standard index in the standard index data according to the preference information, the vehicle information and the historical behavior data;

[0023] calculating the comprehensive score value of the corresponding charging pile position according to each standard index and the matched dynamic weight.

[0024] By adopting the technical scheme, the preference information preset by the user is acquired, and the personal preference of the user can be integrated into the calculation of the comprehensive score. For example, some users may pay more attention to the charging price and are willing to run a longer distance for a lower price, and some users may pay more attention to the charging speed and hope to complete the charging quickly. According to the preferences, the dynamic weights are assigned to the standard indicators, so that the comprehensive score can better meet the individual needs of different users and provide the charging pile recommendation that meets the expectations of the user. The vehicle information is also crucial for the selection of the charging pile. Different new energy vehicles may have different requirements for the charging power. Some vehicles support fast charging, and some vehicles can only accept slow charging. When assigning the weights, the standard indicators that match the vehicle can be highlighted more accurately by considering the vehicle information. The historical behavior data of the user can be retrieved to understand the habits and preferences of the user in using the charging pile in the past. For example, if the user often chooses the charging pile that is close to the user and has a high idle rate in the past, the weights of the two standard indicators may be relatively high when assigning the dynamic weights. The weight assignment based on the historical behavior data can more accurately reflect the actual needs and usage habits of the user, and provide the charging pile recommendation that is more suitable for the actual situation of the user, thereby improving the accuracy of the recommendation. The dynamic weights are assigned to the standard indicators in the standard indicator data by comprehensively considering the preference information, vehicle information and historical behavior data of the user, so that the calculation of the comprehensive score is more comprehensive and accurate. This multi-factor consideration method can avoid the limitations brought by a single factor or fixed weights, and more truly reflects the comprehensive value of each charging pile for a specific user. The user can select the charging pile more accurately according to the more accurate comprehensive score value, thereby improving the efficiency and satisfaction of the selection.

[0025] Optionally, the step of calculating the optimal path comprises:

[0026] After the user selects the target pile in the list of alternative charging pile sites, the map API is called;

[0027] The GPS coordinate position and the target pile position are taken as the starting and ending points, and the real-time traffic data is combined to calculate the initial optimal path;

[0028] Based on the current power of the vehicle and the length of the initial optimal path, the remaining power to reach the target pile is predicted;

[0029] It is judged whether the predicted remaining power is lower than the safety threshold;

[0030] If yes, the nearby alternative pile along the way is pushed in real time, and the initial optimal path is corrected;

[0031] If no, when a full load occurs during the navigation to the target pile, the optimal path is recalculated according to the alternative charging pile along the way.

[0032] By adopting the above technical solution, the initial optimal path is calculated by calling the map API, taking the GPS coordinate position of the user and the target pile position as the starting and ending points, and combining real-time traffic data, so that the planned path can fully consider the current traffic conditions and avoid congested road sections, greatly saving the user's time to go to the charging pile and improving the travel efficiency. When the target pile position suddenly reaches full load during navigation, the system automatically plans a backup charging pile position along the way and recalculates the optimal path. This real-time response mechanism can timely solve the sudden problems encountered by the user during charging. Based on the current electric quantity of the vehicle and the length of the initial optimal path, the remaining electric quantity to reach the target pile position is predicted. This function allows the user to have a clearer understanding of the electric quantity of their vehicle and to plan ahead for electric quantity. If the predicted remaining electric quantity is lower than the safety threshold, the system will push the nearest backup pile position along the way and modify the initial optimal path, providing an effective solution for the insufficient electric quantity and ensuring that the user can successfully charge. This guarantee mechanism improves the safety and reliability of the user using new energy vehicles, allowing the user to use new energy vehicles for long-distance or daily travel with greater confidence. From efficient path planning to safe electric quantity management to real-time response to sudden situations, the entire process provides users with comprehensive protection and convenience, which can significantly enhance the user's satisfaction with new energy vehicle charging services and further promote the popularization and development of new energy vehicles.

[0033] Optionally, the step of recalculating the optimal path according to the backup charging pile position along the way before the step of recalculating the optimal path includes:

[0034] predicting the driving duration of the user from the current location to the target pile position;

[0035] retrieving charging pile information with a remaining charging duration less than the driving duration in the target pile position;

[0036] analyzing whether the corresponding charging pile has an idle trend according to the charging pile information;

[0037] if yes, maintaining the current navigation unchanged;

[0038] if no, recalculating the optimal path according to the backup charging pile position along the way.

[0039] By adopting the technical scheme, the driving time length of the user from the current position to the target pile position is predicted, and the charging pile information with the remaining charging time less than the driving time length is called, so that the user can more likely directly use the idle charging pile when reaching the target pile position. The need for a long waiting time after reaching for the vehicle being charged to leave is avoided, thereby greatly saving the time of the user and improving the charging efficiency. By analyzing whether the corresponding charging pile has an idle trend, the optimal path is recalculated in time according to the passing backup charging pile position when there is no idle trend, so that the situation that the user goes to the target pile position and finds no available charging pile and wastes time and energy is avoided. The user can plan to go to the position with the idle charging pile in advance, ensure the smooth progress of the charging process, and improve the efficiency of the entire charging trip.

[0040] Optionally, the step after analyzing whether the corresponding charging pile has an idle trend comprises:

[0041] acquiring actual temperature data and environmental temperature and humidity data of the charging pile with the idle trend;

[0042] inputting the actual temperature data, the environmental temperature and humidity data, and the predicted driving time length into a pre-constructed thermal failure risk threshold model to generate a predicted temperature when reaching the target pile position;

[0043] judging whether the predicted temperature exceeds a temperature threshold value;

[0044] if yes, marking the charging pile as high risk;

[0045] if no, marking the charging pile as a backup charging pile;

[0046] if the number of the backup charging piles reaches a set number value, maintaining the current navigation unchanged;

[0047] if the number of the backup charging piles does not reach the set number value, recalculating the optimal path according to the passing backup charging pile position.

[0048] By adopting the technical scheme, by introducing the thermal failure risk threshold model, the real-time temperature of the charging pile and the environmental temperature and humidity are combined, the working state of the charging pile when the user reaches can be more accurately predicted, and the failure or safety accident caused by overheating of the charging pile is effectively avoided, thereby 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 comprehensively considers multiple dimensions such as thermal risk, environmental conditions, and arrival time, so that the path planning is more intelligent and accurate, the user is avoided to navigate to the unusable charging pile, thereby reducing the invalid trip and improving the practicability of the navigation system and the user satisfaction. By introducing the multi-dimensional data fusion, thermal failure risk prediction, and dynamic path optimization mechanism, the leap from “static navigation” to “intelligent navigation + safety guarantee” is realized.

[0049] Optionally, according to the backup charging pile position of the route, the step of recalculating the optimal path comprises:

[0050] Obtaining real-time state data and positions of all backup charging pile positions of the route;

[0051] Generating a candidate path set according to the current real-time position of the user and the positions of the backup charging pile positions, and combining real-time traffic data;

[0052] According to the current vehicle remaining power of the user, the comprehensive score value of each backup charging pile position, and the real-time state data corresponding to each backup charging pile position, calculating a path score of a candidate path in the candidate path set;

[0053] Taking the candidate path with the highest path score as the optimal path.

[0054] By using the above technical solution, the optimal path is dynamically recalculated according to real-time state data and traffic data, so that the charging pile resources can be more reasonably allocated, the users can be guided to go to the charging piles with idle and better comprehensive conditions, the overall use efficiency of the charging piles is improved, and resource waste is reduced.

[0055] In a second aspect, the application provides a charging pile position positioning system for new energy vehicles, which adopts the following technical solution:

[0056] A charging pile position positioning system for new energy vehicles comprises:

[0057] A GPS position acquisition module is configured to acquire a GPS coordinate position of a user;

[0058] A data processing module is configured to, after verifying that the GPS coordinate position is valid, call a database API, and calculate a comprehensive score value of each charging pile position within a radius of x km;

[0059] A list display module is configured to sort the candidate charging pile positions in descending order of scores, and display a TopN candidate charging pile position list, where TopN represents the top N; the data processing module is further configured to, after a user selects a target pile position from the candidate charging pile position list, calculate an optimal path;

[0060] An instruction generation module is configured to generate navigation instructions according to the optimal path.

[0061] In a third aspect, the application provides a terminal, which adopts the following technical solution:

[0062] A terminal comprises:

[0063] A memory stores a charging pile position positioning program for new energy vehicles;

[0064] A processor is configured to execute programs stored in the memory to implement the steps of the new energy vehicle charging pile positioning method.

[0065] In summary, the present application has at least the following beneficial effects:

[0066] By obtaining the GPS coordinate position of the user, the search for the charging pile can be closely focused on the current location of the user, so that the searched charging pile is highly relevant to the actual location of the user, avoiding wasting time and effort in unnecessary areas to find the charging pile, improving the search efficiency, and enabling the user to quickly find a charging facility close to himself. After verifying the validity of the GPS coordinate position, the database API is called to calculate the comprehensive score value of each charging pile within a radius of x km, and the TopN candidate charging pile list is displayed in descending order of score. This method can help users filter out charging piles with better comprehensive conditions from a large number of charging piles. When the user selects a target pile from the candidate charging pile list, the optimal path is calculated, and navigation instructions are generated based on this, greatly saving the user's time to go to the charging pile, reducing detours and unnecessary travel, and improving travel efficiency. Especially for users of new energy vehicles with low power, they can quickly reach the charging pile for charging, reducing the risk of insufficient power, and helping to optimize the allocation of charging pile resources. By displaying charging piles with high comprehensive scores, users are guided to preferentially select these high-quality charging piles, which can improve the utilization rate of high-quality charging piles, and also encourage some charging pile operators to improve their service quality, thereby improving the service level and resource utilization efficiency of the entire charging pile industry. In addition, from precise search for charging piles to efficient navigation, 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 new energy vehicle charging difficulties, and further promote the popularization and development of new energy vehicles. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 is the first flowchart of the method embodiment of the present application;

[0068] Figure 2 is the second flowchart of the method embodiment of the present application;

[0069] Figure 3 is the third flowchart of the method embodiment of the present application;

[0070] Figure 4 is the fourth flowchart of the method embodiment of the present application;

[0071] Figure 5 is the fifth flowchart of the method embodiment of the present application;

[0072] Figure 6is a seventh flow chart of the method embodiment of the present application.

[0073] Figure 7 is a seventh flow chart of the method embodiment of the present application. DETAILED DESCRIPTION

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will combine the accompanying drawings in the embodiments of the present application to further describe the embodiments of the present application in detail. Figure 1 - the accompanying drawings Figure 7 The technical solutions in the embodiments of the present application are described clearly and completely, and obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0075] The first embodiment of the present application discloses a charging pile positioning method for new energy vehicles. Referring to Figure 1 The charging pile positioning method includes S110-S150:

[0076] S110, acquiring the GPS coordinate position of the user;

[0077] S120, after verifying the validity of the GPS coordinate position, calling a database API to calculate the comprehensive score value of each charging pile within a radius of x km;

[0078] S130, sorting in descending order according to the score, and displaying a list of TopN candidate charging piles, where TopN represents the top N;

[0079] S140, after the user selects a target pile in the list of candidate charging piles, calculating an optimal path;

[0080] S150, generating navigation instructions according to the optimal path.

[0081] Specifically, in the S110 step, when acquiring the GPS coordinate position of the user, the charging applet on the user side can be used: after the user opens the applet on the mobile device, the system uses the built-in GPS module of the device to capture the geographic coordinates (e.g., longitude and latitude) of the user in real time. This function is directly embedded in the "pile searching and navigation" function on the user side, allowing the user to input an address or automatically map positioning, ensuring the accuracy and low latency of coordinate acquisition.

[0082] Referring to Figure 2 In S120, the specific steps for calculating the comprehensive score value of the charging pile include S210-S230:

[0083] S210, obtain multi-source index data corresponding to the charging pile position, the multi-source index data including a straight-line distance between the charging pile position and a GPS coordinate position, an idle rate of the charging pile position, a charging price, a user score, a charging power, and a parking fee;

[0084] S220, normalize the multi-source index data into a [0, 1] interval to generate standard index data;

[0085] S230, calculate a comprehensive score value of the corresponding charging pile position according to the standard index data.

[0086] Specifically, in the S210 step, when verifying the GPS coordinate validity and calling the database API to obtain the multi-source index data, the system first checks whether the GPS coordinate is within a reasonable range (such as the latitude being between -90 degrees and 90 degrees, and the longitude being between -180 degrees and 180 degrees), and prompts the user to retry if it is invalid. After verification, the API (supporting cloud-to-cloud connection or SDK connection, integrating data of three parties related to charging) of the unified standard data source is called through the platform access layer to retrieve all charging pile information within a radius of x km, including the straight-line distance (calculated based on the Haversine formula), the idle rate (obtained from the real-time device monitoring module), the charging price (associated with the tariff setting in the site management), the user score (derived from the evaluation data of the order details), the charging power (from the charging pile specifications), and the parking fee (associated with the site management of the distributor end), etc. These data are uniformly processed by the system middle layer to ensure consistent format.

[0087] Subsequently, in the S220 step, considering the dimensional differences of different indicators, the min-max standardization method is adopted, for example, the straight-line distance is mapped to the [0, 1] interval according to the principle that “the closer the distance, the higher the score” (such as setting the maximum effective distance as 10 km, 1 km as 1, and 10 km as 0), the idle rate is directly taken as the ratio of the original value to 1 (such as an idle rate of 80% corresponding to 0.8), the charging price and the parking fee are processed according to the principle that “the lower the price, the higher the score”, the user score (assuming a full score of 5 points) is converted into a score value / 5, and the charging power is standardized according to the principle that “the higher the power, the higher the score” (such as setting the highest reference power as 180 kW, and the ratio of the actual power to 180 kW as the standard value).

[0088] Referring to Figure 3 S230, the specific steps of calculating the comprehensive score value of the corresponding charging pile position according to the standard index data include S310-S340:

[0089] S310, obtain user preset preference information and vehicle information;

[0090] S320, call historical behavior data of the user;

[0091] S330, assign dynamic weights to each standard index in the standard index data according to the preference information, vehicle information and historical behavior data;

[0092] S340, calculate a comprehensive score value of the corresponding charging pile according to each standard index and the matched dynamic weight.

[0093] Specifically, in the S310 step, when the user's preset preference information and vehicle information are obtained, the system reads the preset data through the personal center module of the user end, such as the charging preference set by the user in the applet, the price sensitivity, and the vehicle battery capacity or charging type in the "My Car" function, which are stored in the user management database of the platform management end for quick retrieval.

[0094] Subsequently, in the S320 step, when the user's historical behavior data is retrieved, the system accesses the user analysis module (belonging to the data center function) of the platform management end, extracts the user's historical charging records (such as frequently used sites, charging duration), scoring behavior (through evaluation data in order details) and reservation habits (derived from reservation charging logs), and these data are aggregated and analyzed in combination with the operation trend chart of the distributor end.

[0095] In the S330 step, when the standard index is assigned a dynamic weight according to the preference information, vehicle information and historical behavior data, a hybrid algorithm combining the analytic hierarchy process (AHP) and machine learning model is used: first, the standard index data is divided into four dimensions: basic attributes (such as charging power, interface compatibility), economy (charging unit price, preferential activities), convenience (distance, surrounding facilities), and reliability (device failure rate, operator response speed), each dimension contains 3-5 sub-indices; then, based on the user's preset preference information, the initial weight matrix is calculated through AHP, for example, if the user sets "charging efficiency" as 5-star important, the initial weight of the corresponding charging power index is increased by 20%; combined with vehicle information, 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" is automatically increased by 15%; at the same time, a logistic regression model is introduced to train the historical behavior data, taking the user's past behavior of selecting charging piles as the dependent variable and the historical performance of each index as the independent variable, and the weight coefficients are iteratively optimized through gradient descent method, for example, if it is found that the user's past 80% charging choices are concentrated in charging piles with unit price lower than 1.8 yuan / kWh, then the dynamic weight of the "charging unit price" index is increased by 10% based on the initial value, and finally the AHP initial weight and the logistic regression model optimized weight are weighted and fused according to the ratio of 6:4 to form the final dynamic weight value of each standard index, ensuring that the weight sum is 1.

[0096] Then, in the step S340, when calculating the comprehensive score value of the charging pile, the system applies a weighted summation formula, comprehensive score = ∑ (each standard index value × dynamic weight), and the calculation of each charging pile 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, the multi-source index data of a certain charging pile is standardized to a distance of 0.8, an idle rate of 0.9, a price of 0.7, a score of 0.85, a power of 0.95, and a parking fee of 0.6, and the corresponding weights are 0.15, 0.15, 0.25, 0.2, 0.3, and 0.05, respectively. Then 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.

[0097] Finally, in the step S130, when ranking and displaying the top N candidate list, the system sorts the comprehensive scores in descending order (using the quicksort algorithm), and dynamically displays the top N (such as N = 5) charging piles on the user end interface, for example, in a card list on the "find pile charging" page, including the map location, idle state, and navigation button. This process directly calls the core flow function of the user end, supports one-key navigation or reservation charging after user preview, and realizes a closed-loop experience.

[0098] The entire implementation process is closely integrated: the user end is the interactive entrance to handle S110, S310, and S130; the platform access layer and the system middle layer handle data acquisition in S210 and normalization in S220; the distributor end provides operation data support in S320; the platform management end coordinates the calculation and weight distribution in S330 and S340. Through the integration of the three-party platform, the real-time and accuracy of the data source are ensured, and the soft and hard decoupling design improves the scalability of the system.

[0099] Referring to Figure 4 , in S140, the specific steps for calculating the optimal path include S410-S460:

[0100] S410, after the user selects the target pile in the candidate charging pile list, the map API is called;

[0101] S420, taking the GPS coordinate position and the target pile position as the starting and ending points, and combining real-time traffic data, the initial optimal path is calculated;

[0102] S430, based on the current electric quantity of the vehicle and the length of the initial optimal path, the remaining electric quantity to reach the target pile is predicted;

[0103] S440, it is judged whether the predicted remaining electric quantity is lower than the safety threshold;

[0104] S450, if yes, the nearby candidate pile along the way is pushed in real time, and the initial optimal path is corrected;

[0105] S460, if not, recalculating the optimal path according to the backup charging pile positions along the way when the full load suddenly occurs during navigation to the target pile position.

[0106] Specifically, in S410, after the user selects the target pile position in the list of alternative charging pile positions through the "pile searching and navigation" function of the charging applet, the system connects with the API of the map service provider through the platform access layer, calls the path planning interface, and can access real-time traffic data.

[0107] In S420, the system takes the current GPS coordinates of the user as the starting point and the coordinates of the target pile position as the ending point, combines the real-time traffic data obtained through API connection from the three-party platform, and calculates the initial optimal path using the path algorithm (such as Dijkstra or A* algorithm) built-in the map API.

[0108] In S430, based on the current vehicle power data uploaded by the user in real time (obtained through the interconnection of the charging applet and the vehicle Bluetooth or vehicle system) and the distance parameters of the initial optimal path, the power prediction model in the system middle layer is called to predict the remaining power to reach the target pile position; the power prediction model can be trained by combining historical charging data, vehicle energy consumption curve, and the influence coefficient of real-time traffic on energy consumption.

[0109] S440 compares the predicted remaining power with the safety threshold (such as 20% of the battery capacity) preset by the platform management end to determine whether the path needs to be adjusted. If the predicted remaining power is lower than the safety threshold, the system retrieves the nearby alternative pile position data (including location, idle state, etc.) of the passing area from the real-time device monitoring module of the platform management end, and re-plans the path through the map API to push the recommended pile position to the user end applet for navigation update.

[0110] Referring to Figure 5 , the steps before recalculating the optimal path according to the backup charging pile positions along the way in S460 include S510-S550:

[0111] S510, predicting the driving duration from the current location to the target pile position;

[0112] S520, retrieving charging pile information with charging remaining duration less than the driving duration;

[0113] S530, analyzing whether the corresponding charging pile has an idle trend according to the charging pile information;

[0114] S540, if yes, maintaining the current navigation unchanged;

[0115] S550, if not, recalculating the optimal path according to the backup charging pile positions along the way.

[0116] Specifically, if the predicted remaining power is sufficient, the system enters the target pile position burst full load alternative. At this time, based on the real-time traffic data of the user's current location and the target pile position, the driving time is estimated through the map API, and in S520, the charging pile information with a remaining charging time less than the driving time in the target pile position is called from the charging station operator management module of the platform management end (i.e. the pile position that may be idle when the user arrives).

[0117] In S530, in combination with the equipment visualization monitoring data provided by the distributor end (such as the historical use frequency of the charging pile, 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).

[0118] Referring to Figure 6 , the steps after analyzing whether the corresponding charging pile has an idle trend in S530 include S610-S670:

[0119] S610, the actual temperature data and environmental temperature and humidity data of the charging pile with an idle trend are obtained;

[0120] S620, the actual temperature data, environmental temperature and humidity data, and predicted driving time are input into the pre-constructed thermal failure risk threshold model to generate the predicted temperature when arriving at the target pile position;

[0121] S630, it is judged whether the predicted temperature exceeds the temperature threshold;

[0122] S640, if yes, mark the charging pile as high risk;

[0123] S650, if no, mark the charging pile as a backup charging pile;

[0124] S660, if the number of backup charging piles reaches the set number value, maintain the current navigation unchanged;

[0125] S670, if the number of backup charging piles does not reach the set number value, recalculate the optimal path according to the backup charging pile position along the way.

[0126] Specifically, for the charging pile with an idle trend, S610 obtains its actual temperature data (reported by the charging pile sensor in real time) and environmental temperature and humidity data (obtained from local regulatory platform or weather API) through the platform access layer, and in S620, these data and the predicted driving time are input into the pre-constructed thermal failure risk threshold model to generate the predicted temperature when arriving; the thermal failure risk threshold model is trained based on the charging pile hardware parameters, historical temperature data and environmental factors, and can be called through the unified data source of the system middle layer.

[0127] S630 compares the predicted temperature with a temperature threshold (e.g., 85°C), and if the predicted temperature exceeds the threshold, the charging pile is marked as high risk, otherwise, the charging pile is marked as a backup charging pile.

[0128] When the number of backup charging piles reaches a number value set by the platform management end (e.g., 2), the system maintains the current navigation unchanged.

[0129] Referring to Figure 7 In S670, according to the backup charging pile positions on the route, the specific steps of recalculating the optimal path include:

[0130] S710, obtaining real-time state data and positions of all backup charging pile positions on the route;

[0131] S720, generating a set of candidate paths based on the current real-time position of the user and the positions of the backup charging pile positions, and combining real-time traffic data;

[0132] S730, calculating path scores of candidate paths in the set of candidate paths based on the current vehicle remaining power of the user, the comprehensive score values of the backup charging pile positions, and the real-time state data corresponding to the backup charging pile positions;

[0133] S740, taking the candidate path with the highest path score as the optimal path.

[0134] Specifically, if the number of backup charging piles does not reach the number value set by the platform management end, the real-time state data (including idle rate, charging power, fault record, reservation record, etc.) and position information of all backup charging pile positions on the route are obtained from the platform management end.

[0135] In S720, based on the current real-time position of the user (updated by the applet positioning function) and the positions of the backup pile positions, a set of candidate paths containing different backup pile positions is generated in batches by combining real-time traffic data and using a map API.

[0136] In S730, the intermediate layer of the system integrates the current vehicle remaining power of the user, the comprehensive score values of the backup pile positions, and the real-time state data (such as whether it is reserved), and calculates the path score of each candidate path by a preset algorithm (such as weighted summation, the weight can be personalized set by the user end or defaulted by the system), wherein the factors such as power sufficiency, pile reliability, and traffic time consumption are given different weights.

[0137] Finally, in S740, the candidate path with the highest path score is taken as the optimal path, which is pushed to the user through the navigation module of the charging app, realizing the intelligent decision-making of the whole process from finding piles to navigation. The data docking, device monitoring, and user interaction involved in the whole process are realized based on the "user end-distributor end-platform management end" architecture, ensuring that the technical solution of data interconnection and unified operation and maintenance management is implemented.

[0138] Based on the above method embodiments, the second embodiment of the present application discloses a charging pile positioning system for new energy vehicles. The charging pile positioning system for new energy vehicles of the embodiment of the present application can implement any of the above charging pile positioning methods for new energy vehicles, and the specific working processes of each module in the charging pile positioning system for new energy vehicles can refer to the corresponding processes in the above method embodiments.

[0139] For ease of understanding, the following is an example: a charging pile positioning system for new energy vehicles, comprising:

[0140] a GPS position acquisition module configured to acquire the GPS coordinate position of the user;

[0141] a data processing module configured to, after verifying the validity of the GPS coordinate position, call a database API and calculate the comprehensive score value of each charging pile within a radius of x km;

[0142] a list display module configured to sort the candidate charging pile list in descending order of score and display the top N candidate charging pile list, where top N represents the top N; the data processing module is further configured to, after the user selects a target pile from the candidate charging pile list, calculate an optimal path;

[0143] an instruction generation module configured to generate navigation instructions according to the optimal path.

[0144] The third embodiment of the present application provides a terminal, which can include a memory and a processor as an implementation manner of the terminal, wherein,

[0145] The memory is configured to store a charging pile positioning program for new energy vehicles;

[0146] The processor is configured to execute the program stored on the memory to implement the steps of the above charging pile positioning method for new energy vehicles.

[0147] The memory can be in communication connection with the processor through a communication bus, and the communication bus can be an address bus, a data bus, a control bus, etc.

[0148] In addition, the memory can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0149] 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 gates or transistor logic devices, discrete hardware components, etc.

[0150] The above are only preferred embodiments of the present application and are not used to limit the protection scope of the present application. Any feature disclosed in the specification (including the abstract and drawings) can be replaced by other equivalent or similar features unless otherwise stated. That is, each feature is only an example of a series of equivalent or similar features unless otherwise stated.

Claims

1. A method for locating charging piles for new energy vehicles, characterized in that, include: Obtain the user's GPS coordinates; After verifying the validity of the GPS coordinates, the database API is called to calculate the comprehensive score of each charging station within a radius of x km; Sort by rating in descending order and display the Top N list of alternative charging stations, where Top N represents the top N ranked stations. After the user selects a target charging station from the list of available charging stations, the map API is invoked. Using the GPS coordinates and target pile location as the starting and ending points, and combining real-time traffic data, the initial optimal path is calculated; Based on the vehicle's current battery level and the initial optimal path length, predict the remaining battery level to reach the target kiln location; Determine whether the predicted remaining battery power is below the safety threshold; If so, the nearest alternative stakeout along the route will be pushed in real time, and the initial optimal path will be corrected. If not, then when a sudden full load occurs during navigation to the target pile, predict the travel time for the user to reach the target pile from the current location; Retrieve information on charging piles at the target location whose remaining charging time is less than the driving time; Based on the charging pile information, analyze whether the corresponding charging pile has an idle trend; If not, the optimal route will be recalculated based on the available backup charging stations along the route. If so, obtain the actual temperature data and ambient temperature and humidity data of the charging piles with an idle trend; The actual temperature data, the ambient temperature and humidity data, and the predicted driving time are input into a pre-built thermal failure risk threshold model to generate the predicted temperature when the target pile is reached. Determine whether the predicted temperature exceeds the temperature threshold; If so, mark the charging station as high-risk; If not, mark the charging station as a backup charging station; If the number of backup charging stations reaches the set value, the current navigation will remain unchanged; If the number of backup charging piles does not reach the set value, the optimal path will be recalculated based on the available backup charging pile locations along the route. Based on the optimal path, navigation instructions are generated.

2. The method for locating charging piles for new energy vehicles according to claim 1, characterized in that, The steps for calculating the comprehensive score of a charging station include: Obtain multi-source indicator data corresponding to the charging pile location. The multi-source indicator data includes the straight-line distance between the charging pile location and the GPS coordinate location, the vacancy rate of the charging pile location, the charging price, user rating, charging power, and parking fee. The multi-source index data is normalized to the [0,1] interval to generate standard index data; Based on the aforementioned standard indicator data, calculate the comprehensive score for the corresponding charging station location.

3. The method for locating charging piles for new energy vehicles according to claim 2, characterized in that, The steps for calculating the comprehensive score of the corresponding charging station based on the aforementioned standard indicator data include: Obtain user-preset preference information and vehicle information; Retrieve users' historical behavior data; Based on the preference information, vehicle information, and historical behavior data, dynamic weights are assigned to each standard indicator in the standard indicator data; Based on various standard indicators and matching dynamic weights, the comprehensive score of the corresponding charging station is calculated.

4. The method for locating charging piles for new energy vehicles according to claim 1, characterized in that, The steps for recalculating the optimal route based on the availability of backup charging stations along the way include: Obtain real-time status data and location of all available backup charging stations along the route; Based on the user's current real-time location and the location of each backup charging station, and combined with real-time traffic data, a set of alternative routes is generated; The path score of the alternative paths in the alternative path set is calculated based on the user's current vehicle battery level, the comprehensive score of each backup charging station, and the real-time status data of each backup charging station. The candidate path with the highest path score is selected as the optimal path.

5. A charging pile location positioning system for new energy vehicles, characterized in that, The method for locating charging pile locations for new energy vehicles as described in any one of claims 1-4 includes: The GPS location acquisition module is used to obtain the user's GPS coordinates. The data processing module is used to call the database API after verifying the validity of the GPS coordinates to calculate the comprehensive score of each charging station within a radius of x km; The list display module is used to sort the list of candidate charging stations in descending order of rating and display the Top N list of candidate charging stations, where Top N represents the top N in ranking; the data processing module is also used to calculate the optimal path after the user selects a target charging station from the list of candidate charging stations. The instruction generation module is used to generate navigation instructions based on the optimal path.

6. A terminal, characterized in that, include: The memory stores the location tracking program for charging stations used in new energy vehicles. A processor is configured to execute a program stored in the memory to implement the steps of the charging pile location method for new energy vehicles as described in any one of claims 1-4.

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