Methods, systems, equipment, and media for guiding vehicle charging based on dynamic incentives

Abstract

This application relates to the field of transportation interconnection technology, and in particular to a vehicle charging guidance method, system, device, and medium based on dynamic discounts. The method includes: calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's initial remaining battery power; obtaining the real-time usage status of all first estimated charging stations within the estimated driving range; calculating the optimal charging station based on the real-time usage status and estimated arrival time; calculating the charging discount scheme for the optimal charging station and sending it to the vehicle terminal; if none of the first estimated charging stations within the estimated driving range can meet the charging demand, searching for external charging station resources and connecting them to the current system; generating a list of optimal charging schemes including all available charging resources, locking the target charging pile after user selection, and planning the navigation route. This application can alleviate problems such as inaccurate charging equipment recommendations and uneven distribution of charging resources in existing systems.

Description

Technical Field

[0001] This application relates to the field of transportation interconnection technology, and in particular to a vehicle charging guidance method, system, device and medium based on dynamic incentives. Background Technology

[0002] With the rapid increase in the popularity of new energy vehicles, the distribution and management of charging stations are facing increasingly severe challenges. Despite the continuous increase in the number of charging stations, congestion often occurs at popular service areas during peak hours, while resources remain idle in adjacent service areas, leading to longer waiting times and increased range anxiety for users. This phenomenon not only affects the user's travel experience but also reduces the overall utilization efficiency of charging facilities.

[0003] Existing charging management systems primarily rely on static data for charging station recommendations, such as geographical location, fixed priority, or simply the number of charging piles. They lack dynamic, comprehensive assessments of vehicle remaining battery power, real-time traffic conditions, and actual user needs, resulting in inaccurate recommendations. When multiple users are simultaneously directed to the same popular charging station, it can easily lead to localized resource shortages while other stations have available resources. When the number of vehicles is excessive, it can create an imbalance between supply and demand for charging resources, resulting in a poor charging experience for platform users.

[0004] Therefore, the existing charging management system relies on static data and lacks dynamic response and resource optimization allocation mechanisms, resulting in technical problems such as supply and demand imbalance, long waiting times for users, and increased range anxiety, which urgently need to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a vehicle charging guidance method, system, device and medium based on dynamic incentives, which aims to solve the technical problems of charging management systems relying on static data, lacking dynamic response and resource optimization allocation mechanisms, resulting in supply and demand imbalance, long user waiting time and increased range anxiety.

[0006] To achieve the aforementioned objectives, this application proposes a vehicle charging guidance method based on dynamic incentives, the method comprising:

[0007] Based on the vehicle's initial remaining battery power, calculate the estimated driving range of the vehicle on the current navigation route;

[0008] Obtain the real-time usage status of all first estimated charging stations within the estimated driving range;

[0009] Based on the real-time usage and estimated arrival time, the optimal charging station is calculated;

[0010] Calculate the optimal charging discount scheme for each charging station and send it to the vehicle terminal;

[0011] If none of the first estimated charging stations within the estimated driving range can meet the charging needs, then search for external charging station resources and connect them to the current system.

[0012] Generate a list of optimal charging solutions, including all available charging resources, and after the user selects a target charging station, lock in the target charging station and plan a navigation route.

[0013] Furthermore, the step of calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power includes:

[0014] Identify the first start command, and based on the first start command, identify the vehicle's first remaining battery power.

[0015] Obtain the vehicle's navigation information, and identify the vehicle's current location and the set current navigation route based on the navigation information;

[0016] Based on the remaining battery power and the vehicle's average energy consumption rate, a preliminary estimate of the theoretical driving distance on the current navigation route is made.

[0017] The system obtains real-time traffic information for the current navigation route and adjusts the distance based on the theoretical driving distance using the real-time traffic information to obtain the maximum distance the vehicle can travel on the current navigation route with the current battery level, thus obtaining the estimated driving range.

[0018] Further, the step of obtaining the real-time usage of all first estimated charging stations within the estimated driving range includes:

[0019] Based on the vehicle's estimated driving range and combined with the information of the current navigation route, the information of each internal charging station is filtered out from all possible internal charging station resources within the vehicle's estimated driving range, thus obtaining the information of the first estimated charging station.

[0020] The charging data of each first estimated charging station is collected in real time to obtain the real-time usage status of all first estimated charging stations.

[0021] Furthermore, the step of calculating the optimal charging station based on the real-time usage and estimated arrival time includes:

[0022] Obtain the second navigation information for all preceding vehicles along the navigation route from the current vehicle location to the farthest first estimated charging station;

[0023] If the second estimated charging station in the navigation route of the second navigation information overlaps with any of the first estimated charging stations corresponding to the current vehicle, then the second remaining battery power of the corresponding preceding vehicle is obtained.

[0024] Based on the previous vehicle's historical charging preferences and the discounts offered by the current optimal charging station for the previous vehicle, a selection probability score is given for each of the second estimated charging stations included in the navigation route of the previous vehicle.

[0025] Based on the selection probability score, the estimated usage of each charging station in each of the first estimated charging stations is calculated at the corresponding estimated arrival time of the vehicle.

[0026] Based on the projected usage, the optimal charging stations are selected.

[0027] Furthermore, the step of calculating the optimal charging station's charging discount scheme and sending it to the vehicle terminal includes:

[0028] Obtain the empty charging pile ratio of the optimal charging station corresponding to the current vehicle, the queuing time of each charging station in the first estimated charging station for the estimated arrival time of the current vehicle, and the time-of-use electricity price for the current time period.

[0029] Based on the empty charging pile ratio, the queuing time, and the time-of-use electricity price, the charging discount scheme for the current vehicle corresponding to the optimal charging station is calculated according to a preset weight and sent to the vehicle terminal; wherein the optimal charging station includes one or more.

[0030] Furthermore, the step of searching for external charging station resources and connecting them to the current system if none of the first estimated charging stations within the estimated driving range can meet the charging demand includes:

[0031] If the queuing time of the first estimated charging station within the estimated driving range exceeds the first preset threshold for the estimated arrival time of the vehicle, it is determined that the charging demand cannot be met.

[0032] Based on the number of vehicles whose charging needs cannot be met on the current navigation route and the current electricity price benchmark, calculate the basic price increase ratio and generate a price increase plan;

[0033] Based on the price increase plan, access requests are sent to various external charging station resources within the estimated driving range of the vehicle.

[0034] Based on the response data received and parsed from external charging station resources by the current system, basic information about the external charging station resources is obtained.

[0035] Furthermore, the step of generating an optimal charging scheme list including all available charging resources, locking the target charging station and planning the navigation route after the user selects the option includes:

[0036] Update the status information of all charging stations based on all available charging resources;

[0037] Based on the updated status information of each charging station, the first estimated charging station is updated;

[0038] Arrange the first estimated charging stations corresponding to the current vehicles according to the corresponding discount levels, and generate a list of the optimal charging solutions for the corresponding vehicles.

[0039] Upon receiving the user's first confirmation instruction, the system locks the charging permissions of the target charging station at the corresponding time and plans a navigation route to the user's terminal.

[0040] A second aspect of this application proposes a vehicle charging guidance system based on dynamic incentives, comprising:

[0041] The mileage calculation module is used to calculate the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power.

[0042] The real-time acquisition module is used to acquire the real-time usage status of all first estimated charging stations within the estimated driving range;

[0043] The optimal calculation module is used to calculate the optimal charging station based on the real-time usage and estimated arrival time.

[0044] The discount generation module is used to calculate the optimal charging discount scheme for charging stations and send it to the vehicle terminal;

[0045] An external access module is used to search for external charging station resources and access the current system if none of the first estimated charging stations within the estimated driving range can meet the charging needs.

[0046] The recommended planning module generates a list of optimal charging solutions, including all available charging resources. After the user selects a target charging station, it locks in the target charging station and plans the navigation route.

[0047] A third aspect of this application also provides an apparatus comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the methods described above.

[0048] A fourth aspect of this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0049] Beneficial effects

[0050] This solution dynamically calculates available charging stations within the estimated driving range by combining the vehicle's remaining battery power and the current navigation route. It then selects the optimal charging station based on real-time usage and estimated arrival time, improving the accuracy and real-time performance of the recommendations. By calculating and sending charging discount schemes corresponding to the optimal charging station to the vehicle, it helps guide users to choose charging stations rationally and optimize resource allocation. When all charging stations within the estimated driving range cannot meet the demand, the system can proactively search for and access external charging resources, expanding the range of available stations and improving the system's adaptability and service continuity. Finally, the system generates a list of optimal charging schemes including all available resources. After user confirmation, the system locks the target charging station and plans the navigation route, improving user efficiency and charging experience. This solution enables intelligent scheduling and closed-loop management of the charging guidance process, effectively alleviating problems such as inaccurate recommendations and uneven resource allocation caused by the reliance on static information in existing systems, and improving the overall utilization and service level of the charging network. Attached Figure Description

[0051] Figure 1 This is a schematic flowchart illustrating a vehicle charging guidance method based on dynamic incentives according to an embodiment of this application.

[0052] Figure 2 This is a schematic block diagram of a vehicle charging guidance system based on dynamic incentives, according to an embodiment of this application.

[0053] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application;

[0054] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is referred to as “connected” or “coupled” to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein may include wireless connection or wireless coupling. The term “and / or” as used herein includes all or any modules and all combinations of one or more associated listed items.

[0057] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0058] Reference Figure 1 This invention provides a vehicle charging guidance method based on dynamic incentives, including steps S1-S6, specifically:

[0059] S1. Based on the vehicle's initial remaining battery power, calculate the estimated driving range of the vehicle on the current navigation route;

[0060] S2. Obtain the real-time usage status of all first estimated charging stations within the estimated driving range;

[0061] S3. Based on the real-time usage and estimated arrival time, calculate the optimal charging station;

[0062] S4. Calculate the optimal charging discount scheme for charging stations and send it to the vehicle terminal;

[0063] S5. If none of the first estimated charging stations within the estimated driving range can meet the charging needs, then search for external charging station resources and connect them to the current system.

[0064] S6. Generate a list of optimal charging solutions, including all available charging resources, and lock the target charging station and plan the navigation route after the user selects it.

[0065] In step S1, the system first receives a "first start command" from the vehicle terminal or mobile application. This command can be initiated by the user (e.g., clicking the "Find a charging station" button), or it can be automatically triggered by the system when it detects that the remaining battery level is below a certain threshold (e.g., 20%); or it can be a command received by the system from the user starting the vehicle or activating the corresponding navigation. At this point, the system begins to calculate the estimated driving range. Then, the system obtains the "first remaining battery level" information from the vehicle's CAN (Controller Area Network) bus or Battery Management System (BMS) and combines it with the vehicle's historical energy consumption data (e.g., the average energy consumption per 100 kilometers of the last 30 trips) to estimate the theoretical driving distance. For example, if the current remaining battery level is 40 kWh and the historical average energy consumption is 15 kWh / 100 km, the theoretical driving distance is approximately 267 km. Subsequently, the system calls the navigation module to obtain the current vehicle location, destination, and navigation route, and obtains real-time traffic conditions (e.g., congestion index, speed limit changes, weather effects, etc.) through the network interface. Based on these dynamic factors, the theoretical driving distance is updated and corrected in real time. For example, in areas with frequent highway travel or during rainy or snowy weather, the system will appropriately reduce the driving range; while in slow-moving urban traffic, it may moderately extend it. The resulting "estimated driving range" is used to define the geographical area that the vehicle can reach under the current conditions, providing a basis for the next step of selecting charging stations within that area.

[0066] Suppose a user is driving on a highway with 30% battery remaining and a historical average energy consumption of 16 kWh / 100km. The system identifies their navigation route as from x to z, with a total distance of approximately 280km. The system calculates a theoretical remaining driving range of 240km, but due to heavy traffic and a rain warning, the estimated range is adjusted to 210km. Therefore, the vehicle can only reach the service area at segment y at most, and cannot continue to z. This provides a basis for subsequent decisions regarding searching for available charging stations only within segment y. This step represents a shift from static battery display to dynamic range assessment, avoiding misjudgments caused by ignoring road conditions and driving habits in traditional methods. It improves the accuracy and practicality of subsequent recommendations and is the premise and foundation of the entire charging guidance logic.

[0067] As described in step S2 above, after calculating the estimated driving range of the vehicle, the core task of step S2 is to filter out all possible charging stations based on this mileage range and the current navigation route, and obtain the latest operating status data of these stations to provide data support for subsequent optimal station recommendations. First, the system combines the estimated driving range with the navigation route information to determine the geographical area that the vehicle can cover in its current battery state. For example, if the estimated driving range is 180 kilometers and the navigation route extends along a highway, the system will filter out all charging stations located within 180 kilometers of this route and directly reachable via the navigation path from the built-in or cloud-based charging station database, forming a "first estimated charging station list." This process not only relies on static map data but also needs to be dynamically adjusted based on real-time traffic conditions to ensure that the selected stations are actually reachable.

[0068] Next, the system obtains its operational status data in real time through the backend data. Since all charging stations rely on the same backend data for management and operation, this makes data acquisition more unified and efficient. Here, "all charging stations" refers to the internal charging resources currently managed by the system. The usage and operational data of these internal charging resources are managed based on the system's backend and corresponding database. Typically, the corresponding charging stations are those with established long-term management partnerships or those operated by the system itself. This data usually includes the total number of charging piles and their current availability, the number of people queuing and the estimated waiting time for the current time period, whether the station is under maintenance or experiencing a malfunction, and whether it supports the user's charging protocol (such as DC fast charging), etc.

[0069] Suppose an electric vehicle is traveling on a highway, currently 250 kilometers from its destination, with 30% battery remaining and a historical average energy consumption of 16 kWh / 100km. Based on this information, the system calculates an estimated remaining driving range of 187.5 kilometers. Subsequently, based on backend data, the system identifies three charging stations within this range: Station A, Station B, and Station C. All of these stations are managed and operated by the system platform.

[0070] Site A: The information obtained shows that this site has a total of 10 charging piles, 2 of which are currently available. There are 0 people in the queue, and the estimated waiting time is 0 minutes. It supports DC fast charging.

[0071] Station B: This station has 5 charging stations, all of which are occupied. There is 1 person in the queue, and the estimated waiting time is 40 minutes. DC fast charging is not supported.

[0072] Station C: This station has 10 charging stations, all of which are occupied. There are 2 people in the queue, and the estimated waiting time is 60 minutes. It supports DC fast charging.

[0073] Based on the above information, the system can dynamically perceive and update the real-time status of charging stations that the vehicle may choose, which facilitates accurate recommendations and discount calculations in the future.

[0074] As described in step S3 above, the core task of step S3 is to comprehensively consider the current usage status of each station, the user's expected arrival time, and the selection habits of the vehicle in front, and use a multi-dimensional scoring model to select the most suitable charging station as the recommendation result. This process not only relies on static data, but also combines dynamic load prediction and user behavior analysis to ensure the accuracy and practicality of the recommendation results.

[0075] First, the system identifies all vehicles traveling in front of the current vehicle (within the estimated driving range) within this system. For each vehicle in front, the system obtains its corresponding "second navigation information", including the vehicle's current location, destination, remaining battery power, and navigation route.

[0076] For each preceding vehicle, the system analyzes its historical charging preferences. For example, a preceding vehicle typically chooses to charge when its battery level drops to around 40%, and tends to choose the most favorable charging station recommended by the system. The system records these preferences, forming a behavioral pattern database for each preceding vehicle. Based on the preference for each choice, the system determines the weighting of the corresponding preference tag, and then uses this weighting to determine the likelihood of the preceding vehicle choosing any charging station at that moment.

[0077] Specifically, the system compares the navigation route of the preceding vehicle with the navigation route of the current vehicle to find charging stations that the two vehicles may visit together (i.e., overlapping stations). For example, if the navigation route of the preceding vehicle 1 passes through stations E, F, and G, while the navigation route of the current vehicle is E, F, and H, then the overlapping stations are E and F, proving that the preceding vehicle 1 may also choose two of the estimated charging stations of the current vehicle for charging.

[0078] For each overlapping stop, the system calculates the probability that the preceding vehicle will choose that stop based on its historical selection preferences and current remaining battery power. For example, if the preceding vehicle 1 currently has 45% battery power, and its historical data shows that it has an 80% probability of choosing the system's recommended best-off stop when its battery power is below 50%, then the system will score stops E and F respectively. Assuming that stop E is the best-off stop for the preceding vehicle 1, then stop E will receive a higher score.

[0079] Combining the selection probability score of the preceding vehicle with the real-time usage of the current station (such as the number of available charging stations and the number of people in the queue), the system predicts the actual usage of the current vehicle when it arrives at each station. For example, suppose station E currently has 8 available charging stations, but considering that multiple preceding vehicles are highly likely to choose this station, the system predicts that the number of available charging stations may decrease to 4 within the next 15 minutes. However, at the same time, some vehicles will finish charging and leave the corresponding charging station within this 15-minute period (the time the current vehicle arrives at E) (for example, the background data shows that a vehicle can finish charging in 12 minutes). Therefore, the final calculation shows that when the current vehicle arrives at E, there are predicted to be 5 available charging stations, and no queuing is required.

[0080] The same method is used to estimate the usage of other charging stations. Based on the predicted usage, the system combines other factors (such as distance, estimated arrival time, supported charging protocols, etc.) and employs a pre-defined weighted scoring model to calculate a comprehensive score for each station. Finally, the stations with the highest scores are recommended as the optimal charging stations.

[0081] For example, the comprehensive scoring formula mainly considers the following four key factors:

[0082] Queue length: The shorter the queue, the better, as it directly affects the user experience;

[0083] Empty charging station ratio: The more empty charging stations there are, the better, to ensure that users can charge immediately upon arrival;

[0084] Charging protocol compatibility: Sites that support the user's required charging protocol (such as DC fast charging) will be given priority;

[0085] User charging habits: Adjust site priority based on user charging habits (e.g., more willing to charge when battery is below 40%).

[0086] The corresponding comprehensive score weighting value can be 0.4 × Empty Charging Pile Ratio + 0.3 × Reciprocal of Queue Time + 0.2 × Charging Protocol Matching Degree + 0.1 × User's Habitual Charging Power Matching Degree. Where: Empty Charging Pile Ratio: The proportion of currently idle charging piles out of the total number of charging piles; the higher the value, the higher the score; Reciprocal of Queue Time: The shorter the queue time, the higher the score; using the reciprocal is to give higher weight to stations with shorter waiting times; Charging Protocol Matching Degree: Stations that support the charging protocol required by the user score higher; User's Habitual Charging Power Matching Degree: Adjusting station priority based on the user's historical charging habits (e.g., more willing to charge when the battery is below 40%).

[0087] The system recommends one or more optimal charging stations for users to choose from. For example, the system may recommend stations E and F, where station E has the highest score, but station F also has a high score. That is, when the score of a corresponding station exceeds a first preset threshold, both are recommended as optimal charging stations to suit users with different needs.

[0088] In summary, step S3, by introducing a preceding vehicle behavior prediction mechanism, significantly improves the accuracy and flexibility of the recommendations. Compared to relying solely on static site data or simple priority ranking, this solution can better predict future site usage, preventing users from being directed to sites nearing full capacity, while also improving the overall utilization and service quality of the charging network. Furthermore, recommending multiple optimal sites further enhances users' autonomy in their choices, improving the user experience.

[0089] In this way, the system not only achieves accurate recommendations for charging stations, but also fully considers users' actual needs and behavioral habits, thus providing a more personalized service experience.

[0090] In step S4, based on the load difference between the currently recommended station and other estimated stations, as well as the matching degree between the station and the user's charging behavior habits, a personalized charging discount plan is dynamically generated and pushed to the vehicle terminal to enhance the user's willingness to go to the target station, especially to provide stronger incentives in the case of resource shortages or mismatched user behavior.

[0091] First, calculate the total load of the first estimated charging station and the load of the current station:

[0092] Total load = Σ(number of people queuing at each station + (1 - percentage of empty piles at each station) * total number of piles at each station)

[0093] Current station load = Current station queue number of people + (1 - Current station empty station percentage) * Current station total number of stations

[0094] Load ratio = Total load / Current site load

[0095] For example: Suppose there are three sites A, B, and C.

[0096] Site A: Empty pile ratio 0.2, 8 people in queue, total number of piles 10;

[0097] Station B: Empty pile ratio 0.6, 2 people in queue, total number of piles 10;

[0098] Station C: Empty pile ratio 0.5, 3 people in queue, total number of piles 10;

[0099] The total load is then:

[0100] (8+(1-0.2)*10)+(2+(1-0.6)*10)+(3+(1-0.5)*10)=8+10+5+2+4+8=37;

[0101] If the recommended optimal site includes the current site A, its load = 8 + (1 - 0.2) * 10 = 18; load ratio = 37 / 18 ≈ 2.06;

[0102] The system then considers the user's charging habits, such as the user's usual starting charge threshold (e.g., usually starting to charge when the battery is below 30%), preferred charging times (e.g., preferring to charge during off-peak hours at night), and price sensitivity (whether the user has changed their choice due to discounts).

[0103] The corresponding discount coefficient = α × load ratio + β × user habit deviation;

[0104] Wherein, α: load ratio weight, reflecting the impact of the site's load level relative to other sites on the discount; β: user habit deviation weight, reflecting the compensation demand caused by mismatch in user behavior; the larger the load ratio, the more serious the user habit deviation → the higher the discount coefficient → the greater the discount. After calculating the corresponding discount, a corresponding discount plan is generated based on the time-of-use electricity price at that time. The discount plan is pushed to the user's terminal device, such as the in-vehicle central control screen or mobile app, through the vehicle network communication interface (such as the in-vehicle TSP platform, 4G / 5G connection); the display format includes but is not limited to pop-up prompts, card-style lists, voice broadcasts, etc.; each discount information is marked with key elements: discount content, effective time, usage conditions, validity period, etc., to ensure that users are informed and can make reasonable decisions.

[0105] Step S4 enables a truly dynamic discount generation mechanism, where the discount level is no longer fixed but intelligently adjusted based on both site load and user behavior. This enhances the system's guidance capabilities, especially when resources are scarce or user behavior is mismatched, by increasing discounts to improve recommendation acceptance rates. It also optimizes the overall network resource scheduling efficiency, alleviating congestion pressure on popular sites while promoting the utilization rate of less popular sites. Furthermore, it enhances user experience and platform stickiness, ensuring that users receive reasonable incentives even in "less than ideal" situations.

[0106] As described in step S5, in some cases, all charging stations within the estimated driving range may be unable to meet the user's charging needs (e.g., all stations are operating at full capacity or do not support the required charging protocol). After confirming that internal stations cannot meet the demand, the system initiates an extended search mechanism to find external charging station resources within the estimated driving range and integrates them into the current system to ensure that the user can find a suitable charging solution. These external charging station resources include personal charging stations or charging stations with designated operators or operating platforms, typically managed by independent operating systems and databases. The system first checks the status of all charging stations within the estimated driving range, including but not limited to the percentage of available charging piles, the number of people queuing, and whether they support the required charging protocol. If it is found that all stations cannot provide readily available charging piles, or the expected waiting time is too long (e.g., exceeding the user's acceptable time), it is determined that "charging demand cannot be met." For each found external station, the system first calculates a reasonable electricity price based on factors such as the current market electricity price and supply and demand. The system then sends a bid request to the external station, which typically includes the electricity price, the usage period, and the expected number of charging piles. After receiving a bid request, an external site decides whether to accept it based on its own operational strategy. If accepted, it returns a confirmation message to this system. Once the external site accepts the bid request, the system integrates the site's resources into the current system. The system reads some of the backend data provided by the external site through API or protocol interfaces, mainly including: real-time status data: such as the current number of available charging piles, the number of people in the queue, etc.; historical usage data: used to predict future load conditions; and billing data: used for subsequent settlement.

[0107] This solution provides users with effective charging solutions even in extreme conditions; it enhances the user experience, avoiding anxiety caused by not being able to find a suitable charging station; it promotes interconnectivity between different charging networks, contributing to the construction of a more complete and efficient charging infrastructure ecosystem; it optimizes overall operational efficiency, making resource allocation more rational and reducing inconvenience for users on the go; it increases the diversity of charging resources, expanding users' choices and improving charging convenience by integrating external site resources; and it implements dynamic pricing and resource scheduling, better balancing supply and demand and improving resource utilization through bidding and auction mechanisms.

[0108] As described in step S6 above, all internal and external charging resources are integrated, and a "list of optimal charging solutions" containing multiple preferred options is generated based on factors such as real-time status, user preferences, preferential strategies, and load forecasting, for users to choose from. Once selected, the system will lock onto the target charging station and plan the optimal navigation path for it.

[0109] In another embodiment, if the vehicle's remaining battery power is lower than a preset minimum threshold, the system determines whether the number of reachable charging stations is less than a preset number based on the navigation route. If it is less than the preset number, it determines whether the current target charging station has an available charging pile or is in a queue. If there is no available charging pile or the vehicle is in a queue, it identifies whether the remaining battery power of the vehicle currently charging or queuing is higher than a second preset threshold. If it is higher than the second preset threshold, it calculates a list of subsequent charging stations that can be reached with the corresponding battery power and calculates the corresponding estimated queuing time. If there is an alternative station with a shorter queuing time than the current station, it generates recommendation information and pushes the recommendation information, including preferential incentive content, to eligible vehicles.

[0110] In this embodiment, if the remaining battery power of a vehicle is lower than a preset minimum threshold, and the navigation route indicates that the remaining battery power can only support access to a number of charging stations that are less than a preset number (e.g., only one charging station can be reached in the current direction of travel), and the corresponding charging station has no empty charging piles or requires queuing, then the remaining battery power of the vehicle currently charging at the charging pile or the vehicle currently in the queue is identified. If the remaining battery power is higher than a second preset threshold (which, after calculation, allows access to subsequent charging stations, including one or more charging stations), the estimated usage of the charging stations that can be reached with the battery power of the second preset threshold is calculated. If the estimated usage shows that the corresponding queuing time is lower than the queuing time of the current charging station, then the corresponding preferential information is calculated, and the corresponding charging station is recommended to the vehicle with the remaining battery power higher than the second preset threshold or the queuing vehicle, so that vehicles with the remaining battery power lower than the preset minimum threshold can reduce queuing time, charge as soon as possible, and improve the charging experience.

[0111] In one embodiment, the step of calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power includes:

[0112] S10. Identify the first start command, and identify the first remaining battery power of the vehicle based on the first start command;

[0113] S11. Obtain the vehicle's navigation information, and identify the vehicle's current location and the set current navigation route based on the navigation information;

[0114] S12. Based on the remaining battery power and the vehicle's average energy consumption rate, make a preliminary estimate of the theoretical driving distance on the current navigation route;

[0115] S13. Obtain real-time traffic information for the current navigation route, and adjust the distance based on the theoretical driving distance based on the real-time traffic information to obtain the maximum distance that the vehicle can travel on the current navigation route based on the existing battery power, and obtain the estimated driving range.

[0116] In this embodiment, after receiving the user's start command, the system first identifies the vehicle's current remaining battery power and obtains the vehicle's navigation information, including its current location and the set driving route. Subsequently, the system combines the vehicle's historical average energy consumption rate to preliminarily estimate the theoretical distance the vehicle can travel with the current battery level. To improve the accuracy of the estimate, the system further integrates real-time traffic data, such as congestion, speed limit changes, and weather factors, to dynamically adjust the theoretical driving distance, ultimately determining the maximum distance the vehicle can travel along the current navigation route under current conditions—the estimated driving range. This process achieves an intelligent evolution from static battery level assessment to dynamic route adaptation, providing a reliable basis for subsequent charging station recommendations.

[0117] In one embodiment, the step of obtaining the real-time usage of all first estimated charging stations within the estimated driving range includes:

[0118] S20. Based on the vehicle's estimated mileage and combined with the information of the current navigation route, filter out the information of each internal charging station among all possible internal charging station resources within the vehicle's estimated mileage range, thus obtaining the information of the first estimated charging station.

[0119] S21. Collect charging data of each first estimated charging station in real time to obtain the real-time usage status of all first estimated charging stations.

[0120] In this embodiment, the system first uses the vehicle's estimated mileage and current navigation route information, combined with road topology and the spatial distribution of charging stations, to accurately filter all charging stations within the vehicle's reach, forming a first estimated charging station list. Subsequently, the system obtains real-time operational data for each station through its internal database or by connecting to a third-party platform interface. This data includes, but is not limited to, the total number of charging piles, the number of currently available charging piles, the number of vehicles in queue, the average waiting time, the current electricity price, and whether the charging protocol required by the vehicle is supported. This data is uniformly integrated and dynamically updated to ensure the system can accurately grasp the real-time usage of each estimated charging station, providing comprehensive data support for subsequent optimal station recommendations, load prediction, and preferential strategy generation.

[0121] In one embodiment, the step of calculating the optimal charging station based on the real-time usage and estimated arrival time includes:

[0122] S30. Obtain the second navigation information for all preceding vehicles in the navigation route between the current vehicle location and the farthest first estimated charging station.

[0123] S31. If the second estimated charging station in the navigation route of the second navigation information overlaps with any of the first estimated charging stations corresponding to the current vehicle, then obtain the second remaining battery power of the corresponding vehicle.

[0124] S32. Based on the previous vehicle's historical charging preference and the discount of the current optimal charging station corresponding to the previous vehicle, score the selection probability of each of the second estimated charging stations included in the navigation route of the previous vehicle.

[0125] S33. Based on the selection probability score, calculate the expected usage of each charging station in each of the first estimated charging stations at the corresponding estimated arrival time of the vehicle.

[0126] S34. Based on the expected usage, select the optimal charging stations.

[0127] In this embodiment, the system intelligently recommends optimal charging stations by comprehensively analyzing the driving and charging behaviors of the current vehicle and vehicles ahead. Specifically, the system first obtains the second navigation information of all vehicles ahead on the navigation route from the current vehicle's location to the farthest estimated charging station, and determines whether these vehicles are likely to choose the same charging station as the current vehicle. If there are overlapping stations, the system further obtains the remaining battery information of the preceding vehicles and, combined with their historical charging preferences and the corresponding station's preferential policies, scores the likelihood of selecting each estimated charging station. Based on this score, the system predicts the future usage of each charging station for the current vehicle at different estimated arrival times, including key indicators such as changes in the number of available charging stations and queuing trends. Finally, based on the predicted station load status and combined with the user's preferences for factors such as distance, waiting time, and electricity price, the system selects the stations with the highest comprehensive scores as the optimal charging stations, thereby improving recommendation accuracy and user experience.

[0128] In one embodiment, the step of calculating the optimal charging station's charging discount scheme and sending it to the vehicle terminal includes:

[0129] S40. Obtain the empty charging pile ratio of the optimal charging station corresponding to the current vehicle, the queuing time of each charging station in the first estimated charging station at the estimated arrival time corresponding to the current vehicle, and the time-of-use electricity price for the current time period.

[0130] S41. Based on the empty charging pile ratio, the queuing time, and the time-of-use electricity price, calculate the charging discount scheme for the current vehicle corresponding to the optimal charging station according to the preset weights, and send it to the vehicle terminal; wherein the optimal charging station includes one or more.

[0131] In this embodiment, the system dynamically evaluates the match between the load status of optimal charging stations and user demand, intelligently generates personalized charging discount schemes, and pushes these schemes to the vehicle terminal in real time. Specifically, the system first obtains key parameters such as the proportion of available charging piles at the optimal charging station recommended to the current vehicle, the queue time corresponding to the estimated arrival time, and the time-of-use electricity price for the current time period in the area. Subsequently, the system performs comprehensive calculations on these parameters based on a preset weight model, where the proportion of available charging piles reflects the resource scarcity, the queue time reflects the user's waiting cost, and the time-of-use electricity price serves as the basis for price adjustment. Through weighted analysis, the system generates differentiated discount strategies for each optimal charging station, such as electricity price discounts, points rewards, and priority charging privileges, to incentivize users to visit the target station. Finally, the discount scheme is packaged and sent to the user's in-vehicle terminal or mobile device, supporting users to compare and decide among multiple recommended stations, thereby achieving multiple goals such as traffic guidance, optimized resource allocation, and improved user experience.

[0132] In one embodiment, the step of searching for external charging station resources and connecting them to the current system if none of the first estimated charging stations within the estimated driving range can meet the charging demand includes:

[0133] S50. If the queuing time of the charging stations in the first estimated charging stations within the estimated driving range exceeds the first preset threshold for the estimated arrival time of the vehicle, it is determined that the charging demand cannot be met.

[0134] S51. Based on the number of vehicles whose charging needs cannot be met on the current navigation route and the current electricity price benchmark, calculate the basic price increase ratio and generate a price increase plan.

[0135] S52. Based on the price increase plan, send access requests to various external charging station resources within the estimated driving range corresponding to the vehicle.

[0136] S53. Based on the response data received and parsed from the external charging station resources by the current system, obtain the basic information of the external charging station resources.

[0137] In this embodiment, when the system determines that the queuing time at all the first estimated charging stations within the estimated driving range exceeds a set first preset threshold at the vehicle's estimated arrival time, it determines that the current charging demand cannot be met. At this point, the system further calculates a reasonable base price increase ratio based on the number of vehicles with the same problem on the current navigation route and the regional electricity price benchmark, and generates a corresponding price increase plan as the basis for requesting external charging resources. Subsequently, based on this price increase plan, the system actively initiates access requests to various external charging station resources within the vehicle's driving range, attempting to introduce more available charging resources. After receiving the request, the external station returns response data, which the system parses to obtain basic information including station location, charging pile type, idle status, supported protocols, and billing methods, completing the effective access and integration of external resources, thereby expanding the range of charging stations available to the user and ensuring the user can successfully complete their charging task.

[0138] In one embodiment, the step of generating an optimal charging scheme list including all available charging resources, locking the target charging station and planning a navigation route after the user selects the option includes:

[0139] S60: Update the status information of all charging stations based on all available charging resources;

[0140] S61. Based on the updated status information of each charging station, update the first estimated charging station;

[0141] S62. Arrange the first estimated charging stations corresponding to the current vehicles according to the corresponding discount rates, and generate a list of the optimal charging solutions for the corresponding vehicles.

[0142] S63. After receiving the user's first confirmation instruction, lock the charging permission of the target charging pile at the corresponding time and plan a navigation route to the user terminal.

[0143] In this embodiment, the system ensures the accuracy and timeliness of the recommended data by updating the status information of all available charging resources (including internal stations and external access stations) in real time. Subsequently, based on the updated status information, the first estimated charging station list is dynamically adjusted, removing unavailable or fully loaded stations and adding new available resources. Next, the system sorts the charging stations based on multiple factors such as current discounts, distance, estimated arrival time, and queuing status, generating a personalized optimal charging solution list, which is then pushed to the user's terminal for reference. When the user selects a target station by issuing a first confirmation command through the interactive interface, the system immediately locks the charging access of the corresponding charging pile at that station for a specified time period to prevent resources from being occupied by others. Simultaneously, it plans a navigation path from the current location to the target charging pile, including route guidance, estimated arrival time, and traffic condition information, ensuring the user arrives smoothly and efficiently and completes the charging operation. The entire process achieves closed-loop management from data updates, intelligent sorting, user interaction to resource locking and path guidance, significantly improving the intelligence level of charging services and user experience.

[0144] Reference Figure 2 This is a structural block diagram of a vehicle charging guidance system based on dynamic incentives in one embodiment of this application. The system includes:

[0145] The mileage calculation module 100 is used to calculate the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power.

[0146] The real-time acquisition module 200 is used to acquire the real-time usage status of all first estimated charging stations within the estimated driving range;

[0147] The optimal calculation module 300 is used to calculate the optimal charging station based on the real-time usage and estimated arrival time.

[0148] The discount generation module 400 is used to calculate the optimal charging discount scheme for charging stations and send it to the vehicle terminal.

[0149] The external access module 500 is used to search for external charging station resources and access the current system if none of the first estimated charging stations included within the estimated driving range can meet the charging needs.

[0150] The recommended planning module 600 is used to generate a list of optimal charging solutions, including all available charging resources. After the user selects a target charging station, it locks in the target charging station and plans the navigation route.

[0151] Furthermore, the mileage calculation module 100 includes:

[0152] A start-up identification unit is used to identify a first start-up command and, based on the first start-up command, identify the vehicle's first remaining battery power.

[0153] The navigation acquisition unit is used to acquire the vehicle's navigation information and identify the vehicle's current location and the set current navigation route based on the navigation information.

[0154] The distance estimation unit is used to make a preliminary estimate of the theoretical driving distance on the current navigation route based on the first remaining battery power and the vehicle's average energy consumption rate;

[0155] The output unit is adjusted to obtain real-time traffic information of the current navigation route, and the distance is adjusted based on the theoretical driving distance based on the real-time traffic information to obtain the maximum distance that the vehicle can travel on the current navigation route based on the existing battery power, thus obtaining the estimated driving range.

[0156] Furthermore, the real-time acquisition module 200 includes:

[0157] The station filtering unit is used to filter out the information of each internal charging station among all possible internal charging station resources within the range of the vehicle's estimated driving mileage, based on the vehicle's estimated driving mileage and combined with the information of the current navigation route, so as to obtain the information of the first estimated charging station.

[0158] The data collection unit is used to collect charging data from each of the first estimated charging stations in real time, and obtain the real-time usage status of all the first estimated charging stations.

[0159] Furthermore, the optimal calculation module 300 includes:

[0160] The path acquisition unit is used to obtain the second navigation information of all preceding vehicles in the navigation route between the current vehicle location and the farthest first estimated charging station.

[0161] The station comparison unit is used to obtain the second remaining battery power of the corresponding vehicle if the second estimated charging station in the navigation route of the second navigation information overlaps with any of the first estimated charging stations corresponding to the current vehicle.

[0162] The preference scoring unit is used to score the selection probability of each of the second estimated charging stations included in the navigation route of the preceding vehicle based on the preceding vehicle's historical charging selection preferences and the discount of the current optimal charging station corresponding to the preceding vehicle.

[0163] The predictive analysis unit is used to calculate the expected usage of each charging station in each of the first estimated charging stations at the corresponding estimated arrival time of the vehicle, based on the selection probability score.

[0164] The optimal filtering unit is used to filter out the optimal charging stations based on the expected usage.

[0165] Furthermore, the discount generation module 400 includes:

[0166] The empty charging pile detection unit is used to obtain the proportion of empty charging piles at the optimal charging station corresponding to the current vehicle, the queuing time of each charging station in the first estimated charging station at the estimated arrival time corresponding to the current vehicle, and the time-of-use electricity price for the current time period.

[0167] The scheme calculation unit is used to calculate the charging discount scheme of the current vehicle corresponding to the optimal charging station according to the empty charging pile ratio, the queuing time and the time-of-use electricity price, according to the preset weight, and send it to the vehicle terminal; wherein the optimal charging station includes one or more.

[0168] Furthermore, the external access module 500 includes:

[0169] The demand judgment unit is used to determine that the charging demand cannot be met if the queuing time of the charging stations in the first estimated charging stations within the estimated driving range exceeds a first preset threshold at the estimated arrival time of the vehicle.

[0170] The price increase calculation unit is used to calculate the basic price increase ratio and generate a price increase plan based on the number of vehicles that cannot meet the charging demand on the current navigation route and the current electricity price benchmark.

[0171] The resource request unit is used to publish access requests to various external charging station resources within the estimated driving range corresponding to the vehicle based on the price increase plan.

[0172] The response parsing unit is used to obtain basic information about the external charging station resources by receiving and parsing the response data from the external charging station resources currently being used by the system.

[0173] Furthermore, the recommended planning module 600 includes:

[0174] The status refresh unit is used to update the status information of all charging stations based on all available charging resources;

[0175] The site update unit is used to update the first estimated charging site based on the updated status information of each charging site;

[0176] The sorting generation unit is used to sort the first estimated charging stations corresponding to the current vehicle according to the corresponding discount level, and generate a list of the optimal charging schemes for the corresponding vehicle.

[0177] The permission locking unit is used to lock the charging permission of the target charging pile at the corresponding time after receiving the user's first confirmation instruction, and to plan a navigation path to the user terminal.

[0178] Reference Figure 3 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, the computer device includes a processor, internal memory, a storage medium (non-volatile storage medium), and a network interface connected via a system bus. The processor provides computing and control capabilities. The computer device's memory includes the aforementioned storage medium (non-volatile storage medium) and internal memory. The storage medium (non-volatile storage medium) stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the storage medium (non-volatile storage medium). The computer device's database stores usage data, such as data related to a dynamic preferential vehicle charging guidance method. The computer device's network interface is used for communication with external terminals via a network connection. Furthermore, the computer device may also include input devices and a display screen. When the aforementioned computer program is executed by a processor, it implements a vehicle charging guidance method based on dynamic discounts, comprising the following steps: calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power; obtaining the real-time usage status of all first estimated charging stations within the estimated driving range; calculating the optimal charging station based on the real-time usage status and the estimated arrival time; calculating the charging discount scheme of the optimal charging station and sending it to the vehicle terminal; if none of the first estimated charging stations within the estimated driving range can meet the charging demand, searching for external charging station resources and connecting them to the current system; generating a list of optimal charging schemes including all available charging resources, locking the target charging pile after user selection, and planning the navigation route. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0179] One embodiment of this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements a vehicle charging guidance method based on dynamic discounts, including the following steps: calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power; obtaining the real-time usage of all first estimated charging stations within the estimated driving range; calculating the optimal charging station based on the real-time usage and estimated arrival time; calculating the charging discount scheme of the optimal charging station and sending it to the vehicle terminal; if none of the first estimated charging stations within the estimated driving range can meet the charging demand, searching for external charging station resources and connecting them to the current system; generating a list of optimal charging schemes including all available charging resources, locking the target charging pile after user selection, and planning the navigation route. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0182] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A vehicle charging guidance method based on dynamic incentives, characterized in that, The method includes: Based on the vehicle's initial remaining battery power, calculate the estimated driving range of the vehicle on the current navigation route; Obtain the real-time usage status of all first estimated charging stations within the estimated driving range; Based on the real-time usage and estimated arrival time, the optimal charging station is calculated; Calculate the optimal charging discount scheme for each charging station and send it to the vehicle terminal; If none of the first estimated charging stations within the estimated driving range can meet the charging needs, then search for external charging station resources and connect them to the current system. Generate a list of optimal charging solutions including all available charging resources, lock the target charging station and plan the navigation route after the user selects it; The step of calculating the optimal charging station based on the real-time usage and estimated arrival time includes: Obtain the second navigation information for all preceding vehicles along the navigation route from the current vehicle location to the farthest first estimated charging station; If the second estimated charging station in the navigation route of the second navigation information overlaps with any of the first estimated charging stations corresponding to the current vehicle, then the second remaining battery power of the corresponding preceding vehicle is obtained. Based on the previous vehicle's historical charging preferences and the discounts offered by the current optimal charging station for the previous vehicle, a selection probability score is given for each of the second estimated charging stations included in the navigation route of the previous vehicle. Based on the selection probability score, the estimated usage of each charging station in each of the first estimated charging stations is calculated at the corresponding estimated arrival time of the vehicle. Based on the projected usage, the optimal charging stations are selected.

2. The vehicle charging guidance method based on dynamic incentives according to claim 1, characterized in that, The step of calculating the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power includes: Identify the first start command, and based on the first start command, identify the vehicle's first remaining battery power. Obtain the vehicle's navigation information, and identify the vehicle's current location and the set current navigation route based on the navigation information; Based on the remaining battery power and the vehicle's average energy consumption rate, a preliminary estimate of the theoretical driving distance on the current navigation route is made. The system obtains real-time traffic information for the current navigation route and adjusts the distance based on the theoretical driving distance using the real-time traffic information to obtain the maximum distance the vehicle can travel on the current navigation route with the current battery level, thus obtaining the estimated driving range.

3. The vehicle charging guidance method based on dynamic incentives according to claim 1, characterized in that, The step of obtaining the real-time usage status of all first estimated charging stations within the estimated driving range includes: Based on the vehicle's estimated driving range and combined with the information of the current navigation route, the information of each internal charging station is filtered out from all possible internal charging station resources within the vehicle's estimated driving range, thus obtaining the information of the first estimated charging station. The charging data of each first estimated charging station is collected in real time to obtain the real-time usage status of all first estimated charging stations.

4. The vehicle charging guidance method based on dynamic incentives according to claim 1, characterized in that, The step of calculating the optimal charging station discount scheme and sending it to the vehicle terminal includes: Obtain the empty charging pile ratio of the optimal charging station corresponding to the current vehicle, the queuing time of each charging station in the first estimated charging station for the estimated arrival time of the current vehicle, and the time-of-use electricity price for the current time period. Based on the empty charging pile ratio, the queuing time, and the time-of-use electricity price, the charging discount scheme for the current vehicle corresponding to the optimal charging station is calculated according to a preset weight and sent to the vehicle terminal; wherein the optimal charging station includes one or more.

5. The vehicle charging guidance method based on dynamic incentives according to claim 1, characterized in that, The step of searching for external charging station resources and connecting them to the current system if none of the first estimated charging stations within the estimated driving range can meet the charging demand includes: If the queuing time of the first estimated charging station within the estimated driving range exceeds the first preset threshold for the estimated arrival time of the vehicle, it is determined that the charging demand cannot be met. Based on the number of vehicles whose charging needs cannot be met on the current navigation route and the current electricity price benchmark, calculate the basic price increase ratio and generate a price increase plan; Based on the price increase plan, access requests are sent to various external charging station resources within the estimated driving range of the vehicle. Based on the response data received and parsed from external charging station resources by the current system, basic information about the external charging station resources is obtained.

6. The vehicle charging guidance method based on dynamic incentives according to claim 1, characterized in that, The steps of generating an optimal charging solution list including all available charging resources, locking the target charging station and planning the navigation route after the user selects the option include: Update the status information of all charging stations based on all available charging resources; Based on the updated status information of each charging station, the first estimated charging station is updated; Arrange the first estimated charging stations corresponding to the current vehicles according to the corresponding discount levels, and generate a list of the optimal charging solutions for the corresponding vehicles. Upon receiving the user's first confirmation instruction, the system locks the charging permissions of the target charging station at the corresponding time and plans a navigation route to the user's terminal.

7. A vehicle charging guidance system based on dynamic incentives, characterized in that, include: The mileage calculation module is used to calculate the estimated driving range of the vehicle on the current navigation route based on the vehicle's first remaining battery power. The real-time acquisition module is used to acquire the real-time usage status of all first estimated charging stations within the estimated driving range; The optimal calculation module is used to calculate the optimal charging station based on the real-time usage and estimated arrival time. The discount generation module is used to calculate the optimal charging discount scheme for charging stations and send it to the vehicle terminal; An external access module is used to search for external charging station resources and access the current system if none of the first estimated charging stations within the estimated driving range can meet the charging needs. The recommended planning module generates a list of optimal charging solutions, including all available charging resources. After the user selects a target charging station, the module locks in the target charging station and plans the navigation route. The optimal calculation module includes: The path acquisition unit is used to obtain the second navigation information of all preceding vehicles in the navigation route between the current vehicle location and the farthest first estimated charging station. The station comparison unit is used to obtain the second remaining battery power of the corresponding vehicle if the second estimated charging station in the navigation route of the second navigation information overlaps with any of the first estimated charging stations corresponding to the current vehicle. The preference scoring unit is used to score the selection probability of each of the second estimated charging stations included in the navigation route of the preceding vehicle based on the preceding vehicle's historical charging selection preferences and the discount of the current optimal charging station corresponding to the preceding vehicle. The predictive analysis unit is used to calculate the expected usage of each charging station in each of the first estimated charging stations at the corresponding estimated arrival time of the vehicle, based on the selection probability score. The optimal filtering unit is used to filter out the optimal charging stations based on the expected usage.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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