Intelligent refueling navigation method and system, mobile client terminal and vehicle

Abstract

The invention relates to an intelligent refueling navigation method and system, a mobile client terminal and a vehicle, and belongs to the technical field of vehicle navigation. The intelligent refueling navigation method comprises the following steps: acquiring comprehensive fuel consumption information, real-time fuel quantity information and navigation information of the vehicle; based on the comprehensive fuel consumption information and the navigation information, calculating to obtain a target fuel quantity required for completing the current travel; based on the comparison result of the real-time oil mass information and the target oil mass, whether refueling warning is conducted or not is judged; when the difference value between the real-time fuel quantity information and a safety threshold value determined based on the comprehensive fuel consumption information is smaller than or equal to the target fuel quantity, it is determined that refueling warning needs to be conducted; in response to determining that refueling alarm needs to be carried out, acquiring information of a plurality of gas stations in a preset range based on the navigation information, grading and sorting the gas stations, and generating a refueling recommendation list comprising at least one recommended gas station; and pushing the refueling recommendation list to the user at a preset time.

Description

Technical Field

[0001] This application relates to the technical field of vehicle navigation, and more particularly to an intelligent refueling navigation method, system, mobile client terminal, and vehicle. Background Technology

[0002] With the continuous growth of car ownership and the increasing frequency of user travel, how to efficiently and economically manage fuel supply during long-distance or daily driving has become an important issue for improving driving experience and road safety. Traditional vehicle fuel management mainly relies on the driver's subjective judgment by observing the fuel gauge, which is subject to significant randomness and the risk of misjudgment. In recent years, with the development of vehicle networking and mobile navigation technologies, some intelligent solutions have emerged that attempt to combine refueling reminders with navigation services. The inventors of this application have discovered that existing intelligent refueling navigation solutions generally suffer from the following shortcomings: On the one hand, its alarm mechanism is a passive trigger mode, only activating the reminder when the vehicle's remaining fuel level is below a certain fixed threshold, which often leaves users with limited time and options when faced with refueling choices. On the other hand, when predicting fuel level and assessing the feasibility of the trip, such solutions fail to integrate the vehicle's real-time comprehensive fuel consumption characteristics, nor do they fully consider the impact of dynamic road conditions and environmental changes on fuel consumption during actual driving, resulting in a significant deviation between the predicted results and actual needs. In addition, in the gas station recommendation stage, existing solutions mostly rely on simple sorting based on limited dimensions such as distance and fuel price, lacking a systematic and comprehensive evaluation of multiple dimensions such as the user's real-time fuel urgency, route smoothness, and service waiting time, making it difficult to generate a truly personalized and optimal refueling plan. Summary of the Invention

[0003] This application addresses, to at least some extent, one of the technical problems in the related art.

[0004] Therefore, this application aims to provide an intelligent refueling navigation method, system, and vehicle.

[0005] To achieve the above objectives, in a first aspect, this application provides an intelligent refueling navigation method, which includes: Obtain comprehensive vehicle fuel consumption information, real-time fuel level information, and navigation information; Based on the comprehensive fuel consumption information and the navigation information, the target fuel quantity required to complete the current journey is calculated. Based on the comparison between the real-time fuel level information and the target fuel level, it is determined whether to issue a refueling alarm; when the difference between the real-time fuel level information and the safety threshold determined based on the comprehensive fuel consumption information is less than or equal to the target fuel level, it is determined that a refueling alarm needs to be issued. In response to the determination that a refueling alarm is needed, information on multiple gas stations within a preset range is obtained based on the navigation information, and the gas stations are scored and sorted using the analytic hierarchy process to generate a refueling recommendation list containing at least one recommended gas station. The list of recommended gas stations will be pushed to users at preset times.

[0006] The technical solution introduces a safety threshold related to the vehicle's overall fuel consumption and constructs refueling alarm judgment rules to achieve early and accurate proactive warnings. This allows the system to provide a relaxed refueling planning window before the user's fuel level actually becomes critical. By integrating navigation information to dynamically correct overall fuel consumption and calculate the target fuel level, the accuracy and reliability of fuel demand prediction are greatly improved, effectively avoiding misjudgments caused by changes in road conditions. By performing multi-dimensional, weighted scoring and ranking of gas stations, the system can recommend the optimal refueling plan to the user. Finally, by pushing notifications at preset times, the user experience is significantly improved, enhancing driving safety and the intelligence level of navigation services.

[0007] In some embodiments of this application, the target fuel consumption required to complete the current journey is calculated based on the comprehensive fuel consumption information and the navigation information, including: The remaining mileage of the current trip is determined based on the navigation information; Obtain real-time traffic information associated with the remaining mileage; The target fuel quantity is calculated based on the comprehensive fuel consumption information, the remaining mileage, and the road condition correction factor determined according to the real-time road condition information.

[0008] The technical solution introduces a road condition correction coefficient to dynamically adjust the theoretical fuel demand calculated based on the vehicle's overall fuel consumption and remaining mileage. This ensures that the predicted target fuel quantity closely matches the actual driving environment. This overcomes the prediction bias caused by using a fixed fuel consumption coefficient or ignoring road condition changes in existing technologies, significantly improving the accuracy and scenario adaptability of fuel demand assessment. The system can more realistically simulate the vehicle's energy consumption during subsequent journeys, thus providing a solid and reliable data foundation for intelligent judgments on refueling needs and subsequent refueling plan recommendations.

[0009] In some embodiments of this application, obtaining information on multiple gas stations within a preset range based on the navigation information includes: Based on the navigation information, extract information on multiple gas stations along the current travel path or within its preset range; The gas station information includes gas station attribute information and traffic association information related to the current travel route.

[0010] The technical solution categorizes gas station information into two main types: gas station attribute information and traffic-related information. This lays a refined data foundation for subsequent multi-dimensional intelligent evaluation. This design overcomes the limitations of traditional solutions, which suffer from mixed information and a single evaluation dimension. The organic combination of these two aspects allows the system to comprehensively depict candidate gas stations from both service supply and trip matching perspectives. This supports subsequent multi-objective comprehensive evaluation and ranking that better meets users' actual needs, ultimately generating personalized and optimal refueling recommendations.

[0011] In some embodiments of this application, the step of using the analytic hierarchy process (AHP) to score and rank the gas stations to generate a gas station recommendation list containing at least one recommended gas station includes: Multiple evaluation dimensions and the weight corresponding to each evaluation dimension are determined. The evaluation dimensions include dimensions related to the traffic-related information and dimensions related to the gas station attribute information. Based on the gas station attribute information of each gas station, a score is calculated under the evaluation dimension related to the gas station attribute information; Based on the traffic association information of each gas station, a score is calculated under the evaluation dimension related to the traffic association information; The scores are weighted according to the weight of each evaluation dimension to obtain the comprehensive score of each gas station. The multiple gas stations are sorted based on the comprehensive score to generate the recommended gas station list.

[0012] In this technical solution, a multi-dimensional and quantifiable comprehensive evaluation model is constructed, fundamentally improving the gas station recommendation strategy from simple conditional selection to intelligent weighted decision-making. First, a multi-dimensional evaluation dimension encompassing traffic relevance and gas station service attributes is defined, and each dimension is assigned configurable weights to flexibly adapt to different user preferences. Then, based on gas station attribute information and traffic relevance information, the performance of each gas station in each dimension is converted into objective scores, and a comprehensive score is calculated through weighted summation. Finally, a ranking list generated based on the comprehensive score provides users with a clear and objective optimal selection sequence, significantly improving the objectivity, personalization, and user decision-making efficiency of the recommendation results, overcoming the shortcomings of existing technologies that suffer from single recommendation dimensions and a lack of systematic balancing.

[0013] In some embodiments of this application, it further includes: Receive user's selection instruction for at least one recommended gas station in the recommended list; Based on the selection instructions, the target gas station is determined and a corresponding navigation route is generated; If the selection instruction is not received within a preset time, the gas station ranked first will be selected as the target gas station by default.

[0014] The technical solution significantly improves interactivity and decision-making flexibility by introducing a user-interventionible decision-making loop. While providing intelligent sorting and recommendations, this mechanism respects and retains the user's final decision-making power. If the user is satisfied with the default recommendation, no action is required; the system will automatically execute the optimal solution after a preset time, achieving seamless intelligent service and minimizing driving interference. If the user wishes to select other options from the list based on personal preference, the system can also respond instantly to their selection command and replan the route. This design organically combines machine intelligence recommendation with human experience judgment, making the final refueling navigation solution scientific, personalized, and human-centered, effectively enhancing the user experience.

[0015] In some embodiments of this application, it further includes: When the real-time fuel level is below a safety threshold, a refueling reminder message is output.

[0016] The technical solution establishes a dual, tiered fuel level warning mechanism by setting a safety threshold as an independent condition for triggering basic refueling alarms. In addition to intelligent, advance refueling planning, a tiered safety warning system is added. When the real-time fuel level falls below this safety threshold, regardless of current travel needs, the system will immediately trigger an alarm, alerting the user that the fuel level is extremely low and a gas station needs to be found as soon as possible. This mechanism serves as a safety fallback for the intelligent recommendation algorithm, ensuring that in any situation, such as the user ignoring intelligent reminders or significant changes to the itinerary, the risk of vehicle breakdown due to running out of fuel is avoided, fundamentally improving driving safety and system robustness.

[0017] In some embodiments of this application, generating a refueling recommendation list containing at least one recommended gas station further includes: Based on the target fuel quantity, the real-time fuel quantity information, and the safety threshold, the recommended fuel quantity for the recommended gas station is calculated. The suggested refueling amount is included in the refueling recommendation list.

[0018] The technical solution intelligently calculates and provides specific suggested refueling amounts based on trip requirements, real-time fuel levels, and safety thresholds, achieving complete functionality for the refueling recommendation list. It not only recommends the best gas stations but also informs users of the suggested refueling amount. This calculation comprehensively considers the need to reach the destination, current fuel levels, and safety thresholds set to ensure redundancy. This ensures that the suggested refueling amount meets the needs of the subsequent journey while restoring the fuel level to a safe level. This effectively avoids the risk of over- or under-fueling due to user inexperience or anxiety, significantly improving the economy and safety of refueling and greatly optimizing the user experience.

[0019] In a second aspect, the present invention provides an intelligent refueling navigation system, comprising: Mobile client terminals are used to provide navigation information to cloud servers; The vehicle-mounted equipment is used to collect real-time fuel level information and comprehensive fuel consumption information of the vehicle, and send the real-time fuel level information and comprehensive fuel consumption information to the cloud server. A cloud server is used to calculate the target amount of fuel required to complete the current journey based on the real-time fuel quantity information, the comprehensive fuel consumption information, and the navigation information. Whether to issue a refueling alarm is determined based on the comparison result between the real-time fuel level information and the target fuel level; when the difference between the real-time fuel level information and the safety threshold determined based on the comprehensive fuel consumption information is less than or equal to the target fuel level, it is determined that a refueling alarm needs to be issued. In response to determining that the refueling alarm is needed, information on multiple gas stations within a preset range is obtained based on the navigation information, and the gas stations are scored and sorted to generate a refueling recommendation list containing at least one recommended gas station. The list of recommended refueling stations is sent to the mobile client terminal.

[0020] In a third aspect, the present invention provides a mobile client terminal for executing the intelligent refueling navigation method as described in the first aspect.

[0021] In a fourth aspect, the present invention provides a vehicle including an in-vehicle terminal, the in-vehicle terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the intelligent refueling navigation method as described in the first aspect.

[0022] As can be seen from the above technical solutions, additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] Figure 1This is a schematic diagram of the architecture of an intelligent refueling navigation system according to an embodiment of this application; Figure 2 This is a schematic diagram of the data transmission logic according to the embodiments of this application; Figure 3 This is a schematic diagram of the intelligent refueling navigation method according to the embodiments of this application; Figure 4 This is a schematic diagram of the structure of an intelligent refueling navigation system according to an embodiment of this application; Figure 5 This is a schematic diagram of a computer device according to an embodiment of this application.

[0024] In the above figures: 100. Mobile client terminals; 200. In-vehicle equipment; 300. Cloud servers; 40. Bus; 41. Processor; 42. Memory; 43. Communication interface. Detailed Implementation

[0025] In this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0026] The present application will now be described in detail through exemplary embodiments. However, it should be understood that, without further description, elements, structures, and features in one embodiment may be advantageously incorporated into other embodiments. In the automotive sector, vehicles can report key data, including remaining fuel level and vehicle model, to the cloud in real time through in-vehicle TSP (Telematics Service Provider) platforms. Meanwhile, mobile navigation applications can provide accurate route planning, real-time traffic conditions, and abundant information on gas station points of interest (POIs).

[0027] The in-vehicle TSP (Telematics Service Provider) database is the core data platform in the vehicle-to-everything (V2X) system, responsible for aggregating and managing real-time and historical data such as vehicle status, location, and fault codes reported by in-vehicle terminals.

[0028] At the vehicle hardware level, the device responsible for collecting and uploading real-time and historical data such as vehicle status, location, and fault codes is usually called a T-BOX (Telematics Box). It is a key hardware module for enabling communication between the vehicle and the cloud-based TSP platform.

[0029] In the field of navigation and location services, POI (Point of Interest) refers to a location on a map with specific attributes, such as a gas station, restaurant, or parking lot. It is the basic data unit for navigation recommendation services.

[0030] The primary drawback of existing technologies lies in their overly passive and delayed warning mechanisms. Current solutions generally employ a simple low-fuel-level trigger mode, only issuing a warning when the vehicle's remaining fuel level falls below a preset, fixed threshold. This design means the system can only issue an alert when fuel is nearly depleted, leaving users with limited time and options when they receive the warning, hindering their planning and potentially forcing them to visit inconvenient, expensive, or long-queueed gas stations. Ultimately, this fails to address users' anxiety about fuel availability during their journeys.

[0031] Existing technologies have shortcomings in fuel consumption prediction and trip feasibility assessment, as their prediction models are disconnected from actual vehicle characteristics and dynamic driving environments. These solutions neither effectively utilize the vehicle's overall fuel consumption—a key energy consumption parameter—nor do they ignore the dynamic and significant impact of real-time road conditions and environmental factors on fuel consumption. Therefore, their predictions based on fixed fuel consumption or simple mileage calculations often deviate significantly from actual needs, potentially leading to unnecessary false alarms or failing to provide timely reminders when refueling is truly needed, resulting in low reliability and practicality.

[0032] Furthermore, existing technologies for recommending gas stations appear simplistic and one-sided. They largely rely on limited dimensions such as distance and fuel price for simple sorting, lacking a systematic, multi-dimensional, and quantifiable comprehensive evaluation system. For example, they fail to quantify the urgency users feel when refueling based on remaining fuel and distance to their destination, fail to accurately calculate the smoothness of routes to different gas stations and detour costs, and fail to systematically integrate diverse factors such as real-time queue times and gas station brand services. This crude recommendation logic struggles to generate truly personalized and optimal refueling solutions, failing to meet users' comprehensive needs for economy, convenience, and user experience.

[0033] Based on this, this application proposes an intelligent refueling navigation method, system, and vehicle. By introducing a dynamic safety threshold linked to the vehicle's overall fuel consumption and combining real-time traffic information to dynamically correct and predict the fuel required for the trip, an intelligent judgment model of "available fuel ≤ required fuel" is constructed. Then, when refueling is needed, a hierarchical analysis method is used to comprehensively evaluate and rank gas stations from multiple dimensions, achieving proactive, accurate, and personalized intelligent planning for energy replenishment throughout the journey. This method solves the problems of delayed alarms, inaccurate predictions, and limited recommendation dimensions in existing technologies, completely eliminating users' range anxiety and significantly improving driving safety and the intelligence level of navigation services.

[0034] In the following, embodiments of this application will be described in detail with reference to the accompanying drawings.

[0035] Figure 1 This is a schematic diagram of the architecture of an intelligent refueling navigation system according to an embodiment of this application; Figure 2 This is a schematic diagram of the data transmission logic according to the embodiments of this application; the following is in conjunction with... Figure 1 and Figure 2 This application provides a detailed explanation of the connection and collaboration relationships between the mobile client terminal, the vehicle-mounted device, and the cloud server involved in the embodiments.

[0036] like Figure 1 As shown, the intelligent refueling navigation method proposed in this application can be implemented through the collaboration of a mobile client terminal, a vehicle manufacturer's TSP (Telematics Service Provider) data platform, and a cloud computing service platform.

[0037] It should be noted that mobile client terminals include smartphones (such as Android phones, iOS phones, etc.), smart glasses, smartwatches, smart bracelets, tablets, laptops, personal digital assistants, and other mobile internet devices that can conduct wireless communication.

[0038] The mobile client terminal has a navigation application installed, serving as both the service initiator and the interaction terminal. Users first need to log into their personal accounts within the navigation application and bind their vehicles. Afterward, the mobile client terminal undertakes the core interactive functions of receiving user navigation commands, presenting intelligent refueling recommendations from the cloud, and proactively pushing information via voice or image. It acts as a bridge connecting users to cloud-based intelligent services. As a vehicle database, the automaker's TSP data platform provides accurate and real-time vehicle status input for cloud computing.

[0039] Specifically, the vehicle continuously collects vehicle data via its built-in T-BOX (Telematics Box) terminal, including but not limited to: remaining fuel level, vehicle model, combined fuel consumption, and fuel tank capacity. This vehicle data is then uploaded in real-time to the automaker's TSP (Telematics Service Provider) data platform via mobile communication networks, such as 4G / 5G, for aggregation, storage, and management.

[0040] The cloud service platform, serving as an intelligent computing and decision-making center, is deployed on a cloud server cluster. Through standard application programming interfaces (APIs), it retrieves real-time data of the bound vehicles from the automaker's TSP data platform. Simultaneously, it typically obtains detailed information about the navigation route, remaining mileage, real-time traffic conditions, weather, altitude, and all POIs (Points of Interest) along the route from the navigation application on the mobile client terminal.

[0041] Based on the aforementioned multi-source heterogeneous data, the cloud service platform executes intelligent algorithms, including using a dynamic fuel consumption model adjusted for road conditions to accurately calculate the target fuel quantity, combining safety thresholds to determine refueling demand, and employing the Analytic Hierarchy Process (AHP) to comprehensively evaluate and rank gas stations from multiple dimensions. Ultimately, it generates a personalized refueling plan that includes the optimal recommended gas station, suggested fuel quantity, and estimated cost. This plan is then distributed to the corresponding mobile client terminal via the communication network, completing the closed-loop delivery of the service.

[0042] like Figure 2 As shown, the data transmission logic between the mobile client terminal, the vehicle manufacturer's TSP (Telematics Service Provider) data platform, and the cloud computing service platform is as follows.

[0043] First, the user activates the intelligent refueling navigation service and completes the identity matching process. Specifically, users enter their destination and activate navigation in a navigation application on their mobile client. The navigation application then sends navigation requests and vehicle registration information to the cloud computing service platform through the user account system.

[0044] Based on this information, the cloud service initiates a query and matching request to the automaker's TSP data platform to obtain real-time data access to the target vehicle that is uniquely bound to the user account, thereby ensuring that all subsequent calculations are based on the correct vehicle status.

[0045] Furthermore, after a successful match, the process proceeds to the multi-source data synchronous acquisition stage.

[0046] Specifically, in response to the retrieval request from the vehicle manufacturer's TSP data platform, the vehicle-mounted T-BOX (Telematics Box) terminal sends a fuel level detection command to the fuel level detector. The fuel level detector collects real-time fuel level information and continuously transmits the real-time fuel level information back to the vehicle-mounted T-BOX (Telematics Box) terminal at fixed intervals.

[0047] Meanwhile, the in-vehicle T-BOX (Telematics Box) terminal collects vehicle data, including vehicle model, official combined fuel consumption, and fuel tank capacity, and uploads real-time fuel information and vehicle data to the automaker's TSP data platform for aggregation and management via mobile communication networks (such as 4G / 5G).

[0048] On the other hand, the cloud computing service platform simultaneously obtains contextual and environmental data related to this navigation request through the navigation application interface of the mobile client terminal, including detailed route planning and remaining mileage, real-time traffic conditions, weather conditions, altitude information, and detailed information of all gas station POIs along the way, such as location, real-time fuel price, estimated queue time, and business status.

[0049] Furthermore, after the cloud computing service platform aggregates vehicle data and navigation data, it executes the core algorithm.

[0050] Specifically, the system calculates the precise fuel demand for the trip based on a dynamic road condition correction model and makes intelligent refueling decisions based on safety thresholds. If refueling is determined to be necessary, the gas station evaluation model is immediately activated. The Analytic Hierarchy Process (AHP) is used to score and rank gas stations within a preset range from multiple dimensions, generating a recommended gas station list. This list includes recommended gas stations, suggested fuel quantities, and estimated costs.

[0051] Furthermore, the cloud computing service platform will generate a list of recommended gas stations and distribute it to the mobile client terminal that initiated the request. The mobile client terminal will then proactively push this list to the user at a pre-set time through voice announcements and user interface display.

[0052] Users can passively accept the default optimal solution or actively choose from the gas station recommendation list. The selection process on the mobile client terminal will generate or update the navigation route.

[0053] After the navigation information shows that the user has driven away from the gas station at a preset distance, if the cloud computing platform determines that refueling is still needed based on the latest vehicle location and fuel level data, a new round of intelligent refueling navigation process will be automatically triggered to recommend the next optimal refueling point to the user, thus forming a closed-loop, adaptive intelligent refueling navigation service.

[0054] If the cloud computing platform determines that refueling is not required, the mobile user terminal receives the "no refueling" instruction and actively pushes it to the user through voice broadcast and interface display, thus ending the intelligent refueling navigation.

[0055] Figure 3 The following is a schematic diagram of the intelligent refueling navigation method according to the embodiments of this application, in conjunction with... Figure 3 This paper details the intelligent refueling navigation method.

[0056] In one illustrative embodiment of the intelligent refueling navigation method of this application, it includes: S1: Obtain vehicle's overall fuel consumption information, real-time fuel level information, and navigation information.

[0057] Preferably, the multi-source data acquired by the cloud computing platform can be divided into two categories based on the data source: vehicle data and navigation data.

[0058] Specifically, the vehicle data cloud computing platform obtains the data by calling the interface of the automaker's TSP data platform.

[0059] Furthermore, vehicle data includes at least: real-time fuel level, vehicle model, overall fuel consumption, required fuel type, and fuel tank capacity.

[0060] Specifically, the vehicle's real-time fuel level information is obtained through a fuel level detector installed inside the vehicle's fuel tank. This analog or digital signal is transmitted to the onboard T-BOX terminal via the vehicle's CAN (Controller Area Network) bus. After processing the signal, the onboard T-BOX terminal uploads it to the automaker's TSP data platform via a mobile network.

[0061] Fuel level detectors can be fuel level sensors, such as float-type resistance sensors, capacitive sensors, or ultrasonic sensors.

[0062] The fuel level sensor receives fuel level detection commands from the vehicle-mounted T-BOX terminal, and performs fuel level height or volume detection based on the fuel level detection commands, converts them into analog or digital electrical signals, and continuously transmits real-time fuel level information back to the vehicle-mounted T-BOX terminal at fixed intervals.

[0063] Furthermore, vehicle model, overall fuel consumption information, required fuel type, and fuel tank capacity are inherent attributes of the vehicle. This vehicle data is typically determined at the factory and stored in the vehicle's onboard electronic control unit or in the vehicle's database.

[0064] The vehicle-mounted T-BOX terminal can read inherent vehicle data through the diagnostic interface or pre-stored data, and report it to the vehicle-mounted TSP data platform along with real-time fuel information.

[0065] Specifically, the comprehensive fuel consumption information is the official energy consumption value of a vehicle under a standard test cycle, expressed in liters per 100 kilometers, and is used to characterize the inherent energy consumption level of a vehicle.

[0066] The fuel tank capacity and the required fuel type are used to calculate the maximum amount of fuel to be refueled and to screen for compatible gas stations.

[0067] Furthermore, the navigation data cloud computing platform obtains the data by calling the interface of the navigation application on the mobile client terminal.

[0068] Furthermore, navigation data includes at least: the user's real-time location, destination location and remaining distance, planned route information, real-time traffic conditions, real-time weather conditions, real-time altitude, and information on gas stations along the route.

[0069] Specifically, gas station information should include at least real-time queue status, fuel prices, gas station ratings, and operational status.

[0070] S2: Calculates the target fuel consumption required to complete the current journey based on comprehensive fuel consumption information and navigation information.

[0071] In some embodiments, calculating the target fuel quantity required to complete the current journey includes: Determine the remaining distance of the current trip based on navigation information; Obtain real-time traffic information associated with remaining mileage; The target fuel quantity is calculated based on comprehensive fuel consumption information, remaining mileage, and a road condition correction factor determined according to real-time road condition information.

[0072] Preferably, a vehicle's actual fuel consumption is not fixed but is dynamically and significantly affected by driving environment and conditions. If only the vehicle's official standard combined fuel consumption is used for fuel quantity prediction, the result will deviate greatly from the energy consumption in real driving scenarios, leading to inaccurate predictions and thus false or missed warnings. Therefore, a road condition correction factor needs to be introduced.

[0073] Furthermore, the road condition correction factor includes at least: road correction factor, traffic congestion correction factor, and weather correction factor.

[0074] Preferably, the road correction coefficient is used to quantify the differentiated impact of different road types on the vehicle's overall fuel consumption during the trip. Its calculation relies on a detailed analysis of the current navigation route, decomposing the total trip into road segments with different attributes, and assigning a preset fuel consumption impact coefficient to each road segment.

[0075] Road types include at least: urban roads, expressways, village roads, and elevated / expressways.

[0076]

[0077] Table 1. Example Table of Road Type Correction Coefficients Table 1 shows an example of road type correction coefficients. As shown in Table 1, the system extracts the mileage of each type of road from the navigation path and calculates its proportion of the total remaining mileage. The road correction coefficient is calculated by weighting and summing the mileage proportion of each type of road with its corresponding correction coefficient, subtracting the baseline value of 1, and then converting it into a percentage.

[0078] Furthermore, the road correction factor is expressed as: Road correction factor = (a*1.05+ b*1.03+ g*1.06+ d*0.97-1)*100%.

[0079] It should be noted that the road types and correction coefficients listed in Table 1 above are for illustrative purposes only. In practical applications, the classification of road types is not limited to this and can be expanded or adjusted according to specific navigation map data specifications and energy consumption analysis models. For example, it may also include other types such as national highways, provincial highways, tunnels, and off-road sections.

[0080] Similarly, the specific correction coefficient values ​​for various road types can be preset or dynamically calculated based on measured data, statistical models, or specific strategies, and can be manually fine-tuned by the user according to their driving habits. The core of this application lies in introducing and quantifying the impact of road type on fuel consumption, rather than being limited to the specific types and values ​​listed in Table 1.

[0081] Preferably, the traffic congestion correction coefficient is used to quantify the dynamic impact of real-time traffic flow conditions on actual vehicle fuel consumption. During navigation trips, the degree of traffic congestion significantly alters vehicle operating conditions. Vehicles on congested roads frequently idle, follow other vehicles at low speeds, and repeatedly accelerate and decelerate, causing the engine to operate in an inefficient range for extended periods, resulting in a significant increase in fuel consumption.

[0082] Traffic congestion levels include at least three categories: smooth traffic, light congestion, and heavy congestion. Based on real-time navigation traffic information, the system identifies road segments within the remaining mileage that fall under different congestion levels and calculates their mileage percentage. Table 2 shows the preset unit fuel consumption impact coefficients corresponding to different congestion levels.

[0083]

[0084] Table 2 Example of Traffic Congestion Level Correction Coefficient Furthermore, the traffic congestion correction factor is expressed as: Traffic congestion correction factor = (e*1.05 + f*1.1-1)*100%.

[0085] It should be noted that the traffic congestion level classifications, specific correction coefficient values, and their calculation methods relative to mileage listed in Table 2 above are merely illustrative examples, intended to clearly illustrate the core calculation logic of this application. In practical applications, the granularity of congestion level classifications and the corresponding correction coefficient values ​​for each level can be preset, fitted, or dynamically calculated based on more refined traffic flow data or specific energy consumption models. The core of this application lies in introducing and quantifying the impact of the dynamic dimension of real-time traffic congestion status on fuel consumption, and its protection scope is not limited to the specific values ​​and classification methods listed in Table 2.

[0086] Preferably, the weather correction factor is used to quantify the additional impact of special weather conditions on the actual fuel consumption of the vehicle. During navigation trips, real-time weather conditions such as rain, snow, and strong winds directly change the vehicle's driving environment and resistance, thereby affecting the engine load and energy consumption.

[0087] Specifically, the weather types include at least: sunny, dry, and windless weather; rainy / snowy weather; and headwind weather. Based on weather information provided by the real-time navigation service, the system identifies route segments within the remaining mileage that may be affected by specific weather conditions and calculates their mileage percentage. As shown in Table 3, different weather conditions correspond to preset unit fuel consumption impact coefficients.

[0088]

[0089] Table 3. Example of Weather Type Correction Coefficient Furthermore, the weather correction factor is expressed as: Weather correction factor = (k*1.05 + j*1.06 - 1)*100%.

[0090] It should be noted that the weather types, correction coefficients, and calculation methods listed in Table 3 above are merely illustrative examples. In practical applications, weather factors that can be considered, such as fog, dust storms, and temperature, along with their specific influence coefficient values ​​and the logic for judging their impact, can be defined and adjusted based on meteorological models, measured data, or specific strategies. The core of this application lies in introducing and quantifying the impact of real-time weather conditions as an environmental dimension on fuel consumption.

[0091] Preferably, the altitude correction factor is used to quantify the impact of changes in air density and engine intake conditions caused by altitude variations on vehicle fuel consumption. As altitude increases, air density decreases, which may lead to insufficient engine intake and changes in combustion efficiency, thereby increasing fuel consumption.

[0092] Specifically, the altitude correction factor = (H / 100) × 1%.

[0093] The formula indicates that for every 100-meter increase in altitude, the vehicle's overall fuel consumption is expected to increase by 1%.

[0094] It should be noted that the above calculation formula is for illustrative purposes only. In practical applications, the specific impact coefficient per unit altitude can be more precisely calibrated and preset based on the engine characteristics of the vehicle model, the application of turbocharging technology, and measured data models. The core of this application lies in introducing and quantifying the impact mechanism of cumulative altitude changes on fuel consumption.

[0095] Furthermore, by combining the aforementioned road correction coefficient, traffic congestion correction coefficient, weather correction coefficient, and altitude correction coefficient, a road condition correction coefficient is calculated to comprehensively quantify the total impact of the dynamic driving environment throughout the entire journey on the vehicle's standard fuel consumption.

[0096] Specifically, the road condition correction factor is calculated by combining the independent sub-correction factors through multiplication, representing the combined effect of various influencing factors on fuel consumption, and is expressed as: Road condition correction factor R = (1 + road correction factor) × (1 + traffic congestion correction factor) × (1 + altitude correction factor) × (1 + weather correction factor).

[0097] In some embodiments, the target fuel quantity is calculated based on comprehensive fuel consumption information, remaining mileage, and a road condition correction factor determined according to real-time road condition information.

[0098] Preferably, the target oil quantity is calculated using the following formula:

[0099] Where D represents the remaining distance of the current journey as determined based on navigation information, in kilometers; L indicates the vehicle's overall fuel consumption information, expressed in liters per 100 kilometers; R represents the total road condition correction coefficient, which incorporates the dynamic effects of multiple dimensions such as roads, traffic, altitude, and weather. It is dimensionless.

[0100] S3: Based on the comparison between real-time fuel level information and target fuel level, determine whether to issue a refueling alarm; when the difference between real-time fuel level information and safety threshold determined based on comprehensive fuel consumption information is less than or equal to the target fuel level, it is determined that a refueling alarm needs to be issued.

[0101] In some embodiments, when the real-time fuel level is below a safety threshold, a refueling reminder message is output.

[0102] Preferably, a safety threshold Ls is set based on the comprehensive fuel consumption information L.

[0103] The safety threshold is an oil quantity buffer value preset based on the comprehensive vehicle fuel consumption information (L) and is used to implement hierarchical early warnings. The value of the safety threshold is usually set to be not less than the comprehensive fuel consumption value (L), representing the amount of fuel required for the vehicle to travel approximately 100 kilometers under standard working conditions.

[0104] The safety threshold can be set by the system default. For example, by default, set Ls = L and allow users to manually adjust it according to their personal driving habits.

[0105] The core function of the safety threshold is that, on the one hand, it serves as the absolute safety bottom line for triggering an emergency warning of too low oil quantity; on the other hand, it serves as a safety buffer quantity deducted in advance from the current oil quantity for calculating the net available oil quantity available for subsequent trips, thereby supporting intelligent and early fueling demand prediction.

[0106] Further, based on the comparison result between the real-time oil quantity information and the target oil quantity, determining whether to issue a fueling warning specifically includes: Based on the navigation information, determine whether the current or upcoming driving route enters a special section. Special sections include, but are not limited to, desolate routes such as uninhabited areas, deserts, and grasslands where gas stations are sparse and resupply is difficult.

[0107] If entering or about to enter the special section, regardless of the comparison result between the real-time oil quantity information (T) and the target oil quantity Qneed, at the first gas station passed before entering the special section, and the last two gas stations, forcefully trigger a reminder to prompt the user to fill up the fuel tank. This strategy aims to provide the maximum safety redundancy for driving in special sections.

[0108] If not entering the special section, perform a conventional intelligent fueling reminder judgment and execute a gradient early warning judgment: [[ID=<<MASK_E>>19]]Basic safety warning check, determine whether the real-time oil quantity information (T) is lower than the safety threshold (Ls). If T < Ls, immediately trigger an emergency warning of too low oil quantity.

[0109] Intelligent reminder check, if T ≥ Ls, further calculate and determine whether (T - Ls) ≤ Qneed holds. If it holds, trigger an intelligent fueling reminder, meaning that even if a safety buffer oil quantity is reserved, the current oil quantity is already tight for completing the remaining trip and it is necessary to plan a fueling plan in advance; if it does not hold, remain silent and do not issue a reminder.

[0110] S4: In response to determining that a fueling warning is required, obtain information on multiple gas stations within a preset range based on the navigation information, and use the analytic hierarchy process to score and rank the gas stations to generate a fueling recommendation list containing at least one recommended gas station.

[0111] In some embodiments, information on multiple gas stations along the current travel path or within its preset range is extracted based on navigation information; The gas station information includes gas station attribute information and traffic-related information associated with the current travel route.

[0112] Preferably, the gas station attribute information includes at least one of the following: real-time fuel price, estimated queue time, operating status, user rating, and gas station brand.

[0113] Traffic-related information includes at least one of the following: the location of the gas station, the distance from the current vehicle location, and the detour distance required to reach the gas station.

[0114] In some embodiments, the analytic hierarchy process (AHP) is used to score and rank gas stations, generating a gas station recommendation list containing at least one recommended gas station, specifically including: Multiple evaluation dimensions and the corresponding weights for each evaluation dimension are determined. The evaluation dimensions include those related to traffic-related information and those related to gas station attribute information. Based on the gas station attribute information of each gas station, calculate the score under the evaluation dimensions related to the gas station attribute information; Based on the traffic association information of each gas station, a score is calculated under the evaluation dimensions related to the traffic association information; The scores are weighted according to the weight of each evaluation dimension to obtain the comprehensive score of each gas station. Multiple gas stations are ranked based on their overall scores, and a recommended refueling list is generated.

[0115] Preferably, the evaluation dimensions include at least the urgency of fuel availability, proximity to the destination, fuel price, waiting time, gas station brand, additional services, and safety factor.

[0116]

[0117] Table 4. Example of Evaluation Dimensions, Weights, and Scoring Rules for Gas Stations As shown in Table 4, the urgency of refueling is strongly correlated with the distance to the current vehicle location and the vehicle's real-time fuel status in the traffic-related information. The core of this approach is to quantify the risk of traveling to the gas station. A higher numerator value in the scoring rule, indicating a larger fuel tank capacity, suggests more fuel remaining upon arrival at the station, lower refueling urgency, and a more relaxed user experience; therefore, a higher priority score is awarded.

[0118] The route convenience rating is directly determined by the detour distance in the traffic-related information. The route convenience rating quantifies the extra travel cost incurred by the user for refueling; the less detour, the higher the score, reflecting the optimization of route efficiency.

[0119] The fuel price is calculated based on the real-time fuel price information in the gas station's attribute information. It directly reflects the economics of refueling; the more the fuel price is lower than the market average, the higher the score, saving users money.

[0120] Queue time is calculated based on the estimated queue time in the gas station's attribute information. This quantifies the user's time cost; the shorter the queue time, the higher the score, thus preventing users from experiencing long waits while refueling.

[0121] Gas station brands are based on the gas station attribute information and use a preset mapping. For example: PetroChina = 1, Sinopec = 1, other brands, such as private brands = 0.8. This reflects users' potential trust preferences regarding fuel quality and service reliability.

[0122] Additional services are scored based on the service descriptions in the gas station's attribute information, such as whether car washes, restaurants, and spacious areas are provided. For example, each approved service can add 0.2 points. Additional services enhance the richness and added value of the recommended experience.

[0123] Safety is assessed comprehensively, taking into account location and time information from traffic-related data. For example, refueling at night in a remote area will result in a deduction of 0.3 points. This dimension incorporates considerations for personal and property safety in specific scenarios.

[0124] Based on the evaluation dimensions, weights, and scoring rules defined in Table 4, the formula for calculating the comprehensive score of a gas station is as follows: Gas station overall rating =

[0125] It should be noted that the evaluation dimensions, specific weight allocations, scoring rules and scoring methods shown in Table 4 are all examples, intended to clearly illustrate the core idea of ​​this application using the Analytic Hierarchy Process (AHP) for multi-dimensional comprehensive evaluation.

[0126] In practice, the types and number of evaluation dimensions, the weight of each dimension, and the specific scoring function can be configured, adjusted, and optimized according to different business strategies, regional characteristics, or user preferences. The key point of this application is to propose a methodology that maps the static attributes, dynamic status, and correlation with the trip of a gas station to multiple evaluation dimensions, and obtains a comprehensive score through weighted calculation for ranking and recommendation, rather than being limited to the specific implementation parameters listed in Table 4.

[0127] Preferably, based on the above-described method of using the analytic hierarchy process (AHP) for multi-dimensional scoring and ranking, the system can generate a clear and actionable list of recommended gas stations. Table 5 shows an example list generated using this method.

[0128]

[0129] Table 5 Recommended Gas Station List As shown in Table 4, the gas station recommendation list not only includes basic information about the gas stations, but also displays comprehensive scores based on multi-dimensional evaluation and summary reasons for the recommendations. This provides users with an intuitive comparison and decision-making basis, allowing them to accept the default optimal option or make personalized choices based on the detailed information in the list.

[0130] In some embodiments, the fuel recommendation list also includes suggested fuel quantities.

[0131] Specifically, based on the target fuel quantity, real-time fuel quantity information, and safety thresholds, a recommended refueling quantity for the recommended gas station is calculated; the recommended refueling quantity is then included in the refueling recommendation list.

[0132] Preferably, the calculation of the recommended refueling amount aims to provide users with accurate refueling guidance, and the specific calculation rules are as follows: When the real-time fuel level T is less than the safety threshold Ls, it indicates that the current fuel level is below the safety line. In this case, it is recommended that the refueling amount Qadd not only replenish the required fuel Qneed to reach the destination, but also add additional fuel to ensure that the total fuel level after refueling is at least above the safety threshold Ls. The calculation formula is: Qadd = Qneed + Ls - T This rule prioritizes ensuring that the vehicle is immediately out of a low fuel emergency situation after refueling.

[0133] When the real-time fuel level T is greater than or equal to the safety threshold Ls, it indicates that the current fuel level is within a safe range. In this case, it is recommended that the fuel amount Qadd only be enough to complete the remaining journey. The calculation formula is: Qadd = Qneed This rule aims to meet travel needs while avoiding unnecessary over-fueling, thus reflecting economic efficiency.

[0134] S5: Push the list of recommended gas stations to users at preset times.

[0135] In some embodiments, the system receives a user's selection instruction for at least one recommended gas station from the recommended list. Based on the selected instructions, the target gas station is determined and a corresponding navigation route is generated; If no selection instruction is received within the preset time, the gas station ranked first will be selected as the target gas station by default.

[0136] Preferably, after generating the gas station recommendation list, the system initiates a preset selection waiting period and clearly displays the gas station recommendation list. During this period, the user can actively select any gas station from the list. If the user does not take any action, the system defaults to selecting the gas station with the highest overall rating as the target gas station and automatically generates a navigation route. This ensures efficient and uninterrupted execution of the optimal recommendation plan while retaining the user's right to intervene based on personal experience and preferences, achieving an organic combination of intelligent decision-making and human judgment.

[0137] Furthermore, preset timing refers to a series of trigger points where the system proactively pushes refueling recommendations or performs key operations to the user, aiming to intervene at an appropriate frequency and timing to avoid interfering with driving concentration while ensuring the timeliness of reminders.

[0138] Specific timings include, but are not limited to: When navigation begins, after the user sets the destination and starts navigation, if the system calculates that refueling is needed, it will immediately provide the first voice and interface recommendation, offering a full refueling plan so that the user knows as early as possible.

[0139] When approaching a recommended gas station, a second voice prompt will be given when the vehicle is a certain distance away from the default or selected recommended gas station, such as 5 kilometers, for trip confirmation and preparation.

[0140] After leaving the recommended gas station, if the user does not refuel at the recommended gas station and drives more than a certain distance, such as 1 kilometer, the system will recalculate based on the latest fuel level and location. If refueling is still needed, the system will proactively recommend the next optimal gas station, thus achieving relay planning.

[0141] Upon arrival at the recommended gas station, when the navigation displays that the target gas station area has been reached, the system will again provide a pre-calculated suggested amount of fuel via voice prompt, facilitating communication between the user and the gas station attendant or self-service refueling.

[0142] Before entering special road sections, when the navigation route is about to enter special road sections with sparse gas stations, such as uninhabited areas or deserts, the system will forcibly trigger a reminder at the last two gas stations along the way, suggesting that the user fill up the tank. This is a mandatory strategy based on safety considerations.

[0143] It should be noted that the implementation architecture of the aforementioned intelligent refueling navigation method is not limited to a cloud-based collaborative model. Depending on the configuration of computing resources and data acquisition, this method can also employ other deployment methods. For example, a vehicle-side computing model can be adopted, in which the vehicle's own computing unit, such as the in-vehicle infotainment system or domain controller, obtains information such as navigation information, overall vehicle fuel consumption and real-time fuel level, and completes target fuel level calculation, alarm judgment and gas station rating and ranking on the vehicle side, generates a recommendation list and pushes it to the user through the vehicle display device or voice.

[0144] In addition, a mobile terminal computing mode can be adopted, in which the user's mobile terminal, such as a smartphone, obtains or receives various information from the vehicle and network services, completes all calculations and decisions locally on the mobile terminal, and pushes the information through the mobile terminal's interactive interface.

[0145] These different deployment methods are all based on the core logic of the intelligent refueling navigation method proposed in this application. By adjusting the subject that performs the data processing and computing tasks, they can adapt to different product forms, network conditions, or user preferences, and are all within the scope of protection of this application.

[0146] like Figure 4 As shown, in a second aspect, the present invention provides an intelligent refueling navigation system for implementing the intelligent refueling navigation method of the first aspect, including a mobile client terminal 100, an in-vehicle device 200, and a cloud server 300.

[0147] Mobile client terminal 100 is used to provide navigation information to cloud server 300; The vehicle-mounted device 200 is used to collect real-time fuel level information and comprehensive fuel consumption information of the vehicle, and send the real-time fuel level information and comprehensive fuel consumption information to the cloud server 300. The cloud server 300 is used to calculate the target amount of fuel required to complete the current journey based on real-time fuel level information, comprehensive fuel consumption information, and navigation information. The decision to issue a refueling alarm is based on the comparison between real-time fuel level information and target fuel level information; when the difference between real-time fuel level information and safety threshold determined based on comprehensive fuel consumption information is less than or equal to target fuel level, a refueling alarm is required. In response to a refueling alarm, the system retrieves information on multiple gas stations within a preset range based on navigation information, scores and sorts the gas stations, and generates a refueling recommendation list containing at least one recommended gas station. The list of recommended refueling stations will be sent to mobile client terminals 100.

[0148] In a third aspect, the present invention provides a mobile client terminal for executing the intelligent refueling navigation method as described in the first aspect.

[0149] In a fourth aspect, the present invention provides a vehicle including an in-vehicle terminal, the in-vehicle terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the intelligent refueling navigation method as described in the first aspect.

[0150] Combination Figure 5 As shown, this embodiment discloses a specific implementation of a computer device. The computer device may include a processor 41 and a memory 42 storing computer program instructions.

[0151] Specifically, the processor 41 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0152] The memory 42 may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 42 may include removable or non-removable (or fixed) media. Where appropriate, the memory 42 may be internal or external to the data processing device. In a particular embodiment, the memory 42 is non-volatile. Volatile memory. In a particular embodiment, memory 42 includes read-only memory. ROM (ROM-only memory) and RAM (Random Access Memory). Where appropriate, the ROM can be a mask-programmed ROM or a programmable ROM. Only Memory (PROM) and Erasable Programmable Read Memory (PROM) The RAM can be a type of RAM, such as EPROM (Electrically Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), EAROM (Electrically Alterable Read-Only Memory), or FLASH (Flash Memory), or a combination of two or more of these. Where appropriate, the RAM can be a Static Random Access Memory (SRAM). Access Memory (SRAM) or Dynamic Random Access Memory (DRAM) can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Out Dynamic Random Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0153] The memory 42 can be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 41.

[0154] The processor 41 implements the intelligent refueling navigation method in the above embodiment by reading and executing computer program instructions stored in the memory 42.

[0155] In some embodiments, the computer device may further include a communication interface 43 and a bus 40. For example, Figure 5 As shown, the processor 41, memory 42, and communication interface 43 are connected through bus 40 and complete communication with each other.

[0156] Communication interface 43 is used to enable communication between modules, devices, units and / or equipment in the embodiments of this application.

[0157] Communication port 43 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0158] Bus 40 includes hardware, software, or both, that couples components of a computer device together. Bus 40 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. For example, and not as a limitation, bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, and a PCI bus. Express (PCI X) bus, Serial Advanced Technology Accessory (Seria l Advanced) The bus may be a Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 40 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0159] Furthermore, in conjunction with the intelligent refueling navigation method in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the intelligent refueling navigation methods in the above embodiments.

[0160] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned intelligent refueling navigation method.

[0161] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described intelligent refueling navigation method.

[0162] Although embodiments of the invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the invention.

[0163] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An intelligent refueling navigation method, characterized in that, It comprises: Obtaining vehicle comprehensive fuel consumption information, real-time fuel quantity information and navigation information; Based on the comprehensive fuel consumption information and the navigation information, the target fuel quantity required to complete the current trip is calculated; Based on the comparison result of the real-time fuel quantity information and the target fuel quantity, it is judged whether to perform refueling warning; When the difference between the real-time fuel quantity information and the safety threshold determined based on the comprehensive fuel consumption information is less than or equal to the target fuel quantity, it is determined that refueling warning is needed; In response to determining that refueling warning is needed, based on the navigation information, a plurality of gas station information within a preset range is obtained, and the gas stations are scored and sorted to generate a refueling recommendation list containing at least one recommended gas station; The refueling recommendation list is pushed to the user at a preset time.

2. The intelligent refueling navigation method of claim 1, wherein, Based on the comprehensive fuel consumption information and the navigation information, the target fuel quantity required to complete the current trip is calculated, including: Determine the remaining mileage of the current trip based on the navigation information; Obtain real-time traffic information associated with the remaining mileage; Based on the comprehensive fuel consumption information, the remaining mileage, and the road condition correction coefficient determined according to the real-time traffic information, the target fuel quantity is calculated.

3. The intelligent refueling navigation method of claim 1, wherein, The navigation information is used to obtain a plurality of gas station information within a preset range, including: Based on the navigation information, a plurality of gas station information along the current trip path or within a preset range thereof is extracted; Wherein, the gas station information includes gas station attribute information and traffic-related information related to the current trip path.

4. The intelligent refueling navigation method of claim 3, wherein, The gas stations are scored and sorted to generate a refueling recommendation list containing at least one recommended gas station, including: Determine a plurality of evaluation dimensions and the weight corresponding to each evaluation dimension, the evaluation dimensions include dimensions related to the traffic-related information and dimensions related to the gas station attribute information; Based on the gas station attribute information of each gas station, the score in the evaluation dimension related to the gas station attribute information is calculated; Based on the traffic-related information of each gas station, the score in the evaluation dimension related to the traffic-related information is calculated; According to the weight of each evaluation dimension, the corresponding score is weighted to obtain the comprehensive score of each gas station; According to the comprehensive score, the plurality of gas stations are sorted to generate the refueling recommendation list.

5. The intelligent refueling navigation method of claim 1, wherein, It also includes: Receive the user's selection instruction for at least one recommended gas station in the recommendation list; According to the selection instruction, determine the target gas station and generate the corresponding navigation route; If the selection instruction is not received within a preset time, the first ranked gas station is determined as the target gas station by default.

6. The intelligent refueling navigation method of claim 1, wherein, It also includes: When the real-time fuel quantity information is lower than the safety threshold, output a refueling prompt information.

7. The intelligent refueling navigation method of claim 1, wherein, The generation of the refueling recommendation list containing at least one recommended gas station also includes: Based on the target fuel quantity, the real-time fuel quantity information and the safety threshold, the recommended refueling quantity for the recommended gas station is calculated; The recommended refueling quantity is included in the refueling recommendation list.

8. An intelligent refueling navigation system characterized by, It includes: Mobile client terminal for providing navigation information to cloud server; A vehicle-mounted device is configured to collect real-time fuel information and comprehensive fuel consumption information of a vehicle, and send the real-time fuel information and the comprehensive fuel consumption information to a cloud server. The cloud server is configured to calculate a target fuel amount required to complete a current trip based on the real-time fuel information, the comprehensive fuel consumption information and navigation information. A comparison result of the real-time fuel information and the target fuel amount is used to determine whether to issue a refueling alert. When a difference between the real-time fuel information and a safety threshold determined based on the comprehensive fuel consumption information is less than or equal to the target fuel amount, it is determined that the refueling alert is needed. In response to the determination that the refueling alert is needed, a plurality of gas station information within a preset range is obtained based on the navigation information, and the gas stations are scored and sorted to generate a refueling recommendation list containing at least one recommended gas station. The refueling recommendation list is sent to a mobile client terminal.

9. A mobile client terminal, characterized in that A device for performing the intelligent refueling navigation method of any one of claims 1-7.

10. A vehicle comprising an on-board terminal, characterized in that The vehicle-mounted terminal includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the intelligent refueling navigation method of any one of claims 1-7 is implemented.

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