Multi-source information and dynamic regulation and control fused oil mass display method and system

Abstract

The invention relates to the technical field of vehicle instrument display, in particular to an oil mass display method and system fusing multi-source information and dynamic regulation, and the method comprises the steps: carrying out the fusion processing of multi-source data collected in real time, and training a pre-established oil mass consumption prediction model based on the multi-source data after the fusion processing; through the trained fuel consumption prediction model and in combination with a navigation planning route, predicting the remaining fuel quantity and the travelable mileage of the vehicle when the vehicle travels to the destination; according to the oil mass states of the vehicle under different working conditions, a graded oil mass display strategy is executed, and oil mass display under each working condition is dynamically regulated and controlled; wherein the vehicle running state comprises a normal running working condition, a congestion working condition, a high-speed running working condition, a bumpy road section working condition and a slope parking working condition. According to the scheme, accurate, stable and comprehensive fuel quantity information is provided for a driver, and the driving experience and the driving safety are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle instrument display, and in particular relates to a fuel level display method and system integrating multi-source information and dynamic regulation. Background Art

[0002] Currently, the fuel level display of a vehicle mostly relies on a single fuel level sensor, which obtains fuel level data through the change of float linkage resistance. However, this method is easily interfered by factors such as vehicle bumps, tilts, and fuel sloshing, resulting in large fluctuations in the displayed values ​​and poor accuracy, making it difficult for the driver to accurately judge the remaining fuel level and difficult to meet the user's needs for the accuracy, reliability, and safety of vehicle fuel information. Under various operating conditions and different environments of the vehicle, the disadvantages of the existing fuel level display method are becoming increasingly obvious. For example, in special circumstances such as vehicle refueling and oil leakage, it is impossible to timely and accurately reflect changes and anomalies in the fuel level, posing a safety hazard. SUMMARY OF THE INVENTION

[0003] Based on this, it is necessary to provide a fuel level display method and system that integrates multi-source information and dynamic control to address the above problems, so as to solve the problems of poor vehicle fuel level display accuracy, insufficient stability and inability to meet the needs of various complex working conditions in the existing technology, thereby improving the driving experience and driving safety.

[0004] In a first aspect, the present application provides a method for displaying fuel quantity by integrating multi-source information and dynamic control, the method comprising:

[0005] Fuse the multi-source data collected in real time and train the pre-established fuel consumption prediction model based on the fused multi-source data;

[0006] By using the trained fuel consumption prediction model and combining it with the navigation route planning, the remaining fuel and mileage of the vehicle to the destination are predicted;

[0007] According to the fuel level status of the vehicle under different working conditions, a graded fuel level display strategy is implemented to dynamically adjust the fuel level display under each working condition;

[0008] The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

[0009] Optionally, the fusing of multi-source data collected in real time includes:

[0010] Real-time multi-source data collection through a multi-source sensor group; wherein the sensor group includes an oil level sensor, a pressure sensor, a temperature sensor, a flow sensor, a fuel tank cap induction switch, an oil pump float, and a conversion resistor;

[0011] The multi-source data includes fuel tank status parameters and vehicle operation data;

[0012] Filter the fuel tank status parameters and vehicle operation data to remove noise interference in the data;

[0013] Combined with the oil pump float conversion resistance data, the oil level sensor measurement value is corrected, and the corrected measurement value is recalibrated according to the fuel temperature and pressure;

[0014] Taking into account vehicle driving data, posture data, geographic location and weather data, the calibrated data is fused based on the Kalman filter algorithm to establish a multi-dimensional fuel quantity calculation model and calculate the fuel quantity value;

[0015] Combined with vehicle operating parameters, the fuel quantity value is corrected in real time to calculate the fuel quantity under different conditions; and the fuel quantity data obtained under different conditions is processed; wherein, the vehicle operating parameters include vehicle load information and traffic congestion level information.

[0016] Optionally, collecting multi-source data in real time through a multi-source sensor group includes:

[0017] The oil level sensor, pressure sensor, temperature sensor, and flow sensor are deployed in the oil tank; the induction switch and oil pump float are installed at the oil tank cap; the oil pump float is connected to the conversion resistor; the conversion resistor outputs the corresponding resistance value as the oil pump float position changes; wherein,

[0018] The oil level sensor is used to collect the fuel level reading in real time;

[0019] The pressure sensor is used to monitor the pressure changes in the fuel tank;

[0020] The temperature sensor is used to obtain the fuel temperature;

[0021] The flow sensor is used to detect the fuel inlet and outlet flow;

[0022] The induction switch is used to feedback the open and closed status of the fuel tank cap;

[0023] It also includes: collecting vehicle driving data, posture data, as well as vehicle geographic location information and real-time weather data;

[0024] The vehicle driving data includes: vehicle speed, engine speed, mileage, driving mode, fuel injection amount, and ignition status;

[0025] The posture data includes: vehicle tilt angle and acceleration.

[0026] Optionally, the real-time correction of the fuel quantity value in combination with the vehicle operating condition parameters includes:

[0027] Obtain load information through vehicle suspension system sensors and establish a load-fuel consumption coefficient mapping relationship table;

[0028] Dynamically adjust the idle fuel consumption coefficient based on traffic congestion information;

[0029] Differentiate between cold start and hot start conditions using engine coolant temperature sensor data.

[0030] Optionally, the calculating the oil volume under different conditions includes:

[0031] Obtain load information through the vehicle suspension system sensor and establish a dynamic resistance-oil volume mapping relationship table between the oil pump float conversion resistance and oil volume;

[0032] Monitor resistance changes in real time, obtain initial oil volume data through table lookup, and perform weighted fusion calculations in combination with other sensor data;

[0033] The mapping table parameters are dynamically adjusted according to the vehicle's ambient temperature and operating conditions.

[0034] Optionally, processing the oil quantity data obtained under different conditions includes:

[0035] Using a fuzzy logic algorithm, the system integrates vehicle speed, engine speed, accelerator pedal position, and braking frequency parameters. When the congestion membership exceeds a preset threshold, a high-frequency fuel level update mode is triggered.

[0036] When the vehicle acceleration change rate exceeds the set value, the weighted average algorithm is used to smooth the fuel quantity data;

[0037] Detect the vehicle's tilt angle and start oil level data compensation when the tilt angle exceeds the preset angle.

[0038] Optionally, the fuel consumption prediction model is constructed using a long short-term memory (LSTM) network of deep learning, which includes:

[0039] At the moment of vehicle startup, a fuel consumption prediction model for the startup phase is established based on the engine model, fuel injection volume at startup, idle speed parameters, and historical startup fuel consumption data;

[0040] Combined with vehicle speed, engine speed, road conditions, and weather data, the startup fuel consumption prediction model is used to calculate the real-time fuel consumption rate;

[0041] The long short-term memory network (LSTM) in deep learning is used to predict fuel consumption and obtain a fuel consumption prediction model.

[0042] Optionally, the training of a pre-established fuel consumption prediction model based on the fused multi-source data includes:

[0043] The pre-collected historical operating condition sequence data is used as network training data and input into a long short-term memory (LSTM) network based on an attention mechanism. The LSTM network based on the attention mechanism is trained to learn fuel consumption patterns under different operating conditions and environmental conditions, thereby obtaining a trained fuel consumption prediction model.

[0044] The historical operating condition sequence data includes a time series of vehicle speed, engine load, and ambient temperature parameters.

[0045] Optionally, the implementation of a graded fuel level display strategy based on the fuel level status of the vehicle under different operating conditions includes: displaying the fuel level according to normal driving conditions, congested conditions, high-speed driving conditions, and bumpy road conditions; implementing a refueling detection strategy, an oil leak detection strategy, a display strategy when the fuel sensor is abnormal, a refueling strategy when the vehicle is stalled, an ignition refueling strategy, and a slope parking stability strategy to optimize the fuel level display.

[0046] In a second aspect, the present application provides a fuel level display system integrating multi-source information and dynamic control, the system comprising:

[0047] The data processing module integrates and processes the multi-source data collected in real time, and trains the pre-established fuel consumption prediction model based on the integrated multi-source data;

[0048] The prediction module is used to predict the remaining fuel and mileage of the vehicle to the destination by combining the trained fuel consumption prediction model with the navigation route planning;

[0049] The graded display module is used to implement a graded fuel level display strategy based on the fuel level status of the vehicle under different working conditions, and dynamically adjust the fuel level display under each working condition;

[0050] The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

[0051] The proposed fuel level display method and system, integrating multi-source information with dynamic control, includes fusing real-time multi-source data and training a pre-established fuel consumption prediction model based on the fused data. The trained fuel consumption prediction model, combined with navigation route planning, predicts the vehicle's remaining fuel and mileage to its destination. Based on the vehicle's fuel level status under different operating conditions, a differentiated fuel level display strategy is implemented to dynamically control the fuel level display under various operating conditions. This includes predicting fuel consumption during startup and adjusting the display frequency, maintaining fuel level stability under different driving conditions, detecting and displaying fuel leaks during refueling, and handling sensor anomaly display. This solution provides drivers with accurate, stable, and comprehensive fuel level information. Based on the vehicle's operating conditions and status, the corresponding fuel level display strategy is implemented, displaying the calculated fuel level information to the driver through a dynamic visualization interface. The displayed content and format are updated promptly based on fuel level changes.

[0052] The present invention integrates multiple sources of information, including oil level sensors, pressure sensors, temperature sensors, flow sensors, oil pump float conversion resistors, as well as vehicle driving data, posture data, geographic location and weather data. It uses the Kalman filter algorithm to remove noise interference and combines it with a multi-dimensional oil quantity calculation model to significantly improve the accuracy of oil quantity calculation and reduce the risk of drivers making misjudgments due to inaccurate oil quantity display.

[0053] This solution incorporates comprehensive operating condition adaptation strategies, including stable fuel level display strategies for various operating conditions, including vehicle startup, normal driving, congestion, highway driving, and bumpy roads. This ensures stable and reliable fuel level display in a variety of complex driving scenarios, enhancing the vehicle's applicability and practicality in various operating conditions. Furthermore, a refueling and oil leak detection mechanism has been established to promptly and accurately detect the vehicle's refueling and oil leak status, alerting the driver through instrument displays and alarms, effectively ensuring driving safety and preventing traffic accidents caused by abnormal fuel levels.

[0054] This invention develops a corresponding display strategy when a fuel sensor anomaly occurs, improving the system's robustness in the event of a fault, reducing information loss due to sensor issues, ensuring the driver can still obtain valuable fuel level information, and enhancing the system's reliability and stability. It also optimizes the fueling strategy for both vehicle shutdown and ignition refueling. When the vehicle is shut down, non-essential tasks are suspended to conserve resources and focus on refueling-related data collection and display. During ignition refueling, measures such as lowering the refueling flow rate limit and real-time monitoring of key data effectively reduce safety risks and enhance the intelligence of vehicle fuel management.

[0055] Furthermore, this invention incorporates a stabilization strategy for hilly parking situations. By incorporating information such as the vehicle's tilt angle into the fuel level sensor data, this strategy ensures the accuracy and stability of the fuel level display during hilly parking, further enhancing vehicle safety in these challenging parking situations. This provides drivers with more intuitive, clear, and comprehensive fuel level information and fuel consumption trends, optimizing the human-machine interaction experience and improving both the overall driving experience and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0057] Figure 1 This is a flow chart of a method for displaying fuel quantity by integrating multi-source information and dynamic control in an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the structure of a fuel level display system that integrates multi-source information and dynamic control in an embodiment of the present invention;

[0059] Figure 3 FIG. 4 is a diagram showing the internal structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0061] The present invention provides a method and system for displaying fuel levels that integrates multi-source information and dynamic control. This method can be applied to terminals, servers, or systems comprising both terminals and servers, and is implemented through interaction between the two. The terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, and the like. PC-based monitoring and mobile-based monitoring can be used in conjunction with this method.

[0062] The specific embodiments of the present invention specifically relate to an intelligent instrument fuel level display system and method that integrates multi-source data and has the functions of refueling leakage detection, dynamic anti-shake, working condition adaptation, sensor abnormality processing, ignition and refueling management, and slope parking stability.

[0063] Through the fusion of multi-type sensor data and intelligent algorithms, the accuracy of fuel quantity calculation is improved; a refueling and oil leak detection mechanism is established to ensure fuel safety; a stable fuel quantity display strategy is designed for vehicle startup and different operating conditions; a display strategy for fuel sensor abnormalities, a refueling strategy when the vehicle is shut down, an ignition refueling strategy, and a stable parking strategy on slopes are added to provide drivers with accurate, stable, and comprehensive fuel quantity information, improving the driving experience and driving safety.

[0064] In one embodiment, an embodiment of the present invention provides a fuel level display method that integrates multi-source information and dynamic control. Through multi-source data fusion and intelligent algorithms, the accuracy of fuel level display is significantly improved, reducing the risk of driver misjudgment. A comprehensive working condition adaptation strategy ensures stable and reliable fuel level display in different scenarios. The refueling and oil leak detection mechanism promptly warns of fuel anomalies to ensure driving safety. The display strategy when the fuel sensor is abnormal improves the robustness of the system in the event of a fault and reduces information loss caused by sensor problems. The refueling strategy when the vehicle is turned off optimizes the refueling experience and improves the intelligence level of vehicle fuel management. The ignition refueling strategy effectively reduces the safety hazards of refueling in the ignition state. The slope parking stability strategy ensures the accuracy of the fuel level display and the safety of the vehicle when the vehicle is parked on a slope. The dynamic visual display optimizes human-computer interaction, provides drivers with efficient and convenient fuel level information services, and comprehensively improves the overall driving experience and driving safety. At the same time, the optimized data processing algorithm further improves the accuracy of the fuel level display, allowing drivers to more accurately understand the vehicle's fuel status.

[0065] Based on this, the embodiments of the present invention are described in conjunction with the accompanying drawings. Figure 1 As shown, the embodiment of the present invention proposes a method for displaying fuel quantity by integrating multi-source information and dynamic control, which specifically includes the following steps:

[0066] S101 integrates and processes the multi-source data collected in real time, and trains the pre-established fuel consumption prediction model based on the integrated multi-source data;

[0067] S102 uses a trained fuel consumption prediction model and navigation route planning to predict the vehicle's remaining fuel and mileage to the destination.

[0068] S103 executes a graded fuel level display strategy based on the fuel level status of the vehicle under different operating conditions, and dynamically adjusts the fuel level display under each operating condition;

[0069] The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

[0070] Specifically, the above step S101 of fusing the multi-source data collected in real time includes:

[0071] Real-time multi-source data collection through a multi-source sensor group; wherein the sensor group includes a fuel level sensor, a pressure sensor, a temperature sensor, a flow sensor, a fuel tank cap induction switch, a fuel pump float, and a conversion resistor; the multi-source data includes fuel tank status parameters and vehicle operation data;

[0072] Filter the fuel tank status parameters and vehicle operation data to remove noise interference in the data;

[0073] Combined with the oil pump float conversion resistance data, the oil level sensor measurement value is corrected, and the corrected measurement value is recalibrated according to the fuel temperature and pressure;

[0074] Taking into account vehicle driving data, posture data, geographic location and weather data, the calibrated data is fused based on the Kalman filter algorithm to establish a multi-dimensional fuel quantity calculation model and calculate the fuel quantity value;

[0075] Combined with vehicle operating parameters, the fuel quantity value is corrected in real time to calculate the fuel quantity under different conditions; and the fuel quantity data obtained under different conditions is processed; wherein, the vehicle operating parameters include vehicle load information and traffic congestion level information.

[0076] The multi-dimensional fuel quantity calculation model established in the above embodiment is a generated relational model constructed by taking into account fuel tank status parameters, vehicle operation data and various external factors, and is used to calculate the final fuel quantity value.

[0077] In the multi-dimensional oil quantity calculation model, the calculation of oil quantity data includes the following steps:

[0078] Calculate the volume of oil in the tank based on parameters such as oil level, oil temperature, and oil pressure;

[0079] Calculate oil consumption based on vehicle driving data and flow sensor data;

[0080] Adjust the density and volume of oil based on geographic location and weather data;

[0081] Taking all factors into consideration, the final oil volume value is determined.

[0082] The calculated fuel quantity value is output to the vehicle's instrument panel or related equipment for use by the driver or the system.

[0083] Specifically, the above step S101 of collecting multi-source data in real time through the multi-source sensor group includes:

[0084] The oil level sensor, pressure sensor, temperature sensor, and flow sensor are deployed in the oil tank; the induction switch and oil pump float are installed at the oil tank cap; the oil pump float is connected to the conversion resistor; the conversion resistor outputs the corresponding resistance value as the oil pump float position changes; wherein,

[0085] The oil level sensor is used to collect the fuel level reading in real time;

[0086] The pressure sensor is used to monitor the pressure changes in the fuel tank;

[0087] The temperature sensor is used to obtain the fuel temperature;

[0088] The flow sensor is used to detect the fuel inlet and outlet flow;

[0089] The induction switch is used to feedback the open and closed status of the fuel tank cap;

[0090] It also includes: collecting vehicle driving data, posture data, as well as vehicle geographic location information and real-time weather data;

[0091] The vehicle driving data includes: vehicle speed, engine speed, mileage, driving mode, fuel injection amount, and ignition status;

[0092] The posture data includes: vehicle tilt angle and acceleration.

[0093] In the above embodiment, the real-time correction of the fuel level value in combination with the vehicle operating condition parameters includes:

[0094] Obtain load information through vehicle suspension system sensors and establish a load-fuel consumption coefficient mapping relationship table;

[0095] Dynamically adjust the idle fuel consumption coefficient based on traffic congestion information;

[0096] Differentiate between cold start and hot start conditions using engine coolant temperature sensor data.

[0097] Specifically, the calculation of the oil volume under different conditions in step S101 includes:

[0098] Obtain load information through the vehicle suspension system sensor and establish a dynamic resistance-oil volume mapping relationship table between the oil pump float conversion resistance and oil volume;

[0099] Monitor resistance changes in real time, obtain initial oil volume data through table lookup, and perform weighted fusion calculations in combination with other sensor data;

[0100] The mapping table parameters are dynamically adjusted according to the vehicle's ambient temperature and operating conditions.

[0101] In the above embodiment, processing the oil quantity data obtained under different conditions includes:

[0102] Using a fuzzy logic algorithm, the system integrates vehicle speed, engine speed, accelerator pedal position, and braking frequency parameters. When the congestion membership exceeds a preset threshold, a high-frequency fuel level update mode is triggered.

[0103] When the vehicle acceleration change rate exceeds the set value, the weighted average algorithm is used to smooth the fuel quantity data;

[0104] Detect the vehicle's tilt angle and start oil level data compensation when the tilt angle exceeds the preset angle.

[0105] Specifically, the fuel consumption prediction model in step S101 is constructed using a long short-term memory (LSTM) network of deep learning, and includes:

[0106] At the moment of vehicle startup, a fuel consumption prediction model for the startup phase is established based on the engine model, fuel injection volume at startup, idle speed parameters, and historical startup fuel consumption data;

[0107] Combined with vehicle speed, engine speed, road conditions, and weather data, the startup fuel consumption prediction model is used to calculate the real-time fuel consumption rate;

[0108] The long short-term memory network (LSTM) in deep learning is used to predict fuel consumption and obtain a fuel consumption prediction model.

[0109] Specifically, the above step S101 includes training the pre-established fuel consumption prediction model based on the fused multi-source data:

[0110] The pre-collected historical operating condition sequence data is used as network training data and input into a long short-term memory (LSTM) network based on an attention mechanism. The LSTM network based on the attention mechanism is trained to learn fuel consumption patterns under different operating conditions and environmental conditions, thereby obtaining a trained fuel consumption prediction model.

[0111] The historical operating condition sequence data includes a time series of vehicle speed, engine load, and ambient temperature parameters.

[0112] Specifically, step S103 executes a graded fuel level display strategy according to the fuel level status under different operating conditions of the vehicle, including: fuel level display according to normal driving conditions, congested conditions, high-speed driving conditions, and bumpy road conditions; executes a refueling detection strategy, an oil leak detection strategy, a display strategy when the fuel sensor is abnormal, a refueling strategy when the vehicle is turned off, an ignition refueling strategy, and a slope parking stability strategy to optimize the fuel level display.

[0113] Example 1: In one embodiment, the following further illustrates the intelligent instrument fuel level display based on multi-source information fusion and dynamic regulation:

[0114] 1. Multi-source data collection

[0115] A fuel level sensor, pressure sensor, temperature sensor, and flow sensor are deployed inside the fuel tank. An inductive switch is installed at the fuel tank cap, and a conversion resistor is connected to the fuel pump float. The fuel level sensor collects real-time fuel level data, the pressure sensor monitors changes in tank pressure, the temperature sensor obtains fuel temperature, the flow sensor detects fuel inflow and outflow, the inductive switch provides feedback on the open / close status of the fuel tank cap, and the conversion resistor outputs a corresponding resistance value as the position of the fuel pump float changes. Simultaneously, the vehicle's electronic control unit collects driving data such as vehicle speed, engine speed, mileage, driving mode, fuel injection volume, and ignition status. An inertial measurement unit collects vehicle attitude data such as tilt angle and acceleration, and a communication unit acquires vehicle geographic location information and real-time weather data.

[0116] 2. Data fusion processing

[0117] All collected data is transmitted to the data processing module, where a Kalman filter algorithm is used to remove noise. The fuel level sensor's measurement is corrected using the pump float's conversion resistance data, and the fuel level data is recalibrated based on fuel temperature and pressure. A multidimensional fuel level calculation model is constructed, integrating vehicle driving, attitude, geographic location, and weather data to improve accuracy.

[0118] Multi-dimensional fuel quantity calculation model optimization: Building on the existing multi-dimensional fuel quantity calculation model, additional influencing factors are introduced. Vehicle load is taken into account, and load information is obtained through sensors in the vehicle's suspension system. Increased vehicle load leads to increased fuel consumption. Furthermore, real-time traffic congestion information (available through the communication unit) is combined to more accurately model fuel consumption under different levels of congestion. For example, in severely congested conditions, in addition to considering engine idling time and the number of starts and stops, the fuel consumption coefficient is dynamically adjusted based on the duration of congestion, making the fuel quantity calculation more accurate to actual conditions.

[0119] 3. Oil resistance strategy

[0120] A mapping table is established between the pump float's conversion resistance and fuel level. The data processing module monitors resistance changes in real time, obtains initial fuel level data through table lookup, and performs weighted fusion calculations based on data from other sensors. Dynamically adjust the mapping table parameters based on changes in the vehicle's ambient temperature and operating conditions to ensure accurate fuel level calculations under varying conditions.

[0121] 4. Vehicle start-up fuel reduction strategy

[0122] At the moment the vehicle starts, the data processing module builds a fuel consumption prediction model for the start-up phase based on parameters such as engine model, fuel injection volume at startup, and idle speed, combined with historical startup fuel consumption data. Initially, the fuel level display is updated at a lower frequency to avoid display anomalies caused by data fluctuations at startup. As the vehicle completes startup and enters stable operation, the fuel level display update frequency is gradually increased to a normal level, and the displayed value is adjusted in real time based on actual fuel consumption.

[0123] Optimization of the startup prediction model: The existing startup fuel consumption prediction model now takes the engine's thermal state into account. This information is obtained through the engine coolant temperature sensor, as the engine's fuel consumption characteristics differ during cold and hot starts. During a cold start, the engine requires more fuel to reach normal operating temperature, so the fuel consumption coefficient in the prediction model is appropriately increased. During a hot start, the fuel consumption coefficient is reduced, resulting in a more accurate fuel level display during startup.

[0124] 5. Fuel Level Display under Different Operating Conditions - Normal Driving Conditions: Utilizing a fuel consumption prediction model to calculate real-time fuel consumption, combining vehicle speed, engine speed, road conditions, weather, and other data, the instrument panel displays fuel level and remaining mileage at a fixed frequency, maintaining standard display accuracy.

[0125] Congestion Condition: When the vehicle speed remains below a set threshold (e.g., 15 km / h) for two minutes, congestion is detected. A congestion fuel consumption model is activated, calculating fuel consumption based on factors such as engine idling time and the number of starts and stops. The instrument panel updates the fuel level display at a higher frequency (e.g., every 30 seconds), using prominent signage to alert the driver to changes in fuel level.

[0126] High-speed driving conditions: When the vehicle speed exceeds 80 km / h, the fuel consumption prediction model is adjusted based on the vehicle's high-speed driving characteristics. The remaining fuel range at high speeds is highlighted on the instrument panel, and the display duration and font size of the range reminder are increased.

[0127] Bumpy Road Condition: The inertial measurement unit detects changes in vehicle acceleration and tilt angle, determining whether the vehicle is on a bumpy road. If the fuel injection amount changes by less than 15%, the fuel level display update frequency is reduced to once every 10 seconds. A weighted average algorithm is used to smooth the fuel level data, combining historical fuel level data with vehicle posture information, to stabilize the displayed value.

[0128] Optimized operating condition identification: To more accurately identify different operating conditions, a fuzzy logic algorithm is used to comprehensively analyze multi-source data. For example, when determining whether a vehicle is in a congested condition, not only vehicle speed is considered, but also factors such as engine speed, accelerator pedal position, and brake pedal usage frequency. By establishing a fuzzy rule base, the degree of membership in a congested condition is calculated based on the different value ranges of these factors. When the membership exceeds a certain threshold, a congested condition is determined, thereby improving the accuracy of operating condition identification and providing a more reliable basis for subsequent fuel quantity calculation and display strategies.

[0129] 6. Dynamic prediction of fuel quantity

[0130] Based on the integrated multi-source data, a machine learning algorithm is used to train a fuel consumption prediction model. Combining the vehicle's real-time location, navigation route, road conditions, terrain, weather, and other information, the model predicts the vehicle's remaining fuel and mileage to its destination, providing drivers with a reference for fuel planning.

[0131] Machine Learning Model Optimization: Fuel consumption prediction is performed using a long short-term memory (LSTM) network, a deep learning framework. LSTM networks are capable of processing time series data and capturing long-term dependencies in fuel consumption. The LSTM network is trained using extensive historical data to learn fuel consumption patterns under different operating and environmental conditions. Furthermore, an attention mechanism is introduced to allow the model to focus on factors that significantly influence fuel consumption, such as vehicle speed and engine load, improving prediction accuracy.

[0132] 7. Refueling detection strategy Refueling detection strategy, oil leak detection strategy, fuel sensor abnormality display strategy, vehicle stall refueling strategy, ignition refueling strategy, slope parking stability strategy and fuel level display optimization

[0133] When the fuel tank cap sensor detects an open state and the flow sensor detects a fuel flow rate greater than 0.5L / min for 10 seconds, the vehicle is considered to be refueling. The instrument display module uses dynamic animation to display the refueling progress and shows the amount of fuel added in real time. After refueling is completed, the actual amount of fuel is calculated based on the fuel level data before and after refueling, and the instrument display updates the fuel level.

[0134] 8. Oil Leak Detection Strategy

[0135] The data processing module continuously analyzes data from the flow sensor, fuel tank pressure sensor, and fuel level sensor. When the flow sensor detects a fuel outflow exceeding 0.1L / min, and the fuel tank pressure and fuel level continue to drop, the fuel leak alarm is triggered. The instrument panel alerts the driver with a flashing red icon and a beeping sound, and the leak notification is also pushed to the owner's mobile device.

[0136] 9. Display strategy when fuel sensor is abnormal

[0137] Fuel level sensor abnormality: When the data processing module detects a sudden change in fuel level sensor data (a change exceeding 20% ​​of the total tank capacity and not corresponding to a reasonable operating condition) or a prolonged period of no change (no update for more than 5 minutes), and when other sensor data show no obvious abnormalities, the fuel level sensor is considered abnormal. At this point, the instrument display module switches to a backup display mode. Based on the fuel pump float resistor data, pressure sensor data, and historical fuel level data, the data processing module's estimation model calculates a temporary fuel level value. The instrument display displays "Fuel level sensor abnormality, current fuel level is estimated" in orange font. The fuel level display update frequency is reduced to once every 30 seconds to avoid display confusion caused by abnormal data.

[0138] Pressure sensor abnormality: If the pressure sensor data exceeds the normal range and does not match the trend of other sensor data, the pressure sensor is considered abnormal. The instrument display module displays a blue icon next to the fuel level display area indicating "Pressure sensor abnormality." The data processing module then reduces the weight of the pressure sensor data when calculating fuel level, relying more on the data from the fuel level sensor, temperature sensor, and the fuel pump float resistor conversion, ensuring that the fuel level display remains reliable.

[0139] Temperature sensor abnormality: When the temperature sensor data shows unreasonable values, the instrument display module displays a purple icon indicating "Temperature sensor abnormality." During fuel quantity calculation, the data processing module replaces the abnormal data with the default standard fuel temperature parameter and combines it with other sensor data to calculate fuel quantity. A message indicating that the actual temperature data is not being used in the current fuel quantity calculation is displayed on the instrument interface.

[0140] Simultaneous multiple sensor anomalies: If at least two of the fuel level, pressure, or temperature sensors exhibit an anomaly simultaneously, the instrument display module displays a red alert stating, "Multiple fuel sensors are anomaly; fuel level display may be inaccurate." Regular fuel level updates cease, and only the message "Fuel level data abnormal, please inspect as soon as possible" appears. At this point, the data processing module generates a rough fuel level estimate based on limited normal data (such as flow sensor data and historical vehicle fuel consumption data), attempting to update the display every five minutes to provide the driver with a minimum fuel level reference.

[0141] 10. Refueling strategy when the vehicle is turned off

[0142] When the vehicle is turned off and the fuel tank cap sensor detects that it's open, the data processing module immediately suspends all non-essential data collection and computation tasks (such as driving data collection and fuel consumption prediction), retaining only data from refueling-related sensors (flow sensor, fuel level sensor). During refueling, the instrument display module displays refueling progress in a simple and clear interface. In addition to showing the amount of fuel added, it also calculates and displays the estimated time required to fill the tank in real time (based on the current refueling flow rate and remaining tank capacity). If the vehicle experiences any abnormalities during refueling (such as a sudden interruption or unusual fluctuation in flow detected by the flow sensor), the data processing module issues an alert to alert the refueler and driver. After refueling, the data processing module restarts all data collection and computation tasks, updates the fuel resistance mapping table based on accurate fuel level data, and synchronizes the latest fuel level information to the instrument display module and the vehicle information system.

[0143] 11. Ignition and Refueling Strategy

[0144] When the vehicle's ignition is on and the fuel tank cap sensor detects an open state, and the flow sensor detects a fuel flow rate exceeding 0.5 L / min for 10 seconds, the data processing module determines that refueling is in progress. At this point, the instrument display module displays a prominent red warning message: "Refueling with the vehicle's ignition on. Safety risk!" and provides a voice notification to the driver. Simultaneously, the data processing module lowers the maximum allowable refueling flow rate (for example, from 1.5 L / min to 0.8 L / min), slowing refueling and mitigating safety risks. During refueling, key data such as engine status and fuel tank pressure are monitored in real time. If an anomaly is detected (such as sudden engine jerking or an abnormally high fuel tank pressure), refueling is immediately stopped and an emergency alarm is triggered. A flashing red icon and a beeping sound indicate "Refueling abnormality, refueling stopped" on the instrument display module. After refueling, the fuel level display and related data records are updated according to normal procedures.

[0145] 12. Slope parking stability strategy

[0146] When the inertial measurement unit detects that the vehicle's tilt angle exceeds a set threshold (e.g., 15 degrees) and the vehicle speed is zero, the data processing module determines that the vehicle is parked on a slope. At this point, the data processing module enhances the fuel level anti-shake strategy. In addition to using the existing weighted average algorithm to process fuel level data, it also compensates for the fuel level sensor data based on the vehicle's tilt direction and angle to ensure a stable fuel level display without jumps.

[0147] 13. Fuel level display optimization

[0148] The instrument display module features a dynamic visual design. When the fuel level is sufficient, the fuel gauge fills in green. When the fuel level is low but sufficient for the current trip, the gauge displays the estimated mileage in yellow. When the fuel level is nearing depletion, the gauge flashes red and sounds an alarm. A fuel consumption trend graph displays historical and predicted fuel consumption trends in real time.

[0149] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0150] Based on the same inventive concept, embodiments of the present application also provide a fuel level display system that integrates multi-source information and dynamic control. The implementation solution provided by this system is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the fuel level display system integrating multi-source information and dynamic control provided below can be referenced to the limitations of the fuel level display method integrating multi-source information and dynamic control, and will not be further elaborated here. It should be understood that the description of the method above also applies to the description of the system.

[0151] In one embodiment, a fuel quantity display system integrating multi-source information and dynamic control is also provided. The embodiments of the present invention are described below with reference to the accompanying drawings. Figure 2 As shown, the system specifically includes:

[0152] The data processing module 210 performs fusion processing on the multi-source data collected in real time, and trains the pre-established fuel consumption prediction model based on the fused multi-source data;

[0153] The prediction module 220 is used to predict the remaining fuel and mileage of the vehicle to the destination by using the trained fuel consumption prediction model in combination with the navigation planning route;

[0154] The graded display module 230 is used to execute a graded fuel level display strategy according to the fuel level status under different working conditions of the vehicle, and dynamically adjust the fuel level display under each working condition;

[0155] The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

[0156] It is worth noting that although only some basic functional modules are disclosed in the embodiment of the present invention, it does not mean that the composition of the present system is limited to the above basic functional modules. On the contrary, what this embodiment wants to express is that on the basis of the above basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the existing technology to form an infinite number of embodiments or technical solutions. In other words, this system is open rather than closed. Just because this embodiment only discloses individual basic functional modules, it cannot be considered that the scope of protection of the claims of the present invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above devices are described in terms of functions, which are divided into various units and modules. Of course, when implementing the present invention, the functions of each unit and module can be implemented in the same or one or more software and / or hardware.

[0157] At the same time, the application also proposes a computer-readable storage medium and an electronic device.

[0158] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements any one of the method steps S101 to S103.

[0159] In one embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication), or other technologies. When the computer program is executed by the processor, it implements any one of the methods from S101 to S103. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the electronic device housing, or an external keyboard, touchpad, or mouse.

[0160] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for displaying fuel quantity by integrating multi-source information and dynamic control, characterized in that: The method comprises: Fuse the multi-source data collected in real time and train the pre-established fuel consumption prediction model based on the fused multi-source data; By using the trained fuel consumption prediction model and combining it with the navigation route planning, the remaining fuel and mileage of the vehicle to the destination are predicted; According to the fuel level status of the vehicle under different working conditions, a graded fuel level display strategy is implemented to dynamically adjust the fuel level display under each working condition; The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

2. The method according to claim 1, wherein The fusion processing of multi-source data collected in real time includes: Real-time multi-source data collection through a multi-source sensor group; wherein the sensor group includes an oil level sensor, a pressure sensor, a temperature sensor, a flow sensor, a fuel tank cap induction switch, an oil pump float, and a conversion resistor; The multi-source data includes fuel tank status parameters and vehicle operation data; Filter the fuel tank status parameters and vehicle operation data to remove noise interference in the data; Combined with the oil pump float conversion resistance data, the oil level sensor measurement value is corrected, and the corrected measurement value is recalibrated according to the fuel temperature and pressure; Taking into account vehicle driving data, posture data, geographic location and weather data, the calibrated data is fused based on the Kalman filter algorithm to establish a multi-dimensional fuel quantity calculation model and calculate the fuel quantity value; Combined with vehicle operating parameters, the fuel quantity value is corrected in real time to calculate the fuel quantity under different conditions; and the fuel quantity data obtained under different conditions is processed; wherein, the vehicle operating parameters include vehicle load information and traffic congestion level information.

3. The method according to claim 2, wherein The real-time collection of multi-source data by the multi-source sensor group includes: The oil level sensor, pressure sensor, temperature sensor, and flow sensor are deployed in the oil tank; the induction switch and oil pump float are installed at the oil tank cap; the oil pump float is connected to the conversion resistor; the conversion resistor outputs the corresponding resistance value as the oil pump float position changes; wherein, The oil level sensor is used to collect the fuel level reading in real time; The pressure sensor is used to monitor the pressure changes in the fuel tank; The temperature sensor is used to obtain the fuel temperature; The flow sensor is used to detect the fuel inlet and outlet flow; The induction switch is used to feedback the open and closed status of the fuel tank cap; It also includes: collecting vehicle driving data, posture data, as well as vehicle geographic location information and real-time weather data; The vehicle driving data includes: vehicle speed, engine speed, mileage, driving mode, fuel injection amount, and ignition status; The posture data includes: vehicle tilt angle and acceleration.

4. The method according to claim 2, wherein The real-time correction of the fuel quantity value in combination with the vehicle operating condition parameters includes: Obtain load information through vehicle suspension system sensors and establish a load-fuel consumption coefficient mapping relationship table; Dynamically adjust the idle fuel consumption coefficient based on traffic congestion information; Differentiate between cold start and hot start conditions using engine coolant temperature sensor data.

5. The method according to claim 2, wherein The calculation of oil volume under different conditions includes: Obtain load information through the vehicle suspension system sensor and establish a dynamic resistance-oil volume mapping relationship table between the oil pump float conversion resistance and oil volume; Monitor resistance changes in real time, obtain initial oil volume data through table lookup, and perform weighted fusion calculations in combination with other sensor data; The mapping table parameters are dynamically adjusted according to the vehicle's ambient temperature and operating conditions.

6. The method according to claim 2, wherein The processing of the oil quantity data obtained under different conditions includes: Using a fuzzy logic algorithm, the system integrates vehicle speed, engine speed, accelerator pedal position, and braking frequency parameters. When the congestion membership exceeds a preset threshold, a high-frequency fuel level update mode is triggered. When the vehicle acceleration change rate exceeds the set value, the weighted average algorithm is used to smooth the fuel quantity data; Detect the vehicle's tilt angle and start oil level data compensation when the tilt angle exceeds the preset angle.

7. The method according to claim 2, wherein The fuel consumption prediction model is constructed using a deep learning long short-term memory (LSTM) network, which includes: At the moment of vehicle startup, a fuel consumption prediction model for the startup phase is established based on the engine model, fuel injection volume at startup, idle speed parameters, and historical startup fuel consumption data; Combined with vehicle speed, engine speed, road conditions, and weather data, the startup fuel consumption prediction model is used to calculate the real-time fuel consumption rate; The long short-term memory network (LSTM) in deep learning is used to predict fuel consumption and obtain a fuel consumption prediction model.

8. The method according to claim 7, wherein The training of the pre-established fuel consumption prediction model based on the fused multi-source data includes: The pre-collected historical operating condition sequence data is used as network training data and input into a long short-term memory (LSTM) network based on an attention mechanism. The LSTM network based on the attention mechanism is trained to learn fuel consumption patterns under different operating conditions and environmental conditions, thereby obtaining a trained fuel consumption prediction model. The historical operating condition sequence data includes a time series of vehicle speed, engine load, and ambient temperature parameters.

9. The method according to claim 7, wherein The implementation of a graded fuel level display strategy based on the fuel level status under different vehicle operating conditions includes: displaying the fuel level according to normal driving conditions, congested conditions, high-speed driving conditions, and bumpy road conditions; and implementing a refueling detection strategy, an oil leak detection strategy, a display strategy when the fuel sensor is abnormal, a refueling strategy when the vehicle is stalled, an ignition refueling strategy, and a slope parking stability strategy to optimize the fuel level display.

10. A fuel level display system integrating multi-source information and dynamic control, characterized in that: The system comprises: The data processing module integrates and processes the multi-source data collected in real time, and trains the pre-established fuel consumption prediction model based on the integrated multi-source data; The prediction module is used to predict the remaining fuel and mileage of the vehicle to the destination by combining the trained fuel consumption prediction model with the navigation route planning; The graded display module is used to implement a graded fuel level display strategy based on the fuel level status of the vehicle under different working conditions, and dynamically adjust the fuel level display under each working condition; The vehicle operating status includes normal driving conditions, congested conditions, high-speed driving conditions, bumpy road conditions and slope parking conditions.

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