System and method for calculating remaining flight duration of unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle remaining flight time calculation system and method, and the method comprises the steps: carrying out the temperature compensation of the current nominal battery capacity of an unmanned aerial vehicle according to the current temperature of an unmanned aerial vehicle battery, obtaining the actual effective battery capacity of the unmanned aerial vehicle, and carrying out the current integration and voltage correction of the actual effective battery capacity of the unmanned aerial vehicle. Obtaining the remaining available electricity quantity of the unmanned aerial vehicle battery; the equivalent wind speed of the unmanned aerial vehicle is calculated according to the wind speed and wind direction data of the unmanned aerial vehicle in the current flight state, and the flight power consumption of the unmanned aerial vehicle in the current flight state is calculated through an aerodynamic model based on the unmanned aerial vehicle airspeed, the equivalent wind speed and the environment temperature; and adopting a rolling horizon estimation method to calculate the comprehensive remaining duration of the flight of the unmanned aerial vehicle based on the remaining available power of the unmanned aerial vehicle and the flight power consumption of the unmanned aerial vehicle in the current flight state, and performing monotone decreasing smoothing processing on the comprehensive remaining duration to obtain the current display remaining duration of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight time calculation, specifically to a system and method for calculating the remaining flight time of a UAV. Background Technology

[0002] Currently, vehicle-mounted unmanned aerial vehicle (UAV) systems are playing an increasingly important role in low-altitude economic scenarios such as emergency rescue, line inspection, and logistics delivery. The safe and efficient execution of their flight missions highly depends on the accurate estimation of remaining flight time. However, most existing systems have significant shortcomings in achieving this crucial function: they typically estimate the remaining flight time by simply dividing the remaining battery power, a static model that completely ignores the impact of dynamic environmental changes on the UAV's flight time. In reality, the remaining flight time is a complex variable influenced by the coupling of multiple physics fields. Fluctuations in ambient temperature directly affect the battery's internal resistance and discharge efficiency, while wind speed and direction (specifically, the angle between the flight direction and the wind speed) significantly alter the UAV's aerodynamic drag and required propulsion power. These factors collectively determine the real-time flight power consumption, thus dynamically affecting the actual flight time. Current technologies fail to integrate these key environmental parameters, resulting in a significant disconnect between estimated flight time and actual endurance. When faced with sudden changes in wind direction or temperature, the displayed remaining time may exhibit unreasonable jumps or even increase in the opposite direction. This not only severely damages the user experience but also misleads operators into making dangerous flight or mission planning decisions, such as misjudging the return-to-home timing, thereby jeopardizing the safety of the drone platform and mission success rate. Therefore, the industry urgently needs an intelligent prediction method that can integrate multi-source environmental information to achieve an accurate, smooth, and physically consistent display of remaining flight time. Summary of the Invention

[0003] The purpose of this invention is to provide both a system and a method for calculating the remaining flight time of a drone, aiming to completely solve the significant shortcomings of traditional vehicle-mounted drone systems in terms of flight endurance estimation. Traditional solutions typically calculate the remaining flight time by dividing the drone's remaining battery capacity (SOC) by a fixed or empirical average power consumption value, ignoring the dynamic and complex nature of the flight environment. In actual missions, the drone's true power consumption is significantly affected by equivalent wind speed (headwinds greatly increase power consumption), ambient temperature (affecting battery efficiency and air density), and flight attitude and airspeed. Therefore, when a drone encounters a strong headwind or a drastic temperature change, its actual power consumption will instantly deviate from the preset average value, causing drastic and discontinuous jumps in the remaining flight time calculated based on the drone's remaining battery capacity, such as a sudden flashing of 30 minutes remaining to 10 minutes remaining. This instability and unreliability in the display makes it difficult for operators to accurately understand the drone's mission capabilities and safety boundaries. More dangerously, it can severely mislead operators' critical decisions. For example, when battery power is already low, incorrectly displaying ample remaining flight time might tempt the operator to continue the mission, ultimately leading to a forced landing or crash due to battery depletion. This invention introduces an aerodynamic power consumption model that integrates airspeed, equivalent wind speed, and ambient temperature, and employs a rolling time-domain estimation algorithm to dynamically integrate real-time flight status and prediction data, achieving accurate and smooth prediction of remaining flight time. The remaining flight time display for the drone also incorporates monotonically decreasing filtering and descent rate limiting processing to ensure the stability and predictability of the information output.

[0004] To achieve this objective, the present invention provides a system for calculating the remaining flight time of a UAV, comprising: The remaining power calculation module is used to perform temperature compensation on the current nominal battery capacity of the drone based on the current temperature of the drone battery to obtain the actual effective battery capacity of the drone. By performing current integration and voltage correction on the actual effective battery capacity of the drone, the remaining usable power of the drone battery is obtained. The environmental drag and power consumption calculation module is used to calculate the equivalent wind speed of the UAV based on the wind speed and wind direction data of the UAV in its current flight state, and to calculate the flight power consumption of the UAV in its current flight state based on the UAV airspeed, equivalent wind speed and ambient temperature through an aerodynamic model. The remaining time calculation module uses a rolling time-domain estimation method to calculate the comprehensive remaining flight time of the drone based on the drone's remaining available power and flight power consumption in the current flight state. The comprehensive remaining time is then subjected to monotonically decreasing smoothing to obtain the drone's current displayed remaining time.

[0005] Furthermore, the method for obtaining the actual effective battery capacity of the drone by temperature compensation based on the current nominal battery capacity of the drone includes: based on the influence of battery temperature on battery capacity, using a temperature compensation model, and calculating the actual effective battery capacity of the drone based on the current nominal battery capacity of the drone, the temperature compensation coefficient, the current battery temperature, and the reference temperature.

[0006] Furthermore, the formula for calculating the actual effective battery capacity of the drone is as follows: ; in, This represents the actual effective battery capacity of the drone battery at the current temperature. This refers to the current nominal battery capacity of the drone. This is the temperature compensation coefficient. This is the current battery temperature. This is a reference temperature.

[0007] Furthermore, the remaining usable power of the drone battery is calculated based on the remaining power of the drone battery based on time integration, the current integration weighting coefficient, the remaining power of the drone battery based on voltage, and the voltage correction weighting coefficient.

[0008] Furthermore, the formula for calculating the remaining usable power of the drone battery is as follows: , ; in, The remaining usable power of the drone's battery. The remaining battery power of the drone is calculated based on time integral. For current integral weighting coefficients, For voltage correction weighting coefficients, The remaining battery power of the drone is based on voltage.

[0009] Furthermore, the remaining power of the drone battery based on time integration is calculated according to the actual effective battery capacity of the drone battery at the current temperature and the power consumed by the drone battery.

[0010] Furthermore, the formula for calculating the remaining battery power of the drone based on time integration is as follows: ; in, The remaining battery power of the drone is calculated based on time integral. This represents the actual effective battery capacity of the drone battery at the current temperature. The drone's battery has consumed power.

[0011] Furthermore, methods for obtaining the consumed power of the drone battery include: integrating the real-time current of the drone battery over time to obtain the consumed power. ; in, The drone battery has consumed power. The start time of drone battery use. The total time the drone battery is used. for Real-time current of the drone's battery.

[0012] Furthermore, the method for obtaining the remaining capacity of the drone battery based on voltage includes: obtaining the state of charge (SOC) value of the drone battery based on voltage by querying a preset battery and capacity characteristic curve based on the estimated open-circuit voltage of the drone battery; and calculating the remaining capacity of the drone battery based on voltage based on the SOC value and the actual effective battery capacity of the drone battery at the current temperature. Furthermore, the formula for calculating the remaining battery capacity of the drone based on voltage is as follows: ; in, The remaining battery power of the drone is based on voltage. This is a voltage-based state of charge (SOC) value for the drone battery. This represents the actual effective battery capacity of the drone battery at the current temperature.

[0013] Furthermore, the method for obtaining the open-circuit voltage of the drone battery includes: calculating the open-circuit voltage of the drone battery based on the terminal voltage of the drone battery, the operating current of the drone battery, and the internal resistance of the drone battery.

[0014] Furthermore, the formula for calculating the open-circuit voltage of the drone battery is as follows: ; in, This refers to the open-circuit voltage of the drone battery. This refers to the terminal voltage of the drone battery. This is the operating current of the drone battery. This represents the internal resistance of the drone battery.

[0015] Furthermore, methods for calculating the equivalent wind speed of the drone based on wind speed and direction data during its current flight state include: Calculate the angle between the drone's velocity vector relative to the air and the wind speed vector. : ; According to the included angle Calculate the equivalent wind speed of a drone : ; in, For the drone's heading angle, The wind direction angle, The effective wind speed is the same as the ambient wind speed. When the drone flies with the wind, the effective wind speed is positive. When the drone flies against the wind, the effective wind speed is negative.

[0016] Furthermore, methods for calculating the flight power consumption of a drone in its current flight state using an aerodynamic model, based on the drone's airspeed, equivalent wind speed, and ambient temperature, include: ; in, This represents the flight power consumption of the drone in its current flight state. For drone airspeed The base power consumption curve lookup table value, This is the drag coefficient. This is the temperature influence coefficient. The air temperature of the environment in which the drone is located. For reference temperature, Let be the square of the equivalent wind speed of the drone.

[0017] Furthermore, the basic power consumption curve based on UAV airspeed is obtained by running the UAV at different airspeeds in a virtual environment with no wind, constant temperature and standard atmospheric pressure set in flight dynamics simulation software, and recording the basic power consumption of the UAV at different airspeeds.

[0018] Furthermore, the method of calculating the comprehensive remaining flight time of the UAV based on the remaining available power and the flight power consumption in the current flight state using the rolling time-domain estimation method includes: Under an ideal continuous-time model, the remaining duration is considered. for: ; Under the discrete-time model, the remaining time is considered. for: ; in, The total remaining flight time of the drone. To find the maximum value function, This is the maximum flight time for the drone. for The flight power consumption of the drone's battery at all times. The remaining usable power of the drone's battery. This is the critical period for drone flight. In the critical period Predicted flight power consumption within the range, In the first Predicted flight power consumption of the drone within a given time period For the first The length of a time period.

[0019] Furthermore, methods for obtaining the current remaining display time of the drone by performing monotonically decreasing smoothing on the overall remaining time include: Design a monotonically decreasing filter: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. The sign for taking the minimum value.

[0020] Within the set time period, The drone's current remaining time is as predicted. The maximum single downward adjustment is greater than the remaining flight time within a forecast period. The rate of decline remained below The drone's time-predicted display shows the remaining time. At that time, based on Start descent rate limit: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. To take the sign of the maximum value, This represents the maximum single downward adjustment of the remaining flight time within a prediction period.

[0021] Furthermore, a method for calculating the remaining flight time of a drone based on the system includes: Temperature compensation is performed on the current nominal battery capacity of the drone based on the current battery temperature to obtain the actual effective battery capacity of the drone. The remaining usable power of the drone battery is obtained by integrating the current and correcting the voltage based on the actual effective battery capacity. The equivalent wind speed of the drone is calculated based on the wind speed and wind direction data of the drone in its current flight state. Based on the drone's airspeed, equivalent wind speed and ambient temperature, the flight power consumption of the drone in its current flight state is calculated through an aerodynamic model. The rolling time-domain estimation method is used to calculate the overall remaining flight time of the drone based on the remaining available power of the drone and the flight power consumption of the drone in the current flight state. The overall remaining time is then subjected to monotonically decreasing smoothing to obtain the current displayed remaining time of the drone.

[0022] The beneficial effects of this invention are as follows: Existing technologies for drone power estimation rely on static models or single sensors, making it difficult to cope with dynamic and complex environments, resulting in inaccurate estimated flight time. Furthermore, the remaining time display is directly refreshed based on calculated values, which is prone to frequent changes in interface values ​​due to signal fluctuations or estimation jumps, interfering with user judgment. In addition, system parameters are fixed and cannot self-optimize according to changes in the usage environment and seasons. Moreover, drones and vehicles are often two independent systems, leading to delayed data exchange and a fragmented user experience. This invention firstly achieves dynamic perception and fusion analysis of multi-dimensional environmental factors such as wind speed, temperature, and light intensity through multi-source environmental fusion prediction, integrating real-time data from airborne sensors and network meteorological information. This constructs a high-precision dynamic power consumption estimation model, fundamentally improving the accuracy of the prediction. Based on this, a monotonically decreasing remaining time display mechanism is introduced. The core remaining flight time display value is subjected to monotonically decreasing filtering and a rate-limiting process, ensuring that the interface value only decreases steadily and predictably, completely eliminating display jumps caused by short-term fluctuations in underlying data, making the displayed information intuitive and reliable. Furthermore, the system possesses online learning and collaborative optimization capabilities, continuously collecting historical operational data from the local machine and networked similar devices in different regions and seasons. Utilizing swarm intelligence, it optimizes key parameters such as power consumption and temperature prediction models online, enabling the system to adapt to varying regional climates and long-term performance degradation, becoming increasingly accurate with use. Finally, through an integrated vehicle-to-drone bidirectional communication design, the control, status monitoring, and data interaction functions of the drone system are deeply embedded into the vehicle's main unit system. Based on the vehicle's high-speed bus and low-latency communication protocols, seamless and real-time exchange of commands, status, and sensor data is achieved, and all data is presented in a unified vehicle interface. This technical solution effectively solves a series of problems such as inaccurate power consumption estimation in dynamic environments, unreliable displayed information, poor system adaptability, and fragmented cross-device interaction experiences. It not only significantly improves the safety and reliability of drone operation planning but also extends effective battery life through adaptive optimization and provides a smooth and intuitive integrated user experience through deep integration, achieving a leap from isolated functions to an intelligent fusion platform. Attached Figure Description

[0023] Figure 1 This is a diagram illustrating the overall system architecture and data flow of the present invention. Figure 2 This is a logic diagram illustrating the effect of the angle between wind speed and flight direction on power consumption in this invention. Figure 3 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 3 As shown, a system for calculating the remaining flight time of a drone includes: The remaining power calculation module is used to perform temperature compensation on the current nominal battery capacity of the drone based on the current temperature of the drone battery to obtain the actual effective battery capacity of the drone. By performing current integration and voltage correction on the actual effective battery capacity of the drone, the remaining usable power of the drone battery is obtained. The environmental drag and power consumption calculation module is used to calculate the equivalent wind speed of the UAV based on the wind speed and wind direction data of the UAV in its current flight state, and to calculate the flight power consumption of the UAV in its current flight state based on the UAV airspeed, equivalent wind speed and ambient temperature through an aerodynamic model. The remaining time calculation module uses a rolling time-domain estimation method to calculate the comprehensive remaining flight time of the drone based on the drone's remaining available power and flight power consumption in the current flight state. The comprehensive remaining time is then subjected to monotonically decreasing smoothing to obtain the drone's current displayed remaining time.

[0026] In some embodiments, the overall architecture and data flow of this system are as follows: Figure 1As shown, the environment acquisition module receives real-time data such as temperature, wind speed, and wind direction inside and outside the hangar from onboard sensors and outputs environmental parameters. The network information module acquires network information such as meteorological, terrain, and historical databases and outputs predictive and auxiliary data. The UAV flight control and airborne module integrates real-time data representing the UAV's "uplink and flight status," including voltage, current, and temperature data provided by the battery management system, attitude and heading measured by the inertial navigation unit (IMU), and readings from sensors such as the airspeed meter, to the airborne data acquisition and preprocessing module. This data is further transmitted via wireless data link telemetry / remote control to the flight duration estimation algorithm module. The flight duration estimation algorithm module integrates the received environmental parameters, predictive and auxiliary data, and the power model within the module, executing the function P=P0+k1·V. 2 +k2·ΔT calculates the flight power consumption P of the UAV in its current flight state, where V is the wind speed, k1 is the wind resistance coefficient, k2 is the temperature coefficient, P0 is the basic power consumption based on the UAV's airspeed, and ΔT is the temperature difference between the air temperature of the UAV's environment and the reference temperature. The remaining flight time prediction module inside the flight time estimation algorithm module predicts the remaining flight time of the UAV based on the mathematical model tpred=fQ, P (i.e., the mathematical formula for calculating the comprehensive remaining flight time of the UAV under the ideal continuous time model or discrete time model). This completes the closed loop from environmental perception, data fusion, cross-domain transmission to airborne intelligent computing, providing core decision-making basis for UAV mission planning and vehicle-mounted temperature control strategies. The real-time data, network information, and remaining UAV flight time (tpred) are aggregated at the data fusion center for processing. After further transmission to the display smoothing module for optimization, they are processed by the interface rendering module. Simultaneously, the predicted remaining UAV flight time information, after downlink smoothing, is also optimized by the display smoothing module and processed by the interface rendering module. Finally, the remaining flight time prediction data processed by the interface rendering module is intuitively presented on the vehicle system and display interface. This completes a closed loop from environmental perception, data fusion, cross-domain transmission to onboard intelligent computing, providing core decision-making basis for UAV mission planning and onboard temperature control strategies.

[0027] In some technical solutions, the method of obtaining the actual effective battery capacity of the drone by temperature compensation based on the current nominal battery capacity of the drone includes: based on the influence of battery temperature on battery capacity, using a temperature compensation model, and calculating the actual effective battery capacity of the drone based on the current nominal battery capacity of the drone, the temperature compensation coefficient, the current battery temperature and the reference temperature.

[0028] By introducing a temperature compensation model, the inherent characteristics of battery performance being affected by temperature are overcome, thereby improving the accuracy of estimating the actual usable capacity of drone batteries under different ambient temperatures.

[0029] By dynamically compensating for nominal capacity, temperature compensation coefficient, real-time temperature, and reference temperature in the temperature compensation model formula, the system can obtain a more realistic and accurate actual effective battery capacity, rather than the theoretical nominal value. This directly makes flight time prediction, range planning, and return-to-home decisions based on remaining battery power more accurate and reliable, thereby avoiding misjudgments of endurance caused by low-temperature capacity decay or high-temperature performance changes, and enhancing the safety and predictability of UAV missions in complex temperature environments.

[0030] In some technical solutions, the formula for calculating the actual effective battery capacity of a drone is as follows: ; in, This represents the actual effective battery capacity of the drone battery at the current temperature. This refers to the current nominal battery capacity of the drone. This is the temperature compensation coefficient. This is the current battery temperature. This is a reference temperature.

[0031] This formula constructs a linear compensation model based on temperature difference. By dynamically correcting the nominal battery capacity of the drone through the deviation between the real-time monitored battery temperature and the preset reference temperature, it obtains the effective usable capacity under the current actual operating conditions. The temperature compensation coefficient in the formula... It is a key empirical parameter that quantifies the linear impact of temperature deviation from a reference temperature on battery capacity, and is typically obtained through experimental calibration; reference temperature The standard ambient temperature (e.g., 25°C) for battery performance testing is generally set to represent the nominal capacity value. The corresponding baseline conditions; the current battery temperature. The temperature data, collected in real time by the drone's built-in temperature sensor, reflects the actual thermal environment of the battery. Below At that time, the difference If the value is negative, the correction factor is greater than 1, and the system will adjust the effective capacity accordingly to reflect the situation where the actual usable capacity at low temperatures is lower than the nominal value; conversely, when the value is negative, the system will adjust the effective capacity accordingly. Higher than When the correction factor is less than 1, the capacity is appropriately reduced to address the potential acceleration of battery aging due to high temperatures or the decrease in actual usable capacity caused by protection mechanisms. This improves the accuracy and reliability of drone battery status assessment, enabling the system to predict flight duration, manage power consumption, and plan missions based on more realistic usable energy rather than idealized nominal values. Especially in extreme high and low temperature environments, it can effectively avoid premature shutdown or misjudgment of flight mileage caused by capacity estimation errors, thereby enhancing flight safety, mission success rate, and battery lifespan. This is a key foundation for achieving intelligent energy management.

[0032] The drone's current nominal battery capacity is obtained directly from the battery power display. The reference temperature is usually the battery's nominal capacity test temperature, including but not limited to 25°C, and includes a temperature compensation coefficient. This represents the relative rate of change of capacity when the temperature deviates from the reference temperature by 1°C (unit: °C). -1 This coefficient is determined by the battery characteristics and can be obtained through experimental measurement or by consulting the battery manual. The typical value of this coefficient is about 0.005~0.02 / ℃ (that is, for every 1℃ decrease, the capacity decreases by 0.5%~2%).

[0033] In some technical solutions, the remaining usable power of the drone battery is obtained by integrating the current and correcting the voltage based on the actual effective battery capacity of the drone. The methods include: calculating the remaining usable power of the drone battery based on the remaining power of the drone battery based on time integration, the current integration weighting coefficient, the remaining power of the drone battery based on voltage, and the voltage correction weighting coefficient.

[0034] This fusion estimation method based on current integration and voltage correction effectively overcomes the inherent limitations of single methods by weighting and fusing the integrated charge reflecting cumulative consumption with the voltage-charge ratio reflecting the battery's transient terminal voltage characteristics. While the current integration method can continuously track charge changes, its accuracy decreases over time due to sensor zero drift and accumulated errors. Although the voltage method provides an absolute reference point, its readings fluctuate drastically due to load current, temperature, and battery aging. By combining the two methods with dynamic or fixed weighting coefficients, complementary advantages are achieved. The accumulated error of the integration method can be periodically calibrated using voltage readings, while the integration method smooths out instantaneous jumps in voltage readings during periods of severe load fluctuation. This results in a more stable and reliable remaining charge estimate than any single method across various operating conditions and the entire battery lifespan, providing crucial and accurate data for UAV flight time prediction and safe return-to-base decisions.

[0035] In some technical solutions, the formula for calculating the remaining usable power of a drone battery is as follows: , ; in, The remaining usable power of the drone's battery. The remaining battery power of the drone is calculated based on time integral. For current integral weighting coefficients, For voltage correction weighting coefficients, The remaining battery power of the drone is based on voltage.

[0036] Drone battery remaining power The calculation formula is essentially a fusion estimation strategy. This formula uses a weighted average to combine the results of the coulomb counting method based on current-time integral. Results compared with voltage lookup table method based on battery terminal voltage They are organically combined, and the weighting coefficients satisfy... The constraints ensure the rationality and normalization of the fused output values. Specifically, The current integration method directly accumulates the consumed electricity by monitoring and integrating the battery's discharge current in real time. This method is straightforward and generally accurate in the mid-range discharge range, but its error accumulates over time and is susceptible to battery aging, temperature changes, and charge / discharge efficiency. The remaining capacity is estimated by measuring the battery's open-circuit voltage or load voltage under the current condition and comparing it with the battery's voltage-capacity curve. This method often has good indicative accuracy in the nonlinear region where the battery is close to full charge or nearly discharged, but its readings are easily affected by instantaneous load, changes in internal resistance, and battery relaxation effects. By introducing dynamically or statically configurable weighting coefficients... and This formula achieves the complementary advantages and disadvantage suppression of the two methods: for example, during the stable discharge phase of the battery, it can impart... Higher weights rely on direct charge measurement; however, they can be increased during the battery's initial or final charge stages. The weighting is calibrated using the inflection point information of voltage characteristics. This method significantly improves the overall accuracy, robustness, and reliability of remaining battery power estimation under all operating conditions. It effectively smooths out the inherent defects of single methods and reduces the risk of unexpected power outages, mission interruptions, or battery over-discharge damage to the drone due to misjudgment of battery power. Thus, it provides a more reliable data foundation for accurate endurance prediction, safe return-to-home decisions, and battery life management.

[0037] In some embodiments, the current integral weighting coefficient and voltage correction weighting factor To automatically adjust the adaptive weighting coefficient based on the current operating conditions, when the current changes drastically, the remaining battery capacity of the drone based on voltage will have a large error due to the polarization effect caused by the drastic current changes. In this case, the current integral weighting coefficient can be increased. This method focuses on improving the accuracy of calculating the remaining usable power of a drone battery by utilizing the remaining power based on time integration. When the current is stable, the system places more trust in the results estimated based on the battery voltage model. This method is very accurate under conditions where the current is stable and near static (such as when the drone is hovering or cruising at a constant speed), and can be used to correct long-term deviations caused by sensor zero drift and accumulated errors in the current integration method.

[0038] In some technical solutions, the remaining power of the drone battery based on time integration is calculated according to the actual effective battery capacity of the drone battery at the current temperature and the power consumed by the drone battery.

[0039] By introducing the dynamic parameter of the actual effective battery capacity at the current temperature to replace the fixed nominal capacity, the estimated remaining power can reflect the actual impact of temperature on battery performance in real time, thereby improving the accuracy of power estimation. This real-time operating condition-based integral calculation method effectively overcomes the power estimation deviation caused by the decay of usable battery capacity in extreme temperature environments, providing a more reliable power basis for UAV flight safety, mission planning, and return-to-home decisions.

[0040] In some technical solutions, the formula for calculating the remaining battery capacity of a drone based on time integration is as follows: ; in, The remaining battery power of the drone is calculated based on time integral. This represents the actual effective battery capacity of the drone battery at the current temperature. The drone's battery has consumed power.

[0041] In this formula, This represents the remaining battery charge calculated based on the Coulomb integral principle, and it directly reflects the total amount of charge that the battery can currently consume. It is a key environmental adaptability parameter, specifically referring to the effective capacity that the battery can actually release at the current ambient temperature. It is not the nominal capacity of the battery, but the upper limit of real-time available energy that dynamically shrinks or changes as the temperature decreases or increases, reflecting the significant impact of temperature on the battery's internal chemical activity and internal resistance. This represents the total amount of electricity consumed from the time the battery was last fully charged until the current moment, through continuous integration of the discharge current over time. This formula incorporates temperature-adaptive... These parameters enable the remaining power estimate to reflect in real time the capacity decay caused by low temperatures or the protection limits that may be triggered by high temperatures, thus providing a more realistic baseline of available energy. Simultaneously, the precisely integrated consumed power is directly subtracted from this dynamic baseline. The method for obtaining remaining battery power is intuitive and computationally efficient, avoiding reliance on complex electrochemical models or multi-parameter states of the battery and the associated identification challenges. This improves the reliability of real-time estimation during flight, providing a reliable basis for predicting the endurance of the UAV flight control system. It supports the system in making safe flight planning, mission scheduling, or timely return-to-home decisions, effectively preventing the risk of accidental crashes caused by false battery power reports or sudden drops. This enhances the operational safety and mission predictability of the UAV system under different environmental conditions.

[0042] Some technical solutions for obtaining the consumed power of a drone battery include integrating the real-time current of the drone battery over time to obtain the consumed power. ; in, The drone battery has consumed power. The start time of drone battery use. The total time the drone battery is used. for Real-time current of the drone's battery.

[0043] The consumed power of the drone battery is calculated using the current integral formula. In the current integral formula... The integral variable represents the total amount of electricity consumed by the drone's battery during the time interval from the initial time t0 to the current time t. Indicates at any instant The measured real-time current value. This method abandons the coarse approach of estimating based on average current or terminal voltage, directly capturing and accumulating the true trajectory of current changes over time. This allows for precise reflection of the fluctuating current consumption caused by dynamic motor power adjustments during complex flight maneuvers (such as acceleration, climb, hovering, or dealing with gusts of wind). It achieves millisecond-level high-precision tracking of battery energy outflow, transforming power monitoring from discrete, phased estimations into a real-time, continuous, and precise measurement process. This provides an extremely reliable data foundation for real-time endurance assessment and dynamic range prediction of drones. The flight control system can use this precise consumption data, combined with the drone's total battery capacity and current power consumption, to calculate the remaining flight time in real time, supporting optimal path planning, mission decisions, or emergency return-to-home judgments. Simultaneously, the continuous and accurate historical power consumption data provides crucial data support for subsequent analysis of energy consumption characteristics under different flight modes and environmental conditions, optimization of battery management strategies, and improvement of overall energy efficiency.

[0044] By integrating the real-time current of the drone battery over time to obtain the consumed power, a high-precision, continuous, and dynamic measurement of battery energy consumption can be achieved. This method captures and accumulates the actual current output at every moment during flight, accurately reflecting the true cumulative power consumption of the drone under different flight attitudes, loads, and environmental conditions. This provides a direct and reliable real-time data foundation for assessing remaining battery power, predicting remaining flight time, and triggering low battery warnings, thereby improving the accuracy of power management and the safety of flight mission planning.

[0045] In some technical solutions, the method for obtaining the remaining power of a drone battery based on voltage includes: obtaining the state of charge (SOC) value of the drone battery based on voltage by querying a preset battery and power characteristic curve based on the estimated open-circuit voltage of the drone battery; and calculating the remaining power of the drone battery based on voltage based on the SOC value and the actual effective battery capacity of the drone battery at the current temperature.

[0046] By combining voltage characteristics and temperature compensation, the accuracy and environmental adaptability of battery remaining capacity estimation are significantly improved: it uses open-circuit voltage to obtain the basic state of charge (SOC) value, which effectively reflects the battery's chemical potential and the true remaining capacity under resting conditions; then, it introduces the actual effective battery capacity at the current temperature for calibration, compensating for the significant impact of temperature on the battery's usable capacity, and overcoming the serious deviation caused by relying solely on voltage estimation in high or low temperature environments; this enables the system to obtain more reliable remaining capacity information under all climatic conditions, providing key and accurate data input for UAV mission time prediction, return-to-home decision-making, and vehicle energy distribution management.

[0047] In some technical solutions, the formula for calculating the remaining battery capacity of a drone based on voltage is as follows: ; in, The remaining battery power of the drone is based on voltage. This is a voltage-based state of charge (SOC) value for the drone battery. This represents the actual effective battery capacity of the drone battery at the current temperature.

[0048] The core of the formula lies in decoupling the estimation of the remaining battery capacity into two key factors with clear physical meaning and independent calibrability—namely, the instantaneous state of charge percentage obtained based on real-time terminal voltage lookup table or model mapping. The actual usable capacity determined by the compensation curve based on the current battery temperature. Then, the two factors are multiplied together to achieve a dynamic and adaptive estimation of the battery's remaining energy. It reflects the relative charge level corresponding to the battery voltage under current load and aging conditions, and it can quickly respond to voltage transients caused by load changes; while This quantifies the direct impact of temperature on the total amount of usable active material in the battery chemistry system; low temperatures significantly reduce it, while high temperatures may induce degradation. This calculation method cleverly combines the real-time dynamic characteristics of voltage with the gradual, systematic impact of temperature changes. It leverages the advantages of fast voltage response and direct measurability to detect transient changes, while simultaneously correcting capacity in real time through temperature adjustments. This overcomes the inherent errors of relying solely on voltage or coulomb integration methods in scenarios with drastic temperature changes and load fluctuations. It can obtain a more reliable and accurate estimate of remaining energy than traditional methods, closer to the actual physical quantity, even under the complex thermal environment and variable load conditions of UAV flight.

[0049] In some technical solutions, the method for obtaining the open-circuit voltage of the drone battery includes: calculating the open-circuit voltage of the drone battery based on the terminal voltage, the operating current, and the internal resistance of the drone battery.

[0050] By calculating the open-circuit voltage of a drone battery in real time using terminal voltage, operating current, and internal resistance, the influence of load can be eliminated, reflecting the current state of charge and health of the battery more realistically and accurately. This provides a key basis for precise battery management, remaining power estimation, and prevention of overcharging and over-discharging, effectively improving the predictability of drone flight safety and mission endurance.

[0051] In some technical solutions, the formula for calculating the open-circuit voltage of the drone battery is as follows: ; in, This refers to the open-circuit voltage of the drone battery. This refers to the terminal voltage of the drone battery. This is the operating current of the drone battery. This represents the internal resistance of the drone battery.

[0052] The formula for calculating the open-circuit voltage of a drone battery is based on the terminal voltage that can be directly measured under load. and operating current Combined with known or online estimable battery internal resistance This allows for the indirect calculation of the battery's open-circuit voltage under static, no-load conditions. From a physical standpoint, when a battery is working, its terminal voltage... A voltage drop will occur due to the current flowing through the internal resistance. The direction of this voltage drop is related to the direction of the current. During discharge, it manifests as a terminal voltage lower than the open-circuit voltage. Therefore, the measured terminal voltage... With internal resistance voltage drop By adding them together, the battery's true potential when there is no external current, i.e., the open-circuit voltage, can be approximately compensated. This approach avoids the limitation that open-circuit voltage can only be directly measured under static conditions without a load, enabling the battery management system to monitor the voltage during drone operation. Real-time, online estimation. Since open-circuit voltage is strongly correlated with the battery's state of charge (SOC) and state of health (SOH), it is a fundamental parameter for accurate SOC estimation and SOH assessment. Therefore, this formula provides accurate input for UAVs to achieve high-precision battery status monitoring, remaining range prediction, and battery balancing management during dynamic operation. This not only enhances the real-time performance and adaptability of the battery management system but also lays a reliable data foundation for UAVs to make energy-based intelligent decisions in complex missions (such as autonomous return and power consumption adjustment), thereby improving overall flight safety and mission reliability.

[0053] Some technical solutions calculate the equivalent wind speed of the drone based on wind speed and direction data during its current flight state. Calculate the angle between the drone's velocity vector relative to the air and the wind speed vector. : ; According to the included angle Calculate the equivalent wind speed of a drone : ; in, For the drone's heading angle, The wind direction angle, The effective wind speed is the same as the ambient wind speed. When the drone flies with the wind, the effective wind speed is positive. When the drone flies against the wind, the effective wind speed is negative.

[0054] In UAV flight control technology, the equivalent wind speed is calculated using the formula... Determine the angle between the drone's velocity vector relative to the air and the wind speed vector. ,in This represents the drone's heading angle, which is the angle between the drone's nose and a geographic reference direction (such as true north). This represents the wind direction angle, which is the angle between the wind speed vector and the same geographical reference direction. This angle directly reflects the relative azimuth between the wind direction and the drone's flight direction. Then, using the formula... Calculate the equivalent wind speed, where It is the magnitude of the ambient wind speed. It is an included angle The cosine value of the formula is obtained by decomposing the ambient wind speed into the drone's flight direction through vector projection, thus yielding a scalar value with directional significance. When the drone flies with the wind, Approaching 0 degrees Celsius ≈1, Positive and close to This indicates that the wind provides assistance to the drone's forward movement; when the drone flies against the wind... Approximately 180 degrees ≈-1, Vweff is negative and close to - This indicates that wind creates resistance to the drone's forward movement. This calculation method simplifies the complex and multidimensional dynamic effects of wind fields into a real-time calculable equivalent wind speed scalar. This scalar directly quantifies the degree to which wind promotes or hinders the drone's forward movement, enabling the drone control system to easily integrate wind factors into navigation algorithms, path planning, stability control, and energy consumption models.

[0055] This technical solution calculates the scalarized equivalent wind speed along the UAV's flight path by vector decomposing and synthesizing the UAV's airspeed vector and the ambient wind speed vector in three-dimensional space, and then calculating the cosine of their angle. This scalar value intuitively quantifies the actual boost or drag effect of the ambient wind on the UAV's flight: a positive value indicates a tailwind, which provides the UAV with additional forward speed and reduces the propulsion power required to maintain the same ground speed; a negative value indicates a headwind, meaning the UAV must consume additional energy to overcome wind resistance to maintain the predetermined airspeed and flight path. This provides a key input for subsequent flight dynamics models, enabling the UAV energy management system to more accurately assess flight power consumption based on real-time equivalent wind speed, thereby improving the accuracy of remaining flight time prediction. It also provides direct decision-making basis for the flight control system in path planning, airspeed management, and power distribution, such as automatically increasing power output in headwinds or optimizing throttling strategies in tailwinds, ultimately achieving the core objectives of improving flight time, ensuring mission reliability, and optimizing overall energy efficiency.

[0056] Some technical solutions involve calculating the flight power consumption of a drone in its current flight state using an aerodynamic model, based on the drone's airspeed, equivalent wind speed, and ambient temperature. ; in, This represents the flight power consumption of the drone in its current flight state. For drone airspeed The base power consumption curve lookup table value, This is the drag coefficient. This is the temperature influence coefficient. The air temperature of the environment in which the drone is located. For reference temperature, Let be the square of the equivalent wind speed of the drone.

[0057] By establishing a three-stage power consumption calculation model encompassing basic power consumption, drag correction, and temperature correction, high-precision real-time estimation of UAV flight power consumption can be achieved. The complex dynamics of flight power consumption are dynamically deconstructed into independently quantifiable and calibrable physical factors, thereby improving the accuracy of power consumption prediction and environmental adaptability. This technical solution allows the system to quickly obtain the baseline power consumption at the current airspeed through table lookup, and accurately quantifies the additional load caused by drag using the product of the drag coefficient and the square of the equivalent wind speed. Simultaneously, it effectively compensates for the impact of changes in motor efficiency and air density caused by ambient temperature variations through the product of the temperature coefficient and temperature difference. This power consumption calculation method not only ensures that the power consumption estimate closely matches the actual flight state of the UAV, providing accurate input for precise endurance prediction and flight path planning, but also supports the system in making proactive energy management decisions when facing sudden changes in wind fields or temperature differences, thereby enhancing the overall reliability of mission execution and the energy efficiency of the aircraft.

[0058] In some embodiments, the drag effect coefficient and temperature influence coefficient Obtained through calibration, to ensure the influence coefficient of wind resistance. With temperature influence coefficient The accuracy of the calibration requires systematic measurements in a controlled experimental environment. Using a quadcopter drone as the test object, the entire calibration process was completed in a wind tunnel laboratory and a temperature-controlled environmental chamber: Under the reference conditions of no wind and a constant ambient temperature (i.e., ΔT=0), the minimum power required for the drone to maintain stable hovering was measured, and this value was recorded as the basic power consumption. Then the drag coefficient was calculated. Calibration: With the ambient temperature fixed, in the wind tunnel, starting from zero wind speed, the wind speed V is gradually increased at a set gradient, for example, from 0 m / s, 2 m / s, 4 m / s up to 10 m / s. At each stable wind speed point, the total motor power consumed by the UAV to maintain the same hovering position and attitude in the same space is precisely recorded. This series of wind speed and corresponding power data points constitutes a dataset. A quadratic polynomial fitting is performed on the relationship between the power increment and the square of the wind speed in the dataset. The coefficient of the quadratic term in the fitted curve is the wind resistance influence coefficient. Wind resistance influence coefficient In a physical sense, it represents the proportional relationship between the power consumption caused by airflow resistance and the square of the wind speed.

[0059] Finish Temperature coefficient after calibration Calibration: Under windless conditions with the wind tunnel closed, the system operates within a temperature-controlled ambient chamber. Keeping other conditions constant, the system alters the chamber temperature, creating a series of differences ΔT between the ambient temperature and the reference temperature, for example, from -10°C, 0°C, 10°C to 40°C. At each set stable temperature point, the drone's hovering power consumption is measured. A set of temperature difference ΔT and corresponding power increment data is obtained. Linear regression analysis is performed on this data; the slope of the resulting straight line is the temperature influence coefficient. This coefficient quantifies the power consumption offset effect caused by factors such as changes in battery internal resistance, air density, and motor efficiency due to temperature variations.

[0060] The experiment determined , , By substituting the three parameters into the flight power consumption calculation formula, the flight power consumption of the UAV under any given combination of airspeed, equivalent wind speed and ambient temperature can be predicted.

[0061] In some embodiments, such as Figure 2 As shown, the method for quantifying the impact of the angle between wind speed and flight direction on power consumption is as follows: First, obtain the real-time measured wind speed vector. ,wind direction The airspeed vector of the UAV itself and its heading angle These basic data are fed as input parameters into the included angle calculation module, and calculated using the formula... Calculate the relative wind angle between the drone's heading and the wind direction. Based on calculations The system then enters the typical scenario analysis phase: when... A value of 0° corresponds to a completely tailwind condition. The wind field will reduce the power required for the drone to maintain its position on the ground; when φ is 180°, it corresponds to a completely headwind state ( Wind fields will significantly increase flight power consumption; when When the angle is 90°, it corresponds to a pure crosswind state. The wind field has a relatively small direct impact on propulsion power consumption, but it mainly generates indirect power consumption by causing the UAV to adjust its attitude; while when When the angle is between 0° and 90° (or between 90° and 180°), it falls under the category of a slanted wind scenario, and its impact lies between the aforementioned typical cases. Based on a typical scenario, a scenario example is provided, proceeding to the equivalent wind speed decomposition step, according to the formula... The equivalent wind speed component along the drone's heading is calculated, with the downwind direction defined as positive. This decomposition yields the equivalent wind speed for the drone. Simultaneously used in two parallel computing branches: one branch for calculating the ground velocity, according to the formula... The actual speed of the drone relative to the ground is obtained (to predict the drone's estimated landing time), where, For the drone's ground speed, The airspeed of the drone is used; another core branch is used for the power consumption impact model, based on the formula ΔPwind=k1·Vweff 2 Quantitatively calculate the additional power consumption increment ΔPwind caused by wind resistance (or the power consumption reduction when the wind is tailwind), k1 is the wind resistance influence coefficient, and Vweff 2 This is the square of the equivalent wind speed for the drone. The entire computational framework completes the entire data flow and implementation process, from inputting raw wind speed, wind direction, airspeed, and heading data, to calculating the relative wind direction angle, qualitative analysis of the flight scenario, and equivalent wind speed vector decomposition, until it achieves ground speed estimation and quantitative assessment of the impact of wind resistance and power consumption.

[0062] In some technical solutions, the basic power consumption curve based on UAV airspeed is obtained by running the UAV at different airspeeds in a virtual environment with no wind, constant temperature and standard atmospheric pressure set in flight dynamics simulation software, and recording the basic power consumption of the UAV at different airspeeds.

[0063] By constructing a fully controlled virtual environment in high-fidelity flight dynamics simulation software, the mapping curve between the basic power consumption and airspeed of a UAV can be obtained efficiently, safely, and with high accuracy, establishing a reliable benchmark reference covering the entire speed range for the entire physical power consumption model. Under ideal simulation conditions of no wind, constant temperature, and standard atmospheric pressure, by setting different UAV airspeed commands and recording the corresponding steady-state power consumption, noise interference caused by random wind disturbances, temperature fluctuations, and air pressure changes in reality can be completely isolated. This yields a set of basic power consumption data points that only reflect the UAV's aerodynamic shape, weight distribution, propeller efficiency, and motor characteristics, and vary only with airspeed. This not only avoids the technical challenge of completely eliminating the influence of environmental variables in actual flight testing, ensuring the accuracy and repeatability of basic power consumption data, but also allows for the safe and low-cost exploration of power consumption characteristics across the entire airflow envelope from hovering to maximum design speed, especially obtaining power consumption values ​​under high-risk or difficult-to-maintain airspeed conditions in actual flight. The basic power consumption curve P0(V) calibrated through simulation serves as the core lookup table function, providing a solid and accurate benchmark for the subsequent superposition of wind resistance and temperature correction terms. This makes the structure of the integrated power consumption prediction model, which incorporates the effects of wind speed and temperature, clearer and the physical meaning of the parameters more explicit, thereby significantly improving the extrapolation prediction accuracy and reliability of the model under different flight conditions and environments.

[0064] Some technical solutions employ a rolling time-domain estimation method to calculate the overall remaining flight time of the UAV based on its remaining available battery power and flight power consumption during its current flight state. These methods include: Under an ideal continuous-time model, the remaining duration is considered. for: ; Under the discrete-time model, the remaining time is considered. for: ; in, The total remaining flight time of the drone. To find the maximum value function, This is the maximum flight time for the drone. for The flight power consumption of the drone's battery at all times. The remaining usable power of the drone's battery. This is the critical period for drone flight. In the critical period Predicted flight power consumption within the range, In the first Predicted flight power consumption of the drone within a given time period For the first The length of a time period.

[0065] In the ideal continuous-time model, its mathematical expression is to find a maximum time limit T such that from the current moment to the future moment... Up to now, flight power consumption The integral over time (i.e., total energy consumption) does not exceed the remaining usable battery capacity. This critical value That is, the theoretical maximum remaining flight time. It obtains the time boundary of battery depletion in the continuous time domain through integration. Based on the ideal continuous-time model, and in the discrete-time model implemented in actual engineering, the flight mission or prediction time domain is divided into N continuous time periods, where the length of the first N-1 time periods is... and the corresponding predicted power consumption If the energy consumption is known or planned, the estimated energy consumption for these known phases is first accumulated, and then the remaining total electricity is used. Subtracting this energy consumption yields the remaining energy available for the last (Nth) critical stage. Finally, divide this remaining energy by the predicted power consumption of the critical stage. This yields the flight time that can be sustained within the critical phase. Adding this to the sum of the durations of all previously known time periods gives the final comprehensive remaining flight time prediction. This method transforms endurance from a static estimate into a dynamic, continuously updated process. By continuously combining the latest monitored remaining battery power with real-time estimated flight power consumption based on current flight conditions (speed, wind, temperature), and performing energy consumption accumulation and judgment within a forward-rolling finite time domain, it can more realistically reflect the impact of future mission profiles or environmental changes on energy consumption. The prediction result is no longer a single point in time, but a decision-making basis that is bound to specific flight plans or predictive assumptions and can be dynamically adjusted as input information is updated. This significantly improves the reliability and practicality of endurance prediction, providing real-time, quantitative key inputs for UAV autonomous mission replanning, safe return-to-home decisions, and energy-optimal path tracking.

[0066] By introducing a rolling time-domain estimation method, two sets of time models—one continuous and one discrete—are constructed based on the continuous and the other discrete time domains to calculate the overall remaining flight time of the UAV, achieving dynamic, adaptive, and robust prediction of endurance. This transforms traditional static power calculation into a rolling optimization process that integrates real-time flight status and future power consumption prediction, significantly improving the accuracy and practicality of remaining flight time estimation. This scheme selects a reliable value between the ideal continuous model estimation result and the preset maximum flight time through a maximum value function, ensuring that the prediction result does not exceed physical limits and providing a safety boundary for decision-making. The discrete-time model divides future time into several periods and accumulates the predicted flight power consumption within each period, allowing the flight time estimation process to flexibly incorporate factors such as mission planning, anticipated maneuvers, and environmental changes, giving endurance prediction a forward-looking perspective. Introducing the concept of critical flight periods allows the system to concentrate computational resources at key decision points for precise prediction of future power consumption, achieving a good balance between computational complexity and estimation accuracy. Ultimately, this provides accurate, reliable, and real-time data input for UAV autonomous energy management, online trajectory replanning, and safe return-to-home decisions.

[0067] Some technical solutions involve performing monotonically decreasing smoothing on the total remaining time to obtain the current displayed remaining time for the drone. Design a monotonically decreasing filter: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. The sign for taking the minimum value.

[0068] Within the set time period, The drone's current remaining time is as predicted. The maximum single downward adjustment is greater than the remaining flight time within a forecast period. The rate of decline remained below The drone's time-predicted display shows the remaining time. At that time, based on Start descent rate limit: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. To take the sign of the maximum value, This represents the maximum single downward adjustment of the remaining flight time within a prediction period.

[0069] This technical solution optimizes the display logic of the drone's remaining flight time through two progressive mathematical processing steps. The first step is a monotonically decreasing filter. The core mechanism of the monotonically decreasing filter is to always set the current displayed value to the smaller of the previous displayed value and the latest predicted value. This means that the displayed time will only remain unchanged or decrease, and will never increase. This forcibly guarantees the monotonically decreasing characteristic of the displayed value over time, effectively filtering out the accidental rebound of the predicted time caused by transient sensor noise or environmental disturbances. This ensures that the displayed information always presents an intuitive trend of time steadily consuming, avoiding the transmission of fluctuating signals that may cause misjudgments to the drone operator. The second step is a descent rate limit, which is triggered when the displayed value is lower than the predicted value for several consecutive cycles and decreases too rapidly. The descent rate limit formula is as follows: This represents the maximum allowable downward adjustment within a prediction period. The formula's logic is that the current displayed value can only be reduced by a maximum of the value displayed at the previous moment. However, it must not be lower than the latest forecast value. This design provides dual protection: firstly, it limits the rate of decline of the displayed value to a psychologically acceptable and smooth range that allows for informed decision-making. Even if the underlying forecast drops sharply due to sudden strong winds or rapid power consumption, the time displayed to the user will not abruptly decrease, preventing operator panic or hasty decisions. Secondly, by using the maximum value calculation, it ensures that the displayed value does not deviate from the lower limit of the actual forecast. That is, when the forecast value is already very low, the displayed value will immediately reflect the true urgency, eliminating dangerous delays in warnings caused by smoothing. The monotonically decreasing smoothing method and the rate of decline limiting method achieve a delicate balance between information accuracy and display stability. It retains the forecast algorithm's keen perception of the actual remaining time while optimizing the raw data from a user experience perspective through mathematical constraints, ultimately outputting a reliable remaining time, significantly enhancing the operator's trust in the system and their confidence in making decisions.

[0070] In some embodiments, the remaining flight time estimation and control process is as follows: After the system starts and completes initialization, it first acquires initial parameters including the nominal battery capacity, environmental data, and aerodynamic parameters, and then enters a continuous monitoring loop. In the monitoring loop, the system collects real-time data from multiple sources, such as battery voltage, temperature Tb, wind speed Vw, attitude, and airspeed Va. After preprocessing such as filtering and anomaly removal, the preprocessed data is split and processed in parallel: one preprocessed data stream is used for environmental and flight state analysis to calculate the angle between the heading and the wind direction. The equivalent wind speed was obtained. The system obtains the current airspeed Va; another preprocessed data stream is used for battery status analysis, calculating the battery capacity through temperature compensation. The remaining usable power Qleft is estimated; these parameters are then incorporated into the power consumption model. The real-time flight power consumption was calculated. The system then calculates the basic remaining battery life estimate using the instantaneous remaining time, traw = Qleft / P. The system then determines whether to enable predictive mode: if enabled, it obtains a more forward-looking comprehensive remaining time tpred by combining rolling time-domain estimation with predicted weather simulation of future power consumption sequences; otherwise, it directly uses the instantaneous value tpred = traw. Then, tpred undergoes monotonically decreasing filtering, i.e. This is to ensure that the displayed value does not jump, and to further determine whether the rate of descent exceeds the limit (i.e., Δt > Δtmax). If it does, a rate of descent limit is applied. To avoid a sudden drop in displayed values, or otherwise maintain the current displayed values; finally, the system updates the vehicle's infotainment interface display. The system determines whether to continue the flight. If not, it records the various parameters of the flight for subsequent model optimization. Then the system terminates, thus completing a closed-loop process from data acquisition, status analysis, model calculation, prediction correction to safety display.

[0071] Compared to existing technologies that rely on static models or single sensors for drone power estimation, making it difficult to cope with dynamic and complex environments and resulting in inaccurate estimated flight time, this invention addresses the problems of fixed system parameters that cannot adapt to changes in the environment and seasons, and the fact that drones and vehicles are often two independent systems with delayed data exchange and a fragmented user experience. This invention firstly addresses these issues by integrating real-time data from airborne sensors and network meteorological information through multi-source environmental fusion prediction. This enables dynamic perception and fusion analysis of multi-dimensional environmental factors such as wind speed, temperature, and light intensity, thereby constructing a high-precision dynamic power consumption estimation model and fundamentally improving the accuracy of the estimation. Furthermore, it introduces a monotonically decreasing remaining time display mechanism, applying monotonically decreasing filtering and rate-limiting processing to the core remaining flight time display value. This ensures that the interface value decreases steadily and predictably, completely eliminating display jumps caused by short-term fluctuations in underlying data, making the displayed information intuitive and reliable. Furthermore, the system possesses online learning and collaborative optimization capabilities, continuously collecting historical operational data from the local machine and networked similar devices in different regions and seasons. Utilizing swarm intelligence, it optimizes key parameters such as power consumption and temperature prediction models online, enabling the system to adapt to varying regional climates and long-term performance degradation, becoming increasingly accurate with use. Finally, through an integrated vehicle-to-drone bidirectional communication design, the control, status monitoring, and data interaction functions of the drone system are deeply embedded into the vehicle's main unit system. Based on the vehicle's high-speed bus and low-latency communication protocols, seamless and real-time exchange of commands, status, and sensor data is achieved, and all data is presented in a unified vehicle interface. This technical solution effectively solves a series of problems such as inaccurate power consumption estimation in dynamic environments, unreliable displayed information, poor system adaptability, and fragmented cross-device interaction experiences. It not only significantly improves the safety and reliability of drone operation planning but also extends effective battery life through adaptive optimization and provides a smooth and intuitive integrated user experience through deep integration, achieving a leap from isolated functions to an intelligent fusion platform.

[0072] Example 2 A method for calculating the remaining flight time of a UAV based on the system includes: Temperature compensation is performed on the current nominal battery capacity of the drone based on the current battery temperature to obtain the actual effective battery capacity of the drone. The remaining usable power of the drone battery is obtained by integrating the current and correcting the voltage based on the actual effective battery capacity. The equivalent wind speed of the drone is calculated based on the wind speed and wind direction data of the drone in its current flight state. Based on the drone's airspeed, equivalent wind speed and ambient temperature, the flight power consumption of the drone in its current flight state is calculated through an aerodynamic model. The rolling time-domain estimation method is used to calculate the overall remaining flight time of the drone based on the remaining available power of the drone and the flight power consumption of the drone in the current flight state. The overall remaining time is then subjected to monotonically decreasing smoothing to obtain the current displayed remaining time of the drone.

[0073] Example 3 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in Embodiment 2.

[0074] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0075] It will be readily understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, combinations, substitutions, improvements, etc., made under the spirit and principles of the present invention are included within the protection scope of the present invention.

[0076] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A system for calculating the remaining flight time of a drone, characterized in that, It includes: The remaining power calculation module is used to perform temperature compensation on the current nominal battery capacity of the drone based on the current temperature of the drone battery to obtain the actual effective battery capacity of the drone. By performing current integration and voltage correction on the actual effective battery capacity of the drone, the remaining usable power of the drone battery is obtained. The environmental drag and power consumption calculation module is used to calculate the equivalent wind speed of the UAV based on the wind speed and wind direction data of the UAV in its current flight state, and to calculate the flight power consumption of the UAV in its current flight state based on the UAV airspeed, equivalent wind speed and ambient temperature through an aerodynamic model. The remaining time calculation module uses a rolling time-domain estimation method to calculate the comprehensive remaining flight time of the drone based on the drone's remaining available power and flight power consumption in the current flight state. The comprehensive remaining time is then subjected to monotonically decreasing smoothing to obtain the drone's current displayed remaining time.

2. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 1, characterized in that: The method for obtaining the actual effective battery capacity of a drone by temperature compensation based on the current nominal battery temperature includes: using a temperature compensation model based on the influence of battery temperature on battery capacity, and calculating the actual effective battery capacity of the drone based on the current nominal battery capacity, temperature compensation coefficient, current battery temperature, and reference temperature.

3. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 2, characterized in that: The formula for calculating the actual effective battery capacity of a drone is as follows: ； in, This represents the actual effective battery capacity of the drone battery at the current temperature. This refers to the current nominal battery capacity of the drone. This is the temperature compensation coefficient. The current temperature of the battery. This is a reference temperature.

4. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 3, characterized in that: The method for obtaining the remaining usable power of a drone battery by integrating current and correcting voltage based on the actual effective battery capacity includes: calculating the remaining usable power of the drone battery based on the remaining power of the drone battery based on time integration, the current integration weighting coefficient, the remaining power of the drone battery based on voltage, and the voltage correction weighting coefficient.

5. A system for calculating the remaining flight time of a UAV according to claim 1 or 4, characterized in that: The formula for calculating the remaining usable power of a drone's battery is as follows: ， ； in, The remaining usable power of the drone's battery. The remaining battery power of the drone is calculated based on time integral. For current integral weighting coefficients, For voltage correction weighting coefficients, The remaining battery power of the drone is based on voltage.

6. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 5, characterized in that: The remaining power of the drone battery based on time integration is calculated according to the actual effective battery capacity of the drone battery at the current temperature and the power consumed by the drone battery.

7. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 6, characterized in that: The formula for calculating the remaining battery power of a drone based on time integration is as follows: ； in, The remaining battery power of the drone is calculated based on time integral. This represents the actual effective battery capacity of the drone battery at the current temperature. The drone battery has consumed power.

8. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 7, characterized in that: Methods for determining the consumed power of a drone battery include: integrating the real-time current of the drone battery over time to obtain the consumed power. ； in, The drone battery has consumed power. The start time of drone battery use. The total time the drone battery is used. for Real-time current of the drone's battery.

9. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 5, characterized in that: The method for obtaining the remaining power of a drone battery based on voltage includes: obtaining the state of charge (SOC) value of the drone battery based on voltage by querying a preset battery and power characteristic curve based on the estimated open-circuit voltage of the drone battery; and calculating the remaining power of the drone battery based on voltage based on the SOC value and the actual effective battery capacity of the drone battery at the current temperature.

10. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 9, characterized in that: The formula for calculating the remaining battery capacity of a drone based on voltage is as follows: ； in, The remaining battery power of the drone is based on voltage. This is a voltage-based state of charge (SOC) value for the drone battery. This represents the actual effective battery capacity of the drone battery at the current temperature.

11. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 10, characterized in that: The method for obtaining the open-circuit voltage of the drone battery includes: calculating the open-circuit voltage of the drone battery based on the terminal voltage, operating current, and internal resistance of the drone battery.

12. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 11, characterized in that: The formula for calculating the open-circuit voltage of the drone battery is as follows: ； in, This refers to the open-circuit voltage of the drone battery. This refers to the terminal voltage of the drone battery. This is the operating current of the drone battery. This represents the internal resistance of the drone battery.

13. The unmanned aerial vehicle (UAV) remaining flight time calculation system according to claim 1, characterized in that: Methods for calculating the equivalent wind speed of a drone based on wind speed and direction data during its current flight state include: Calculate the angle between the drone's velocity vector relative to the air and the wind speed vector. : ; According to the included angle Calculate the equivalent wind speed of a drone : ; in, For the drone's heading angle, The wind direction angle, The effective wind speed is the same as the ambient wind speed. When the drone flies with the wind, the effective wind speed is positive. When the drone flies against the wind, the effective wind speed is negative.

14. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 1 or 13, characterized in that: Methods for calculating the flight power consumption of a UAV under its current flight state using an aerodynamic model, based on the UAV's airspeed, equivalent wind speed, and ambient temperature, include: ； in, This represents the flight power consumption of the drone in its current flight state. For drone airspeed The base power consumption curve lookup table value, This is the drag coefficient. This is the temperature influence coefficient. The air temperature of the environment in which the drone is located. For reference temperature, Let be the square of the equivalent wind speed of the drone.

15. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 14, characterized in that: The basic power consumption curve based on UAV airspeed is obtained by running the UAV at different airspeeds in a virtual environment with no wind, constant temperature and standard atmospheric pressure set in flight dynamics simulation software, and recording the basic power consumption of the UAV at different airspeeds.

16. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 14, characterized in that: The method for calculating the comprehensive remaining flight time of a UAV using the rolling time-domain estimation method, based on the UAV's remaining available power and flight power consumption in the current flight state, includes: Under an ideal continuous-time model, the remaining duration is considered. for: ; Under the discrete-time model, the remaining time is considered. for: ； in, The total remaining flight time of the drone. To find the maximum value function, This is the maximum flight time for the drone. for The flight power consumption of the drone's battery at all times. The remaining usable power of the drone's battery. This is the critical period for drone flight. In the critical period Predicted flight power consumption within the range, In the first Predicted flight power consumption of the drone within a given time period For the first The length of a time period.

17. A system for calculating the remaining flight time of an unmanned aerial vehicle (UAV) according to claim 16, characterized in that: Methods for obtaining the current displayed remaining time of the drone by performing monotonically decreasing smoothing on the overall remaining time include: Design a monotonically decreasing filter: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. To take the sign of the minimum value; Within the set time period, The drone's current remaining time is predicted. The maximum single downward adjustment is greater than the remaining flight time within a forecast period. The rate of decline remained below The drone's time prediction displays the remaining time. At that time, based on Start descent rate limit: ; in, for The time-based drone forecast currently displays the remaining time. for The drone's time-predicted duration is displayed. for The estimated total remaining flight time of the drone at each moment. To take the sign of the maximum value, This represents the maximum single downward adjustment of the remaining flight time within a prediction period.

18. A method for calculating the remaining flight time of a UAV based on the system described in claim 1, characterized in that, include: Temperature compensation is performed on the current nominal battery capacity of the drone based on the current battery temperature to obtain the actual effective battery capacity of the drone. The remaining usable power of the drone battery is obtained by integrating the current and correcting the voltage based on the actual effective battery capacity. The equivalent wind speed of the drone is calculated based on the wind speed and wind direction data of the drone in its current flight state. Based on the drone's airspeed, equivalent wind speed and ambient temperature, the flight power consumption of the drone in its current flight state is calculated through an aerodynamic model. The rolling time-domain estimation method is used to calculate the overall remaining flight time of the drone based on the remaining available power of the drone and the flight power consumption of the drone in the current flight state. The overall remaining time is then subjected to monotonically decreasing smoothing to obtain the current displayed remaining time of the drone.

19. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 18.

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