Energy-saving multi-rotor express delivery unmanned aerial vehicle flight control method

Abstract

The invention discloses an energy-saving multi-rotor express delivery unmanned aerial vehicle flight control method, and relates to the technical field of unmanned aerial vehicle flight control, in the sensor deployment and data acquisition step, sensors are reasonably configured, original data are accurately acquired, and basic information is provided for subsequent control; through the sensor data preprocessing step, the data quality is improved, and the accuracy and stability of data are ensured; by means of the key action node recognition step, dynamic sensing of the loading and unloading process is achieved, and key nodes such as lifting, translation and placement are precisely recognized; in the step of state parameter analysis, quantitative parameters are extracted for different nodes, and a scientific basis is provided for power adjustment; and finally, in the rotor power output dynamic adjustment step, according to the analysis result of the state parameters of each node, a targeted power adjustment strategy is adopted, so that the energy consumption waste caused by instantaneous high power in the initial hoisting stage is effectively avoided, the extra power consumption for maintaining the stable attitude in the translation stage is reduced, and the power distribution in the placement stage is optimized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, specifically to a flight control method for an energy-saving multi-rotor delivery UAV. Background Technology

[0002] Multi-rotor drones, with their vertical takeoff and landing capabilities and flexible hovering performance, have demonstrated enormous application potential in numerous fields, especially in the express delivery and logistics sector, where they have become an indispensable tool for last-mile delivery. In the entire flight operation of express delivery drones, the loading and unloading phase occupies a significant portion of the time, and the hovering process during this phase is crucial for the stable operation of the drone and the quality of mission completion. Meanwhile, energy consumption control, as a key factor affecting the drone's endurance, is also a major concern during the loading and unloading hovering process. How to effectively reduce energy consumption during this phase while ensuring loading and unloading efficiency and hovering stability has become a critical issue that urgently needs to be addressed in the current development of express delivery drone technology.

[0003] Most existing hovering control methods for multi-rotor drones adopt a fixed power output mode. In this mode, regardless of whether the package load changes or the fuselage attitude shifts during loading and unloading, the rotor maintains a preset power output amplitude and response speed. However, express delivery drones exhibit significant dynamic load changes during loading and unloading. In the initial lifting phase, the weight of the package needs to be overcome to transition from a stationary state to motion. During the translation phase, the shift in package position may cause disturbances in the fuselage attitude. During the placement phase, the load force needs to be gradually reduced. Traditional fixed power output modes fail to fully consider these motion characteristics. In the initial lifting phase, a large instantaneous power output is often used to quickly overcome the load, resulting in significant energy waste. In the translation phase, additional power is required to maintain attitude stability and counteract the hovering disturbances caused by load shifts, further increasing energy consumption. Furthermore, existing control methods do not link loading and unloading actions with hovering control, making it impossible to make targeted power adjustments based on the actual needs of different action nodes. This results in excessively high hovering energy consumption during loading and unloading, severely restricting the endurance and delivery efficiency of express delivery drones. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an energy-saving flight control method for multi-rotor delivery drones. This method ensures the accuracy and real-time performance of raw pressure and position data by deploying sensors and collecting the data in real time. Secondly, it preprocesses the raw data using an adaptive filtering algorithm and a sliding window averaging method to improve data quality and provide a reliable foundation for subsequent analysis. Next, based on the changing trends and displacement characteristics of the pressure and position data, it accurately identifies key action nodes such as lifting, translation, and placement. Then, it constructs load growth trend parameters, fuselage attitude offset parameters, and load unloading trend parameters for different nodes, providing a quantitative basis for power adjustment. Finally, it dynamically adjusts the rotor power output amplitude and response speed based on the state parameter analysis results, employing strategies such as progressive power increase, attitude fine-tuning, and gradual reduction of power output to effectively avoid the waste of instantaneous high-power energy consumption during the initial lifting phase and reduce the additional power consumption for maintaining attitude stability during the translation phase.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a flight control method for an energy-saving multi-rotor delivery drone, the method comprising the following specific steps:

[0006] Sensor deployment and data acquisition: Contact strain gauge pressure sensors and three-dimensional positioning sensors are deployed and calibrated on the package clamping device. Raw data of package contact pressure and three-dimensional position relative to the fuselage are acquired at a frequency matching the action response rate and transmitted to the flight control unit in real time.

[0007] Sensor data preprocessing: Adaptive filtering algorithm is used to remove abnormal noise in the raw data, and sliding window averaging method is used for smoothing to output continuous and stable effective pressure data and effective position data;

[0008] Key action node identification: Based on the changing trend of effective pressure data and the displacement characteristics of effective position data, combined with preset thresholds, the three key action nodes of package lifting, translation and placement are accurately identified;

[0009] State parameter analysis: Load growth trend parameters are constructed for the lifting node, body attitude offset parameters are calculated for the translation node, and load unloading trend parameters are constructed for the placement node to achieve state quantitative analysis;

[0010] Dynamic adjustment of rotor power output: Based on the status parameters of each node, the power is gradually increased during the lifting stage, the disturbance is offset by differential fine-tuning of rotor power during the translation stage, the power output is gradually reduced during the placement stage, and the normal hovering power level is restored after the package is placed.

[0011] Furthermore, in the key action node identification step, the identification logic built into the analysis module of the flight control unit is used to monitor and extract features of effective pressure data and effective position data in real time. A pressure stability judgment threshold based on the pressure reference value of the clamping device in the unloaded state of the UAV and a displacement judgment threshold based on the normal movement range of package loading and unloading are preset. When the effective pressure data shows a continuous upward trend from the reference stable value and the vertical displacement exceeds the preset displacement threshold, the lifting node is determined to be entered. When the effective pressure data remains within the preset stable fluctuation range and the horizontal displacement is greater than the vertical displacement and this continues, the translation node is determined to be entered. When the effective pressure data shows a continuous downward trend from the stable fluctuation range and the vertical displacement gradually decreases to below the preset displacement threshold while the horizontal displacement remains within the allowable error range, the placement node is determined to be entered.

[0012] Furthermore, in the state parameter analysis step, parameters are extracted and quantified for different key action nodes. At the lifting node, based on the time series of effective pressure data, the pressure change rate and cumulative change amplitude per unit time are calculated, and a load growth trend parameter containing growth rate and amplitude characteristics is constructed through linear fitting or piecewise fitting. At the translation node, the real-time effective position data is compared with the preset benchmark position data, and the displacement deviation values ​​of the three-dimensional coordinate system X, Y, and Z axes are calculated. Combined with the fuselage structural parameters, this is converted into attitude offset parameters that quantify the impact of load offset on fuselage balance. At the placement node, based on the time series of effective pressure data, the pressure decay rate per unit time and the pressure change difference between adjacent acquisition cycles are calculated. Combined with the stable state of the effective position data, a load unloading trend parameter containing unloading rate and stability characteristics is constructed.

[0013] Furthermore, in the state parameter analysis step, at the lifting node, based on the time series composed of effective pressure data, differential calculation is performed on the continuously collected effective pressure data at preset time intervals to obtain the pressure change rate per unit time. At the same time, the total change of effective pressure data from the lifting node trigger time to the current time is accumulated to determine the cumulative pressure change amplitude. According to the fluctuation characteristics of the pressure change rate, an appropriate fitting method is selected. If the pressure change rate remains stable, a linear fitting method is used to establish a correlation model between pressure and time. If there are obvious stage differences in the pressure change rate, a piecewise fitting method is used, dividing the time series into multiple intervals according to the boundary points of the rate change, and fitting processing is performed separately. Through the model parameters obtained by fitting, a load growth trend parameter that simultaneously includes the load growth rate characteristics and growth amplitude characteristics is constructed.

[0014] Furthermore, in the state parameter analysis step, at the translation node, the real-time collected valid position data is compared axis by axis with the preset reference position data. The preset reference position data is the position data corresponding to the reference position of the wrapper relative to the fuselage at the beginning of the translation phase. By calculating the coordinate difference between the two in the three-dimensional coordinate system X, Y, and Z axes, the displacement deviation value of each axis is obtained. Then, the preset structural parameters of the UAV fuselage are called, and through coordinate transformation and quantization analysis algorithms, the three-axis displacement deviation value is coupled with the fuselage structural parameters to convert the position deviation caused by the load offset into an attitude offset parameter that can accurately quantify its impact on the fuselage balance.

[0015] Furthermore, in the state parameter analysis step, the attitude offset parameter is expressed as: ,in, This represents the vector transpose operation. It is the roll angle offset. It is the pitch angle offset. This is the yaw angle offset, and its calculation formula is: ,in, This is the X-axis displacement deviation value. This is the Y-axis displacement deviation value. It is the distance from the clamping device to the center of gravity of the machine body. It is the fuselage reference wheelbase. , , It is the attitude calibration coefficient.

[0016] Furthermore, in the state parameter analysis step, at the placement node, based on the time series of effective pressure data, continuous effective pressure data are processed at preset time intervals to obtain the pressure decay rate per unit time. At the same time, the specific difference between effective pressure data between two adjacent acquisition cycles is calculated to capture the dynamic change characteristics during the pressure unloading process. Simultaneously, the stability state of the effective position data is determined by monitoring its fluctuations in each axis of the three-dimensional coordinate system to confirm the positional stability of the package during placement. The pressure decay rate, the pressure change difference between adjacent cycles, and the stability state determination results of the effective position data are fused and analyzed, and combined with a preset load unloading feature extraction algorithm, load unloading trend parameters are constructed.

[0017] Furthermore, in the state parameter analysis step, the pressure decay rate, the pressure change difference between adjacent periods, and the stable state determination results of the valid location data are fused and analyzed. Combined with a preset load unloading feature extraction algorithm, a load unloading trend parameter is constructed, the algorithm formula of which is: ,in, It is a load offloading trend parameter. It is the pressure decay rate weighting coefficient. It is the pressure decay rate. It is a weighting coefficient for the difference in pressure changes between adjacent periods. It is the average of the pressure change differences between adjacent acquisition cycles. These are the positional stability state weighting coefficients. It is the position stability coefficient.

[0018] Furthermore, in the rotor power output dynamic adjustment step, adjustment commands are generated based on the analysis results of the state parameters of each node and transmitted to the rotor power control module. At the lifting node, a progressive power increase strategy is adopted based on the load growth trend parameters, and a power output gradient adjustment rule is set with the trigger condition determined by the load growth rate. The power output amplitude is gradually increased according to the preset gradient to match the load growth rate. At the translation node, the power compensation difference required for each rotor is calculated based on the fuselage attitude offset parameters. The fuselage attitude is finely adjusted by differentially adjusting the power output amplitude of different rotors to offset the hovering disturbance caused by the load offset. At the placement node, a power output gradient reduction rule is set based on the load unloading trend parameters, and the rotor power output amplitude is gradually reduced. When the effective pressure data drops to near the no-load pressure reference value and remains stable, and the effective position data meets the placement completion conditions, the rotor power output is restored to the power output level of the normal hovering state.

[0019] Compared with existing technologies, this energy-saving multi-rotor delivery drone flight control method has the following advantages:

[0020] I. This invention accurately identifies key action nodes such as lifting, translation, and placement, and analyzes state parameters for different nodes to provide a quantitative basis for dynamic adjustment of rotor power output. At the lifting node, a progressive power increase strategy is adopted to avoid energy waste caused by instantaneous high power output. At the translation node, rotor power is adjusted differently to reduce the additional power consumption for maintaining attitude stability. At the placement node, power output is gradually reduced to optimize power distribution. Under the premise of ensuring loading and unloading efficiency and hovering stability, this invention effectively reduces hovering energy consumption during the loading and unloading stage of multi-rotor express delivery drones, thereby extending the drone's range and improving the economy and practicality of express delivery.

[0021] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a flight control method for an energy-saving multi-rotor delivery drone;

[0024] Figure 2 A flowchart illustrating the key action node identification steps in a flight control method for an energy-saving multi-rotor delivery drone;

[0025] Figure 3 This is a flowchart illustrating the state parameter analysis steps of a flight control method for an energy-saving multi-rotor delivery drone. Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structure, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0027] This invention discloses an energy-saving multi-rotor delivery drone flight control method, applicable to multi-rotor delivery drones in various scenarios such as urban last-mile delivery and rural short-distance material transportation. It aims to solve the problems of excessive energy consumption and poor attitude stability caused by the mismatch between power output and load changes during package loading, unloading and transportation of existing multi-rotor drones through precise state perception and dynamic power adjustment, and achieve dual optimization of energy saving and flight stability. The technical solution of this invention will be described in detail below with reference to specific embodiments.

[0028] Contact strain gauge pressure sensors are installed at the clamping points of the package clamping device on the drone. These sensors must have the measurement capability to adapt to the package clamping pressure range and the measurement accuracy to meet control requirements, and be able to accurately capture changes in contact pressure between the package and the clamping device. A three-dimensional positioning sensor is installed in the central area of ​​the clamping device. This sensor needs to integrate data from the accelerometer, gyroscope, and magnetometer to output the three-dimensional position coordinates of the package relative to the drone body. Its positioning accuracy must meet the requirements for relative position detection of the package.

[0029] After the sensors are deployed, they are calibrated: For pressure sensors, zero-point calibration and full-scale calibration are performed. The sensor output value when no package is held is set as the no-load pressure reference value, and full-scale calibration is completed by standard load loading method; For three-dimensional positioning sensors, a three-dimensional coordinate system is established with the center of gravity of the drone as the origin (X-axis along the horizontal axis of the drone, Y-axis along the vertical axis of the drone, and Z-axis perpendicular to the plane of the drone). The position of the drone when it is hovering and the package is not held is set as the reference position, and coordinate calibration is completed.

[0030] During the data acquisition phase, the sensor data acquisition frequency is set. This frequency must match the response rate of the drone's actions such as lifting, translating, and placing the package to ensure that the data changes during the action can be fully captured. The pressure sensor collects raw data of the package's contact pressure in real time, and the three-dimensional positioning sensor collects raw data of the package's three-dimensional position relative to the drone in real time. The collected raw data is transmitted in real time to the drone's flight control unit through a preset data transmission bus. The flight control unit must have high-speed data processing and command output capabilities to meet real-time control requirements.

[0031] After receiving the raw data, the flight control unit first uses an adaptive filtering algorithm to remove abnormal noise from the raw data. This algorithm updates the filtering coefficients iteratively, which can adaptively offset random noise caused by airflow disturbances, sensor errors, etc. When the raw data exceeds the preset reasonable range, it is judged as an outlier and removed by the filtering algorithm. Then, the filtered data stream is smoothed by combining the sliding window averaging method. The sliding window size is set to adapt to the data change characteristics. The average value of the data within the window is used as the effective data at the current moment, which ensures the continuity of the data and avoids the impact of instantaneous fluctuations on subsequent analysis. Finally, through the above preprocessing process, continuous and stable effective pressure data and effective position data are output.

[0032] like Figure 2 As shown, based on the node identification logic, the preprocessed valid pressure data and valid location data are monitored and feature extracted in real time. Two types of judgment thresholds are preset: one is the pressure stability judgment threshold, which is set based on the no-load pressure benchmark value; the other is the displacement judgment threshold, which is set based on the normal movement range of package loading and unloading, used to distinguish the displacement characteristics of different action nodes.

[0033] When the effective pressure data shows a continuous upward trend from the no-load pressure reference value, and the pressure change is greater than 0 in multiple consecutive collection cycles, and the vertical displacement (relative to the reference position) exceeds the preset displacement threshold, the flight control unit determines that the UAV has entered the package lifting node.

[0034] When the effective pressure data is maintained within the preset stable fluctuation range (this range is set based on the pressure value after the package is clamped and stabilized), and the horizontal displacement is greater than the vertical displacement in multiple consecutive collection cycles, it is determined that the drone has entered the package translation node.

[0035] When the effective pressure data starts to show a continuous downward trend from the stable fluctuation range, and the pressure change is less than 0 in multiple consecutive collection cycles, while the vertical displacement gradually decreases to below the preset displacement threshold and the horizontal displacement remains within the preset error allowable range, it is determined that the drone has entered the package placement node.

[0036] like Figure 3 As shown, parameter extraction and quantification analysis were performed on the three key action nodes identified above. The specific process is as follows:

[0037] Based on the time series of effective pressure data after the lifting node is triggered, differential calculation is performed on the continuously collected effective pressure data at preset time intervals to obtain the pressure change rate per unit time. At the same time, the total amount of pressure change data from the lifting node trigger time to the current time is accumulated to determine the cumulative pressure change amplitude.

[0038] Based on the fluctuation characteristics of the pressure change rate, an appropriate fitting method is selected. If the pressure change rate remains stable, a linear fitting method is used to establish a correlation model between pressure and time. If there are obvious stage differences in the pressure change rate, a piecewise fitting method is used. The time series is divided into multiple intervals according to the boundary points of the rate change, and fitting is performed separately for each interval. Through the model parameters obtained by fitting, two core features, the pressure growth rate and the cumulative change amplitude, are extracted to construct load growth trend parameters.

[0039] After the translation node is triggered, the real-time collected valid position data is compared with the preset reference position data (the position data corresponding to the reference position of the wrapping relative to the fuselage at the beginning of the translation phase) axis by axis, and the displacement deviation values ​​in the three axes of the three-dimensional coordinate system X, Y and Z are calculated.

[0040] By calling the pre-set structural parameters of the UAV fuselage and combining them with the pre-set attitude calibration coefficients, the three-axis displacement deviation values ​​are coupled with the fuselage structural parameters through coordinate transformation and quantization analysis algorithms. The fuselage attitude offset parameters are then calculated using the following formula. (in, This represents the vector transpose operation. It is the roll angle offset. It is the pitch angle offset. It is the yaw angle offset), and its calculation formula is: ;in, This is the X-axis displacement deviation value. This is the Y-axis displacement deviation value. It is the distance from the clamping device to the center of gravity of the machine body. It is the fuselage reference wheelbase. , , It is the attitude calibration coefficient, which precisely quantifies the impact of load offset on fuselage balance.

[0041] Based on the time series of effective pressure data after the placement node is triggered, differential calculation is performed on the continuous effective pressure data at preset time intervals to obtain the pressure decay rate per unit time. At the same time, the difference of effective pressure data between two adjacent acquisition cycles is calculated, and the average of multiple cycles is obtained to obtain the average value of the pressure change difference between adjacent acquisition cycles.

[0042] The stability of the valid position data is determined synchronously, and a position stability coefficient is set: when the displacement fluctuation amplitude in each axis of the three-dimensional coordinate system is less than the preset range, the position stability coefficient takes the preset stable value (indicating that the position is stable); otherwise, the preset unstable value (indicating that the position is unstable) is taken.

[0043] Based on preset weighting coefficients, load offloading trend parameters are constructed using the following algorithm formula. : ;in, It is the pressure decay rate weighting coefficient. It is the pressure decay rate. It is a weighting coefficient for the difference in pressure changes between adjacent periods. It is the average of the pressure change differences between adjacent acquisition cycles. These are the positional stability state weighting coefficients. It is the position stability coefficient.

[0044] Based on the analysis results of the status parameters of the above nodes, the flight control unit generates power adjustment commands and transmits them to the rotor power control module. By adjusting the power output amplitude of each rotor differently, energy-saving control that dynamically adapts to load changes is achieved.

[0045] Based on load growth trend parameters, a gradual power increase strategy is adopted, and power output gradient adjustment rules are set: using the normal hovering power at the lifting node as a benchmark, the triggering conditions are set according to the load growth rate, and the power output amplitude is gradually increased according to the preset gradient. When the effective pressure data stabilizes at the stable pressure value of the package clamping, the power increase is stopped, and the current power output is maintained to ensure that the power increase and the load growth rate are accurately matched, avoiding energy waste caused by excessive power.

[0046] Based on the fuselage attitude offset parameters, the required power compensation difference for each rotor is calculated. By adjusting the power output amplitude of rotors at different positions in a differentiated manner (such as adjusting the power of the left and right rotors for roll angle offset, adjusting the power of the front and rear rotors for pitch angle offset, and adjusting the power of rotors in different rotation directions for yaw angle offset), the fuselage attitude is finely adjusted to counteract the hovering disturbance caused by load offset and ensure flight stability.

[0047] Based on the load unloading trend parameters, a power output gradient reduction rule is set. Taking the stable power output during the translation phase as the benchmark, the power output amplitude is gradually reduced according to the preset gradient. When the effective pressure data drops to near the no-load pressure benchmark value and remains stable, and the effective position data meets the preset placement completion conditions, the rotor power output is restored to the power output level of the normal hovering state, thus completing the power control of the entire package delivery process.

[0048] Through the above specific implementation methods, the present invention can dynamically adjust the rotor power output according to different stages of package loading, unloading and transportation, effectively reducing power waste while ensuring flight stability, and has significant energy-saving effect and practical application value.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A flight control method for an energy-saving multi-rotor delivery drone, characterized in that, The method includes the following specific steps: Sensor deployment and data acquisition: Contact strain gauge pressure sensors and three-dimensional positioning sensors are deployed and calibrated on the package clamping device. Raw data of package contact pressure and three-dimensional position relative to the fuselage are acquired at a frequency matching the action response rate and transmitted to the flight control unit in real time. Sensor data preprocessing: Adaptive filtering algorithm is used to remove abnormal noise in the raw data, and sliding window averaging method is used for smoothing to output continuous and stable effective pressure data and effective position data; Key action node identification: Based on the changing trend of effective pressure data and the displacement characteristics of effective position data, combined with preset thresholds, the three key action nodes of package lifting, translation and placement are accurately identified; State parameter analysis: Load growth trend parameters are constructed for the lifting node, body attitude offset parameters are calculated for the translation node, and load unloading trend parameters are constructed for the placement node to achieve state quantitative analysis; Dynamic adjustment of rotor power output: Based on the status parameters of each node, the lifting stage adopts a gradual increase in power, the translation stage uses differential fine-tuning of rotor power to offset disturbances, the placement stage gradually reduces power output, and the normal hovering power level is restored after the package is placed.

2. The flight control method for an energy-saving multi-rotor delivery drone according to claim 1, characterized in that, In the key action node identification step, the identification logic built into the analysis module of the flight control unit performs real-time monitoring and feature extraction of effective pressure data and effective position data. A pressure stability judgment threshold based on the pressure reference value of the clamping device in the unloaded state of the UAV and a displacement judgment threshold based on the normal movement range of package loading and unloading are preset. When the effective pressure data shows a continuous upward trend from the reference stable value and the vertical displacement exceeds the preset displacement threshold, the lifting node is determined to be entered. When the effective pressure data remains within the preset stable fluctuation range and the horizontal displacement is greater than the vertical displacement and this continues, the translation node is determined to be entered. When the effective pressure data shows a continuous downward trend from the stable fluctuation range and the vertical displacement gradually decreases to below the preset displacement threshold while the horizontal displacement remains within the allowable error range, the placement node is determined to be entered.

3. The flight control method for an energy-saving multi-rotor delivery drone according to claim 1, characterized in that, In the state parameter analysis step, parameters are extracted and quantified for different key action nodes. At the lifting node, based on the time series of effective pressure data, the pressure change rate and cumulative change amplitude per unit time are calculated. Load growth trend parameters containing growth rate characteristics and amplitude characteristics are constructed through linear fitting or piecewise fitting. At the translation node, the real-time effective position data is compared with the preset benchmark position data to calculate the displacement deviation values ​​of the three-dimensional coordinate system X, Y, and Z axes. Combined with the fuselage structural parameters, these are converted into attitude offset parameters that quantify the impact of load offset on fuselage balance. At the placement node, based on the time series of effective pressure data, the pressure decay rate per unit time and the pressure change difference between adjacent acquisition cycles are calculated. Combined with the stable state of the effective position data, a load unloading trend parameter containing unloading rate characteristics and stability characteristics is constructed.

4. The flight control method for an energy-saving multi-rotor delivery drone according to claim 3, characterized in that, In the state parameter analysis step, at the lifting node, based on the time series of effective pressure data, differential calculation is performed on the continuously collected effective pressure data at preset time intervals to obtain the pressure change rate per unit time. At the same time, the total change of effective pressure data from the lifting node trigger time to the current time is accumulated to determine the cumulative pressure change amplitude. According to the fluctuation characteristics of the pressure change rate, an appropriate fitting method is selected. If the pressure change rate remains stable, a linear fitting method is used to establish a correlation model between pressure and time. If there are obvious stage differences in the pressure change rate, a piecewise fitting method is used. The time series is divided into multiple intervals according to the boundary points of the rate change, and fitting processing is performed separately. Through the model parameters obtained by fitting, a load growth trend parameter that simultaneously includes the load growth rate characteristics and growth amplitude characteristics is constructed.

5. The flight control method for an energy-saving multi-rotor delivery drone according to claim 3, characterized in that, In the state parameter analysis step, at the translation node, the real-time collected valid position data is compared with the preset reference position data axis by axis. The preset reference position data is the position data corresponding to the reference position of the wrapper relative to the fuselage at the beginning of the translation phase. By calculating the coordinate difference between the two in the three-dimensional coordinate system X, Y, and Z axes, the displacement deviation value of each axis is obtained. Then, the preset structural parameters of the UAV fuselage are called, and the three-axis displacement deviation value is coupled with the fuselage structural parameters through coordinate transformation and quantification analysis algorithms. The position deviation caused by the load offset is converted into an attitude offset parameter that can accurately quantify its impact on the fuselage balance.

6. The flight control method for an energy-saving multi-rotor delivery drone according to claim 3, characterized in that, In the state parameter analysis step, the attitude offset parameter is expressed as: ,in, This represents the vector transpose operation. It is the roll angle offset. It is the pitch angle offset. This is the yaw angle offset, and its calculation formula is: ,in, This is the X-axis displacement deviation value. This is the Y-axis displacement deviation value. It is the distance from the clamping device to the center of gravity of the machine body. It is the fuselage reference wheelbase. , , It is the attitude calibration coefficient.

7. The flight control method for an energy-saving multi-rotor delivery drone according to claim 3, characterized in that, In the state parameter analysis step, at the placement node, based on the time series of effective pressure data, the continuous effective pressure data is processed at a preset time interval to obtain the pressure decay rate per unit time. At the same time, the specific difference between the effective pressure data between two adjacent acquisition cycles is calculated to capture the dynamic change characteristics during the pressure unloading process. The stability of the valid location data is determined simultaneously by monitoring its fluctuations in each axis of the three-dimensional coordinate system to confirm the positional stability of the package during placement. The pressure decay rate, the pressure change difference between adjacent periods, and the stability determination results of the valid location data are fused and analyzed. Combined with the preset load unloading feature extraction algorithm, load unloading trend parameters are constructed.

8. The flight control method for an energy-saving multi-rotor delivery drone according to claim 7, characterized in that, In the state parameter analysis step, the pressure decay rate, the pressure change difference between adjacent periods, and the stable state determination results of the valid location data are fused and analyzed. Combined with a preset load unloading feature extraction algorithm, a load unloading trend parameter is constructed, and the algorithm formula is as follows: ,in, It is a load offloading trend parameter. It is the pressure decay rate weighting coefficient. It is the pressure decay rate. It is a weighting coefficient for the difference in pressure changes between adjacent periods. It is the average of the pressure change differences between adjacent acquisition cycles. These are the positional stability state weighting coefficients. It is the position stability coefficient.

9. The flight control method for an energy-saving multi-rotor delivery drone according to claim 1, characterized in that, In the rotor power output dynamic adjustment step, adjustment commands are generated based on the analysis results of the state parameters of each node and transmitted to the rotor power control module. At the lifting node, a progressive power increase strategy is adopted based on the load growth trend parameters. A power output gradient adjustment rule is set with the trigger condition determined by the load growth rate. The power output amplitude is gradually increased according to the preset gradient to match the load growth rate. At the translation node, the power compensation difference required for each rotor is calculated based on the fuselage attitude offset parameters. The fuselage attitude is finely adjusted by differentially adjusting the power output amplitude of different rotors to offset the hovering disturbance caused by the load offset. At the placement node, a power output gradient reduction rule is set based on the load unloading trend parameters. The rotor power output amplitude is gradually reduced. When the effective pressure data drops to near the no-load pressure reference value and remains stable, and the effective position data meets the placement completion conditions, the rotor power output is restored to the power output level of the normal hovering state.

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