Unmanned aerial vehicle control method, system and equipment for high-rise fire extinguishing and medium

Abstract

The invention relates to an unmanned aerial vehicle control method, system and device for high-rise fire extinguishing and a medium. According to the method, a height-lift mapping model is constructed before a task, and a cooperative controller and a state observer are initialized, so that a foundation is laid for real-time control; during operation, a reference lift is obtained according to a real-time flight height query model, meanwhile, the dynamic resistance loss of a pipeline is estimated by using a state observer, and the reference lift and the dynamic resistance loss are fused to generate an accurate dynamic demand lift; and with the above as a target, a pump rotating speed instruction is output through a feedforward-feedback algorithm, pump power consumption is predicted synchronously, real-time flight power consumption is combined, power distribution is optimized and solved by a cooperative controller, and finally a coordination instruction is generated. Therefore, in the continuous change process of the flight height, accurate maintenance of the fire extinguishing lift and dynamic optimal distribution of flight and pumping power energy are achieved, and the problems of lift pressure self-adaptive regulation and control and airborne energy competition in high-rise fire extinguishing are effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) intelligent control technology, and in particular relates to a UAV control method, system, equipment and medium for high-rise fire fighting. Background Technology

[0002] With the acceleration of urbanization, the number of high-rise and super high-rise buildings is increasing, and fire fighting in these buildings has become a global challenge. Traditional firefighting equipment, such as ladder trucks and aerial ladders, is limited by working height and site requirements, making it difficult to effectively intervene in fires above 100 meters. Drones, with their excellent mobility and flexibility, provide a new technological approach for rapid response to high-rise building fires and have become a research hotspot in the field of fire protection in recent years. However, directly applying drones to high-rise building fire fighting faces a severe core technological challenge: how to ensure that extinguishing agents can be stably and effectively delivered to heights of tens or even hundreds of meters.

[0003] In existing technologies, most drones equipped with fire extinguishing modules use constant-speed pumps or rely on manual remote control by operators to adjust pump output. This crude control method has significant drawbacks. As the drone's flight altitude constantly changes, the static pressure difference and flow resistance within the fire extinguishing agent delivery pipeline fluctuate dramatically. Pumps operating at a fixed speed cannot adapt to these changes, often resulting in excessive pressure and energy waste at low altitudes, while severely insufficient pressure and ineffective fire extinguishing agent spraying at high altitudes. Furthermore, the high-power pump shares limited onboard energy with the drone's flight propulsion system, leading to intense power competition between the two. Simple power allocation models cannot achieve dynamic optimization, often resulting in trade-offs. Either excessive pump energy consumption causes drone instability or even crashes, or fire extinguishing efficiency is sacrificed to ensure flight safety, significantly reducing the reliability and effectiveness of fire extinguishing operations.

[0004] Therefore, existing technologies lack a systematic solution that can intelligently and accurately coordinate the control of pump head and flight energy consumption based on the real-time flight altitude and status of the drone, which severely restricts the practical application and working efficiency of high-rise firefighting drones. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, system, device, and medium for controlling unmanned aerial vehicles (UAVs) used for high-rise firefighting, addressing the aforementioned technical problems.

[0006] In a first aspect, this application provides a method for controlling an unmanned aerial vehicle (UAV) for high-rise firefighting, including:

[0007] S1. Before the UAV performs a firefighting mission, based on the input mission parameters and UAV platform performance data, a height-head dynamic mapping model is constructed, and the pump-flight cooperative controller and extended state observer are initialized. The mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The pump-flight cooperative controller is used to optimize the allocation of flight power and pump power. The extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline.

[0008] S2. During the flight and hovering of the drone towards the target floor, collect the drone's flight altitude data, pump operation status data, and flight power consumption data in real time.

[0009] S3. Based on the flight altitude data, obtain the baseline theoretical required head for the corresponding altitude by querying the altitude-head dynamic mapping model; based on the estimated value of the dynamic resistance loss of the water pipeline by the extended state observer, compensate for the baseline theoretical required head and generate the dynamic required head.

[0010] S4. Taking the dynamic demand head as the control target and combining the pump operating status data, the pump speed control command is output through the feedforward-feedback composite control algorithm.

[0011] S5. Predict pump power consumption based on pump speed control command to obtain pump power consumption prediction data; Based on pump power consumption prediction data and flight power consumption data, optimize the solution through pump-flight cooperative controller to dynamically allocate flight power and pump power, and generate flight power allocation command.

[0012] S6. Control the drone to perform high-rise firefighting missions according to the flight power distribution command.

[0013] Secondly, this application also provides an unmanned aerial vehicle (UAV) control system for high-rise firefighting, used to implement the method described in the first aspect, the system comprising:

[0014] The system modeling and initialization module is used to construct an altitude-lift dynamic mapping model based on the input mission parameters and UAV platform performance data before the UAV performs a firefighting mission, and to initialize the pump-flight cooperative controller and extended state observer. The mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The pump-flight cooperative controller is used to optimize the allocation of flight power and pump power; the extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline.

[0015] The real-time status monitoring module is used to collect real-time data on the drone's flight altitude, pump operation status, and flight power consumption during the drone's flight toward and hovering over the target floor.

[0016] The dynamic head compensation module is used to obtain the baseline theoretical head required at the corresponding altitude by querying the altitude-head dynamic mapping model based on the flight altitude data; and to compensate for the baseline theoretical head required based on the estimated value of the dynamic resistance loss of the water pipeline by the extended state observer, thereby generating the dynamic head required.

[0017] The pump control command generation module is used to output pump speed control commands based on dynamic demand head as the control target and combined with pump operating status data through a feedforward-feedback composite control algorithm.

[0018] The power coordination optimization module is used to predict pump power consumption based on pump speed control commands, and obtain pump power consumption prediction data; based on the pump power consumption prediction data and flight power consumption data, the pump-flight coordination controller performs optimization to dynamically allocate flight power and pump power, and generates flight power allocation commands.

[0019] The flight execution control module is used to control the UAV to perform high-rise firefighting missions according to the flight power distribution instructions.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a drone control method for high-rise firefighting as described in the first aspect.

[0021] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a drone control method for high-rise firefighting as described in the first aspect.

[0022] The aforementioned UAV control method, system, equipment, and medium for high-rise firefighting first establishes a precise benchmark and adaptive compensation capability for subsequent real-time control by constructing an altitude-head dynamic mapping model and initializing a cooperative controller and state observer before the mission. Then, during UAV flight and hovering, real-time data on altitude, pump, and flight status are collected. Using the aforementioned model and observer, the theoretical head requirement determined by altitude is fused with the estimated dynamic resistance loss determined by pipeline status to generate a dynamic head requirement that accurately reflects the current actual operating conditions. Subsequently, using this dynamic head requirement as the target, the pump speed is precisely adjusted through a feedforward-feedback composite control algorithm. Simultaneously, based on the pump power consumption predicted by the speed command and the actual flight power consumption, the limited onboard energy is dynamically allocated through online optimization by the cooperative controller, ultimately generating a coordinated power distribution command. This ensures that the UAV can output sufficient pressure of extinguishing agent at any altitude while maintaining stable flight, effectively solving the core problem of head pressure loss and onboard power competition caused by altitude changes in high-rise firefighting. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a drone control method for high-rise firefighting provided by the present invention;

[0025] Figure 2 This is a schematic diagram of the process for generating dynamic demand head in one optional embodiment of the present invention.

[0026] Figure 3 This invention provides a schematic diagram of the structure of an unmanned aerial vehicle (UAV) control system for high-rise firefighting. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] refer to Figure 1 The application presents a flowchart illustrating a drone control method for high-rise firefighting, which includes the following steps:

[0029] S1. Before the UAV performs a firefighting mission, based on the input mission parameters and UAV platform performance data, construct an altitude-lift dynamic mapping model and initialize the pump-flight cooperative controller and extended state observer. The mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The pump-flight cooperative controller is used to optimize the allocation of flight power and pump power. The extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline.

[0030] Specifically, the mission parameters include target floor height, extinguishing agent type, target flow rate parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The UAV platform performance data includes the power-thrust characteristic curve of the flight propulsion system, and the total capacity and discharge rate limits of the airborne battery. The construction of the altitude-lift dynamic mapping model is based on the fluid dynamics pipeline resistance calculation theory, and its core expression is:

[0031]

[0032] In the formula, For the drone's flight altitude The corresponding baseline theoretical head requirement is the minimum head required to overcome the static pressure difference due to gravity and the theoretical resistance of the pipeline. This refers to the real-time flight altitude of the drone, which is the vertical distance of the drone relative to the ground. The density of the extinguishing agent varies depending on the type of extinguishing agent. The specific value needs to be determined based on the type of extinguishing agent input. For example, the density of water extinguishing agent is taken as the standard value at room temperature, while the density of foam extinguishing agent needs to be corrected based on the foam mixing ratio. The acceleration due to gravity is taken in accordance with general physical standards; The theoretical resistance loss head of a water pipeline is composed of friction loss and local resistance loss, and the specific calculation formula is as follows:

[0033]

[0034] In the formula, Frictional resistance loss refers to the energy loss caused by viscous friction during the flow of the extinguishing agent in the straight section of the pipeline. Local resistance loss refers to the energy loss caused by sudden changes in flow pattern when the extinguishing agent flows through local structures such as pipe joints, bends, and valves. The friction factor needs to be corrected based on the viscosity characteristics of the extinguishing agent and the flow state in the pipeline. Under turbulent conditions, it can be determined using the Moody formula, the specific expression of which is: ,in The roughness of the inner wall of the pipeline refers to the average height of the unevenness of the inner wall of the pipeline. The roughness varies for different materials of the pipeline. For example, steel pipes and plastic pipes need to be measured separately. It can be determined by consulting a fluid mechanics handbook. The Reynolds number is a core dimensionless number used to determine whether the flow state in a pipeline is laminar or turbulent. The formula for calculating the Reynolds number is: , The dynamic viscosity of the extinguishing agent reflects its viscosity and varies with temperature and type of extinguishing agent. It needs to be determined according to the actual working conditions. The length of the water supply pipeline is the total length of the pipeline from the pump outlet to the nozzle, including the equivalent length of straight pipe sections and local structures. The inner diameter of the water supply pipeline is the actual flow diameter of the pipeline, excluding the pipe wall thickness. The flow velocity of the extinguishing agent within the pipeline is calculated from the target flow rate and the pipeline cross-sectional area. The formula for calculating the pipeline cross-sectional area is: ,in For pi, the corresponding formula for calculating flow velocity is: , The target flow rate of the water pipeline is the amount of extinguishing agent delivered per unit time required for firefighting operations, which is determined by the size of the fire and the characteristics of the nozzles. The local resistance coefficient is related to the type and number of local structures such as pipe joints and bends. The local resistance coefficient of different structures needs to be determined by experimental calibration or by consulting fluid mechanics handbooks. When multiple local structures are connected in series, the total local resistance coefficient needs to be accumulated to ensure that the resistance effect of all locations where the flow regime changes abruptly is covered.

[0035] The core of initializing the pump-flight cooperative controller is determining the optimization objective function and constraints. The optimization objective is to minimize the total energy consumption or maximize the flight time of the UAV while meeting the firefighting range requirements. Constraints include the minimum output power of the flight propulsion system, the maximum output power of the pump, and the maximum discharge power of the onboard battery. The minimum output power of the flight propulsion system is the critical power for maintaining stable flight of the UAV, and its calculation formula is as follows: In the formula The total mass of the drone (including fire extinguishing agent and equipment load) is the sum of the weight of the drone itself and the weight of the supplies it carries. The air density varies with flight altitude, temperature, and humidity and needs to be corrected according to real-time environmental parameters; The swept area of ​​a drone rotor is calculated by multiplying the circular area formed by a single rotor by the number of rotors. The formula is as follows: , For the number of rotors, The rotor diameter; The aerodynamic drag coefficient reflects the magnitude of air resistance experienced by the UAV during flight. It is related to the UAV's shape and flight attitude and is calibrated through wind tunnel experiments. Hovering speed is the vertical flight speed at which the drone maintains a stationary state in the air, and it is usually taken as 0 or a very small value. The rotor propulsion efficiency, i.e., the ratio of the effective thrust power output by the rotor to the input power of the rotor motor, is affected by the rotor speed and air density; the maximum output power of the pump is determined by the rated parameters of the pump motor, and the calculation formula is as follows. ,in This refers to the rated voltage of the motor, which is the rated voltage value for the motor to operate safely for a long period of time. This is the rated current of the motor, which is the maximum allowable current for the motor to operate safely for a long period of time. The motor power factor reflects the ratio of the motor's active power to its apparent power; under inductive loads, its value typically ranges from 0.7 to 0.9. The maximum discharge power of the onboard battery is limited by battery performance, and its calculation formula is... , This refers to the battery's rated voltage, i.e., the battery's nominal operating voltage. The maximum allowable discharge current of the battery is determined by the battery capacity, discharge rate, and temperature characteristics to avoid over-discharge and damage to the battery. The controller can use either model predictive control or particle swarm optimization. During the initialization phase, core parameters such as the iteration step size and convergence threshold of the algorithm need to be set to lay the foundation for subsequent power distribution optimization.

[0036] Initializing the extended state observer requires determining the observer's order and gain parameters. Its core function is to estimate the dynamic resistance loss of the water pipeline, which includes unmodeled disturbances such as additional resistance from pipe bends caused by UAV attitude changes and additional resistance from fluctuations in fire extinguishing agent flow velocity. For the extended state observer of a second-order system, its state equation is:

[0037]

[0038] In the formula, The observed pipeline resistance loss head is the estimated total pipeline resistance loss output by the observer. The rate of change of resistance loss reflects the trend of pipeline resistance loss over time, and the unit is m / s; For extended disturbance state quantities, namely dynamic resistance loss of water pipelines, it specifically characterizes the additional resistance caused by unmodeled disturbances; , , These are the estimated components of the corresponding states. , , Regarding time The first derivative of represents the rate and trend of change of each state variable with time; The actual resistance loss is calculated from the pressure difference between the inlet and outlet of the pipeline collected by the sensor. The calculation formula is as follows: , This refers to the inlet pressure of the pipeline, which is the pipeline pressure at the pump outlet. This refers to the outlet pressure of the pipeline, i.e., the pipeline pressure at the nozzle inlet. This is the pump speed control quantity, i.e., the speed command value output to the pump motor, in r / min; The control gain coefficient is determined by the pump's speed-head gain characteristic, and the calculation formula is as follows: pump head For speed The partial derivatives are obtained by fitting the pump's rated performance curve using the least squares method to ensure that the gain coefficient accurately reflects the degree of influence of the rotational speed on the head. , , The gain parameter of the observer is determined during initialization using the pole placement method. The goal of pole placement is to place the closed-loop poles of the observer in a reasonable position in the left half of the complex plane to ensure that the observer has fast convergence and stability and can accurately track the changes in dynamic resistance loss of the pipeline. Generally, the larger the absolute value of the real part of the pole, the faster the convergence speed.

[0039] S2. During the flight and hovering of the drone towards the target floor, real-time data on the drone's flight altitude, pump operation status, and flight power consumption are collected.

[0040] Specifically, during the drone's flight and hovering towards the target floor, a multi-sensor fusion scheme is used to collect three types of core data in real time: drone flight altitude data, pump operation status data, and flight power consumption data. This ensures the accuracy and real-time nature of data collection, providing reliable support for subsequent control decisions. Flight altitude data is collected through a fusion strategy of a barometric altimeter and GPS. The barometric altimeter obtains relative altitude data, while GPS obtains absolute altitude data. The fusion algorithm uses Kalman filtering, and its discretized state equation and observation equation are as follows:

[0041]

[0042] In the formula, Let k be the state vector at time k, and let its value be... The superscript ⊤ indicates matrix transpose, which converts a row vector into a column vector to satisfy the rules of matrix operations. Let k be the altitude of the drone. Let be the vertical flight velocity of the drone at time k, with upward being the positive direction; This is the state transition matrix, with values... , The sampling period is the time interval between two consecutive data acquisitions, which is the reciprocal of the sensor sampling frequency. To control the input matrix, the value is... Its elements are derived from the kinematic equations and reflect the influence of acceleration commands on state quantities; The vertical acceleration command at time k-1 is generated by the UAV flight control system; The process noise at time k-1 follows a mean of 0 and a variance of . Gaussian distribution, This is the process noise variance matrix, which is usually set as a diagonal matrix, with the diagonal elements corresponding to the process noise variances of height and velocity, respectively, and is determined through experimental calibration. Let be the observation vector at time k, with values... , This is the measurement value of the barometric altimeter at time k. This is the measurement value at GPS time k; The observation matrix takes values ​​of , used to map state vectors to observation vectors; The observation noise at time k-1 follows a mean of 0 and a variance of . Gaussian distribution, To observe the noise variance matrix, a diagonal matrix was also used, with diagonal elements corresponding to the measurement noise variances of the barometric altimeter and GPS, respectively, determined based on sensor factory parameters and actual testing. A Kalman filter prediction-update iterative process was employed to eliminate the influence of environmental pressure changes and GPS signal drift on the measurement results, ensuring the stability of the altitude data.

[0043] Pump operating status data includes three key parameters: real-time pump speed, pump outlet pressure, and pipeline flow rate, each acquired through corresponding sensors. The speed sensor is installed on the pump motor shaft to collect real-time pump speed. The Hall effect speed sensor calculates the speed by counting the number of changes in the magnetic field at the motor shaft end per unit time. The calculation formula is... , This represents the number of pulses within the sampling period, i.e., the number of magnetic field changes detected by the sensor. The number of magnets on the motor shaft end refers to the number of permanent magnets evenly attached to the motor shaft end, typically 4, 6, or 8. A pressure transmitter is installed at the pump outlet to collect the pump outlet pressure. An electromagnetic flowmeter is installed in the middle of the pipeline to collect the pipeline flow rate; it measures flow based on the principle of electromagnetic induction, and its measurement formula is... , This is the instrument constant, calibrated at the factory by the flow meter, and its unit is [unit missing]. , The magnetic field strength generated by the built-in coil of the flowmeter, measured in Tesla. The inner diameter of the flow meter's measuring tube must match the inner diameter of the water supply pipeline to avoid flow loss. To ensure the average flow rate of the extinguishing agent within the pipeline, the sampling frequencies of all sensors are kept consistent to guarantee data synchronization. Flight power consumption data is collected collaboratively by current and voltage sensors to obtain the input current and voltage of the UAV's flight propulsion system in real time. Flight power consumption is calculated by multiplying the voltage and current. , Input voltage to the flight propulsion system, For the input current, and considering the line losses of the power system, the corrected formula is as follows: , The equivalent resistance of the circuit, i.e., the total equivalent resistance of the wires, connectors, etc., from the battery to the motor, is measured using the DC bridge method. During the data acquisition process, the current and voltage data need to be filtered using a moving average; the filtering formula is as follows: , The filter window length, i.e., the number of historical data points used in the averaging calculation, is set based on the noise frequency. This is the original data at time i. The data at time k is filtered to reduce the fluctuations in power consumption data caused by motor operating noise, ensuring that the power consumption data can accurately reflect the energy consumption status of the flight propulsion system.

[0044] S3. Based on the flight altitude data, obtain the baseline theoretical required head for the corresponding altitude by querying the altitude-head dynamic mapping model; based on the estimated value of the dynamic resistance loss of the water pipeline by the extended state observer, compensate for the baseline theoretical required head to generate the dynamic required head.

[0045] Specifically, the baseline theoretical head requirement reflects the basic head needed to overcome gravity and theoretical pipeline resistance at the current height, providing a foundation for subsequent head adjustments. Based on the estimated dynamic resistance loss of the water supply pipeline output in real time by the extended state observer, the baseline theoretical head requirement is compensated to generate the dynamic head requirement. The compensation logic involves adding the dynamic resistance loss to the baseline head to ensure that the actual head output by the pump can simultaneously overcome the static pressure difference and real-time pipeline resistance, avoiding failure of extinguishing agent delivery due to changes in dynamic resistance. The formula for calculating the dynamic head requirement is:

[0046]

[0047] In the formula, The dynamic demand head refers to the actual target head that the pump needs to output. Based on the baseline theoretical head required by real-time flight altitude queries, The real-time flight altitude of the drone is determined by fusing data using Kalman filtering. The dynamic resistance loss of the water supply pipeline is estimated using an extended state observer. During actual compensation, the validity of the dynamic resistance loss estimate needs to be assessed using the following formula: ,in This represents the minimum reasonable value for dynamic resistance loss, i.e., the minimum resistance fluctuation value when there are no additional disturbances in the pipeline. The maximum reasonable value for dynamic drag loss, i.e., the maximum additional drag value under the extreme attitude of the UAV, is determined experimentally based on the pipeline structure and the range of UAV attitude changes. When the estimated value exceeds this range, it is judged as sensor malfunction or observer divergence. In this case, the historical mean is used instead. The formula for calculating the historical mean is... , The historical data length refers to the number of past dynamic drag loss estimates used in the mean calculation. Typically, data from 10-20 sampling periods are used to avoid abnormal data causing head control failure and to ensure the continuity of firefighting operations.

[0048] S4. Taking the dynamic demand head as the control target and combining the pump operating status data, the pump speed control command is output through the feedforward-feedback composite control algorithm.

[0049] Specifically, the feedforward control section is based on the pump's rated performance curve. This curve reflects the inherent relationship between pump speed, head, and flow rate, and its mathematical expression can be obtained through curve fitting. A commonly used quadratic fitting formula is... ,in , , These are the fitting coefficients. For pump speed, For traffic, The head is determined based on the pump's factory performance data. The least squares method is used for fitting the equation, which is obtained through multiple sets of measured data on speed, flow rate, and head, ensuring the fitting formula matches the actual characteristics of the pump. Based on the dynamic head requirement and target flow rate, the feedforward speed command is obtained by solving this fitting equation. The solution process involves... , Substitute into the fitting equation ( (for target traffic), get information about The quadratic equation of The equation follows the standard form of a quadratic equation in one variable. The coefficient of the quadratic term is The coefficient of the linear term is The constant term is Solve the equation and take a reasonable positive root as the feedforward speed command. Discarding negative roots (where the speed has no negative value) and roots exceeding the motor's rated speed range. The core function of feedforward control is to utilize the inherent characteristics of the pump to achieve coarse speed adjustment, reduce the adjustment burden of feedback control, and improve control response speed.

[0050] The feedback control section employs a proportional-integral-derivative (PI-DE) control algorithm, using the deviation between the pump outlet pressure and the target pressure as input. The target pressure is calculated based on the dynamic demand head and the pressure loss characteristics of the nozzle at the end of the pipeline. The calculation formula is as follows:

[0051]

[0052] In the formula, The target pressure is the pressure value that the pump outlet needs to reach. For dynamic demand head; Density of the extinguishing agent; It is the acceleration due to gravity; The rated operating pressure of the nozzle is used to ensure that it can properly spray the extinguishing agent and form a jet that meets the fire extinguishing requirements. The nozzle pressure loss must also be calculated in conjunction with the nozzle flow coefficient, using the following formula: , The nozzle flow coefficient is determined by design parameters such as nozzle orifice diameter, structural shape, and spray angle. It is calculated by experimentally measuring the actual flow rate of the nozzle under different pressures, and the unit is 1 / 2 liters. The output of proportional-integral-derivative control is the feedback speed correction, and its discretized calculation formula is as follows:

[0053]

[0054] In the formula, The feedback speed correction at time k is used to fine-tune the feedforward speed command and compensate for the deviation of the feedforward control. This is the sampling time, i.e., the time point number of the current data acquisition and control calculation. The proportional gain is used to speed up the control response and reduce dynamic deviation. The larger the value, the faster the response, but too large a value can easily lead to system oscillation. Let k be the pressure deviation at time k, which is the difference between the target pressure and the real-time pressure at the pump outlet. The calculation formula is: , The real-time pump outlet pressure at time k is obtained by a pressure transmitter. This is the integral gain, used to eliminate static bias and prevent pressure from deviating from the target value in steady state. The larger the value, the stronger the integral effect, but too large a value can easily lead to overshoot. The sampling period should be consistent with the data acquisition period. The differential gain is used to suppress system oscillations, improve control stability, and predict the trend of pressure deviation changes. The larger the value, the better the oscillation suppression effect, but if it is too large, it is easily affected by noise. The pressure deviation at time k-1 is the pressure deviation value at the previous sampling time. The final speed command of the feedforward-feedback composite control is the superposition of the feedforward speed command and the feedback speed correction, i.e. At the same time, upper and lower limits are set for the speed command. This range is determined by the rated speed parameters of the pump motor, with the upper limit being the rated speed of the motor. This refers to the maximum speed at which the motor can operate safely for extended periods, with the lower limit being the critical speed required to maintain the minimum pump flow rate. This refers to the pump speed at which the pipeline flow rate is not lower than the minimum target flow rate. It is determined by the pump characteristic curve to prevent motor damage caused by overspeed operation and to ensure the safety of the pumping equipment.

[0055] S5. Based on the pump speed control command, predict the pump power consumption to obtain pump power consumption prediction data; based on the pump power consumption prediction data and flight power consumption data, optimize the solution through the pump-flight cooperative controller to dynamically allocate flight power and pump power, and generate flight power allocation command.

[0056] Specifically, the pump power consumption prediction model is constructed based on the pump efficiency characteristic curve, which is... Reflects pump speed ,flow With efficiency The relationship between them is fitted by the following formula: , , , , The fitting coefficients were calibrated using pump experimental data, which consisted of measured values ​​of pump effective power and shaft power at different speeds and flow rates. The least squares method was used for fitting to ensure accurate efficiency calculations. The formula for calculating pump shaft power is:

[0057]

[0058] In the formula, Pump shaft power, i.e., the mechanical power output by the pump impeller; Density of the extinguishing agent; It is the acceleration due to gravity; The target flow rate for the water supply pipeline; For dynamic demand head; To determine the pump efficiency at the commanded speed and target flow rate, we can substitute the values ​​into the above fitting formula. The calculation shows that the pump power consumption is the ratio of shaft power to pump motor efficiency. Motor efficiency is obtained by consulting a motor efficiency curve, and the formula for fitting the motor efficiency curve is: , , , , The fitting coefficients are calibrated using measured data of the motor's input and output power at different speeds and torques. The output torque of the motor is the torque required for the motor to drive the pump. The torque calculation formula is: In the formula, 9550 is a unit conversion factor used to convert shaft power (kW) and speed (r / min) into torque (N·m), thereby obtaining pump power consumption prediction data. The calculation formula is as follows: This refers to the input power of the pump motor, which reflects the energy consumption of the pumping system.

[0059] Based on pump power consumption prediction data and real-time acquired flight power consumption data, an optimization solution is performed using a pump-flight cooperative controller to dynamically allocate flight power and pump power, generating flight power allocation commands. The optimization objective function of the cooperative controller is set as follows:

[0060]

[0061] In the formula, This represents the total power consumption of the UAV, which is the sum of the power consumption of the flight propulsion system and the pumping system. The real-time flight power consumption is obtained by collecting and correcting data from sensors. Based on the predicted pump power consumption data, the optimization objective is to minimize the total power consumption and improve the drone's endurance. The optimization process must satisfy several constraints, as shown in the constraint equations:

[0062]

[0063] In the formula, For drones at the current altitude With flight speed The minimum flight power is determined by the aerodynamic model and power system characteristics of the UAV. To meet dynamic head requirements With target traffic The minimum pump power is calculated using the following formula: , The maximum efficiency of the pump, i.e. the highest efficiency value in the pump characteristic curve, is determined by the pump's factory parameters. This represents the maximum discharge power of the onboard battery. The formula for calculating the single adjustment amount of flight power is as follows: , This represents the flight power allocation amount corresponding to the flight power allocation command at time k, which is the target output power of the flight power system at the current time. The flight power allocation amount corresponding to the flight power allocation command at time k-1 is the target output power at the previous time. The absolute value ensures that the adjustment amount is non-negative. This represents the maximum allowable adjustment of flight power to prevent sudden power surges that could cause UAV attitude instability. The value is based on the UAV attitude control accuracy calibration and is typically 10% to 20% of the minimum flight power. The cooperative controller uses a particle swarm optimization algorithm, where the particle dimension represents the flight power allocation, meaning each particle corresponds to a target flight power value. The particle fitness function is the objective function value. The algorithm iteration formula is as follows:

[0064]

[0065]

[0066] in, The inertial weight controls the influence of the particle's original velocity on its current velocity. Its value decreases with the number of iterations, typically from 0.9 to 0.4, balancing global and local search capabilities. , The learning factor controls the degree to which the particle learns towards the individual optimum and the global optimum, respectively, and is usually set to 1.5-2.0. , Use random numbers in the interval [0,1] to increase the randomness of the algorithm's search; The optimal position of an individual is the optimal fitness value corresponding to the i-th particle during the iteration process. This represents the globally optimal position, which is the optimal fitness value corresponding to all particle iterations. Let be the velocity of the i-th particle at time k. Let be the position of the i-th particle at time k. Through iterative optimization, the optimal flight power allocation command that satisfies all constraints is obtained, ensuring the rationality and safety of power allocation.

[0067] S6. Control the drone to perform high-rise firefighting missions according to the flight power distribution command.

[0068] Specifically, the power adjustment of the flight propulsion system is achieved by changing the pulse width modulation (PWM) duty cycle of the motor. PWM controls the average motor voltage by adjusting the duty cycle of the pulse signal, thereby adjusting the motor's output power. The correspondence between the PWM duty cycle and output power is determined by the propulsion system calibration experiment. The calibration process needs to cover different voltage and load conditions to establish a precise duty cycle-power mapping relationship. The fitting formula is as follows: , , , The fitting coefficients are obtained by fitting measured motor output power data under different duty cycles. The duty cycle is the ratio of the high-level time to the period within one pulse cycle, and its value ranges from [0,1]. When D=0, the motor stops, and when D=1, the motor outputs maximum power. The required duty cycle command can be derived from the flight power distribution command using this formula, ensuring the accuracy of power adjustment.

[0069] Simultaneously, the pump motor operates according to speed control commands, driving the extinguishing agent along the water pipeline to the nozzle and spraying it onto the target fire area, completing the high-rise firefighting operation. During the mission, a real-time monitoring and emergency response mechanism must be established to dynamically monitor the pump head and UAV attitude. The actual pump head is calculated using the inlet and outlet pressures; the calculation formula is as follows: , For the pipeline inlet pressure, To ensure fire suppression efficiency, when the actual pump head is detected to be lower than the dynamic required head by a reasonable percentage (e.g., 90%), the co-controller prioritizes increasing the pump power while reducing unnecessary flight power consumption. The UAV's attitude angles are measured using a fusion of gyroscope and accelerometer measurements. The roll angle is the UAV's rotation angle around its longitudinal axis (the direction the nose points), and the pitch angle is the UAV's rotation angle around its transverse axis (the horizontal direction perpendicular to the longitudinal axis). The stable range is determined by the control accuracy of the UAV's flight control system. Typically, the absolute values ​​of the roll and pitch angles do not exceed 5° to 10°. When the roll and pitch angles are detected to be outside the stable range, the co-controller prioritizes increasing the flight power to ensure UAV flight safety. Once the attitude stabilizes, the pump power is adjusted to achieve a dynamic balance between flight safety and fire suppression efficiency, ensuring the successful completion of the fire suppression mission.

[0070] The aforementioned UAV control method for high-rise firefighting first constructs an altitude-head dynamic mapping model and initializes a cooperative controller and state observer before the mission, establishing a precise benchmark and adaptive compensation capability for subsequent real-time control. Then, during UAV flight and hovering, real-time data on altitude, pump, and flight status are collected. Using the aforementioned model and observer, the theoretical head requirement determined by altitude is fused with the estimated dynamic drag loss determined by pipeline status to generate a dynamic head requirement that accurately reflects the current actual operating conditions. Subsequently, using this dynamic head requirement as the target, a feedforward-feedback composite control algorithm precisely adjusts the pump speed. Simultaneously, based on the pump power consumption predicted by the speed command and the actual flight power consumption, the cooperative controller performs online optimization to dynamically allocate limited onboard energy, ultimately generating a coordinated power distribution command. This ensures that the UAV can output sufficient pressure of extinguishing agent at any altitude while maintaining stable flight, effectively solving the core problem of head-pressure loss and onboard power competition caused by altitude changes in high-rise firefighting.

[0071] refer to Figure 2In one optional embodiment, the dynamic demand head is generated by compensating for the baseline theoretical head based on the estimated dynamic resistance loss of the water pipeline using an extended state observer, including the following steps:

[0072] S11. Obtain the pump speed and actual pump head of the UAV in the historical period as historical historical data; wherein, the actual pump head is calculated based on the pump inlet and outlet pressure difference measurement data in the pump operation status data.

[0073] Specifically, the selection of the historical period needs to be determined comprehensively based on the UAV's flight speed, the rate of change of the fire situation, and the sensor sampling frequency. For example, selecting the first 10-20 sampling periods as the historical period ensures that the amount of data is sufficient to support adaptive estimation while avoiding estimation lag due to outdated historical data. The sampling period should be consistent with the data acquisition period in claim 1 to ensure data synchronization. The historical pump speed data comes from a Hall speed sensor installed at the pump motor shaft end, and is calculated by counting the number of changes in the magnetic field at the motor shaft end per unit time. The specific calculation formula is as follows: ,in This represents the number of pulses within a single sampling period. This refers to the number of magnets at the motor shaft end. The historical data needs to be processed by moving average filtering to eliminate fluctuations caused by motor operating noise. The filter window length is set to 5 sampling periods to ensure the stability of the speed data.

[0074] The calculation of the actual pump head is based on the measurement data of the pressure difference between the pump inlet and outlet. Essentially, it involves converting the pressure difference to obtain the actual output head value of the pump. The calculation formula is as follows: In the formula Let t be the actual pump head. The pump inlet pressure at time t is collected by a pressure transmitter installed in the pump inlet pipeline. The pump outlet pressure at time t is collected by a pressure transmitter at the pump outlet location. The measurement accuracy of the two pressure transmitters must be no less than 0.01 MPa to ensure the accuracy of the differential pressure calculation. The extinguishing agent density is determined based on the extinguishing agent type specified in the task parameter input step S1. The acceleration due to gravity is used, following general physical standards. The collected inlet and outlet pressure difference data must first undergo outlier removal; the removal rule is that when the pressure difference exceeds the normal operating range... ( When the standard deviation of the historical differential pressure data is used, it is judged as abnormal data. The average differential pressure of two adjacent sampling periods is used as a substitute, and then the actual pump head is calculated by substituting it into the above formula. Finally, the pump speed of each historical sampling period is stored one by one with the corresponding calculated actual pump head to form a complete historical data set, which provides data support for subsequent adaptive estimation.

[0075] S12. Based on historical data, the additional resistance loss head caused by pipe bending and deformation is estimated in real time by using the adaptive estimation algorithm in the extended state observer, and the estimated value of the additional resistance loss head is obtained.

[0076] The adaptive estimation algorithm observes the additional drag loss head as an extended state of the pump head dynamic system modeled by the extended state observer. The process of the adaptive estimation algorithm is as follows: based on the input pump speed and the actual pump head, the state update equation of the Luneburg observer is used to calculate and output a state estimation vector containing the estimated value of the additional drag loss head. The estimated value of the additional drag loss head component is extracted from the state estimation vector and used as the estimated value of the additional drag loss head.

[0077] The state estimation update equation for the Luneburger observer is as follows:

[0078]

[0079] in, Let be the state estimation vector at time t, defined as , This represents the estimated value of the demand head component at time t. This represents the estimated value of the head component of the additional drag loss at time t, indicated by the superscript. Represents the transpose of a vector; The system input at time t is the pump speed; The measured output of the pump head dynamic system at time t is the actual pump head. , , The state space matrix is ​​predetermined based on the physical characteristics of the UAV's airborne pump and water pipeline; For the pre-designed observer gain matrix; Represents the state estimation vector Regarding time The derivative; This represents the estimated pump head output by the dynamic system at time t.

[0080] Specifically, the adaptive estimation algorithm observes the additional drag loss head as an extended state of the pump head dynamic system modeled by the extended state observer. Its core objective is to quantify unmodeled sudden disturbances such as pipe bends and deformations into observable state variables, enabling accurate tracking of this type of dynamic drag loss and avoiding the shortcomings of traditional fixed models that cannot adapt to changes in pipe attitude. The core of the adaptive estimation algorithm is the Luneburg observer, which models and observes the pump head dynamic system in real time through state update equations, outputting a state estimation vector containing the estimated additional drag loss head value. The target component is then extracted from this vector to obtain the final estimated additional drag loss head value.

[0081] The Luneburger observer is an asymptotic observer based on the system's state-space description. It can estimate in real-time the system's state variables that cannot be directly measured using system input and output data. Here, by incorporating the additional drag loss head as an extended state into the observation system, it achieves indirect measurement of this type of implicit drag loss. The state estimation update equation of the Luneburger observer is as follows:

[0082]

[0083] in, Let be the state estimation vector at time t, defined as superscript The transpose operation represents the transformation of a vector, converting a row vector into a column vector to satisfy the rules of matrix operations, where... This represents the estimated value of the demand head component at time t, corresponding to the dynamic estimate of the baseline theoretical demand head generation stage in step S3, reflecting the estimated result of the head required to overcome gravity and inherent pipeline resistance at the current height. This represents the estimated value of the additional resistance loss head component at time t, which is the additional resistance loss head caused by pipe bends and deformations. It is the core objective quantity of this adaptive estimation algorithm, and its value is positively correlated with the pipe bend angle and the degree of deformation. The larger the bend angle and the more obvious the deformation, the larger this value.

[0084] The system input at time t is the pump speed, which is the actual speed value collected by the Hall speed sensor at time t and filtered. The unit is r / min. This value is used as the input excitation signal of the observer to reflect the influence of the pump's operating state on the system output. The measured output of the pump head dynamic system at time t is the actual pump head, calculated using the inlet and outlet pressure difference in step S11. As a feedback signal from the observer, it is used to correct the deviation of the state estimate. , , The state-space matrix, which is pre-determined based on the physical characteristics of the UAV's airborne pump and water pipeline, is the core parameter for the Luneburger observer model. Its value needs to be determined through experimental calibration and system identification to ensure consistency with the actual pump-pipeline system characteristics.

[0085] Wherein, the state matrix It is a 2×2 matrix reflecting the coupling relationship between the internal states of the system. It is derived based on the pump speed-head characteristics and pipeline resistance characteristics, and its specific form is as follows: , The pump head response coefficient to rotational speed is obtained by fitting the pump's rated performance curve, and the fitting formula is as follows: (in For pump speed, (flow rate), relative to rotational speed Find the partial derivative and take the mean to get The input matrix B is a 2×1 matrix that reflects the influence of the system input on the state, in the form of: , The input gain coefficient is determined by experimentally measuring the change in pump head at different speeds. The output matrix C is a 1×2 matrix that reflects the mapping relationship between the system state and the output, in the form of: This indicates that the system output is the superposition of the required head component and the additional resistance loss head component, which is consistent with the physical composition of the actual pump head.

[0086] The pre-designed observer gain matrix is ​​a 1×2 matrix. Its design goal is to asymptotically converge the observation error of the Romberg observer to zero, ensuring the stability and speed of state estimation. This can be achieved using the pole placement method, which involves placing the closed-loop poles of the observer in appropriate positions in the left half of the complex plane. The larger the absolute value of the real part of the pole, the faster the observation error converges. However, it is necessary to avoid poles that are too far away, which could cause system oscillations. The specific design process relies on the observer's characteristic equation, which is: ,in, The determinant operation is used to solve for the determinant value of a matrix. The eigenvalues ​​(i.e., closed-loop poles) of the observer can be obtained by the condition that the determinant is zero. It is the core operation of the pole placement method. These are complex frequency variables, belonging to the complex plane, used to characterize the dynamic properties of the system. The roots (poles) of the characteristic equation are... The solution; The identity matrix, here compared to the state matrix. It has the same dimensions and is a 2×2 identity matrix, in the form of Its function is to ensure dimension matching in matrix operations, so that... and It can perform subtraction operations; This is the state matrix, reflecting the coupling relationship of the internal states of the dynamic system of pump head; The observer gain matrix to be designed is the core optimization objective of the pole placement method; The output matrix reflects the mapping relationship between the system state and the output.

[0087] During the design process, the closed-loop pole positions of the observer should be preset first. , (usually taken) , (The unit is rad / s; the negative real roots in the left half of the complex plane ensure the stability of the observer). Then, substitute the preset poles into the characteristic equation, and combine... , Given the values ​​of the matrix, construct about Matrix elements , The system of equations can be solved to obtain the gain matrix. . Represents the state estimation vector The derivative with respect to time t reflects the rate of change of the state estimation vector with time, and is used to describe the dynamic evolution of the system state. The pump head estimate output by the dynamic system at time t represents the pump head estimate at time t, which is obtained by combining the state estimation vector and the output matrix. The theoretical output value obtained from the calculation is used to compare with the actual measured output. The comparison forms the observation error, providing a basis for correcting the state estimate.

[0088] The specific calculation process of the Luneburger observer is as follows: First, the historical data is compared with the pump speed at time t acquired in real time. Actual pump head The input is fed into the observer; then the derivative of the state estimate vector is calculated using the first state update equation. The equation consists of three parts, namely the internal state evolution term of the system. Input incentive items and error correction items The error correction term corrects the state estimate in real time by adjusting the deviation between the actual output and the estimated output, thereby reducing the estimation error; then, the second equation is used to calculate the estimated pump head. This forms a closed-loop feedback; numerical integration methods are used to... Integrating, we obtain the state estimation vector at time t. The integration method uses Euler's integral method, and the discretization formula is as follows: Where k is the sampling time, The sampling period is used; finally, the state estimation vector is used. Extracting the second component yields the estimated additional drag loss head at time t. In actual calculations, the initial state of the observer needs to be determined in conjunction with historical data. Initialization is performed, with the initial value being the average of the required head and the additional drag loss head from the historical data, to ensure that the observer starts from a steady state and avoids excessive initial error.

[0089] S13. Add the baseline theoretical required head to the estimated head for additional drag loss to obtain the dynamic required head.

[0090] Specifically, the baseline theoretical required head is calculated using the height-head dynamic mapping model. This reflects the basic head required to overcome the static pressure difference caused by gravity and the inherent resistance of the pipeline at the current flight altitude, but it does not consider the additional resistance caused by pipeline bends and deformations; the estimated head due to additional resistance loss. This precisely quantifies these sudden additional resistances, and the sum of the two ensures that the final dynamic head requirement completely covers all types of resistance under the current operating conditions, ensuring that the pump output head is sufficient to overcome all types of resistance and achieve stable delivery of the extinguishing agent.

[0091] The specific calculation formula is as follows: In the formula This is the final dynamically generated demand head. During the actual addition process, the validity of the estimated additional drag loss head needs to be verified. The verification rules are consistent with the general rules for generating the dynamic demand head in step S3, i.e., when... When the value exceeds the preset reasonable range, it is determined to be an abnormality in the observer's estimation. In this case, the average value of the additional drag loss head in the historical data is used as the replacement. The formula for calculating the average value is as follows: Where M is the length of historical data, taking data from 10-20 sampling periods to avoid deviations in dynamic demand head caused by abnormal estimates, thus ensuring the stability and reliability of firefighting operations. Simultaneously, the summed dynamic demand head needs to be fed back to the pump speed feedforward-feedback composite control algorithm in step S4, serving as the core objective of pump speed control to achieve precise matching between pump output and actual resistance requirements.

[0092] In one optional embodiment, taking dynamic demand head as the control objective and combining pump operating status data, a feedforward-feedback composite control algorithm is used to output a pump speed control command, including the following steps:

[0093] S21. Based on the dynamic demand head and the current target flow parameters of the water pipeline, use the pump rated performance curve data in the task parameters to inversely solve the target pump speed; generate a feedforward speed command based on the target pump speed.

[0094] Specifically, the dynamic demand head is the final head generated in step S3. This refers to the actual head target that the pump needs to meet, which already covers the static pressure difference due to gravity, the inherent resistance of the pipeline, and the dynamic additional resistance; the target flow rate parameter of the water delivery pipeline is the task parameter input in step S1. The temperature is determined by the fire level and the nozzle spray characteristics, and it remains constant throughout the process to ensure the fire extinguishing effect.

[0095] The pump rated performance curve data is also a task parameter input in step S1. This curve is core characteristic data provided by the pump manufacturer, reflecting the pump speed. Output head With pipeline flow The inherent correspondence among these three is usually stored in the form of discrete data points. In practical applications, it needs to be transformed into a continuous mathematical model through curve fitting. The least squares method is used for fitting, and a quadratic polynomial model is selected. ,in , , The fitting coefficients are obtained by considering the rotational speeds under multiple rated operating conditions. ,flow Yangcheng The data was obtained by solving the model to ensure that the deviation between the model and the actual pump characteristics is within the allowable range.

[0096] The inverse process of determining the target pump speed is essentially about converting the dynamic demand head into a specific value. With target traffic Substitute the values ​​into the above fitting model to solve for the corresponding rotational speed. , Substituting into the fitting equation, we can obtain information about the rotational speed. The quadratic equation of The equation follows the standard quadratic equation form. The coefficient of the quadratic term coefficient of the first term constant term After solving, the roots need to be screened for rationality, discarding negative roots (those with no negative speed values) and roots exceeding the pump's rated speed range (those marked on the pump's rated performance curve in the task parameters of step S1). The root of the feedforward speed command is used, retaining the positive root that conforms to the operating conditions as the target pump speed. This means directly using the target pump speed. Its core function is to achieve coarse speed adjustment based on the inherent characteristics of the pump, quickly approach the required value, and reduce the adjustment amount and response time of subsequent feedback control.

[0097] S22. Using the dynamic demand head as the set value, and the actual pump head calculated based on the pump inlet and outlet pressure difference measurement data in the pump operation status data as the feedback value, calculate the first deviation between the set value and the feedback value.

[0098] Specifically, the setpoint is the dynamic head requirement generated in step S3. The target value is the ideal pump head; the feedback value is the actual pump head. It needs to be calculated using the pump operating status data (inlet and outlet pressure difference) collected in step S2. The calculation formula is as follows: , in the formula For pump inlet pressure, The pump outlet pressure is collected by the pressure transmitter deployed in step S2 (the inlet pressure transmitter is installed in the pump inlet pipeline, and the outlet pressure transmitter is installed in the pump outlet pipeline). The extinguishing agent density is determined by the extinguishing agent type in the task parameters of step S1. The acceleration due to gravity is used, following general physical standards. Before calculation, the inlet and outlet pressure difference data needs to be preprocessed to remove outliers (those exceeding the normal operating range). , (The standard deviation of historical pressure difference data) Outliers are replaced by the average pressure difference of two adjacent sampling periods, and then substituted into the formula to calculate the actual head, so as to avoid noise interference causing the feedback value to be distorted.

[0099] first deviation The calculation logic is to subtract the feedback value from the set value, that is... This deviation directly reflects the difference between the actual pump output head and the ideal requirement: when When the pump speed is insufficient, it indicates that the actual head is insufficient and the pump speed needs to be increased; when When the actual head is excessive, the pump speed needs to be reduced; when This indicates that the actual head matches the demand, and no adjustment is needed. The first deviation serves as the core input for subsequent PID control, and its accuracy directly determines the precision of the feedback control.

[0100] S23. Perform proportional-integral-derivative control calculations on the first deviation to obtain the feedback speed correction amount.

[0101] Specifically, the proportional-integral-derivative (PID) control calculation uses a discretization algorithm to adapt to the digital computing characteristics of the UAV control system. The calculation formula is as follows: ,in At the current sampling time, This is the feedback speed correction amount at time k, used to fine-tune the feedforward speed command. For proportional gain, its core function is to speed up the control response and reduce dynamic deviation. Its value needs to be calibrated according to the pump speed-head response characteristics, and the typical range is 0.5-2.0. If the value is too large, it will easily cause system oscillation, and if it is too small, the response will be slow. The first deviation at time k, This represents the first deviation at time k-1, reflecting the temporal change of the deviation. This is the integral gain, used to eliminate static bias and avoid residual deviation between the actual head and the set value in steady state. The value range is 0.01 to 0.1. If the value is too large, it will lead to an increase in overshoot and affect the stability of the system. The sampling period is consistent with the data acquisition period in step S2 to ensure timing synchronization; The differential gain is used to predict the trend of deviation changes, suppress system oscillations, and improve control stability. Its value ranges from 0.1 to 1.0. If the value is too large, it is easily affected by noise interference, and if it is too small, the oscillation suppression effect will be poor.

[0102] The core logic of PID control is to correct deviation through the coordinated action of the proportional, integral, and derivative components. The proportional component responds to the current deviation in real time, the integral component accumulates past deviations to eliminate steady-state error, and the derivative component anticipates changes in deviation trends. The calculated feedback speed correction is then used. It is a fine-tuning amount for the feedforward speed command, which can be positive or negative, corresponding to the increase or decrease of the speed, to ensure that the pump speed can accurately track the changes in dynamic head requirements.

[0103] S24. Add the feedback speed correction amount to the pump speed parameter in the feedforward speed command to obtain the pump speed control command.

[0104] Specifically, feedforward speed command The coarse speed adjustment value generated in step S21 is used as the feedback speed correction amount. The fine-tuning amount generated in step S23, when added together, becomes the final pump speed control command. The calculation formula is: .

[0105] After superposition, upper and lower limit constraints need to be applied to the speed control command. The constraint range is determined based on the pump rated performance data in the task parameters of step S1: the upper limit is the rated speed of the pump motor. This refers to the maximum speed at which the motor can operate safely for extended periods, preventing overheating and damage caused by operating at excessive speeds; the lower limit is the minimum stable speed of the pump. This means ensuring that the pipeline flow rate is not less than The critical speed at the minimum value is determined by the pump's rated performance curve to prevent the pump from running dry and the delivery of extinguishing agent from being interrupted due to excessively low speed.

[0106] The final pump speed control command The output will be sent to the pump motor controller to drive the motor to run at the commanded speed. At the same time, the command will be transmitted to step S5 for pump power consumption prediction, providing a basis for the dynamic allocation of flight power and pump power, and realizing the closed-loop connection of the control process.

[0107] In one optional embodiment, based on pump power consumption prediction data and flight power consumption data, an optimization solution is performed by a pump-flight cooperative controller to dynamically allocate flight power and pump power, generating flight power allocation instructions, including the following steps:

[0108] S31. Obtain the estimated maximum safe discharge power of the current battery of the drone, and obtain the minimum safe flight power required to maintain the drone's stable hovering at the current altitude and in the current environment.

[0109] Specifically, the current estimated maximum safe discharge power of the battery. Based on a comprehensive estimation of real-time battery status parameters (remaining charge, temperature, and individual cell voltage) and the battery's factory characteristic curve, the core objective is to avoid damage to the battery from over-discharge while adapting to discharge capabilities under different operating conditions. The specific estimation logic is as follows: First, real-time parameters are collected through the battery management system. Remaining charge (SOC) is measured using a coulomb counter, temperature is collected using the battery's built-in temperature sensor, and individual cell voltage is detected using a voltage divider sampling circuit. Then, combined with the battery discharge characteristic curve (one of the task parameters input in step S1), the maximum discharge current corresponding to the current SOC and temperature is calculated through interpolation. The final estimation formula is: ,in This is the real-time battery terminal voltage, obtained by summing the voltages of individual cells in series (minus line voltage drop). Additionally, a 10%-15% safety margin should be reserved to avoid exceeding the battery's capacity under sudden operating conditions; that is, the maximum safe discharge power actually used for optimization is... .

[0110] Minimum safe flight power to maintain stable hovering of a drone The calculation requires considering the current altitude, environmental parameters (air density, wind speed), and the drone's aerodynamic characteristics. The core objective is to ensure the drone can stably maintain its current altitude without crashing or losing attitude, even without additional load fluctuations. The calculation logic is based on a drone hovering aerodynamic model, and the formula is as follows: ,in The total mass of the drone (including remaining extinguishing agent and equipment load, updated from the initial mass in step S1 and real-time extinguishing agent consumption data). It is the acceleration due to gravity. The environmental wind load compensation (negative value for downwind and positive value for upwind) is calculated based on data collected by wind speed sensors, using the following formula: , For the windward area of ​​the drone, This is the wind resistance coefficient. (For real-time wind speed) The hovering speed at the current altitude (approximately 0, considering only minor attitude adjustment requirements); This represents the actual propulsion efficiency of the UAV rotor at the current altitude and air density. A 20% power redundancy must also be reserved to handle unexpected attitude adjustment needs and ensure flight safety.

[0111] S32. Based on the pump power consumption prediction data and flight power consumption data, establish a collaborative optimization problem with the objectives of minimizing the total power consumption of the flight system and minimizing the pump head tracking error. The optimization variables in the collaborative optimization problem are the flight power allocation value and the pump speed adjustment value. The constraints in the collaborative optimization problem include that the total power consumption does not exceed the maximum safe discharge power and the flight power is not lower than the minimum safe flight power.

[0112] Specifically, among the input parameters, pump power consumption prediction data This refers to the power consumption value predicted based on pump speed commands, i.e., the estimated input power of the pump motor; flight power consumption data. The corrected real-time flight power consumption (excluding line losses) reflects the actual current energy consumption of the rotor motor. The collaborative optimization problem employs a weighted bi-objective model, balancing the minimization of total power consumption (improving endurance) and the minimization of pump head tracking error (ensuring firefighting effectiveness). The objective function expression is:

[0113]

[0114] In the formula, To optimize the objective function value, and to quantify the overall optimization effect of the two objectives, The smaller the value, the better the overall performance in terms of total power consumption and head tracking error; , These are weighting coefficients, and their sum is 1 ( This is used to dynamically adjust the priority of dual targets, and the weight allocation is automatically determined by the flight control system based on real-time operating conditions. The total power consumption of the flight and pumping systems, i.e., the sum of the optimized flight power allocation and the corrected pumping power consumption, is calculated using the following formula: ; The dynamic head requirement refers to the ideal head that the pump needs to achieve. The actual pump head is calculated based on the pressure difference between the pump inlet and outlet. To mitigate pump head tracking error, the absolute value ensures the error is non-negative; a smaller value indicates a closer alignment between pump output and demand. The dynamic weighting adjustment rule is as follows: during critical firefighting stages (when the nozzle is aligned with the fire and a stable spray of extinguishing agent is required), Take 0.6-0.7. Use a value of 0.3-0.4 to prioritize ensuring head tracking accuracy; during cruise or fire mitigation phases (when high-intensity spraying is not required), Take 0.6-0.7. Choose a value of 0.3-0.4 to prioritize reducing total power consumption and extend battery life.

[0115] The optimization variables are set as two core parameters that directly determine the power distribution scheme: one is the flight power distribution value. The first is the target output power of the flight propulsion system, which is the core output parameter for this optimization solution and is used to drive the rotor motor to adjust energy consumption; the second is the pump speed adjustment value. This is for the pump speed command generated in step S4. The fine-tuning amount can be positive or negative (positive value increases speed, negative value decreases speed), and the corrected pump speed is... This, in turn, adjusts the pump power consumption. The revised pump power consumption calculation formula is as follows: In the formula To correct for the actual pump efficiency at the specified speed, the value is obtained by interpolation from the pump's rated efficiency curve; To correct the motor efficiency at the given speed and corresponding torque. The output torque of the pump motor is calculated using the following formula: ( (For pump shaft power). The optimization variables need to have a set range of values: (Meets flight safety constraints) (Minimum safe flight power); maximum allowable adjustment of pump speed is The pump's rated speed can be taken. To avoid sudden changes in speed leading to drastic fluctuations in head, the head should be reduced by 5%-10%. .

[0116] The constraints of optimization problems include hard constraints and implicit constraints. Hard constraints are the safety and stability conditions that must be met, with two core components: one is the total power consumption constraint. , To ensure that the maximum safe discharge power of the battery actually used in the optimization does not exceed the battery's capacity after reserving safety redundancy; secondly, flight power constraints. , To ensure stable hovering of the UAV with minimum safe flight power, an implicit constraint is the pump head constraint, which is indirectly guaranteed by the head tracking error term in the objective function; additionally, a hard constraint on pump speed can be added. ,in The minimum stable speed of the pump (the critical speed that ensures uninterrupted delivery of extinguishing agent). Set the pump's rated speed (the maximum speed at which the motor can operate safely for a long time) to prevent the pump from running dry or being damaged by overload.

[0117] S33. Solve the co-optimization problem in each control cycle to obtain the flight power allocation value, and generate the flight power allocation command based on the flight power allocation value.

[0118] Specifically, the control cycle is kept consistent with the data acquisition cycle of step S2 and the control cycle of step S4 to ensure data synchronization and real-time control loop. The optimization algorithm uses particle swarm optimization, and the specific solution process is as follows: Initialize the particle swarm with a particle dimension of 2 (corresponding to two optimization variables). , The number of particles is set to 20-30, and the position of each particle corresponds to a set of candidate solutions for optimization variables, with the position range corresponding to the value range of the optimization variables; inertia weights are set. (The learning factor decreases from 0.9 to 0.4 with each iteration to balance global and local search) , (All values ​​are set to 1.5 to control the intensity of particle learning towards individual and global optima); iteratively update particle position and velocity, and calculate the objective function value for each particle in each iteration. Filter the optimal position of an individual with the global optimal position The iteration termination condition is reaching the maximum number of iterations (e.g., 10-15 times to ensure real-time performance) or the objective function value converges (the deviation of the global optimum between two adjacent iterations is less than 1%).

[0119] After the iteration terminates, the globally optimal position corresponds to This represents the optimal flight power allocation value. Flight power allocation commands are then generated based on this value. The core process involves converting the power allocation value into control signals (PWM duty cycle) for the UAV rotor motors. The conversion logic is based on a calibrated power-duty cycle fitting model. ( , , (These are the fitting coefficients), and the corresponding duty cycle is obtained by inversely solving the model. That is, the flight power distribution command is a duty cycle signal. .

[0120] The generated flight power distribution command will be output to the UAV flight control system to drive the rotor motor to adjust the output power. At the same time, it will be synchronously fed back to step S2 for real-time updates of flight power consumption data, forming a closed-loop control of power distribution-power consumption acquisition-optimization solution. This ensures that the power distribution in each control cycle is adapted to the current operating conditions and achieves a dynamic balance between flight power and pump power.

[0121] In one optional embodiment, the drone control method for high-rise firefighting further includes the following steps:

[0122] S41. Continuously monitor the second deviation between the actual pump head and the dynamic required head of the drone, as well as the remaining battery power.

[0123] Specifically, the monitoring and calculation of the second deviation uses the actual pump head and dynamic required head as the core inputs. The formula for calculating the second deviation is as follows:

[0124]

[0125] In the formula, This represents the second deviation, used to quantify the degree of deviation between the actual pump head and the dynamic required head. The absolute value calculation ensures that the deviation is non-negative. The actual pump head is calculated using the inlet and outlet pressure difference and obtained after preprocessing. The first deviation serves as the ideal target value for pump operation, reflecting the dynamic demand for pump head. Unlike the first deviation, which is used for emergency detection of long-term deviation anomalies, the second deviation is used for real-time fine-tuning of pump speed. During monitoring, the deviation data needs to be processed by sliding filtering. By setting a reasonable filter window length, false emergency triggers caused by instantaneous noise can be avoided.

[0126] Monitoring of remaining battery charge (SOC) is achieved through the battery management system (BMS) using coulomb counting combined with open-circuit voltage calibration. Coulomb counting calculates the cumulative energy consumption by integrating the charge and discharge current in real time; the specific formula is as follows:

[0127]

[0128] In the formula, This indicates the real-time remaining battery power. This is the initial battery level, determined by the mission parameters. The battery's rated capacity is also an input task parameter. The real-time charge and discharge current of the battery (t is time) is collected in real time by the battery management system. To eliminate integration errors, periodic calibration using the open-circuit voltage method is required to ensure that the SOC measurement accuracy meets the task requirements.

[0129] S42. When the second deviation continues to exceed the first preset threshold for a preset time, a flow reduction operation is triggered to reduce the target flow parameter of the water pipeline, and S3 to S6 are re-executed.

[0130] Specifically, this step addresses the scenario of long-term head tracking failure by reducing the flow rate to alleviate pump load, readjust to the current operating conditions, and avoids forcibly maintaining the original flow rate, which could lead to pump overload or unstable delivery of extinguishing agent.

[0131] The first preset threshold is determined based on a comprehensive consideration of the pump's rated performance and fire extinguishing efficiency requirements, balancing the fire extinguishing effect with the equipment load. If the threshold is too small, it will easily trigger emergency operations frequently, affecting the continuity of the fire extinguishing mission; if the threshold is too large, it will be unable to detect abnormal pump conditions in a timely manner, which may lead to equipment damage.

[0132] The preset time must be set longer than the instantaneous deviation fluctuation period to avoid false triggering caused by instantaneous operating condition fluctuations, ensuring that the emergency procedure is only activated when the deviation persists for a long period. The continuous judgment logic is that if the second deviation consistently exceeds the first preset threshold within multiple consecutive control cycles, a flow reduction operation is triggered.

[0133] When reducing flow rate, the magnitude of the reduction must be controlled to avoid sudden flow changes that could cause pressure surges in the pipeline. Simultaneously, it must be ensured that the reduced flow rate does not fall below the minimum extinguishing flow rate. The minimum extinguishing flow rate is a mission parameter used to guarantee basic extinguishing capacity. After flow rate adjustment, steps S3 to S6 should be executed again to form a closed-loop control system.

[0134] S43. When the remaining battery power is lower than the second preset threshold, the mission termination and return-to-home operation is triggered to prioritize power distribution to the flight system and control the drone to return to home.

[0135] Specifically, this step is a safety measure for scenarios with insufficient power. By terminating the firefighting mission and focusing the flight power supply, it ensures that the drone can safely return to the take-off and landing point and avoids the risk of forced landing in the air.

[0136] The second preset threshold needs to be set with sufficient battery power reserved for return, calculated comprehensively based on flight distance, flight altitude, and environmental resistance. Specifically, the reservation logic is as follows: based on the distance between the drone's current position and the takeoff / landing point, and its current altitude, calculate the minimum battery power required for return. On top of this, reserve an additional safety battery power to cope with sudden wind resistance or attitude adjustment needs. The formula for calculating the minimum battery power required for return is as follows:

[0137]

[0138] In the formula, The minimum amount of electricity required for the return trip. The average return flight power is obtained by reasonably ascending based on the minimum safe flight power to cope with the wind resistance during the return process. The estimated return time is calculated from the flight distance and return speed. For the battery's rated capacity, Both the battery's rated voltage and the default voltage are input mission parameters. The second preset threshold is the sum of the minimum charge required for return and the additional reserve safety charge.

[0139] The core procedures for mission termination and return-to-home are as follows: Immediately cease all firefighting operations, choosing to shut down the pump motors or reduce the flow rate to minimum. Adjust the power distribution priority, setting the flight system to the highest priority. In step S5, the co-controller prioritizes allocating remaining battery power to the rotor motors to ensure flight stability. The flight control system initiates the preset return-to-home path, planning the route using GPS positioning, prioritizing straight lines and avoiding obstacles. After adjusting the flight attitude to the return-to-home route, control the drone to return at a constant altitude and speed. During the return process, continuously monitor battery level and critical component temperature. If the battery level further drops to the emergency threshold, maximize the remaining range by reducing flight speed and adjusting to the minimum safe altitude until reaching the takeoff and landing point.

[0140] The aforementioned UAV control method for high-rise firefighting first constructs an altitude-head dynamic mapping model and initializes a cooperative controller and state observer before the mission, establishing a precise benchmark and adaptive compensation capability for subsequent real-time control. Then, during UAV flight and hovering, real-time data on altitude, pump, and flight status are collected. Using the aforementioned model and observer, the theoretical head requirement determined by altitude is fused with the estimated dynamic drag loss determined by pipeline status to generate a dynamic head requirement that accurately reflects the current actual operating conditions. Subsequently, using this dynamic head requirement as the target, a feedforward-feedback composite control algorithm precisely adjusts the pump speed. Simultaneously, based on the pump power consumption predicted by the speed command and the actual flight power consumption, the cooperative controller performs online optimization to dynamically allocate limited onboard energy, ultimately generating a coordinated power distribution command. This ensures that the UAV can output sufficient pressure of extinguishing agent at any altitude while maintaining stable flight, effectively solving the core problem of head-pressure loss and onboard power competition caused by altitude changes in high-rise firefighting.

[0141] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0142] Based on the same inventive concept, this application also provides a system for implementing the aforementioned drone control method for high-rise firefighting. The solution provided by this system is similar to the implementation described in the above method; therefore, the specific limitations of one or more drone control system embodiments for high-rise firefighting provided below can be found in the limitations of the drone control method for high-rise firefighting described above, and will not be repeated here.

[0143] In one exemplary embodiment, such as Figure 3 As shown, a drone control system 30 for high-rise firefighting is provided to implement the methods in the above-described embodiments. The system includes:

[0144] The system modeling and initialization module 31 is used to construct an altitude-lift dynamic mapping model based on the input mission parameters and UAV platform performance data before the UAV performs a firefighting mission, and to initialize the pump-flight cooperative controller and the extended state observer. The mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The pump-flight cooperative controller is used to optimize the allocation of flight power and pump power. The extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline.

[0145] The real-time status monitoring module 32 is used to collect real-time data on the drone's flight altitude, pump operation status, and flight power consumption during the drone's flight toward and hovering over the target floor.

[0146] The dynamic head compensation module 33 is used to obtain the baseline theoretical head required at the corresponding altitude by querying the altitude-head dynamic mapping model based on the flight altitude data; and to compensate for the baseline theoretical head required based on the estimated value of the dynamic resistance loss of the water pipeline by the extended state observer, thereby generating the dynamic head required.

[0147] The pump control command generation module 34 is used to output pump speed control commands by taking the dynamic demand head as the control target and combining the pump operating status data through a feedforward-feedback composite control algorithm.

[0148] The power coordination optimization module 35 is used to predict pump power consumption based on pump speed control commands to obtain pump power consumption prediction data; based on the pump power consumption prediction data and flight power consumption data, the pump-flight coordination controller performs optimization to dynamically allocate flight power and pump power, and generates flight power allocation commands.

[0149] The flight execution control module 36 is used to control the UAV to perform high-rise firefighting missions according to the flight power distribution command.

[0150] Embodiments of this application also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the aforementioned method embodiments.

[0151] Embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0152] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0153] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for controlling unmanned aerial vehicles (UAVs) used in high-rise firefighting, characterized in that, The method includes: S1. Before the UAV performs a firefighting mission, a height-lift dynamic mapping model is constructed based on the input mission parameters and UAV platform performance data, and the pump-flight cooperative controller and extended state observer are initialized; wherein, the mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump; the pump-flight cooperative controller is used to optimize the allocation of flight power and pump power; the extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline; S2. During the flight and hovering of the drone towards the target floor, collect the drone's flight altitude data, pump operation status data, and flight power consumption data in real time. S3. Based on the flight altitude data, obtain the baseline theoretical required head for the corresponding altitude by querying the altitude-head dynamic mapping model; based on the estimated value of the dynamic resistance loss of the water pipeline by the extended state observer, compensate for the baseline theoretical required head to generate the dynamic required head. S4. Taking the dynamic required head as the control target and combining the pump operating status data, output the pump speed control command through the feedforward-feedback composite control algorithm; S5. Based on the pump speed control command, predict the pump power consumption to obtain pump power consumption prediction data; according to the pump power consumption prediction data and the flight power consumption data, perform optimization solution through the pump-flight cooperative controller to dynamically allocate flight power and pump power, and generate flight power allocation command; S6. Control the UAV to perform high-rise firefighting missions according to the flight power distribution command.

2. The method according to claim 1, characterized in that, The process of compensating for the baseline theoretical head requirement based on the estimated dynamic resistance loss of the water pipeline using the extended state observer to generate the dynamic head requirement includes: S11. Obtain the pump speed and actual pump head of the UAV in the historical period as historical historical data; wherein, the actual pump head is calculated based on the pump inlet and outlet pressure difference measurement data in the pump operation status data; S12. Based on the historical data, the additional resistance loss head caused by pipe bending and deformation is estimated in real time using the adaptive estimation algorithm in the extended state observer, and the estimated value of the additional resistance loss head is obtained. The adaptive estimation algorithm observes the additional drag loss head as an extended state of the pump head dynamic system modeled by the extended state observer. The process of the adaptive estimation algorithm is as follows: based on the input pump speed and the actual pump head, the state update equation of the Lumberjack observer is used to calculate and output a state estimation vector containing the estimated value of the additional drag loss head; the estimated value of the additional drag loss head component is extracted from the state estimation vector as the estimated value of the additional drag loss head. The state estimation update equation of the Luneburger observer is as follows: in, The state estimation vector at time t is defined as follows: , This represents the estimated value of the demand head component at time t. This represents the estimated value of the head component of the additional drag loss at time t, indicated by the superscript. Represents the transpose of a vector; The system input at time t is the pump speed. The measured output of the pump head dynamic system at time t is the actual pump head. , , The state space matrix is ​​predetermined based on the physical characteristics of the UAV's airborne pump and water pipeline; For the pre-designed observer gain matrix; Represents the state estimation vector Regarding time The derivative; This represents the estimated pump head output by the dynamic system at time t. S13. Add the baseline theoretical required head to the estimated additional drag loss head to obtain the dynamic required head.

3. The method according to claim 1, characterized in that, The process, using the dynamic required head as the control target and combining the pump operating status data, outputs a pump speed control command through a feedforward-feedback composite control algorithm, including: S21. Based on the dynamic required head and the current target flow parameters of the water pipeline, the target pump speed is deduced using the pump rated performance curve data in the task parameters; a feedforward speed command is generated based on the target pump speed. S22. Using the dynamic demand head as the set value, and the actual pump head calculated based on the pump inlet and outlet pressure difference measurement data in the pump operating status data as the feedback value, calculate the first deviation between the set value and the feedback value. S23. Perform proportional-integral-derivative control calculations on the first deviation to obtain the feedback speed correction amount; S24. Add the feedback speed correction amount to the pump speed parameter in the feedforward speed command to obtain the pump speed control command.

4. The method according to claim 1, characterized in that, The step of optimizing the pump power consumption prediction data and the flight power consumption data using the pump-flight cooperative controller to dynamically allocate flight power and pump power, and generating flight power allocation commands, includes: S31. Obtain the estimated maximum safe discharge power of the current battery of the drone, and obtain the minimum safe flight power required to maintain the drone's stable hovering at the current altitude and in the current environment; S32. Based on the pump power consumption prediction data and the flight power consumption data, establish a collaborative optimization problem with the objectives of minimizing the total power consumption of the flight system and minimizing the pump head tracking error; wherein, the optimization variables in the collaborative optimization problem are the flight power allocation value and the pump speed adjustment value, and the constraints in the collaborative optimization problem include that the total power consumption does not exceed the maximum safe discharge power and the flight power is not lower than the minimum safe flight power; S33. Solve the cooperative optimization problem in each control cycle to obtain the flight power allocation value, and generate the flight power allocation command based on the flight power allocation value.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: S41. Continuously monitor the second deviation between the actual pump head of the UAV and the dynamic required head, as well as the remaining battery power. S42. When the second deviation continues to exceed the first preset threshold for a preset time, a flow reduction operation is triggered to reduce the target flow parameter of the water supply pipeline, and S3 to S6 are re-executed. S43. When the remaining battery power is lower than the second preset threshold, the mission termination and return-to-home operation is triggered to prioritize the allocation of power to the flight system and control the drone to return to home.

6. A drone control system for high-rise firefighting, used to implement the method according to any one of claims 1 to 5, characterized in that, The system includes: The system modeling and initialization module is used to construct an altitude-lift dynamic mapping model based on input mission parameters and UAV platform performance data before the UAV performs a firefighting mission, and to initialize the pump-flight cooperative controller and extended state observer. The mission parameters include at least the target floor height, fire extinguishing agent type, target flow parameters of the water supply pipeline, characteristic parameters of the water supply pipeline, and rated performance curve data of the airborne pump. The pump-flight cooperative controller is used to optimize the allocation of flight power and pump power. The extended state observer is used to estimate the dynamic resistance loss of the water supply pipeline. The real-time status monitoring module is used to collect real-time data on the drone's flight altitude, pump operation status, and flight power consumption during the drone's flight toward and hovering over the target floor. The dynamic head compensation module is used to obtain the baseline theoretical head required at the corresponding altitude by querying the altitude-head dynamic mapping model based on the flight altitude data; and to compensate for the baseline theoretical head required based on the estimated value of dynamic resistance loss of the water pipeline by the extended state observer, thereby generating the dynamic head required. The pump control command generation module is used to take the dynamic required head as the control target, combine the pump operating status data, and output the pump speed control command through a feedforward-feedback composite control algorithm. The power coordination optimization module is used to predict pump power consumption based on the pump speed control command to obtain pump power consumption prediction data; and to perform optimization solution through the pump-flight coordination controller according to the pump power consumption prediction data and the flight power consumption data to dynamically allocate flight power and pump power and generate flight power allocation command. The flight execution control module is used to control the UAV to perform high-rise firefighting missions according to the flight power distribution command.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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