Firefighting UAV attitude control method, system, medium, product and equipment

Abstract

The present invention belongs to the technical field of attitude control of unmanned aerial vehicles. A method, system, medium, product and equipment for attitude control of a fire-fighting unmanned aerial vehicle are provided. The method dynamically adjusts the process noise covariance and measurement noise covariance of an adaptive Kalman filter in combination with a weight factor. The adaptive Kalman filter determines the true state of the fire-fighting unmanned aerial vehicle based on the sensor data and control input, and uses a linear quadratic regulator to determine the optimal control gain according to the true state of the fire-fighting unmanned aerial vehicle. Based on the adaptive gain and the optimal control gain, the final control input is determined to control the motor operation of the fire-fighting unmanned aerial vehicle. The present invention significantly improves the control robustness in complex environments, achieves high-precision tracking and rapid response, reduces dependence on precise models, enhances the ability to resist sensor interference, has strong scalability, and is applicable to multiple scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) attitude control, and in particular to a method, system, medium, product and equipment for controlling the attitude of a fire-fighting UAV. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Drones are widely used across industries due to their maneuverability, accuracy, and rapid response capabilities. The application of drone systems is also crucial in the field of firefighting and rescue. High-rise firefighting drones can provide significant support to the firefighting industry, especially in today's increasingly frequent fire season. However, the harsh environment faced by firefighting drones at fire scenes can affect their sensors to a certain extent. High temperatures and smoke can cause abnormalities such as increased noise in drone sensors.

[0004] The LQG (Linear Quadratic Gaussian) controller used by current drones consists of a Kalman filter and an LQR controller (Linear Quadratic Regulator). The Kalman part is used to observe the optimal state, and the LQR controller is used for state feedback. However, the traditional Kalman filter uses fixed parameters. If the statistical characteristics of the noise change, the parameters cannot be automatically adjusted, resulting in estimation errors. The LQR controller assumes that the system has no external disturbances and the model is accurate, but in reality, external disturbances may lead to performance degradation. In addition, the LQR controller requires that all state variables can be directly measured, and an observer (such as a Kalman filter) is required to estimate the unmeasurable state. The observation error will reduce the control performance. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present invention provides a fire-fighting drone attitude control method, system, medium, product and equipment, which significantly improves the control robustness in complex environments, achieves high-precision tracking and rapid response, reduces dependence on precise models, enhances the ability to resist sensor interference, has strong scalability, and is applicable to multiple scenarios.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for controlling the attitude of a firefighting drone.

[0008] A firefighting drone attitude control method includes the following steps:

[0009] Obtain sensor data from firefighting drones;

[0010] Dynamically adjusting the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weighting factor, the adaptive Kalman filter determining the true state of the firefighting drone based on the sensor data and control input;

[0011] A linear quadratic regulator is used to determine the optimal control gain according to the actual state of the fire-fighting drone, and based on the adaptive gain and the optimal control gain, a final control input is determined to control the motor operation of the fire-fighting drone.

[0012] In a second aspect, the present invention provides a fire-fighting drone attitude control system.

[0013] A firefighting drone attitude control system, comprising:

[0014] The data acquisition unit is configured to: acquire sensor data of the firefighting drone;

[0015] a state generation unit configured to: dynamically adjust a process noise covariance and a measurement noise covariance of an adaptive Kalman filter in combination with a weight factor, wherein the adaptive Kalman filter determines a true state of the firefighting drone based on the sensor data and control input;

[0016] The attitude control unit is configured to: use a linear quadratic regulator to determine the optimal control gain according to the actual state of the fire-fighting drone, and determine the final control input based on the adaptive gain and the optimal control gain to control the motor operation of the fire-fighting drone.

[0017] In a third aspect, the present invention provides a computer device comprising: a processor and a computer-readable storage medium;

[0018] a processor adapted to execute a computer program;

[0019] A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the firefighting drone attitude control method as described in the first aspect of the present invention.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the firefighting drone attitude control method as described in the first aspect of the present invention.

[0021] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the fire-fighting drone attitude control method as described in the first aspect of the present invention.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention significantly improves control robustness in complex environments. In a fire environment, drones face multiple interferences such as turbulence, thermal airflow, and smoke obstruction. Traditional controllers are prone to state estimation divergence or control instability due to fixed noise parameters and gains. The present invention uses the AKF (Adaptive Kalman Filter) dynamic process and the observation noise covariance matrix to effectively suppress the impact of external disturbances on state estimation.

[0024] 2. The present invention achieves high-precision tracking and rapid response. By adjusting the learning rate and increasing the learning rate to accelerate convergence in an emergency environment, the system can respond faster and more accurately.

[0025] 3. The present invention reduces the dependence on accurate models. Traditional LQR / LRG controllers rely on accurate dynamic models. The present invention uses gain equations and AKF noise estimation to enable the controller to stably track the posture even when there are deviations in the model.

[0026] 4. The present invention enhances the ability to resist sensor interference. AKF dynamically corrects observation noise. When the sensor is interfered with, it can automatically adjust and reduce the sensor weight to maintain system stability.

[0027] 5. The present invention has strong scalability and is applicable to multiple scenarios. It is not limited to quad-rotor drones and fire scenes, but can also be extended to other dynamic systems and different interference environments.

[0028] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0030] Figure 1 A schematic diagram of a flow chart of a method for controlling the attitude of a firefighting drone provided in Example 1 of the present invention;

[0031] Figure 2 A schematic diagram of the principle framework of the firefighting drone attitude control method provided in Example 1 of the present invention;

[0032] Figure 3 A schematic diagram of a firefighting drone attitude control system provided in Example 2 of the present invention;

[0033] Figure 4A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0037] Example 1:

[0038] As mentioned in the background, traditional Kalman filter (KF) and linear quadratic Gaussian (LQG) controllers assume that the process noise covariance and the measurement noise covariance is a fixed value, but in dynamic environments such as fire scenes, noise characteristics (such as turbulence and smoke obscuration) will change dramatically. This implementation method uses adaptive Kalman filtering (AKF) to calculate noise residuals in real time using a sliding window, and dynamically updates the process noise covariance matrix and the measurement noise covariance matrix. It is easier to adapt to sudden noise changes and is more effective in dynamic environments such as fire scenes caused by turbulence. Increase or sensor failure When the state changes suddenly, the state estimation accuracy can still be maintained, which improves the real-time and robustness of the system.

[0039] Moreover, the traditional LQR controller is based on a fixed feedback gain , cannot cope with the uncertainty of system parameters. This implementation introduces Model Reference Adaptive Control (MRAC) to design adaptive gain , dynamically adjust the control input, when facing fire disturbance, Real-time adjustment balances response speed and stability. The integral term in the gain equation can suppress steady-state errors, while the differential term reduces overshoot, improving the system's adaptability and anti-interference capabilities.

[0040] This implementation integrates the gain adjustment of AKF (Adaptive Kalman Filter), LQR (Linear Quadratic Regulator) and adaptive control (MRAC, Model Reference Adaptive Control) into a unified framework to form a closed-loop collaborative optimization. AKF provides high-precision state estimation and dynamic correction of noise parameters; LQR generates optimal control input to minimize energy and tracking error; MRAC is Adaptively compensate for model uncertainty to improve robustness. Each component is independently optimized for scalability. The AKF noise estimate provides reliable state input for the LQR, while the MRAC further optimizes the control rate, forming a closed loop of "estimation-control-compensation."

[0041] More specifically, this implementation proposes a firefighting UAV attitude control method, which enables the UAV to adaptively adjust parameters when facing a fire environment to improve the robustness and control performance of the system, and adapt to abnormal conditions such as noise anomalies through dynamic noise adjustment, such as Figure 1 and Figure 2 As shown, the following process is included:

[0042] S1. Collect the real-time data of the drone itself through its own sensors (the drone is subject to external disturbances during operation), including the drone's three-axis angular velocity, linear acceleration, position, speed, altitude, yaw angle and other information;

[0043] S2, Adaptive Kalman filter is based on the drone sensor data, optimizes the historical state estimation through Kalman smoothing, and combines it with the control input to derive the true state of the drone, and dynamically adjusts the process noise covariance matrix through the weight factor and the measurement noise covariance matrix ;

[0044] The S3, LQR (Linear Quadratic Gaussian) controller obtains the optimal control gain by solving the Riccati equation and derives the control input command based on the actual state estimated by the AKF (Adaptive Kalman Filter).

[0045] S4, by comparing the output with the reference model (the input is the expected control instruction) to make an error, select the appropriate learning rate, and dynamically adjust the adaptive gain , and obtain a more accurate final control input;

[0046] S5. The execution module of the firefighting drone (using a quadrotor drone) converts the final control input into motor thrust to control the drone's attitude.

[0047] In S1 of this implementation, specifically, it includes: establishing a spatial inertial coordinate system and body coordinate system , the state vector of the firefighting drone can be expressed as:

[0048] (1);

[0049] in, Indicates the position of the drone, Indicates that the drone is , , Linear speed on three axes, Represent the roll angle, pitch angle and yaw angle of the drone respectively. is the angular velocity of the UAV on three axes.

[0050] In S2 of this implementation, the discretized linear model of the firefighting drone that introduces disturbance factors such as fire turbulence, thermal airflow, and smoke is:

[0051] (2);

[0052] (3);

[0053] in, is the state transition matrix, is the control input matrix, is the observation matrix, For the system The state value at the moment, For the system The state value at the moment, For the system The input value at the time, For the system The measured value at a moment.

[0054] In formula (2) and formula (3), 、 are the total process noise and measurement noise, respectively, both of which obey Gaussian distribution:

[0055] (4);

[0056] (5);

[0057] in, represents a Gaussian distribution, represents the process noise generated by the system itself, represents the additional process noise introduced by external interference, represents the inherent observation noise of the system’s own sensor, Represents the observation noise caused by external interference, and Kalman filtering uses the covariance matrix , Approximate the influence of external interference, the actual covariance matrix of the total noise is:

[0058] (6);

[0059] (7);

[0060] in, is the total process noise covariance, is the basic process noise covariance (such as motor vibration, etc.), is the process noise covariance of the environmental increment, is the total observation noise covariance, is the basic observation noise covariance, representing the intrinsic error of the sensor, is the observation noise covariance of the environmental increment, express for The covariance matrix of express for The covariance matrix of .

[0061] An adaptive dynamic adjustment method is used for the process and measurement noise matrices. The steps are as follows:

[0062] First, a fixed interval smoothing is used to obtain a smoothed state estimate , based on the current moment All the measurement data of historical moments The optimal estimate of the state of The Kalman filter (KF) is used to estimate the current state. In contrast, fixed interval smoothing estimates use the current and previous measurement data to estimate the past The state at the moment is estimated retrospectively, assuming that the lag step length is , then at every moment , will generate state estimates, , ,…, Corresponding to the current moment and the past A moment.

[0063] Second, based on the smoothed state, the process and measurement noise samples are calculated:

[0064] (8);

[0065] (9);

[0066] in, for The state estimate at time t, for The state estimate at time t, for The control input at the moment, for The measured value at the moment,

[0067] Then in the time window Covariance of internal statistical process and measurement noise:

[0068] (10);

[0069] (11);

[0070] in,

[0071] (12);

[0072] (13);

[0073] in, represents the trace of a matrix, which is the sum of the elements on the main diagonal of a square matrix, is the coordinate transformation matrix for directionally weighting the noise, is a diagonal matrix describing the degree of interference to the sensor, and are process noise samples and measurement noise samples respectively, , and Both are covariance matrices, and are dynamically adjusted covariance matrices, that is, the covariance matrices updated in the previous iteration cycle:

[0074] is the block matrix describing the state transition equation:

[0075] (14);

[0076] in, Represents the historical state transition matrix.

[0077] Then update the state estimate:

[0078] (15);

[0079] in, is the Kalman gain calculated after the update, is the observation matrix, The state at the current moment is predicted based on the state at the previous moment.

[0080] If fire turbulence occurs, the process noise covariance If it increases, the predicted covariance will expand, the Kalman gain will increase, and AKF will trust the measured value more; if it encounters smoke obstruction, the observed noise covariance will increase. As the specific dimension of increases, the corresponding element of the Kalman gain decreases, and AKF trusts the model prediction more and avoids being misled by contaminated measurements.

[0081] By designing as above, the covariance of process and observation noise is dynamically adjusted and , maintaining the robust estimation of adaptive Kalman filter AKF in fire environment, balancing model prediction and measurement trust.

[0082] In S3 and S4 of this implementation, specifically, the following steps are included:

[0083] After the AKF estimates the state of the UAV, the LQR controller obtains the optimal control gain by solving the Riccati equation and obtains the control input command based on the actual state estimated by AKF.

[0084] Specifically, based on the state estimation value obtained by the designed adaptive Kalman filter AKF, the LQR controller is designed in combination with the MRAC adaptive control rate. The obtained AKF-based robust LQG controller is designed as follows:

[0085] The optimal control index function of the UAV system model is set as:

[0086] (16);

[0087] in, is the state weight matrix, which is a symmetric semi-positive definite matrix; is the control weight matrix, which is a symmetric positive definite matrix; For the system The state of the moment, for Control input at any moment.

[0088] Choose a suitable weight matrix and solve P via the Riccati equation:

[0089] (17);

[0090] Among them, P is the matrix solution to be solved, is the state transition matrix, is the control input matrix.

[0091] Thus, the optimal control gain matrix is ​​obtained :

[0092] (18) ；

[0093] The output of the LQR controller is:

[0094] (19);

[0095] Then, by comparing the output with the reference model and taking the error, we select the appropriate learning rate and dynamically adjust the gain ρ equation to obtain a more accurate final control input.

[0096] Specifically, in order to improve the control effect of the LQR controller, the adaptive gain ρ is introduced in combination with MRAC (Model Reference Adaptive Control), and the control rate becomes:

[0097] (20);

[0098] Introducing the second-order system reference model, its transfer function is as follows:

[0099] (twenty one);

[0100] in, is the natural frequency, is the damping ratio.

[0101] Make the measured data output by the actual drone track the ideal data output by the reference model , where X is the drone state (such as actual position, etc.) measured by the quadcopter’s own sensors. The error is the ideal state generated by the reference model according to the expected instruction. , in order to ensure that the error e is closer to 0, the gain ρ equation is improved, and:

[0102] (twenty two);

[0103] in, is the learning rate, which is used to control the gain adjustment speed, and is the weight parameter for adjusting the error dynamics.

[0104] In S5 of this implementation, the execution module of the firefighting drone inputs the final control Converted into motor thrust to control the drone's attitude.

[0105] Example 2:

[0106] like Figure 3 As shown, this implementation provides a firefighting drone attitude control system, including:

[0107] The data acquisition unit is configured to: acquire sensor data of the firefighting drone;

[0108] a state generation unit configured to: dynamically adjust a process noise covariance and a measurement noise covariance of an adaptive Kalman filter in combination with a weight factor, wherein the adaptive Kalman filter determines a true state of the firefighting drone based on the sensor data and control input;

[0109] The attitude control unit is configured to: use a linear quadratic regulator to determine the optimal control gain according to the actual state of the fire-fighting drone, and determine the final control input based on the adaptive gain and the optimal control gain to control the motor operation of the fire-fighting drone.

[0110] The specific working process of the above units is described in Example 1 and will not be repeated here.

[0111] It is understandable that each of the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (or some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0112] According to another embodiment of the present application, the system described in this embodiment can be constructed and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0113] Example 3:

[0114] like Figure 4 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0115] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0116] The processor 1001 (also called CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0117] The processor 1001 is configured to execute the following process:

[0118] Obtain sensor data from firefighting drones;

[0119] Dynamically adjusting the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weighting factor, the adaptive Kalman filter determining the true state of the firefighting drone based on the sensor data and control input;

[0120] A linear quadratic regulator is used to determine the optimal control gain according to the actual state of the fire-fighting drone, and based on the adaptive gain and the optimal control gain, a final control input is determined to control the motor operation of the fire-fighting drone.

[0121] The specific working process is described in Example 1 and will not be repeated here.

[0122] Example 4:

[0123] This implementation provides a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device within an electronic device that stores programs and data. It should be understood that the computer-readable storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides storage space that stores the processing system of the electronic device.

[0124] Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0125] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process:

[0126] Obtain sensor data from firefighting drones;

[0127] Dynamically adjusting the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weighting factor, the adaptive Kalman filter determining the true state of the firefighting drone based on the sensor data and control input;

[0128] A linear quadratic regulator is used to determine the optimal control gain according to the actual state of the fire-fighting drone, and based on the adaptive gain and the optimal control gain, a final control input is determined to control the motor operation of the fire-fighting drone.

[0129] The specific working process is described in Example 1 and will not be repeated here.

[0130] Example 5:

[0131] This implementation provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process:

[0132] Obtain sensor data from firefighting drones;

[0133] Dynamically adjusting the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weighting factor, the adaptive Kalman filter determining the true state of the firefighting drone based on the sensor data and control input;

[0134] A linear quadratic regulator is used to determine the optimal control gain according to the actual state of the fire-fighting drone, and based on the adaptive gain and the optimal control gain, a final control input is determined to control the motor operation of the fire-fighting drone.

[0135] The specific working process is described in Example 1 and will not be repeated here.

[0136] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0137] The above embodiments can be implemented in whole or in part using software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can include magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0138] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A firefighting drone attitude control method, characterized in that: The following processes are included: Obtain sensor data from firefighting drones; Dynamically adjusting the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weight factor, wherein the adaptive Kalman filter optimizes historical state estimation based on the sensor data through Kalman smoothing and determines the actual state of the firefighting drone in combination with a control input; A linear quadratic regulator is used to determine an optimal control gain according to the actual state of the firefighting drone, and a final control input is determined based on the adaptive gain and the optimal control gain to control the motor operation of the firefighting drone; Among them, the process noise covariance of the adaptive Kalman filter is dynamically adjusted in combination with the weight factor and the measurement noise covariance ,include: in, , , is the time window, and are process noise samples and measurement noise samples respectively, Represents the observation data for the next N steps, represent time, represent time, is the coordinate transformation matrix for directionally weighting the noise, is a diagonal matrix describing the degree of interference to the sensor, , is the block matrix describing the state transition equation, is the observation matrix, is the measurement noise covariance, represents the trace of the matrix; The output of the linear quadratic regulator is for: ,in, is the adaptive gain, is the optimal control gain determined by the linear quadratic regulator, The actual status of the firefighting drone; Adaptive gain Calculations include: ； in, is the learning rate, and To adjust the weight parameter of the error dynamics, is the reference model output, is the error, , Error The first derivative of The measurement data output by the actual drone.

2. The firefighting drone attitude control method according to claim 1, characterized in that: The sensor data of the firefighting drone includes: the three-axis angular velocity, linear acceleration, position, speed, altitude and yaw angle of the firefighting drone.

3. The firefighting drone attitude control method according to claim 1, characterized in that: The actual status of the firefighting drone is: ； in, is the recalculated Kalman gain, For the system The measured value at the moment, For the system The observation matrix at time t, For The real status of firefighting drones at all times.

4. A firefighting drone attitude control system, characterized in that: include: The data acquisition unit is configured to: acquire sensor data of the firefighting drone; a state generation unit configured to dynamically adjust the process noise covariance and the measurement noise covariance of an adaptive Kalman filter in combination with a weight factor, wherein the adaptive Kalman filter optimizes the historical state estimation based on the sensor data through Kalman smoothing and determines the actual state of the firefighting drone in combination with a control input; A posture control unit is configured to: determine an optimal control gain according to the actual state of the firefighting drone using a linear quadratic regulator, and determine a final control input based on the adaptive gain and the optimal control gain to control the motor operation of the firefighting drone; Among them, the process noise covariance of the adaptive Kalman filter is dynamically adjusted in combination with the weight factor and the measurement noise covariance ,include: ； in, , , is the time window, and are process noise samples and measurement noise samples respectively, Represents the observation data for the next N steps, represent time, represent time, is the coordinate transformation matrix for directionally weighting the noise, is a diagonal matrix describing the degree of interference to the sensor, , is the block matrix describing the state transition equation, is the observation matrix, is the measurement noise covariance, represents the trace of the matrix; The output of the linear quadratic regulator is for: ,in, is the adaptive gain, is the optimal control gain determined by the linear quadratic regulator, The actual status of the firefighting drone; Adaptive gain Calculations include: ； in, is the learning rate, and To adjust the weight parameter of the error dynamics, is the reference model output, is the error, , Error The first derivative of The measurement data output by the actual drone.

5. A computer device, characterized in that: include: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the firefighting drone attitude control method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the firefighting drone attitude control method according to any one of claims 1 to 3.

7. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the fire-fighting drone attitude control method according to any one of claims 1 to 3.

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