Method, system and computer storage medium for enabling flight control of a drone

Abstract

The application discloses a method and system for enabling flight control of a UAV and a computer storage medium, the method comprising: establishing a gas flow model of associated gas combustion; setting basic control parameters according to the gas flow model and releasing the UAV; collecting flow velocity and temperature information of the ascending gas flow through the UAV, monitoring a real-time state of the associated gas combustion; and calibrating the UAV attitude, predicting the ascending gas flow state and selecting a flight control mode according to the flow velocity and temperature information of the ascending gas flow. The application can adjust the flight state according to the real-time collected gas flow field data, and improve the hovering time. The combustion of the associated gas is directly monitored and the surrounding gas is collected to detect the combustion state.

Description

Technical Field

[0001] This invention relates to the field of petrochemical detection technology, and in particular to a method, system, and computer storage medium for controlling the powered flight of unmanned aerial vehicles (UAVs). Background Technology

[0002] Oil and gas extraction generates a large amount of associated gas, which is produced in large quantities, has a complex composition, and exhibits significant differences in the content of its components, making it difficult to perform precise recovery and utilization. Direct emission of this gas would cause significant atmospheric pollution. Currently, considering environmental protection, economics, and safety, oil and gas extraction well sites both domestically and internationally generally adopt the post-combustion venting method to avoid environmental pollution caused by direct emission of associated gas and corrosion of pipelines by sulfides. During the combustion of associated gas, a flare phenomenon is formed at the well site. The composition of the gas before combustion can be measured by sensors inside the pipeline. However, the gases produced by the flare after combustion and the gases that are not completely burned cannot be monitored, and the mixed gases entering the atmosphere will cause significant potential pollution to the surrounding environment of the well site. Furthermore, the well site is a flammable environment during oil and gas extraction, and the continuous combustion of associated gas poses a safety threat to the well site. Therefore, real-time monitoring of the continuous flame status and the post-combustion gas emission status is essential for the safe, environmentally friendly, and economical development of the well site.

[0003] Existing patent CN102646191B discloses a method for image recognition of associated gas combustion flames in oil drilling. This method indirectly monitors the combustion state of associated gas by identifying and monitoring images of the flames produced during combustion. However, this method cannot directly detect the combustion status of associated gas. Existing patent CN209605172U discloses an associated gas venting combustion monitoring and control flare, which monitors associated gas combustion by placing a temperature sensor at the combustion nozzle. This solution can only monitor the combustion status of associated gas and cannot monitor the specific combustion process or whether the various components mixed in the associated gas are fully combusted, resulting in insufficient monitoring. Existing technologies for monitoring associated gas using unmanned aerial vehicles (UAVs) mostly employ rotary-wing UAVs carrying gas detection devices, which are limited by energy resources and cannot remain airborne for extended periods for monitoring. Summary of the Invention

[0004] This invention provides a method, system, and computer storage medium for controlling the powered flight of unmanned aerial vehicles (UAVs), which solves the problem that existing image monitoring methods cannot directly detect associated gas combustion and that UAV monitoring cannot be continuous.

[0005] On one hand, embodiments of the present invention provide a method for controlling the powered flight of an unmanned aerial vehicle (UAV), including:

[0006] Establish an airflow model for associated gas combustion;

[0007] Set basic control parameters according to the airflow model and launch the drone;

[0008] The drone collects information on the velocity and temperature of the rising airflow to monitor the real-time combustion status of the associated gas.

[0009] The drone's attitude is calibrated based on the updraft velocity and temperature information, the updraft state is predicted, and the flight control mode is selected.

[0010] In one possible implementation, the airflow model is obtained by analyzing the rising airflow generated by the combustion of associated gas.

[0011] In one possible implementation, the basic control parameters are parameters for setting the UAV's ailerons, rudder, elevators, and differential throttle.

[0012] In one possible implementation, the flight control modes include a search mode, a preparation mode, a tracking mode, and an exit mode.

[0013] In one possible implementation, the prediction of the updraft state is estimated and predicted using an extended Kalman filter structure.

[0014] On the other hand, embodiments of the present invention provide an unmanned aerial vehicle (UAV) powered flight control system, including:

[0015] The environmental simulation module is used to establish an airflow model for associated gas combustion;

[0016] A preset launch module is used to set basic control parameters and launch the drone according to the airflow model.

[0017] The data acquisition and monitoring module is used to collect the velocity and temperature information of the updraft through the UAV and monitor the real-time combustion status of the associated gas.

[0018] The flight control module is used to calibrate the UAV attitude, predict the updraft state, and select the flight control mode based on the updraft velocity and temperature information.

[0019] In one possible implementation, the acquisition and monitoring module acquires information on the velocity and temperature of the rising airflow while simultaneously monitoring the flame state during associated gas combustion and measuring the emissions of the post-combustion mixture.

[0020] In one possible implementation, the flight control module selects the flight control mode based on airflow intensity, thermal core location, and flight state maintenance time.

[0021] On the other hand, embodiments of the present invention provide a computer storage medium storing a plurality of computer instructions, which are used to cause a computer to execute the above-described method.

[0022] The UAV powered flight control method, system, and computer storage medium of this invention have the following advantages:

[0023] (1) Adjust the flight status based on the real-time collected airflow field data to increase the loiter time.

[0024] (2) Directly monitor the combustion of associated gas and collect surrounding gas to detect the combustion status. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of an unmanned aerial vehicle (UAV) hovering around the updraft during associated gas combustion, provided in an embodiment of the present invention.

[0027] Figure 2 A schematic diagram illustrating the interference experienced by a drone in an updraft field, as provided in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the updraft state estimation method provided in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the autonomous tracking and control method for thermal cores provided in an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of the updraft distribution state provided in an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the flight trajectory provided in an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the drone trajectory under the recognition mode provided in the embodiment of the present invention;

[0033] Figure 8 This is a schematic diagram of updraft state estimation data for the identification pattern provided in an embodiment of the present invention;

[0034] Figure 9 This is a schematic diagram of the UAV tracking mission trajectory in the tracking mode provided in an embodiment of the present invention;

[0035] Figure 10 This is a schematic diagram of updraft state estimation data for the tracking mode provided in an embodiment of the present invention;

[0036] Figure 11 This is a schematic diagram comparing the altitude changes of a drone under recognition and tracking modes provided in an embodiment of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Figure 1 This invention provides a schematic diagram of a drone hovering around the updraft during associated gas combustion, as shown in the embodiments of the present invention. The embodiments of the present invention also provide a method for controlling the powered flight of a drone, including:

[0039] Establish an airflow model for associated gas combustion;

[0040] Set basic control parameters according to the airflow model and launch the drone;

[0041] The drone collects information on the velocity and temperature of the rising airflow to monitor the real-time combustion status of the associated gas.

[0042] The drone's attitude is calibrated based on the updraft velocity and temperature information, the updraft state is predicted, and the flight control mode is selected.

[0043] For example, such as Figure 1As shown, the control method proposed in this invention enables fixed-wing UAVs to identify and track the updraft generated by associated gas combustion to achieve powered flight. The updraft generated by associated gas combustion, as a special type of vertical wind field interference, can be regarded as a slender heat pipe extending upward from the top of the exhaust tower. The airflow temperature and intensity are greatest at the flame core, and it diffuses outward within a circle of radius R. The airflow intensity and temperature gradually weaken as it extends outward. This can be represented as an airflow model. Then, the UAV take-off and landing site is selected, and the target airspace range and basic parameters of the UAV around the well site are set. The meaning of the parameter setting is that the UAV, during take-off and hovering flight while tracking the thermal core, can achieve powered flight. During the flight, parameter constraints must be met. If the airspace, altitude, or the aforementioned airflow state values ​​exceed the limits, the exit procedure is initiated. Based on instructions from ground station personnel, a decision is made regarding whether to execute a return flight or re-enter the updraft tracking flight mode. When the UAV hovers around the thermal core of the target updraft, local velocity differences on the wings will generate pitch and roll moments. When the UAV is hovering to the left, the updraft will cause it to tend to hover to the right. Under lateral control input, the UAV uses the induced roll moment generated by the updraft to level its attitude, and its flight trajectory forms an arc around the thermal core. The UAV can achieve low-throttle hovering and climbing. The lateral control input, depending on the UAV's configuration, includes ailerons, rudder, elevons, and differential throttle. Then, an extended Kalman filter method is used to dynamically estimate the thermal airflow distribution. The UAV calibrates the airflow state based on the measurement signals and then selects the flight control mode.

[0044] In one possible embodiment, the airflow model is obtained by analyzing the rising airflow generated by the combustion of associated gas.

[0045] For example, the updraft generated by associated gas combustion, as a special type of vertical wind field interference, can be regarded as a slender heat pipe extending upward from the top of the exhaust tower. The airflow temperature and intensity are greatest at the flame core, and it diffuses outward within a circle of radius R. The airflow intensity and temperature gradually weaken as it extends outward. This model can be expressed as:

[0046]

[0047] Where θ is the thermal gas flow state vector, θ = [r WR] T This includes the geographical location r of the updraft's thermal core, the airflow intensity W at the thermal core, the distribution radius R of the updraft, and x, which is the geographical location of the UAV, expressed as x = [x n x e ] T D is the distance from the UAV to the thermal core, and exp represents an exponential function with the natural constant e as the base.

[0048] When describing the relative positional relationship between the UAV and the updraft, a specific reference point can be selected, where χ1 is the heading angle between the thermal core of the updraft and the reference point, and χ... g χ is the heading angle of the UAV relative to the reference point. th Let ψ be the heading angle between the UAV and the thermal core. t This is the yaw angle of the drone.

[0049] In one possible embodiment, the basic control parameters are parameters for setting the UAV's ailerons, rudder, elevators, and differential throttle.

[0050] For example, the basic control parameters require first selecting the UAV take-off and landing site, setting the target airspace range around the well site, and the east-west position [X]. min X max ], North-South position [Y min Y max The altitude range during drone flight [H] min H max Speed ​​range [V] min V max Attitude angle limit (roll angle limit φ) max / φ min Pitch angle limit θ max / θ min The maximum and minimum hovering radii R of the thermal core tracking flight. max R min Climb rate V cl The decline rate V ds The drone uses airflow monitoring equipment to monitor the emissions of associated gas after combustion, including gas composition, flow rate, and temperature. A maximum temperature setting is required. and maximum updraft speed The parameter settings refer to the parameter constraints that the UAV must meet during takeoff and hovering around the hot core. If the airspace, altitude, or the aforementioned airflow state values ​​exceed the limits, the exit procedure is initiated. Based on instructions from ground station personnel, it is determined whether to perform a return flight or re-enter the updraft tracking flight mode. When the UAV hovers around the target updraft's hot core, local velocity differences on the wings will cause pitch and roll moments, such as... Figure 2 As shown, when the UAV is hovering to the left, the updraft causes it to tend to hover to the right. Under the lateral control input, the UAV uses the induced roll moment generated by the updraft to level its attitude, and its flight trajectory is an arc around the thermal core. The UAV can achieve low-throttle hovering and climbing. The basic control parameters of the lateral control input, depending on the UAV's layout, include ailerons, rudder, elevons, and differential throttle.

[0051] In one possible embodiment, the flight control modes include a search mode, a preparation mode, a tracking mode, and an exit mode.

[0052] For example, in order to enable UAVs to achieve powered flight by tracking the updraft formed by the combustion of associated gas, four control modes were designed: search, preparation, tracking, and exit. A Boolean variable b was defined to indicate whether the airflow intensity exists. latch Whether to track the hot core (Boolean variable b) follow Minimum hold time T in each flight mode latch The specific meanings of the four flight modes are as follows:

[0053] In search mode, after takeoff, the drone tracks the set mission route and filters signals based on the airflow intensity it carries. Is it greater than the airflow intensity threshold w? latch and duration greater than threshold T latch The system determines the state transition based on the following conditions. If the condition is less than 1, the airflow intensity does not exist, and the mission path is maintained; if the condition is greater than 1, the updraft exists, and the system switches to ready mode.

[0054] In preparation mode, the UAV tracks and hovers along the mission path, estimating wind speed and updraft conditions based on the relationship between airspeed and ground speed. If the updraft condition θ... k If convergence occurs, the UAV switches to tracking mode and uses the estimated position of the thermal core as the target center of the hovering trajectory; otherwise, it continues to hover around the target radius.

[0055] In tracking mode, the drone orbits the estimated thermal core location, It hovers within a radius, and switches to exit mode when the measured vertical wind speed decreases or the measured values ​​of altitude, temperature, distance, attitude, and power exceed the limits.

[0056] In exit mode, the drone first maintains its current heading angle and flies level. After the minimum flight time required to exit mode is met, it determines whether to enter search, hot core tracking, or return-to-home mode based on the drone's stable state parameters.

[0057] The classification and switching conditions of the four control modes—search, prepare, track, and exit—are shown in Table 1.

[0058] Table 1 Control Mode Classification and Switching Conditions

[0059]

[0060] In one possible embodiment, the prediction of the updraft state is estimated and predicted using an extended Kalman filter structure.

[0061] For example, before estimating and predicting the updraft state using the extended Kalman filter structure, the induced roll moment of the UAV must first be predicted. Based on the aerodynamic distribution characteristics of the UAV's wing in the updraft field, the induced roll moment l th The prediction method first involves calculating the local pressure. Assuming the lift of the wing is linearly distributed along its span, the local pressure of the wing differs under different lift distributions. The local pressure can be expressed as:

[0062]

[0063] Where, ΔC L (b) represents the lift coefficient increment along the span, where b is the wing span, and ΔC L (b) is related to the local angle of attack Δα(b) of the spanwise profile. Assuming a linear relationship, ΔC L (b) is represented as:

[0064]

[0065] in, Let Δα(b) be the derivative of the lift coefficient with respect to the angle of attack, determined by the local airflow. Since the wingspan of the UAV in this case is smaller than the distribution radius R of the updraft (>b), its level flight airspeed is greater than the vertical airflow speed V. a >w.

[0066] Then, the local updraft velocity is calculated. Assuming that the local angle of attack of the UAV also follows a linear distribution, the calculation method for the local angle of attack is as follows:

[0067] Δα(b)≈tan(Δα(b))=Δw(b) / V a (6)

[0068] Where Δw(b) is the vertical wind speed, which can be expressed as the product of the updraft gradient along the span and the wingspan. The calculation method is as follows:

[0069] Δw(b)≈dw / db·b (7)

[0070] Next, the spanwise gradient of the updraft is calculated. By integrating the product of the pressure and spanwise displacement in the spanwise direction, the wing roll moment caused by the uneven lift distribution in the updraft field can be obtained. The calculation method is as follows:

[0071]

[0072] In the above equation, except for the spanwise airflow gradient dw / dD, all other terms are independent of the updraft state.

[0073] Based on the updraft model in the above formula and the positional relationship between the UAV and the thermal core, the calculation method for the spanwise airflow gradient dw / dD is as follows:

[0074]

[0075] Where φ is the roll angle of the UAV and ψ is the yaw angle of the UAV.

[0076] The induced roll moment is calculated by substituting the wing roll moment formula into the spanwise airflow gradient dw / dD formula. The calculation method is as follows:

[0077]

[0078] Finally, the extended Kalman filter method is used to dynamically estimate the thermal gas distribution state, which is divided into the updraft state θ. k The prediction, and combined with w k and l th,k To correct θ k Two parts.

[0079] θ k Prediction methods;

[0080] In the updraft state formed by associated gas combustion, the location of the thermal core is disturbed by a random wind field, while the airflow intensity and distribution radius are not affected by the wind field disturbance. (Airflow state prediction value) and prediction error covariance matrix The calculation method is as follows:

[0081]

[0082]

[0083] Where, θ k-1 P can be calculated using equation (11). k-1 The matrix is ​​the identity matrix, where the subscript k in the above equation represents the current state, k-1 represents the state of the previous calculation step, and V w Let Δt be the horizontal wind speed, Δt be the calculation step size, and Q be the error matrix for state prediction.

[0084] θ k Correction method:

[0085] The UAV calibrates the airflow state based on measurement signals. The nonlinear measurement function for state correction is:

[0086] h(θ) = [wl] th ] T (12)

[0087] Among them, w and l th The predicted signal for the corrected state is calculated using equations (10) and (12). The calculation methods for the measurement results of w and lth are as follows:

[0088] When the updraft velocity w can be directly measured, the signal can be low-pass filtered to obtain the measured value, which can then be used as the input for the correction signal.

[0089]

[0090] Among them, w m This is the measurement signal of the rising airflow velocity from the airborne airflow sensor.

[0091] When the updraft velocity w cannot be directly measured, the drone's energy change rate can be used. Excluding the nominal climb rate caused by lift, tension and vertical overload The method is as follows:

[0092]

[0093] The nominal rate of climb can be considered as the airspeed V. a Vertical overload n z throttle δ t The function is represented in segments based on whether the throttle is greater than the cruise throttle, and the calculation method is as follows:

[0094]

[0095] in, The climb rate, representing the thrust and vertical overload, can be calculated by interpolation using empirical values ​​of throttle, overload, and airspeed. The maximum climb rate of the drone. For cruise throttle, This is the maximum throttle setting.

[0096] Induced rolling torque l th Currently, it is not possible to directly measure this using sensors; instead, it can be calculated using aerodynamic models and other measurement conditions. Assuming the UAV's roll acceleration tends towards 0 over a certain time period, i.e. The induced roll moment consists of two parts: the dynamic moment caused by the roll angular velocity and the roll moment generated during lateral control. The calculation method is as follows:

[0097]

[0098] in, Base throttle, T is propeller thrust, l T To control the lever arm, This is the derivative of the rolling moment coefficient with respect to the rolling angular velocity. δ is the derivative of the roll moment coefficient with respect to the yaw rate. For a UAV with differential control in the lateral direction, δ dtFor differential throttle, To balance the differential throttle; for UAVs controlled by lateral ailerons, δ a As a secondary wing, This is the trim amount for the ailerons.

[0099] like Figure 3 As shown, the state estimation equation under the extended Kalman filter structure is constructed, and the calculation method is as follows:

[0100]

[0101] in, Let I be the correction error covariance matrix, I be the identity matrix, L be the Kalman filter gain matrix, and C(x) be the partial derivative of the state quantities. The constructed Jacobian matrix, R is the measurement error matrix, and z(θ) is the measurement state vector of the updraft and induced roll moment. C(x) can be calculated using the following method:

[0102]

[0103] In the formula:

[0104] This invention also provides an unmanned aerial vehicle (UAV) powered flight control system, comprising:

[0105] The environmental simulation module is used to establish an airflow model for associated gas combustion;

[0106] A preset launch module is used to set basic control parameters and launch the drone according to the airflow model.

[0107] The data acquisition and monitoring module is used to collect the velocity and temperature information of the updraft through the UAV and monitor the real-time combustion status of the associated gas.

[0108] The flight control module is used to calibrate the UAV attitude, predict the updraft state, and select the flight control mode based on the updraft velocity and temperature information.

[0109] In one possible embodiment, the acquisition and monitoring module acquires information on the velocity and temperature of the rising airflow while simultaneously monitoring the flame state during associated gas combustion and measuring the emissions of the post-combustion mixture.

[0110] For example, the drone's flight control module obtains the speed w of the updraft by collecting data from an onboard gas measuring instrument. th and temperature T th When a drone hovers around an updraft field, the updraft velocity at different locations can be determined by the drone's distance D from the thermal core, as well as the updraft intensity W and its distribution radius R. th The calculation method is as follows:

[0111]

[0112] The rate of change of the updraft gradient is obtained by taking the second derivative of D with respect to equation (2), and the calculation method is as follows:

[0113]

[0114] via d 2 w / dD 2 =0 The maximum value of the calculated airflow gradient dw / dD is 0. This indicates that when the drone hovers around the thermal core, the airflow temperature does not exceed [a certain value]. Under the conditions, with The radius of the hovering flight can obtain the maximum vertical overload, which is most advantageous for low-power climb flight.

[0115] In one possible embodiment, the flight control module selects the flight control mode based on airflow intensity, thermal core location, and flight state maintenance time.

[0116] For example, when a drone enters an updraft containing combusted gas after takeoff, and the updraft velocity and induced roll moment both exceed threshold conditions, the altitude meets the maximum and minimum altitude limits, and the distance meets the maximum distance limit. The drone entered search mode and flew along a fixed route at a safe distance from the center of the fire.

[0117] When the updraft velocity is greater than the threshold, the time satisfies T>T latch At this time, the UAV switches to the preparation mode and makes real-time predictions of the updraft intensity, distribution radius, and hot core distribution location according to the updraft estimation method in step 3. The UAV then hovers along the initial value of the predicted distribution radius of the hot core.

[0118] When the airflow remains greater than the threshold, the time satisfies T>5*T. latch At that time, the equivalent input power obtained by the UAV through the updraft is greater than 0.5 times the output power, i.e., ΔP in >0.5ΔP out The drone switched to tracking mode, using the location of the thermal core as the center of its hovering flight. Using a radius of 1, the drone maintains a hovering flight mode with pitch and roll attitudes, while dynamically tracking the thermal core to gain energy and climb.

[0119] If the drone's control status and the monitoring status of its onboard airflow sensors exceed the limits, it will switch to exit mode, and the drone will maintain its current heading T. avoidsoaIf the status value still exceeds the limit after a certain time, it will enter return-to-home mode; if the status value is normal, it will re-enter search mode. In search, preparation, and tracking modes, if the drone's status value fails to meet various safety restrictions, it will exit into updraft tracking mode. The various safety restrictions refer to:

[0120] The conditions for the existence of airflow are that the updraft is greater than the threshold and the duration of its presence must also be greater than the time threshold.

[0121] The airflow persistence condition is that the updraft persists for a duration greater than the threshold, and the duration is greater than 4 to 6 times the time threshold.

[0122] Positional constraints: the communication distance between the drone and the ground station must not exceed the communication limit of the data link;

[0123] Due to safety constraints, the drone's hovering radius and attitude angle are between their maximum and minimum values;

[0124] Temperature constraints apply; the measured temperature of the updraft must be lower than the maximum updraft temperature.

[0125] Through the regulation of the flight control module, by measuring the updrafts distributed around the flame during the combustion of associated gas, the distribution state of the updraft field can be estimated, and the control mode can be switched to achieve active hovering and tracking control of the hot core. This can take into account the limitations of the updraft gradient and airflow temperature, and achieve safe powered flight. Based on the distribution characteristics of the updrafts formed by associated gas combustion, this invention designs four control modes: "search," "prepare," "track," and "exit," enabling the UAV to achieve autonomous flight control including updraft measurement, airflow distribution state estimation, active tracking control of the hot core, and exit from abnormal states.

[0126] This invention also provides a computer storage medium storing a plurality of computer instructions, which are used to cause a computer to execute the above-described method.

[0127] In one possible embodiment, a case simulation is performed, using a fixed-wing UAV with a wingspan of 3m and a weight of 3kg as an example, to simulate the application of the energy-gaining flight control method under associated gas combustion airflow field. The control example is as follows.

[0128] The UAV employs a wing-tail thrust propulsion system, elevators and rudders for attitude control, and a manual takeoff and belly-to-ground landing method. It measures gas emissions during associated gas combustion using onboard airflow sensors. Assuming an input condition of a 3 m / s velocity at the flame core and an airflow distribution radius of 120 m during associated gas combustion at the well site, the gradually decreasing upward airflow distribution at the flame core is as follows: Figure 5 As shown.

[0129] After takeoff, the UAV enters mission mode at an altitude of 600m, with the flame core also at 600m altitude, and its position is at the origin (0,0). The UAV begins its search from its initial waypoints: initial waypoint A (-100, -200), intermediate waypoint B (-100, 100), and mission waypoint C (100, 100). It flies in both hot core identification and hot core tracking modes. In identification mode, the UAV sequentially follows the flight path from A to B to C, then hovers stably at point C with a radius of 80m, executing only updraft identification during flight. In tracking mode, the UAV tracks the hot core based on the identified updraft conditions during flight path tracking. Both modes have the same initial conditions, maintaining a cruise throttle of 0.4 throughout the entire process. The simulation time is 200s. The target flight trajectory is as follows: Figure 6 As shown.

[0130] The extreme parameters, airflow existence conditions, persistence conditions, and exit conditions for the UAV during updraft identification and active tracking are set as shown in Table 2.

[0131] Table 2 Condition Setting Table

[0132]

[0133] In recognition mode, during the tracking of the mission route, when the airflow conditions meet the requirements, the UAV switches to preparation mode and estimates the state of the thermal core. Influenced by the updraft formed by the combustion of associated gas, the UAV's altitude increases slowly during the route tracking process, and the estimated position of the thermal core gradually approaches the true value. The UAV's flight trajectory and the estimated updraft state are shown below. Figure 7 , Figure 8 As shown.

[0134] Airflow intensity estimate W est As the speed increases from 1.5 m / s to 2.7 m / s, the estimated distribution radius R... est The depth was increased from 80m to 110m. During the UAV's hovering around point C, the estimated position of the thermal core was the relative position of the UAV. The average estimated position of the thermal core in the x-direction was -90m, and the average estimated position in the y-direction was -100m. The coordinates of waypoint C were (100, 100), indicating that the predicted geographical location of the thermal core was (10, 0). The updraft state estimation results under this mode show that, under the condition that updrafts are formed by associated gas combustion, the UAV can switch from search mode to preparation mode when tracking the target route. The estimation accuracy of the updraft state estimation method proposed in this invention reaches over 90%, proving the effectiveness of the updraft estimation method in this invention.

[0135] In tracking mode, the UAV estimates updraft conditions while tracking the mission route. When the presence and persistence conditions of the updraft meet the requirements, it switches to preparation and tracking control modes respectively. The UAV then performs hovering flight control based on the estimated hot core position and radius. During the active tracking and hovering process, the UAV gradually increases altitude at cruise throttle, achieving the mission requirement of powered hovering and climbing. The flight trajectory and updraft state estimation results are as follows: Figure 9 , Figure 10 As shown.

[0136] The estimated airflow intensity W during the drone's hovering around the thermal core est As the speed increases from 1.5 m / s to 3 m / s, the estimated distribution radius R est The altitude increases from 80m to 120m, with a hovering radius of 84m, placing the drone at the location of maximum updraft gradient. The mean value of the estimated position of the thermal core is (0,0). The updraft state estimation results under this mode demonstrate that when both the existence and persistence conditions of the updraft are met, the UAV can switch from search mode to preparation mode and then to tracking mode. The updraft estimation method achieves an estimation accuracy of over 95%, proving the effectiveness of the updraft estimation and tracking control method in this invention.

[0137] Comparison of drone altitude changes under recognition and tracking modes, for example Figure 11 As shown.

[0138] The flight time and cruise throttle of the UAV are the same in both modes, meaning that the total output power of the UAV is the same. In recognition mode, the UAV's altitude increases by 90m within 200s of simulation time, and the UAV weighs 3kg, gaining 2646W of gravitational potential energy in the updraft field. In tracking mode, the altitude change within 0-20s of simulation time is the same as in recognition mode. After the airflow continuity condition meets the requirements, the UAV stably hovers and climbs 410m around the thermal core within 200s, gaining 12054W of gravitational potential energy in the updraft field. This proves the effectiveness of the updraft tracking control method in this invention for energy gain.

[0139] Simulation results demonstrate that the proposed control method enables the UAV to hover and climb around the updraft generated by associated gas combustion to gain energy, achieving airflow state estimation and active tracking control to monitor flame state and gas emission velocity after associated gas combustion. A mathematical representation model for the updraft generated by associated gas combustion is also proposed. Furthermore, considering the characteristics of UAVs being affected by updraft field interference, an updraft state estimation method using updraft velocity and induced roll torque as measurement signal inputs is proposed. Additionally, four control modes—search, prepare, track, and exit—and their switching conditions are proposed to meet the mission requirements of the UAV-mounted gas detector for active updraft tracking and gas emission monitoring.

[0140] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0141] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for enabling flight control of a UAV, applied to a scene of combustion monitoring of associated gas of an oil and gas well site, characterized in that, The method comprises: establishing a gas flow model of the combustion of associated gas; parameters of the model include geographical position r of a thermal core of the updraft, gas flow intensity W at the thermal core, and distribution radius R of the updraft; setting basic control parameters including attitude angle limit, maximum and minimum circling radius, maximum temperature, and maximum updraft velocity according to the gas flow model, and releasing the UAV; the basic control parameters are parameters for setting aileron, rudder, elevators, and differential throttle of the UAV; collecting flow velocity and temperature information of the updraft by the UAV, and monitoring flame state during the combustion of the associated gas and emission of mixed gas after the combustion; calibrating UAV attitude and predicting updraft state based on extended Kalman filtering algorithm according to the flow velocity and temperature information of the updraft and induced rolling moment generated by the updraft; selecting a flight control mode according to the predicted updraft state, through gas flow intensity, thermal core position, and flight state retention time, to realize active circling tracking control of the thermal core. 2.The method of claim 1, wherein, The flight control mode includes search mode, preparation mode, tracking mode, and exit mode.

3. An unmanned aerial vehicle flight-enabling control system, comprising: The method comprises: an environment simulation module for establishing a gas flow model of the combustion of associated gas; parameters of the model include geographical position r of a thermal core of the updraft, gas flow intensity W at the thermal core, and distribution radius R of the updraft; a preset release module for setting basic control parameters including attitude angle limit, maximum and minimum circling radius, maximum temperature, and maximum updraft velocity according to the gas flow model, and releasing the UAV; the basic control parameters are parameters for setting aileron, rudder, elevators, and differential throttle of the UAV; a collection and monitoring module for collecting flow velocity and temperature information of the updraft by the UAV, and monitoring flame state during the combustion of the associated gas and emission of mixed gas after the combustion; a flight control module for calibrating UAV attitude and predicting updraft state based on extended Kalman filtering algorithm according to the flow velocity and temperature information of the updraft and induced rolling moment generated by the updraft; selecting a flight control mode according to the predicted updraft state, through gas flow intensity, thermal core position, and flight state retention time, to realize active circling tracking control of the thermal core.

4. A computer storage medium, characterized in that, The computer storage medium stores a plurality of computer instructions for causing a computer to execute the method in any one of claims 1-2.

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