Barrier light control method and system in low-altitude flight environment and lamp

Abstract

The invention discloses an obstruction light control method and system in a low-altitude flight environment and a lamp, and relates to the technical field of aviation safety, and the method comprises the steps: obtaining a current obstruction light parameter corresponding to each aircraft based on preset obstruction light division information in a target flight region; based on the preset flight parameter corresponding to each aircraft, the current obstacle light parameter is adjusted, and an obstacle light initial optimization parameter is obtained; obtaining obstacle light regulation and control environment information corresponding to each aircraft, and obtaining obstacle light adjustment information based on a preset obstacle light parameter corresponding rule and the obstacle light regulation and control environment information; according to the obstacle light adjustment information, the obstacle light initial optimization parameters are adjusted, and environment suitable obstacle light parameters are obtained; and controlling the corresponding obstruction lights to perform dynamic adjustment based on the environment suitable obstruction light parameters. The method has the effect of improving the control efficiency of the aviation obstruction beacon.

Description

Technical Field

[0001] The present application relates to the technical field of aviation safety, and in particular, to a method, a system and a lamp for controlling obstacle lights in a low-altitude flight environment. Background Art

[0002] With the popularization of low-altitude aircraft such as unmanned aerial vehicles and helicopters, the problem of low-altitude flight safety has become increasingly prominent; traditional aviation obstacle lights usually adopt the method of continuous light emission or light emission for a fixed time period, resulting in problems such as energy waste and light pollution; at the same time, existing obstacle lights lack intelligent perception and control capabilities and cannot be dynamically adjusted according to the actual flight environment, so there is a defect in the poor control efficiency of aviation obstacle lights and improvement is needed. Summary of the Invention

[0003] In order to solve the problems of the prior art and improve the control efficiency of aviation obstacle lights, the present application provides a method, a system and a lamp for controlling obstacle lights in a low-altitude flight environment.

[0004] In a first aspect, the invention object of the present application is achieved by adopting the following technical solutions: A method for controlling obstacle lights in a low-altitude flight environment, comprising: Based on the preset obstacle light division information in the target flight area, obtaining the current obstacle light parameters corresponding to each aircraft; Based on the preset flight parameters corresponding to each aircraft, adjusting the current obstacle light parameters to obtain the initial optimized obstacle light parameters; Obtaining the obstacle light regulation environment information corresponding to each aircraft, and obtaining the obstacle light adjustment information based on the preset obstacle light parameter correspondence rule and the obstacle light regulation environment information; According to the obstacle light adjustment information, adjusting the initial optimized obstacle light parameters to obtain the obstacle light parameters suitable for the environment; Based on the obstacle light parameters suitable for the environment, controlling the corresponding obstacle lights to perform dynamic adjustment.

[0005] By adopting the above technical solutions, this solution supports differential control of main / auxiliary lamp groups, realizes dynamic allocation of aircraft lighting resources through obstacle light division information, and avoids interference of lighting signals during multi-aircraft coordination; compared with the traditional single-parameter configuration mode, it improves the accuracy rate of obstacle light resource allocation; combined with the obstacle light regulation environment information (such as environmental parameters such as meteorology and airspace traffic density), it realizes real-time adjustment of lighting parameters (brightness / frequency / color temperature); thus, it helps to improve flight safety, especially in a low-altitude flight environment, reducing the risk of collision between the aircraft and ground obstacles; further, the present application secondarily optimizes the initial optimized obstacle light parameters through the environmental adjustment information to form an environmental dynamic adaptation rate, improving the control efficiency of aviation obstacle lights.

[0006] In a preferred example of the present application: The preset obstacle light division information is obtained through the following method: Obtain flight facility registration information, flight area map information, and airspace control data; determine the aircraft type, flight altitude range, and flight area characteristics; According to the aircraft type, flight altitude range, and flight area characteristics, divide the main obstacle light information and auxiliary obstacle light information corresponding to each aircraft; The obstacle light division information of each aircraft includes main obstacle light parameter configuration and auxiliary obstacle light parameter configuration.

[0007] By adopting the above technical solution, through the analysis of flight facility registration information, flight area map information, and airspace control data, the main obstacle light information and auxiliary obstacle light information can be accurately divided for each aircraft, ensuring appropriate obstacle light configurations under different flight tasks and conditions. At the same time, it can also save the energy consumption of obstacle lights, such as activating only when triggered by an airspace control instruction (such as a temporary no-fly zone), reducing the overall energy consumption.

[0008] In a preferred example of the present application: The preset flight parameters include aircraft type, flight speed, flight attitude, and emergency status identifier. The adjustment of the current obstacle light parameters includes: Based on the aircraft type of the corresponding aircraft, obtain the basic brightness coefficient and the reference value of the flashing frequency; Combined with the flight speed, flight attitude, and emergency status identifier, query the preset dynamic adjustment coefficient table to obtain the real-time correction coefficient; According to the basic brightness coefficient, the reference value of the flashing frequency, and the real-time correction coefficient, adjust the current obstacle light parameters to obtain the initial optimized parameters of the obstacle lights.

[0009] By adopting the above technical solution, an emergency response mechanism for the emergency status identifier (such as mechanical failure) is introduced. For example, in an emergency state, trigger the flashing frequency of the obstacle lights to increase to 1.5 times the reference value; thus, especially in an emergency state, the obstacle light parameters can be quickly adjusted to increase visibility or change the flashing mode, effectively improving the safety performance of the aircraft in complex situations.

[0010] In a preferred example of the present application: The obstacle light control environment information includes meteorological data, airspace traffic density, and ground obstacle distribution. The corresponding rules for the preset obstacle light parameters include: According to the meteorological visibility value in the meteorological data and the preset meteorological visibility threshold, determine whether to activate the infrared supplementary light module and send an alarm instruction to the ground station; Based on the airspace traffic density, calculate the light flashing frequency differentiation coefficient, and trigger the light flashing frequency differentiation strategy based on the light flashing frequency differentiation coefficient; When the detected distance to the ground obstacle is less than the preset safety radius, a dynamic switching instruction for the light color temperature is triggered.

[0011] By adopting the above technical solution, based on multiple environmental impact factors existing in the actual environment, such as meteorological conditions, air traffic conditions, and the position of ground obstacles and other environmental factors, the present application formulates corresponding obstacle light adjustment strategies to provide a highly adaptable warning method. For example, the infrared supplementary light module is activated and an alarm instruction is sent to the ground station in the case of low visibility, or a dynamic switching instruction for the light color temperature is triggered when a ground obstacle is detected approaching, which is beneficial to ensuring flight safety.

[0012] In a preferred example of the present application: adjusting the initial optimization parameters of the obstacle light to obtain obstacle light parameters suitable for the environment, including: Constructing a four-dimensional flight trajectory prediction model and calculating the boundary of the light action range; Through a fuzzy PID controller, converting the aircraft overload value into a flashing frequency gain coefficient of the obstacle light according to the boundary of the light action range; Based on the flashing frequency gain coefficient of the obstacle light, combining with the optimized duty cycle value of the light signal output by the reinforcement learning model, triggering a secondary optimization instruction for the obstacle light parameters; Based on the secondary optimization instruction for the obstacle light parameters, adjusting the initial optimization parameters of the obstacle light to obtain the final obstacle light parameters suitable for the environment.

[0013] By adopting the above technical solution, the four-dimensional flight trajectory prediction model is based on the dynamic trajectory prediction accuracy of flight longitude, latitude, altitude, and time, which is different from the traditional three-dimensional model and has higher prediction accuracy. At the same time, introducing the time dimension to dynamically correct the boundary of the light action range breaks through the limitation of the traditional static boundary setting; the present application converts the overload value through a fuzzy PID to provide a non-linear overload response ability in the case of overload mutation, and at the same time, through the reinforcement learning model, the duty cycle of the light signal is dynamically adapted to the environment (such as the duty cycle is increased to 70% at night and decreased to 30% during the day), improving the adaptability of the obstacle light control strategy to environmental changes.

[0014] In a preferred example of the present application: the light flashing frequency differentiation coefficient is calculated by the following formula: where, is the airspace traffic density (flights / cubic kilometer); is the preset safety traffic density threshold; is the frequency adjustment factor (1.2 ≤ ≤ 1.5); is the attenuation coefficient ( ); is the predicted value of the maximum traffic density.

[0015] By adopting the above technical solution, calculating the differential coefficient of the light flashing frequency, and automatically adjusting the flashing frequency of the obstacle lights according to the current airspace traffic density, the obstacle light control system can perform excellently in dealing with complex and changeable flight conditions; the obstacle light control method with exponential decay is more in line with the human eye visual fatigue curve.

[0016] In a preferred example of the present application: the fuzzy PID controller calculates the gain coefficient through the following formula : where i is the fuzzy rule index; n is the total number of fuzzy rules; is the aircraft overload value; is the triangular membership function of the overload deviation ; where is the lower limit of the membership function; b is the upper limit of the membership function, defining the vertex of the fuzzy interval; is the proportionality coefficient in the preset fuzzy rule base, satisfying: where k is the slope of the S-shaped activation function (k≥0.5); is the fuzzy rule trigger threshold.

[0017] By adopting the above technical solution, the PID parameters are flexibly adjusted according to the aircraft overload value, and the triangular membership function ( =-1g, b = 1g) has improved sensitivity to medium overload ( =0.8g).

[0018] In the second aspect, the invention object of the present application is achieved by adopting the following technical solution: An obstacle light control system in a low-altitude flight environment, used to execute the obstacle light control method in the low-altitude flight environment as described above, the system includes: A parameter acquisition module, used to acquire the current obstacle light parameters corresponding to each aircraft based on the preset obstacle light division information in the target flight area; A parameter optimization engine, used to adjust the current obstacle light parameters based on the preset flight parameters corresponding to each aircraft to obtain the initial optimized obstacle light parameters; An environment perception module, used to acquire the obstacle light regulation environment information corresponding to each aircraft, and obtain the obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light regulation environment information; A dynamic adjustment module, used to adjust the initial optimized obstacle light parameters according to the obstacle light adjustment information to obtain the obstacle light parameters suitable for the environment; A light control execution unit, used to control the corresponding obstacle lights to perform dynamic adjustment based on the obstacle light parameters suitable for the environment.

[0019] In a third aspect, the invention object of the present application is achieved by the following technical solutions: A lighting fixture includes a memory, a processor, and a computer program stored in the memory and executable on the processor. It is characterized in that when the processor executes the computer program, the steps of the obstacle light control method in the low-altitude flight environment described above are implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. By obtaining the current obstacle light parameters of each aircraft in the target flight area and adjusting according to the preset flight parameters, the obstacle lights can automatically optimize their parameters such as brightness and flashing frequency in different flight environments; in the low-altitude flight environment, the risk of collision between the aircraft and ground obstacles is reduced. 2. It can be customized according to the specific type of the aircraft, the flight altitude range, and the characteristics of the flight area, improving the pertinence and efficiency of the use of obstacle lights. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flowchart of a method for controlling obstacle lights in a low-altitude flight environment according to an embodiment of the present application; Figure 2 is a flowchart of step S2 in the method for controlling obstacle lights in a low-altitude flight environment according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following further describes the present application in detail with reference to the accompanying drawings.

[0023] In one embodiment, as Figure 1 shown, the present application discloses a method for controlling obstacle lights in a low-altitude flight environment, which specifically includes the following steps: S1: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft.

[0024] In this embodiment, the target flight area refers to the flight area of the aircraft in the low-altitude environment; the obstacle light division information refers to the pre-defined lighting configuration rules according to the flight facility registration information, the flight area map, and the airspace control data, including the parameter templates of the main obstacle lights (constant light warning) and the auxiliary obstacle lights (dynamic regulation); the current obstacle light parameters include real-time control parameters such as brightness value (unit: candela cd), flashing frequency (Hz), color temperature (K), etc.

[0025] Specifically, obtain the aircraft ID through the aircraft communication bus (such as CAN bus), query the cloud database to obtain the pre-stored main obstacle light parameter configuration file (in JSON format) and the auxiliary obstacle light parameter configuration file, and call the parameter template corresponding to the current flight stage (takeoff / cruise / landing) from the configuration file.

[0026] Specifically, step S1 includes: S11: Obtain the flight facility registration information, flight area map information, and airspace control data; determine the aircraft type, flight altitude range, and flight area characteristics.

[0027] In this embodiment, the aircraft registration information includes the aircraft type and the unique identification information of ground facilities (such as heliports, transmission towers), including facility type, geographical location, altitude range, usage, etc.; the flight area map information refers to the geofence data (such as airport runway boundaries, no-fly zone coordinates) or the geographical coordinates and topology of transmission lines; the airspace control data refers to the real-time control instructions issued by the aviation management department, including temporary no-fly notices, route restrictions, aircraft activity density, etc.

[0028] Specifically, the flight altitude range is determined according to the facility type (such as the main landing area altitude of the heliport is 0 - 120m, and the warning altitude of the transmission tower is 0 - 150m); the regional characteristics of the flight area map information can be classified by GIS map matching to identify the airspace type (such as around the airport, high-voltage line corridor).

[0029] S12: According to the aircraft type, flight altitude range, and flight area characteristics, divide the main obstacle light information and auxiliary obstacle light information corresponding to each aircraft.

[0030] Specifically, the main obstacle light information refers to the configuration of constantly-on, high-brightness lights for all-weather basic warning (such as a constantly-on red light at the top of the transmission tower); the auxiliary obstacle light information refers to the configuration of dynamically adjustable lights for enhancing warning in specific scenarios (such as high-frequency flashing lights during an emergency landing).

[0031] In this embodiment, establish a facility type - altitude - regional characteristics mapping table, for example: .

[0032] S13: The obstacle light division information of each aircraft includes the main obstacle light parameter configuration and the auxiliary obstacle light parameter configuration.

[0033] In this embodiment, the main obstacle light parameter configuration includes static parameters such as brightness reference value, flashing frequency, color temperature, etc.; the auxiliary obstacle light parameter configuration includes variable parameters such as dynamic trigger threshold, gain coefficient, response delay, etc.

[0034] S2: Based on the preset flight parameters corresponding to each aircraft, adjust the current obstacle light parameters to obtain the initial optimized obstacle light parameters.

[0035] In this embodiment, the preset flight parameters include the aircraft type (fixed-wing / multi-rotor), flight speed (m / s), flight attitude (pitch angle / roll angle), and emergency status flag (normal / alarm); the initial optimized parameters refer to the intermediate parameters obtained by weighted calculation of the basic brightness coefficient and the dynamic correction coefficient.

[0036] Specifically, step S2 includes: S21: Based on the aircraft type of the corresponding aircraft, obtain the basic brightness coefficient and the reference value of the flashing frequency.

[0037] In this embodiment, the aircraft type refers to the type of aircraft (such as fixed-wing UAV, multi-rotor UAV, manned aircraft); the unit of the basic brightness coefficient is candela (cd), representing the initial brightness reference value of the obstacle light in the standard environment; the unit of the reference value of the flashing frequency is hertz (Hz), representing the default flashing rate of the obstacle light.

[0038] Establish an aircraft type - brightness / frequency mapping table, for example: .

[0039] S22: Combine the flight speed, flight attitude, and emergency status flag to query the preset dynamic adjustment coefficient table to obtain the real-time correction coefficient.

[0040] Specifically, the flight speed (v): unit m / s, affecting the real-time adjustment of the light brightness and frequency; the flight attitude is represented by the attitude angle which includes the pitch angle and the roll angle (roll), unit degree (°), reflecting the motion state of the aircraft; the emergency status flag (E), expressed by a boolean value (0 / 1), indicating whether the alarm mode is triggered; the dynamic adjustment coefficient table is used to store the mapping relationship between the flight parameters and the correction coefficient. In practical applications, the aircraft sensors obtain real-time data: flight speed (v), attitude angle ( ) and emergency status flag E, and the emergency status flag is triggered by the fault detection module.

[0041] According to the flight speed (v) and the attitude angle ( ), query the preset dynamic adjustment coefficient table (the set flight speed is standardized to [0, 30] m / s; limited within the range of [-30°, 30°]): .

[0042] For example, when the speed v = 15 m / s (in the range of 10 - 20), the correction coefficient is calculated by linear interpolation: brightness correction coefficient = 1.0 - (15 - 10) / 10×(1.0 - 0.8) = 0.9; where the emergency state coverage rule is: when E = 1, the forced coverage correction coefficient is the maximum value (brightness + 50%, frequency × 2).

[0043] S23: Adjust the current obstacle light parameters according to the basic brightness coefficient, the reference value of the flashing frequency, and the real-time correction coefficient to obtain the initial optimized parameters of the obstacle light.

[0044] In this embodiment, the initial optimized parameters of the obstacle light refer to the intermediate control parameters obtained by integrating the basic parameters and dynamic corrections; the light brightness in the embodiments of the present application is subject to the upper and lower limits of brightness constraints according to the actual product specifications of the obstacle light, for example, brightness ∈ [500, 2000] cd; frequency ∈ [1.0, 5.0] Hz.

[0045] Specifically, the optimized brightness = basic brightness × (1 + brightness correction coefficient); the optimized frequency = reference frequency × frequency correction coefficient.

[0046] S3: Obtain the obstacle light control environment information corresponding to each aircraft, and based on the preset obstacle light parameter correspondence rule and the obstacle light control environment information, obtain the obstacle light adjustment information.

[0047] In this embodiment, the obstacle light control environment information includes meteorological data (wind speed, visibility), air traffic control status (flight prohibition / height limit), and ground traffic density (drone activity frequency); the obstacle light adjustment information includes brightness adjustment, frequency switching, and color temperature switching instructions.

[0048] Specifically, the preset obstacle light parameter correspondence rule includes: S31: Determine whether to activate the infrared supplementary lighting module and send an alarm instruction to the ground station according to the meteorological visibility value in the meteorological data and the preset meteorological visibility threshold.

[0049] In this embodiment, the meteorological visibility refers to the maximum distance (unit: meter) at which the observer can clearly identify the target object, which is obtained through meteorological sensors or air traffic control data; the infrared supplementary lighting module emits infrared light that is invisible to the human eye but visible to the camera, enhancing the recognition of obstacles at night or under low visibility conditions; the alarm instruction is a standardized message (such as XML format) sent to the ground station, including the alarm level, location, and duration.

[0050] Specifically, according to the ICAO standard, the infrared supplementary lighting is activated when the visibility < 800 m; the value of the meteorological visibility threshold can be defined according to the type of the aircraft.

[0051] S32: Calculate the differential coefficient of the light flashing frequency based on the airspace traffic density, and trigger the light flashing frequency differentiation strategy based on the differential coefficient of the light flashing frequency.

[0052] In this embodiment, the airspace traffic density refers to the number of aircraft in a unit volume (such as 1 km³), which is obtained through an ADS-B receiver or an air traffic control system; the differential coefficient of the flashing frequency is a frequency scaling factor dynamically adjusted according to the density (such as density × 0.1); the differentiation strategy is to increase the frequency difference at high density (such as ±10%) and keep synchronous at low density.

[0053] Specifically, set up an airspace grid model (such as a 500 m × 500 m cell), and the airspace traffic density = the number in the cell / the volume of the cell. The light flashing frequency differentiation strategy includes a hierarchical frequency control strategy. For example, when the airspace traffic density is less than the first density threshold and the differential coefficient of the flashing frequency is 1.0, the flashing mode is synchronous constant frequency; when the airspace traffic density is greater than or equal to the first density threshold and less than the second density threshold, and the differential coefficient of the flashing frequency is 1.2, the flashing frequency increases by 20%; when the airspace traffic density is greater than or equal to the second density threshold, and the differential coefficient of the flashing frequency is 1.5, the flashing frequency is randomly jittered.

[0054] Furthermore, the differential coefficient of the light flashing frequency is calculated by the following formula: where is the airspace traffic density (flights / cubic kilometer); is the preset safe traffic density threshold; is the frequency adjustment factor (1.2 ≤ ≤ 1.5); is the attenuation coefficient ( ); is the predicted value of the maximum traffic density.

[0055] S33: When it is detected that the distance to the ground obstacle is less than the preset safe radius, trigger the light color temperature dynamic switching instruction.

[0056] In this embodiment, the distance to the ground obstacle is detected by a lidar (LiDAR) or a millimeter-wave radar; the safe radius is a preset safe distance threshold (such as a radius of 30 m for a helicopter landing area); the color temperature switching is from white (5000 K) to red (2700 K) to enhance the visual warning effect.

[0057] S4: Adjust the initial optimization parameters of the obstacle lights according to the obstacle light adjustment information to obtain the obstacle light parameters suitable for the environment.

[0058] In this embodiment, the obstacle light parameters suitable for the environment are the final control parameters that synthesize the initial parameters and the environmental instructions. Specifically, step S4 includes: S41: Construct a four-dimensional flight trajectory prediction model and calculate the boundary of the lighting action range.

[0059] In this embodiment, the four-dimensional flight trajectory prediction model is a trajectory prediction algorithm based on the longitude, latitude, altitude, and time dimensions, and outputs the predicted values of the position and speed of the aircraft in the next 1-5 seconds; the boundary of the lighting action range refers to the geometric boundary of the effective warning range of the obstacle lights (such as a spherical radius R = 50m).

[0060] Specifically, the four-dimensional flight trajectory prediction model uses Kalman filtering to fuse IMU and GPS data to predict the future trajectory: , where is the predicted state at the next moment; is the state transition matrix, which contains the relationship between position and speed; is the state estimation vector at time k, which is the optimal estimated value obtained through Kalman filtering. For example, in the UAV trajectory prediction, it includes information such as position, speed, and acceleration; is the control input matrix, which converts the control input into the change in the state space; is the control input vector, which represents the control amount applied to the aircraft. For example, the acceleration command of the UAV; is the process noise, which can be Gaussian white noise with a mean of zero.

[0061] The calculation of the boundary of the lighting action range is carried out in a spherical space with the current position of the aircraft as the center of the sphere and a radius of R = 50m, and the radius is dynamically adjusted according to the flight speed (R = 50 + v × 0.5). The higher the speed, the larger the coverage range.

[0062] S42: Through a fuzzy PID controller, convert the aircraft overload value into the flashing frequency gain coefficient of the obstacle lights according to the boundary of the lighting action range.

[0063] In this embodiment, the fuzzy PID controller combines fuzzy logic and PID algorithm to map the aircraft overload value to the flashing frequency gain coefficient of the obstacle lights. The aircraft overload value refers to the deviation value between the actual aircraft overload and the safety threshold (such as 9g).

[0064] Specifically, the fuzzy PID controller calculates the gain coefficient through the following formula : where i is the fuzzy rule index; n is the total number of fuzzy rules; is the aircraft overload value; is the triangular membership function of the overload deviation ; where is the lower limit of the membership function; b is the upper limit of the membership function, defining the vertex of the fuzzy interval; is the proportionality coefficient in the preset fuzzy rule base, satisfying: where k is the slope of the S-shaped activation function (k≥0.5); is the fuzzy rule trigger threshold 。

[0065] S43: Based on the flashing frequency gain coefficient of the obstacle light and combining with the optimized duty cycle value of the light signal output by the reinforcement learning model, trigger the secondary optimization instruction for the obstacle light parameters.

[0066] In this embodiment, the reinforcement learning model adopts the Q-learning algorithm, takes the environmental state as the input, and outputs the optimized duty cycle value of the light signal. The optimized duty cycle value refers to the PWM duty cycle (0% to 100%) for controlling the LED driver chip to adjust the light brightness.

[0067] Specifically, the state space (S) in the reinforcement learning model includes the aircraft overload value ( ), airspace traffic density (D), and ground obstacle distance (d); the ground obstacle distance represents the straight-line distance from the aircraft to the nearest ground obstacle (unit: meter); the action space (A) refers to the duty cycle adjustment step size (±5%, ±10%); , the energy consumption refers to the electrical energy consumption of the lighting system (unit: watt), and the false alarm rate refers to the probability of erroneously triggering the light warning (unit: %). In actual applications, correlation weight coefficients are assigned to the energy consumption and the false alarm rate. For example, the energy consumption weight is 1 (direct square term), and the false alarm rate weight is 0.1 (less impact after squaring).

[0068] The goal of the Q-learning algorithm is to make the agent learn to select the optimal action by continuously updating Q(s, a). The Q-table update formula is: , where the adjustment step size determines the amplitude of each action change; Q(s, a) represents the expected cumulative reward for taking action a in state s; R is the immediate reward; is the learning rate, which determines the Q-value update speed, and the value range is 0.1~0.3; is the discount factor, which is used to balance the importance of the current reward and the future reward; represents the maximum Q-value of all possible actions a' in the next state s', that is, the Q-value for selecting the optimal action.

[0069] S44: Based on the secondary optimization instruction for the obstacle light parameters, adjust the initial optimized parameters of the obstacle light to obtain the final obstacle light parameters suitable for the environment.

[0070] In this embodiment, the secondary optimization instruction refers to a composite control instruction that combines the fuzzy PID gain coefficient and the optimized duty cycle value of reinforcement learning; the environmental suitability obstacle lamp parameters are the set of brightness, frequency, and color temperature parameters of the final output.

[0071] Specifically, parameter fusion adopts a weighted fusion formula: ; ; where is the fusion weight (0.6 ≤ ≤ 0.8); in actual application, the actual light intensity is monitored by a photosensitive sensor, and parameter recalculation is triggered when the error exceeds ±5%; and the obstacle lamp is set with safety limits, for example, the maximum brightness does not exceed 3000 cd, and the frequency does not exceed 6 Hz. The specific safety limits are custom-set according to the product parameters of the actual obstacle lamp.

[0072] S5: Based on the environmental suitability obstacle lamp parameters, control the corresponding obstacle lamp for dynamic adjustment.

[0073] In this embodiment, there is a preset Internet of Things module (such as an intelligent control terminal, etc.) for remotely and wirelessly controlling all obstacle lamps in the target flight area; the light parameters are controlled in real time through the Internet of Things module.

[0074] It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0075] In one embodiment, an obstacle lamp control system in a low-altitude flight environment is provided, and this obstacle lamp control system in a low-altitude flight environment corresponds to the obstacle lamp control method in the above embodiment in a low-altitude flight environment.

[0076] The obstacle lamp control system in a low-altitude flight environment includes a parameter acquisition module, a parameter optimization engine, an environmental perception module, a dynamic adjustment module, and a light control execution unit. The detailed descriptions of each functional module are as follows: The parameter acquisition module is used to obtain the current obstacle lamp parameters corresponding to each aircraft based on the preset obstacle lamp division information in the target flight area; The parameter optimization engine is used to adjust the current obstacle lamp parameters based on the preset flight parameters corresponding to each aircraft to obtain the initial optimized obstacle lamp parameters; The environmental perception module is used to obtain the obstacle lamp control environment information corresponding to each aircraft, and obtain the obstacle lamp adjustment information based on the preset obstacle lamp parameter correspondence rule and the obstacle lamp control environment information; The dynamic adjustment module is used to adjust the initial optimized obstacle lamp parameters according to the obstacle lamp adjustment information to obtain the environmental suitability obstacle lamp parameters; A lighting control execution unit is used to control the corresponding obstacle lights for dynamic adjustment based on the environmental suitability obstacle light parameters.

[0077] For the specific limitations of the obstacle light control system in the low-altitude flight environment, reference can be made to the limitations of the obstacle light control method in the low-altitude flight environment described above, which will not be elaborated here; each module in the above obstacle light control system in the low-altitude flight environment can be implemented in whole or in part by software, hardware, and their combinations; the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0078] In one embodiment, a lamp is provided. The lamp includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the obstacle light control method in the low-altitude flight environment as described above are implemented. The lamp includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the lamp is used to provide computing and control capabilities. The memory of the lamp includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the lamp is used to store obstacle light division information, obstacle light regulation environment information, environmental suitability obstacle light parameters, etc. The network interface of the lamp is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an obstacle light control method in a low-altitude flight environment.

[0079] In one embodiment, a lamp is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: S1: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft. S2: Based on the preset flight parameters corresponding to each aircraft, adjust the current obstacle light parameters to obtain the initial optimized obstacle light parameters. S3: Obtain the obstacle light regulation environment information corresponding to each aircraft, and based on the preset obstacle light parameter corresponding rules and the obstacle light regulation environment information, obtain the obstacle light adjustment information. S4: According to the obstacle light adjustment information, adjust the initial optimized obstacle light parameters to obtain the environmental suitability obstacle light parameters. S5: Based on the environmental suitability obstacle light parameters, control the corresponding obstacle lights for dynamic adjustment.

[0080] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. Obstacle light control method in a low-altitude flight environment, characterized in that Including: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft; Based on the preset flight parameters corresponding to each aircraft, adjust the current obstacle light parameters to obtain the initial optimized obstacle light parameters; Obtain the obstacle light control environment information corresponding to each aircraft, and based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information, obtain the obstacle light adjustment information; According to the obstacle light adjustment information, adjust the initial optimized obstacle light parameters to obtain the environment-suitable obstacle light parameters; Based on the environment-suitable obstacle light parameters, control the corresponding obstacle lights for dynamic adjustment.

2. The obstacle lamp control method in a low-altitude flight environment according to claim 1, characterized in that, The preset obstacle light division information is obtained through the following methods: Obtain flight facility registration information, flight area map information, and airspace control data; determine the aircraft type, flight altitude range, and flight area characteristics; According to the aircraft type, flight altitude range, and flight area characteristics, divide the main obstacle light information and auxiliary obstacle light information corresponding to each aircraft; The obstacle light division information of each aircraft includes the main obstacle light parameter configuration and the auxiliary obstacle light parameter configuration.

3. The obstacle light control method in a low-altitude flight environment according to claim 1, wherein The preset flight parameters include the aircraft type, flight speed, flight attitude, and emergency status flag. The adjustment of the current obstacle light parameters includes: Based on the aircraft type of the corresponding aircraft, obtain the basic brightness coefficient and the flashing frequency reference value; Combined with the flight speed, flight attitude, and emergency status flag, query the preset dynamic adjustment coefficient table to obtain the real-time correction coefficient; According to the basic brightness coefficient, the flashing frequency reference value, and the real-time correction coefficient, adjust the current obstacle light parameters to obtain the initial optimized obstacle light parameters.

4. The obstacle light control method in a low-altitude flight environment according to claim 1, characterized in that, The obstacle light control environment information includes meteorological data, airspace traffic density, and ground obstacle distribution. The preset obstacle light parameter corresponding rules include: According to the meteorological visibility value in the meteorological data and the preset meteorological visibility threshold, determine whether to activate the infrared supplementary light module and send an alarm instruction to the ground station; Based on the airspace traffic density, calculate the lighting flashing frequency differentiation coefficient, and trigger the lighting flashing frequency differentiation strategy based on the lighting flashing frequency differentiation coefficient; When it is detected that the distance of the ground obstacle is less than the preset safety radius, trigger the lighting color temperature dynamic switching instruction.

5. The obstacle lamp control method in a low-altitude flight environment according to claim 1, characterized in that, The adjustment of the initial optimized obstacle light parameters to obtain the environment-suitable obstacle light parameters includes: Construct a four-dimensional flight trajectory prediction model and calculate the boundary of the lighting action range; Through a fuzzy PID controller, convert the aircraft overload value into a flashing frequency gain coefficient of the obstacle light according to the lighting action range boundary; Based on the flashing frequency gain coefficient of the obstacle light, combined with the optimized lighting signal duty cycle value output by the reinforcement learning model, trigger the secondary optimization instruction of the obstacle light parameters; Based on the secondary optimization instruction of the obstacle light parameters, adjust the initial optimized obstacle light parameters to obtain the final environment-suitable obstacle light parameters.

6. The obstacle light control method in a low-altitude flight environment according to claim 4, characterized in that The light flashing frequency differentiation coefficient is calculated by the following formula: where is the airspace traffic density (flights / cubic kilometer); is the preset safe traffic density threshold; is the frequency adjustment factor (1.2 ≤ ≤ 1.5); is the attenuation coefficient ( ); is the predicted value of the maximum traffic density.

7. The obstacle light control method in a low-altitude flight environment according to claim 5, characterized in that, The fuzzy PID controller calculates the gain coefficient through the following formula :[[]]END]] where \(i\) is the fuzzy rule index; \(n\) is the total number of fuzzy rules; is the aircraft overload value; is the triangular membership function of the overload deviation ; where is the lower limit of the membership function; \(b\) is the upper limit of the membership function, defining the vertex of the fuzzy interval; is the proportionality coefficient in the preset fuzzy rule base, satisfying: where k is the slope of the S-shaped activation function (k ≥ 0.5); is the fuzzy rule triggering threshold 。 8. Obstacle light control system in a low-altitude flight environment, characterized in that, For implementing the obstacle light control method in the low-altitude flight environment according to any one of claims 1 to 7, the system includes: A parameter acquisition module, configured to acquire current obstacle light parameters corresponding to each aircraft based on preset obstacle light division information in a target flight area; A parameter optimization engine, configured to adjust the current obstacle light parameters based on preset flight parameters corresponding to each aircraft to obtain initial optimized obstacle light parameters; An environment perception module, configured to acquire obstacle light regulation environment information corresponding to each aircraft, and obtain obstacle light adjustment information based on a preset obstacle light parameter correspondence rule and the obstacle light regulation environment information; A dynamic adjustment module, configured to adjust the initial optimized obstacle light parameters according to the obstacle light adjustment information to obtain environment-suitable obstacle light parameters; A lighting control execution unit, configured to control corresponding obstacle lights to perform dynamic adjustment based on the environment-suitable obstacle light parameters.

9. A lighting fixture, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the obstacle light control method in a low-altitude flight environment according to any one of claims 1 to 7.

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