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

Abstract

This application discloses a method, system, and lighting device for controlling obstruction lights in a low-altitude flight environment, relating to the field of aviation safety technology. The method includes obtaining current obstruction light parameters corresponding to each aircraft based on preset obstruction light division information in a target flight area; adjusting the current obstruction light parameters based on preset flight parameters corresponding to each aircraft to obtain initial optimized obstruction light parameters; obtaining obstruction light control environment information corresponding to each aircraft, and obtaining obstruction light adjustment information based on preset obstruction light parameter corresponding rules and the obstruction light control environment information; adjusting the initial optimized obstruction light parameters based on the obstruction light adjustment information to obtain environmentally suitable obstruction light parameters; and dynamically adjusting the corresponding obstruction light based on the environmentally suitable obstruction light parameters. This application improves the control efficiency of aviation obstruction lights.

Description

Technical Field

[0001] The present application relates to the field of aviation safety technology, and in particular to an obstacle light control method, system, and lamp in a low-altitude flight environment. Background Art

[0002] With the popularity of low-altitude aircraft such as drones and helicopters, the safety issue of low-altitude flight has become increasingly prominent; traditional aviation obstruction lights usually adopt the method of continuous lighting or lighting for a fixed time period, which has problems such as energy waste and light pollution; at the same time, existing obstruction lights lack intelligent perception and control capabilities and cannot be dynamically adjusted according to the actual flight environment. Therefore, there is a defect of poor control efficiency of aviation obstruction lights, which needs to be improved. Summary of the Invention

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

[0004] In the first aspect, the invention objectives of this application are achieved by adopting the following technical solutions:

[0005] Obstacle light control methods in low-altitude flight environments include:

[0006] Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft;

[0007] Adjusting the current obstruction light parameters based on the preset flight parameters corresponding to each aircraft to obtain initial optimized obstruction light parameters;

[0008] Obtaining the obstacle light control environment information corresponding to each aircraft, and obtaining the obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information;

[0009] Adjusting the initial optimization parameters of the obstacle light according to the obstacle light adjustment information to obtain environment-appropriate obstacle light parameters;

[0010] Based on the environment, the corresponding obstacle lights are controlled to perform dynamic adjustments based on the obstacle light parameters.

[0011] By adopting the above technical solution, this solution supports differentiated control of the main / auxiliary light groups, and realizes dynamic allocation of aircraft lighting resources by dividing information through obstacle lights, avoiding light signal interference when multiple aircraft work together; compared with the traditional single parameter configuration mode, the accuracy of obstacle light resource allocation is improved; combined with obstacle light control environment information (such as weather, airspace traffic density and other environmental parameters), real-time adjustment of lighting parameters (brightness / frequency / color temperature) is achieved; thereby helping to improve flight safety, especially in low-altitude flight environments, reducing the risk of collision between aircraft and ground obstacles; further, this application uses environmental adjustment information to secondary optimize the initial optimization parameters of the obstacle lights, forming an environmental dynamic adaptation rate, and improving the control efficiency of aviation obstruction lights.

[0012] In a preferred example of the present application, the preset obstacle light division information is obtained by:

[0013] Obtain flight facility registration information, flight area map information, and airspace control data; determine aircraft type, flight altitude range, and flight area characteristics;

[0014] Divide the main obstacle light information and auxiliary obstacle light information corresponding to each aircraft according to the aircraft type, flight altitude range and flight area characteristics;

[0015] The obstacle light division information of each aircraft includes main obstacle light parameter configuration and auxiliary obstacle light parameter configuration.

[0016] By adopting the above technical solution and analyzing flight facility registration information, flight area map information and airspace control data, the main and auxiliary obstacle light information can be accurately divided for each aircraft, ensuring that the appropriate obstacle light configuration is available for different flight missions and conditions. At the same time, the energy consumption of the obstacle lights can be saved. For example, the lights can be activated only when triggered by airspace control instructions (such as temporary no-fly zones), thereby reducing overall energy consumption.

[0017] In a preferred example of the present application, the preset flight parameters include aircraft type, flight speed, flight attitude, and emergency status indicator, and the adjustment of the current obstacle light parameters includes:

[0018] Based on the aircraft type of the corresponding aircraft, obtaining a basic brightness coefficient and a flicker frequency reference value;

[0019] In combination with the flight speed, flight attitude and emergency status indicator, a preset dynamic adjustment coefficient table is searched to obtain a real-time correction coefficient;

[0020] According to the basic brightness coefficient, the flicker frequency reference value and the real-time correction coefficient, the current obstacle light parameters are adjusted to obtain the initial optimized parameters of the obstacle light.

[0021] By adopting the above technical solution, an emergency response mechanism for emergency status identification (such as mechanical failure) is introduced. For example, in an emergency, the flashing frequency of the obstacle lights is triggered to increase to 1.5 times the baseline value. Therefore, especially in an emergency, the obstacle light parameters can be quickly adjusted to increase visibility or change the flashing mode, thereby effectively improving the safety performance of the aircraft in complex situations.

[0022] In a preferred example of the present application, the obstacle light control environment information includes meteorological data, airspace traffic density, and ground obstacle distribution, and the preset obstacle light parameter corresponding rules include:

[0023] Determining whether to activate the infrared fill light module and sending an alarm command to the ground station based on the meteorological visibility value in the meteorological data and a preset meteorological visibility threshold;

[0024] Calculating a light flicker frequency differentiation coefficient based on the airspace traffic density, and triggering a light flicker frequency differentiation strategy based on the light flicker frequency differentiation coefficient;

[0025] When the distance to a ground obstacle is detected to be less than the preset safety radius, the dynamic switching instruction of the light color temperature is triggered.

[0026] By adopting the above-mentioned technical solution, this application formulates corresponding obstacle light adjustment strategies based on multiple environmental influencing factors existing in the actual environment, such as meteorological conditions, air traffic conditions, and the location of ground obstacles, so as to provide a highly adaptable early warning method. For example, when visibility is low, the infrared fill light module is activated and an alarm instruction is sent to the ground station, or when an approaching ground obstacle is detected, a dynamic switching instruction of the light color temperature is triggered, which is beneficial to ensure flight safety.

[0027] In a preferred embodiment of the present application, adjusting the initial optimization parameters of the obstacle light to obtain environmentally suitable obstacle light parameters includes:

[0028] Build a four-dimensional flight trajectory prediction model and calculate the boundaries of the lighting range;

[0029] The aircraft overload value is converted into a flashing frequency gain coefficient of the obstacle light according to the boundary of the light action range through a fuzzy PID controller;

[0030] Based on the flickering frequency gain coefficient of the obstacle light and the optimized value of the light signal duty cycle output by the reinforcement learning model, a secondary optimization instruction for the obstacle light parameters is triggered;

[0031] Based on the obstacle light parameter secondary optimization instruction, the initial optimization parameters of the obstacle light are adjusted to obtain final environment-suitable obstacle light parameters.

[0032] By adopting the above technical solution, the four-dimensional flight trajectory prediction model is a dynamic trajectory prediction accuracy based on flight longitude, latitude, altitude and time. Different from the traditional three-dimensional model, it has higher prediction accuracy. At the same time, it introduces the time dimension to dynamically correct the boundary of the light range, breaking through the limitations of traditional static boundary setting. This application converts the overload value through fuzzy PID to provide nonlinear overload response capability in overload mutation scenarios. 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 reduced to 30% during the day), thereby improving the adaptability of the obstacle light control strategy to environmental changes.

[0033] In a preferred example of this application: the light flicker frequency differentiation coefficient Calculated by the following formula: in, is the airspace traffic density (flights / cubic kilometer); To preset a 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.

[0034] By adopting the above technical solution, the light flashing frequency differentiation coefficient is calculated to automatically adjust the flashing frequency of the obstacle light according to the current airspace traffic density. The obstacle light control system can perform well when dealing with complex and changing flight conditions; the exponential decay obstacle light control method is more in line with the human eye visual fatigue curve.

[0035] In a preferred example of the present application, the fuzzy PID controller calculates the gain coefficient by the following formula: :

[0036] 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 ;in, is the lower limit of the membership function; b is the upper limit of the membership function, which defines the vertex of the fuzzy interval; is the proportional coefficient in the preset fuzzy rule base, satisfying:

[0037] Where k is the slope of the S-type activation function (k ≥ 0.5); Triggering threshold for fuzzy rules.

[0038] By adopting the above technical solution, the PID parameters are flexibly adjusted according to the overload value of the aircraft, and the triangle membership function ( =-1g, b=1g) for medium overload ( =0.8g), the sensitivity is improved.

[0039] In the second aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0040] An obstruction light control system in a low-altitude flight environment, for executing the above-mentioned obstruction light control method in a low-altitude flight environment, comprises:

[0041] A parameter acquisition module is used to obtain the current obstacle light parameters corresponding to each aircraft based on the preset obstacle light division information in the target flight area;

[0042] A parameter optimization engine, configured to adjust the current obstruction light parameters based on preset flight parameters corresponding to each aircraft to obtain initial optimized parameters of the obstruction light;

[0043] An environment perception module is used to obtain the obstacle light control environment information corresponding to each aircraft, and obtain obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information;

[0044] A dynamic adjustment module, configured to adjust the initial optimization parameters of the obstacle light according to the obstacle light adjustment information to obtain environment-appropriate obstacle light parameters;

[0045] The lighting control execution unit is used to control the corresponding obstacle lights to perform dynamic adjustments based on the environment-suitable obstacle light parameters.

[0046] In a third aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0047] A lamp comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for controlling an obstacle light in a low-altitude flight environment when executing the computer program.

[0048] In summary, this application includes at least one of the following beneficial technical effects:

[0049] 1. By acquiring the current obstruction light parameters of each aircraft in the target flight area and adjusting them according to preset flight parameters, the obstruction lights can automatically optimize parameters such as brightness and flashing frequency in different flight environments. In low-altitude flight environments, the risk of collision between aircraft and ground obstacles is reduced.

[0050] 2. It can be personalized according to the specific type of aircraft, flight altitude range and flight area characteristics, which improves the pertinence and efficiency of the use of obstacle lights. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for controlling an obstacle light in a low-altitude flight environment according to an embodiment of the present application;

[0052] Figure 2 This is a flowchart of step S2 in the obstacle light control method in a low-altitude flight environment in one embodiment of the present application. DETAILED DESCRIPTION

[0053] The present application is further described in detail below with reference to the accompanying drawings.

[0054] In one embodiment, if Figure 1 As shown, the present application discloses a method for controlling an obstacle light in a low-altitude flight environment, which specifically includes the following steps:

[0055] S1: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft.

[0056] In this embodiment, the target flight area refers to the flight area of ​​the aircraft in a low-altitude environment; the obstacle light division information refers to the lighting configuration rules predefined based on the flight facility registration information, flight area map and airspace control data, including parameter templates for the main obstacle light (constantly on warning) and the auxiliary obstacle light (dynamically controlled); the current obstacle light parameters include real-time control parameters such as brightness value (unit: candela cd), flashing frequency (Hz), and color temperature (K).

[0057] Specifically, the aircraft ID is obtained through the aircraft communication bus (such as the CAN bus), and the cloud database is queried to obtain the pre-stored main obstacle light parameter configuration file (JSON format) and auxiliary obstacle light parameter configuration file. According to the current flight phase of the aircraft (takeoff / cruise / landing), the parameter template of the corresponding phase is called from the configuration file.

[0058] Specifically, step S1 includes:

[0059] S11: Obtain flight facility registration information, flight area map information and airspace control data; determine aircraft type, flight altitude range and flight area characteristics.

[0060] In this embodiment, aircraft registration information includes the aircraft type and unique identification information of ground facilities (such as heliports and transmission towers), including facility type, geographical location, altitude range, purpose, etc.; flight area map information refers to geographic fence data (such as airport runway boundaries, no-fly zone coordinates) or the geographic coordinates and topological structure of transmission lines; airspace control data refers to real-time control instructions issued by aviation management departments, including temporary no-fly notices, route restrictions, aircraft activity density, etc.

[0061] Specifically, the flight altitude range is determined according to the type of facility (e.g., the main take-off and landing area of ​​a helicopter airport is 0-120m high, and the warning height of a transmission tower is 0-150m high). The regional feature classification of the flight area map information can be used to identify the airspace type (e.g., airport perimeter, high-voltage line corridor) through GIS map matching.

[0062] S12: Divide the main obstacle light information and auxiliary obstacle light information corresponding to each aircraft according to the aircraft type, flight altitude range, and flight area characteristics.

[0063] Specifically, the main obstacle light information refers to the constant, high-brightness lighting configuration, which is used for basic all-weather warnings (such as a constant red light on the top of a transmission tower); the auxiliary obstacle light information refers to the dynamically adjustable lighting configuration, which is used to enhance warnings in specific scenarios (such as high-frequency flashing lights during emergency landing).

[0064] In this embodiment, a facility type-height-region feature mapping table is established, for example: .

[0065] S13: The obstacle light division information of each aircraft includes the main obstacle light parameter configuration and the auxiliary obstacle light parameter configuration.

[0066] In this embodiment, the parameter configuration of the main obstacle light includes static parameters such as brightness reference value, flashing frequency, color temperature, etc.; the parameter configuration of the auxiliary obstacle light includes variable parameters such as dynamic trigger threshold, gain coefficient, response delay, etc.

[0067] S2: Based on the preset flight parameters corresponding to each aircraft, the current obstacle light parameters are adjusted to obtain the initial optimized parameters of the obstacle light.

[0068] In this embodiment, the preset flight parameters include aircraft type (fixed wing / multi-rotor), flight speed (m / s), flight attitude (pitch angle / roll angle), and emergency status indicator (normal / warning); the initial optimization parameters refer to the intermediate parameters obtained by weighted calculation of the basic brightness coefficient and the dynamic correction coefficient.

[0069] Specifically, step S2 includes:

[0070] S21: Based on the aircraft type of the corresponding aircraft, a basic brightness coefficient and a flicker frequency reference value are obtained.

[0071] In this embodiment, the aircraft type refers to the type of aircraft (such as fixed-wing UAV, multi-rotor UAV, manned aircraft); the basic brightness coefficient unit is candela (cd), which represents the initial brightness reference value of the obstacle light under standard conditions; the flashing frequency reference value unit is hertz (Hz), which represents the default flashing rate of the obstacle light.

[0072] Create an aircraft type-brightness / frequency mapping table, for example: .

[0073] S22: Based on the flight speed, flight attitude and emergency status indicator, query the preset dynamic adjustment coefficient table to obtain the real-time correction coefficient.

[0074] Specifically, flight speed (v): unit m / s, affects the real-time adjustment of light brightness and frequency; flight attitude is expressed in attitude angle Indicates, including pitch angle and roll angle (roll), unit angle (°), reflecting the aircraft's motion state; emergency state flag (E), expressed as a Boolean value (0 / 1), indicating whether the alarm mode is triggered; dynamic correction coefficient table is used to store the mapping relationship between flight parameters and correction coefficients. In actual applications, aircraft sensors obtain real-time data: flight speed (v), attitude angle ( ) and emergency status flag E, the emergency status flag is triggered by the fault detection module.

[0075] According to the flight speed (v) and attitude angle ( ), query the preset dynamic correction coefficient table (set the flight speed to be normalized to [0, 30] m / s; limit it to the range of [-30°, 30°]): .

[0076] For example, when the speed v = 15 m / s (between 10 and 20), the correction factor is calculated by linear interpolation: brightness correction factor = 1.0 − (15 − 10) / 10 × (1.0 − 0.8) = 0.9; where the emergency coverage rule is: when E = 1, the mandatory coverage correction factor is the maximum value (brightness + 50%, frequency × 2).

[0077] S23: According to the basic brightness coefficient, the flickering frequency reference value and the real-time correction coefficient, the current obstacle light parameters are adjusted to obtain the initial optimized parameters of the obstacle light.

[0078] In this embodiment, the initial optimization parameters of the obstacle light refer to the intermediate control parameters after comprehensive basic parameters and dynamic correction; the light brightness of the embodiment of the present application is set with upper and lower limit constraints based on the actual product specifications of the obstacle light, such as brightness ∈ [500, 2000] cd; frequency ∈ [1.0, 5.0] Hz.

[0079] Specifically, optimized brightness = basic brightness × (1 + brightness correction coefficient); optimized frequency = reference frequency × frequency correction coefficient.

[0080] S3: Obtaining the obstacle light control environment information corresponding to each aircraft, and obtaining the obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information.

[0081] In this embodiment, the obstacle light control environment information includes meteorological data (wind speed, visibility), air traffic control status (no-fly / height restriction), and ground traffic density (drone activity frequency); the obstacle light adjustment information includes brightness adjustment, frequency switching, and color temperature switching instructions.

[0082] Specifically, the preset obstacle light parameter corresponding rules include:

[0083] S31: Determine whether to activate the infrared fill light module and send an alarm command to the ground station based on the meteorological visibility value in the meteorological data and a preset meteorological visibility threshold.

[0084] In this embodiment, meteorological visibility refers to the maximum distance (unit: meter) at which an observer can clearly identify a target object, which is obtained through meteorological sensors or air traffic control data; the infrared fill light module emits infrared light that is invisible to the human eye but visible to the camera, enhancing obstacle identification at night or in low visibility conditions; the alarm command refers to a standardized message (such as XML format) sent to the ground station, which includes the alarm level, location and duration.

[0085] Specifically, according to ICAO standards, infrared fill light is activated when visibility is less than 800m; the numerical value of the meteorological visibility threshold can be defined according to the type of aircraft.

[0086] S32: Calculate a light flicker frequency differentiation coefficient based on the airspace traffic density, and trigger a light flicker frequency differentiation strategy based on the light flicker frequency differentiation coefficient.

[0087] In this embodiment, airspace traffic density refers to the number of aircraft within a unit volume (e.g., 1 km³), which is obtained through an ADS-B receiver or air traffic control system. The flashing frequency differentiation coefficient is a frequency scaling factor (e.g., density × 0.1) that is dynamically adjusted based on the density. Differentiation strategies include increasing the frequency difference (e.g., ±10%) at high density and maintaining synchronization at low density.

[0088] Specifically, a gridded airspace model (e.g., 500m×500m cells) is set, where airspace traffic density = number of lights within a cell / cell volume. A differentiated light flicker frequency strategy includes a graded frequency control strategy. For example, when airspace traffic density is less than a first density threshold and the flicker frequency differentiation coefficient is 1.0, the flickering mode is synchronized constant frequency. When airspace traffic density is greater than or equal to the first density threshold but less than a second density threshold, the flicker frequency is increased by 20% when the flicker frequency differentiation coefficient is 1.2. When airspace traffic density is greater than or equal to the second density threshold and the flicker frequency differentiation coefficient is 1.5, the flicker frequency is random jitter.

[0089] Furthermore, the light flicker frequency differentiation coefficient Calculated by the following formula: in, is the airspace traffic density (flights / cubic kilometer); To preset a 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.

[0090] S33: When it is detected that the distance of the ground obstacle is less than the preset safety radius, the dynamic switching instruction of the light color temperature is triggered.

[0091] In this embodiment, the distance to ground obstacles is detected by laser radar (LiDAR) or millimeter wave radar; the safety radius refers to a preset safety distance threshold (such as a 30m radius of a helicopter landing and take-off area); the color temperature is switched, such as from white (5000K) to red (2700K), to enhance the visual warning effect.

[0092] S4: According to the obstacle light adjustment information, the initial optimization parameters of the obstacle light are adjusted to obtain obstacle light parameters suitable for the environment.

[0093] In this embodiment, the environmentally suitable obstacle light parameters are final control parameters obtained by integrating the initial parameters and the environmental instructions.

[0094] Specifically, step S4 includes:

[0095] S41: Build a four-dimensional flight trajectory prediction model and calculate the boundaries of the lighting range.

[0096] In this embodiment, the four-dimensional flight trajectory prediction model is a trajectory prediction algorithm based on longitude, latitude, altitude and time dimensions, which outputs the predicted values ​​of the aircraft position and speed in the next 1-5 seconds; the light range boundary refers to the geometric boundary of the effective warning range of the obstacle light (such as the spherical radius R = 50m).

[0097] Specifically, the four-dimensional flight trajectory prediction model uses Kalman filtering to fuse IMU and GPS data to predict future trajectories: ,in, is the predicted state at the next moment; is the state transfer matrix, which contains the relationship between position and velocity; is the state estimation vector at time k, which is the optimal estimate obtained by Kalman filtering. For example, in UAV trajectory prediction, it includes information such as position, velocity, and acceleration; For the control input matrix, the control input Transformed into changes in state space; is the control input vector, which represents the control quantity applied to the aircraft, for example, the acceleration command of the drone; is process noise, and Gaussian white noise with zero mean can be used.

[0098] The light's range is calculated based on a sphere with a radius of 50m and the aircraft's current position as the center. The radius is dynamically adjusted based on flight speed (R=50+v×0.5). The higher the speed, the greater the coverage.

[0099] S42: The aircraft overload value is converted into a flashing frequency gain coefficient of the obstacle light according to the boundary of the light's range through a fuzzy PID controller.

[0100] 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 light. The aircraft overload value refers to the deviation between the actual aircraft overload and the safety threshold (such as 9g).

[0101] Specifically, the fuzzy PID controller calculates the gain coefficient by 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 ;in, is the lower limit of the membership function; b is the upper limit of the membership function, which defines the vertex of the fuzzy interval; is the proportional coefficient in the preset fuzzy rule base, satisfying:

[0102] Where k is the slope of the S-type activation function (k ≥ 0.5); Triggering threshold for fuzzy rules 。

[0103] S43: Based on the flashing frequency gain coefficient of the obstacle light and the optimized value of the light signal duty cycle output by the reinforcement learning model, a secondary optimization instruction for the obstacle light parameters is triggered.

[0104] In this embodiment, the reinforcement learning model adopts the Q-learning algorithm, takes the environmental state as 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%) that controls the LED driver chip to adjust the light brightness.

[0105] Specifically, the state space (S) in the reinforcement learning model contains the aircraft overload value ( ), airspace traffic density (D), ground obstacle distance (d); ground obstacle distance refers to the straight-line distance from the aircraft to the nearest ground obstacle (unit: meter); action space (A) refers to the duty cycle adjustment step size (±5%, ±10%); Energy consumption refers to the electrical energy consumption of the lighting system (unit: watt), and the false alarm rate refers to the probability of falsely triggering the light alarm (unit: %). In practical applications, associated weight coefficients are assigned to energy consumption and false alarm rate. For example, the energy consumption weight is 1 (direct square term), and the false alarm rate weight is 0.1 (the impact is smaller after squaring).

[0106] The goal of the learning algorithm is to enable the agent to learn to choose the optimal action by continuously updating Q(s, a). The Q table update formula is: , where the adjustment step size determines the magnitude of each action change; Q(s, a) represents the expected cumulative reward for taking action a in state s; R is the immediate reward; The learning rate determines the Q value update speed and its value range is 0.1~0.3; is a discount factor used to balance the importance of current rewards and future rewards; It represents the maximum Q value of all possible actions a' in the next state s', that is, the Q value of the optimal action.

[0107] S44: Based on the obstruction light parameter secondary optimization instruction, the initial optimization parameters of the obstruction light are adjusted to obtain the final environment-suitable obstruction light parameters.

[0108] In this embodiment, the secondary optimization instruction refers to a composite control instruction that integrates the fuzzy PID gain coefficient and the reinforcement learning duty cycle optimization value; the environmentally suitable obstacle light parameters are the final output brightness, frequency, and color temperature parameter set.

[0109] Specifically, parameter fusion adopts the weighted fusion formula: ; ;in, is the fusion weight (0.6≤ ≤0.8); in actual application, the actual light intensity is monitored by a photosensor, and parameter recalculation is triggered when the error exceeds ±5%; and the obstruction light is set with safety limits, for example, the maximum brightness does not exceed 3000cd, and the frequency does not exceed 6Hz. The specific safety limits are customized according to the actual product parameters of the obstruction light.

[0110] S5: Dynamically adjust the corresponding obstacle lights based on the environment's suitable obstacle light parameters.

[0111] In this embodiment, an Internet of Things module (such as an intelligent control terminal, etc.) is preset for remote wireless control of all obstacle lights in the target flight area; the lighting parameters are controlled in real time through the Internet of Things module.

[0112] It should be understood that the serial numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0113] In one embodiment, an obstruction light control system in a low-altitude flight environment is provided. The obstruction light control system in a low-altitude flight environment corresponds to the obstruction light control method in a low-altitude flight environment in the above-mentioned embodiment.

[0114] The obstruction light control system in low-altitude flight environments includes a parameter acquisition module, a parameter optimization engine, an environment perception module, a dynamic adjustment module, and a lighting control execution unit. Detailed descriptions of each functional module are as follows:

[0115] A parameter acquisition module is used to obtain the current obstacle light parameters corresponding to each aircraft based on the preset obstacle light division information in the target flight area;

[0116] Parameter optimization engine, used to adjust the current obstruction light parameters based on the preset flight parameters corresponding to each aircraft and obtain the initial optimized parameters of the obstruction light;

[0117] The environment perception module is used to obtain the obstacle light control environment information corresponding to each aircraft, and obtain obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information;

[0118] Dynamic adjustment module, used to adjust the initial optimization parameters of the obstacle light according to the obstacle light adjustment information, and obtain the obstacle light parameters suitable for the environment;

[0119] The lighting control execution unit is used to dynamically adjust the corresponding obstacle lights based on the environment's suitable obstacle light parameters.

[0120] For the specific limitations of the obstruction light control system in a low-altitude flight environment, please refer to the limitations of the obstruction light control method in a low-altitude flight environment mentioned above, which will not be repeated here; the various modules in the above-mentioned obstruction light control system in a low-altitude flight environment can be implemented in whole or in part through software, hardware and their combination; the above-mentioned 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 corresponding operations of the above-mentioned modules.

[0121] In one embodiment, a lighting fixture is provided. The lighting fixture 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 above-described method for controlling obstruction lights in a low-altitude flight environment are implemented. The lighting fixture includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the lighting fixture is configured to provide computing and control capabilities. The lighting fixture's memory 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 operating environment for the operating system and computer program in the non-volatile storage medium. The lighting fixture's database is configured to store information such as obstruction light classification, obstruction light control environment information, and environmentally suitable obstruction light parameters. The lighting fixture's network interface is configured to communicate with an external terminal via a network connection. When the processor executes the computer program, the method for controlling obstruction lights in a low-altitude flight environment is implemented.

[0122] In one embodiment, a lamp is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the following steps:

[0123] S1: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft;

[0124] S2: Based on the preset flight parameters corresponding to each aircraft, adjust the current obstacle light parameters to obtain the initial optimized parameters of the obstacle light;

[0125] S3: Obtaining the obstacle light control environment information corresponding to each aircraft, and obtaining obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information;

[0126] S4: According to the obstacle light adjustment information, the initial optimization parameters of the obstacle light are adjusted to obtain the obstacle light parameters suitable for the environment;

[0127] S5: Dynamically adjust the corresponding obstacle lights based on the environment's suitable obstacle light parameters.

[0128] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0129] 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 aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for controlling an obstacle light in a low-altitude flight environment, characterized in that: include: Based on the preset obstacle light division information in the target flight area, obtain the current obstacle light parameters corresponding to each aircraft; Adjusting the current obstruction light parameters based on the preset flight parameters corresponding to each aircraft to obtain initial optimized obstruction light parameters; Obtaining the obstacle light control environment information corresponding to each aircraft, and obtaining the obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information; Adjusting the initial optimization parameters of the obstacle light according to the obstacle light adjustment information to obtain environment-appropriate obstacle light parameters; Based on the environment suitable for the obstacle light parameters, the corresponding obstacle lights are controlled to perform dynamic adjustments; The preset flight parameters include aircraft type, flight speed, flight attitude and emergency status identification, and the adjustment of the current obstacle light parameters includes: Based on the aircraft type of the corresponding aircraft, obtaining a basic brightness coefficient and a flicker frequency reference value; In combination with the flight speed, flight attitude and emergency status indicator, a preset dynamic adjustment coefficient table is searched to obtain a real-time correction coefficient; Adjust the current obstruction light parameters according to the basic brightness coefficient, the flicker frequency reference value and the real-time correction coefficient to obtain the initial optimized parameters of the obstruction light; The obstacle light control environment information includes meteorological data, airspace traffic density and ground obstacle distribution. The preset obstacle light parameter corresponding rules include: Determining whether to activate the infrared fill light module and sending an alarm command to the ground station based on the meteorological visibility value in the meteorological data and a preset meteorological visibility threshold; Calculating a light flicker frequency differentiation coefficient based on the airspace traffic density, and triggering a light flicker frequency differentiation strategy based on the light flicker frequency differentiation coefficient; When the distance to the ground obstacle is detected to be less than the preset safety radius, the light color temperature dynamic switching instruction is triggered; The step of adjusting the initial optimization parameters of the obstacle light to obtain parameters of the obstacle light suitable for the environment includes: Build a four-dimensional flight trajectory prediction model and calculate the boundaries of the lighting range; The aircraft overload value is converted into a flashing frequency gain coefficient of the obstacle light according to the boundary of the light action range through a fuzzy PID controller; Based on the flashing frequency gain coefficient of the obstacle light and the optimized value of the light signal duty cycle output by the reinforcement learning model, a secondary optimization instruction for the obstacle light parameters is triggered; Based on the obstacle light parameter secondary optimization instruction, the initial optimization parameters of the obstacle light are adjusted to obtain final environment-suitable obstacle light parameters.

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

3. The method for controlling an obstacle light in a low-altitude flight environment according to claim 1, wherein: The light flicker frequency differentiation coefficient K freq Calculated by the following formula: Where ρ is the airspace traffic density, in units of flights / cubic kilometer; ρ0 is the preset safe traffic density threshold; α is the frequency adjustment factor, 1.2≤α≤1.5; β is the attenuation coefficient, β=0.8 / ρ max ρ max is the predicted value of the maximum traffic density.

4. The method for controlling an obstacle light in a low-altitude flight environment according to claim 1, wherein: The fuzzy PID controller calculates the gain coefficient k by the following formula p : Where i is the fuzzy rule index; n is the total number of fuzzy rules; ΔO is the aircraft overload value; μ A (ΔO) is the triangular membership function of the overload deviation; Among them, a is the lower limit of the membership function; b is the upper limit of the membership function, which defines the vertex of the fuzzy interval; k pi is the proportional coefficient in the preset fuzzy rule base, satisfying: Where k is the slope of the S-type activation function, k ≥ 0.5; c i Triggering threshold for fuzzy rules.

5. Obstacle light control system in low-altitude flight environment, characterized by: The system is used to execute the method for controlling an obstacle light in a low-altitude flight environment according to any one of claims 1 to 4, comprising: A parameter acquisition module is used to obtain 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, configured to adjust the current obstruction light parameters based on preset flight parameters corresponding to each aircraft to obtain initial optimized parameters of the obstruction light; An environment perception module is used to obtain the obstacle light control environment information corresponding to each aircraft, and obtain obstacle light adjustment information based on the preset obstacle light parameter corresponding rules and the obstacle light control environment information; A dynamic adjustment module, configured to adjust the initial optimization parameters of the obstacle light according to the obstacle light adjustment information to obtain environment-appropriate obstacle light parameters; A lighting control execution unit, configured to dynamically adjust the corresponding obstacle lights based on the environment-suitable obstacle light parameters; The preset flight parameters include aircraft type, flight speed, flight attitude and emergency status identification, and the adjustment of the current obstacle light parameters includes: Based on the aircraft type of the corresponding aircraft, obtaining a basic brightness coefficient and a flicker frequency reference value; In combination with the flight speed, flight attitude and emergency status indicator, a preset dynamic adjustment coefficient table is searched to obtain a real-time correction coefficient; Adjust the current obstruction light parameters according to the basic brightness coefficient, the flicker frequency reference value and the real-time correction coefficient to obtain the initial optimized parameters of the obstruction light; The obstacle light control environment information includes meteorological data, airspace traffic density and ground obstacle distribution. The preset obstacle light parameter corresponding rules include: Determining whether to activate the infrared fill light module and sending an alarm command to the ground station based on the meteorological visibility value in the meteorological data and a preset meteorological visibility threshold; Calculating a light flicker frequency differentiation coefficient based on the airspace traffic density, and triggering a light flicker frequency differentiation strategy based on the light flicker frequency differentiation coefficient; When the distance to the ground obstacle is detected to be less than the preset safety radius, the light color temperature dynamic switching instruction is triggered; The step of adjusting the initial optimization parameters of the obstacle light to obtain parameters of the obstacle light suitable for the environment includes: Build a four-dimensional flight trajectory prediction model and calculate the boundaries of the lighting range; The aircraft overload value is converted into a flashing frequency gain coefficient of the obstacle light according to the boundary of the light action range through a fuzzy PID controller; Based on the flashing frequency gain coefficient of the obstacle light and the optimized value of the light signal duty cycle output by the reinforcement learning model, a secondary optimization instruction for the obstacle light parameters is triggered; Based on the obstacle light parameter secondary optimization instruction, the initial optimization parameters of the obstacle light are adjusted to obtain final environment-suitable obstacle light parameters.

6. A lamp, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for controlling an obstacle light in a low-altitude flight environment as claimed in any one of claims 1 to 4 are implemented.

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