Agglomerate fog area illumination method and system based on unmanned aerial vehicle group

Abstract

The invention relates to the field of emergency lighting, and provides an agglomerate fog area lighting method and system based on an unmanned aerial vehicle group. The lighting method specifically comprises the following steps: acquiring agglomerate fog physical information and environment information in an agglomerate fog area; according to the agglomerate fog physical information and the environment information, obtaining lighting lamp parameters required by normal passage of the road in the agglomerate fog area; configuring the unmanned aerial vehicle group according to the illumination lamp parameters, and controlling the unmanned aerial vehicle group to fly to the agglomerate fog area; and controlling the illumination lamps of the unmanned aerial vehicle group to realize illumination. According to the technical scheme, it can be ensured that when agglomerate fog appears on the road, road illumination is actively achieved through the illumination lamps of the unmanned aerial vehicle group, so that the road safety passing standard is met, and the accident risk and potential safety hazards of road passing are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency lighting, and particularly to a lighting method and system for a group fog area based on a drone swarm. Background Art

[0002] Group fog is different from ordinary fog and usually appears in environments with large day-night temperature differences and high air humidity. It is a locally sudden, extremely concentrated, and mobile fog. Its visibility can suddenly drop below 10 meters, which is more dangerous than ordinary fog. Especially on highways, it is likely to cause serious traffic accidents and pose a great threat to modern traffic safety.

[0003] In response to the appearance of group fog, the usual countermeasures mainly include monitoring and warning of group fog. On the one hand, the group fog information is reported in a timely manner to dynamically control the traffic; on the other hand, the education and guidance of the public are strengthened to improve the public's safety awareness when facing sudden group fog.

[0004] However, these countermeasures are all "passive". Coupled with the poor lighting effect of existing road lighting facilities when group fog appears, they cannot meet the safety lighting conditions for outdoor road traffic in a timely manner, affecting the normal passage of road vehicles. Summary of the Invention

[0005] The technical solution of the present invention provides a lighting method for a group fog area based on a drone swarm to overcome the defect that the lighting conditions in the area where group fog appears cannot meet the safety passage conditions for outdoor roads, affecting the normal passage of road vehicles.

[0006] To achieve the above object, in a first aspect, the technical solution of the present application provides a lighting method for a group fog area based on a drone swarm. The drone swarm is equipped with lighting fixtures, and the method includes the following steps: obtaining the physical information and environmental information of the group fog in the group fog area; obtaining the lighting fixture parameters required to meet the road safety passage standard of the group fog area according to the physical information and environmental information of the group fog; configuring the drone swarm according to the lighting fixture parameters and controlling the drone swarm to fly to the group fog area; controlling the lighting fixtures of the drone swarm to achieve lighting.

[0007] In the method for illuminating a fog area based on a drone swarm provided by the embodiments of the present application, by collecting and obtaining the physical information and environmental information of the fog in the road in real time, the position and size of the suddenly emerging fog in the road are judged. Then, according to the physical information and environmental information of the fog, combined with the lighting condition requirements for safe road passage in the national standard "Urban Road Lighting Design Standard", the corresponding lighting fixture parameters are obtained by using computer simulation software or lighting algorithms. Then, these lighting fixture parameters are configured on the drones equipped with lighting fixtures, and finally the drones are controlled to go to the area where the fog appears. Lighting is implemented through the lighting fixtures on the drones to significantly improve the road visibility in the fog area, thereby providing good vision conditions for passing vehicles and effectively reducing the safety hazards caused by the sudden emergence of fog. In addition, since drones have the advantages of strong flexibility, high mobility, and fast response speed, using the technical solution of the present application can not only ensure the normal passage of the road when fog appears, but also has high efficiency and a fast response speed when fog appears.

[0008] In combination with the first aspect, in a possible implementation manner, the step of obtaining the lighting fixture parameters required to meet the road safety passage standard for the fog area according to the physical information and environmental information of the fog specifically includes: determining the lighting mode of the lighting fixture according to the environmental information; calculating the illuminance value by using a lighting algorithm according to the lighting mode, the physical information and environmental information of the fog; comparing the illuminance value with the standard illuminance value for normal road passage, and when the illuminance value is not less than the illuminance value of the road safety passage standard, obtaining the number of lighting fixtures.

[0009] Since different lighting effects will be produced when different lighting modes of lighting fixtures are used in different environments, one of the implementation methods of the technical solution of the present application is to first determine the lighting mode of the lighting fixture according to the environmental information, and then calculate the illuminance value by using a lighting algorithm according to the determined lighting mode, the physical information and environmental information of the fog, and continuously adjust the number of lighting fixtures to make the calculated illuminance value result greater than the illuminance value of the lighting conditions for safe road passage in the national standard "Urban Road Lighting Design Standard". The number of lighting fixtures at this time is one of the lighting fixture parameters to be obtained. Using this method can basically ensure that in the area with fog, the illuminance value of the road can meet the safety passage requirements of the national standard.

[0010] In combination with the first aspect, in a possible implementation, further, the lighting modes include a spotlight mode, a mixed mode, and a floodlight mode; the environmental information includes visibility; the step of determining the lighting mode of the lighting fixture according to the environmental information specifically includes: when the visibility is less than a set first visibility threshold value, the lighting mode is the spotlight mode; when the visibility is greater than or equal to the set first visibility threshold value and less than a set second visibility threshold value, the lighting mode is the mixed mode; when the visibility is greater than or equal to the set second visibility threshold value, the lighting mode is the floodlight mode.

[0011] Among them, the lighting fixtures of this technical solution can use high-power LED lighting fixtures. The lighting modes of the lighting fixtures are generally divided into three modes: spotlight mode, mixed mode, and floodlight mode, and are adaptively adjusted according to the different visibility of the environmental information collected on site.

[0012] It should be understood that adopting this method of adaptive adjustment can be flexibly adjusted according to the change of on-site visibility, which can not only significantly improve safety and enhance the user experience, but also save energy and extend the life of the lighting fixture, reducing ineffective energy consumption.

[0013] Further, the step of calculating the illuminance value using the lighting algorithm specifically includes: obtaining the number of initial lighting fixtures according to the lighting mode; the illuminance value is equal to the product of the power of the lighting fixture, the number of the initial lighting fixtures, and the empirical coefficient of the environmental information divided by the road surface area covered by the fog area.

[0014] It should be understood that using the above lighting algorithm can achieve accurate quantification of the lighting effect, avoid the subjectivity of manual calculation and empirical algorithms, can optimize the efficiency of energy use, prevent "over-illumination", thereby saving energy and maintenance costs.

[0015] In combination with the first aspect, in another possible implementation, the step of obtaining the lighting fixture parameters required to meet the road safety passing standard for the fog area according to the fog physical information and environmental information specifically includes: obtaining the illuminance value using the lighting simulation method according to the fog physical information and environmental information; comparing the illuminance value with the illuminance value of the road safety passing standard, and when the illuminance value is not less than the illuminance value of the road safety passing standard, obtaining the number of lighting fixtures.

[0016] Using this method, the real scene can be simulated, which is closer to the actual situation, ensuring the accuracy, efficiency, and reliability of the design of the lighting fixture parameters. At the same time, it can also avoid the errors of manual calculation and empirical estimation methods, greatly improving the implementation efficiency of this technical solution.

[0017] In combination with the first aspect, in a possible implementation manner, further, the lighting simulation method includes: creating a project and basic settings; constructing a model and setting up a scene according to the information of the basic settings; selecting lamp layout and light source settings according to the information of the scene; calculating the illuminance value.

[0018] In combination with the first aspect, in a possible implementation manner, after the step of controlling the lighting lamps of the drone swarm to achieve lighting, an illuminance compensation mechanism is further included: when the illuminance value of the environmental information is less than the illuminance value of the road safety passing standard in the fog area, increase the power of the lighting lamps of the drone swarm until the illuminance value is not less than the national standard of the road safety passing standard.

[0019] It should be understood that in the technical solution of the present application, when the drone swarm implements lighting in the fog area, due to the large fluctuations of the fog, when the illuminance value does not meet the safety passing standard, the power of the lighting lamps of the drone swarm can be increased through the illuminance compensation mechanism, so that the illuminance value in the fog area meets the national standard of the road safety passing standard. Further, the lighting effect in the fog area will not change greatly with the fluctuations of the fog, effectively ensuring the safe passing conditions of the road, and the lighting effect is stable and reliable.

[0020] In a second aspect, the technical solution of the present application also proposes a fog area lighting system based on a drone swarm. The lighting system includes a data collection subsystem, a decision-making subsystem, and an execution subsystem; the data collection subsystem is used to obtain the fog physical information and environmental information in the fog area; the decision-making subsystem is used to obtain the lighting lamp parameters required for normal passage of the road in the fog area according to the fog physical information and environmental information; the execution subsystem includes a drone control center and a drone swarm equipped with lighting lamps, and the drone control center is used to configure the drone swarm according to the lighting lamp parameters and control the drone swarm to fly to the fog area; the drone swarm is used to implement lighting.

[0021] In the fog area lighting system provided by the embodiments of the present application, the fog information and environmental information are collected by the data collection subsystem, and then the collected information is processed by the decision-making subsystem, and then the lighting lamp parameters required for normal passage of the road in the fog area are obtained. The execution subsystem configures the lighting lamps of the drones according to these lighting lamp parameters, and then controls the drones to fly to the fog area to perform the lighting task, so that the illuminance in the fog area can meet the road safety passing standard in the fog area, thereby ensuring the safe passing conditions of the road in the road environment with sudden fog and reducing the occurrence of road traffic accidents.

[0022] In combination with the second aspect, in a possible implementation, the data collection subsystem includes sensors, lidar, and an edge processor. The sensors and lidar are used to collect the physical information and environmental information of the fog bank area, and transmit the physical information and environmental information of the fog bank to the edge processor. The edge processor is used to preprocess the physical information and environmental information of the fog bank and then transmit it to the decision-making subsystem. The sensors include any one or more of the following sensors: thermal imaging sensors, visibility sensors, and illuminance sensors.

[0023] It should be understood that using sensors to collect data can achieve the high efficiency, accuracy, and automation of data, providing a basic guarantee for the rapid implementation of emergency lighting.

[0024] In combination with the second aspect, in a possible implementation, the execution subsystem further includes a smart street pole. The smart street pole is provided with an unmanned aerial vehicle (UAV) and a UAV charging station, which are used to replace the UAV in the fog bank area according to the instructions of the UAV control center or replenish the power of the UAV when the UAV's power is insufficient.

[0025] The smart street pole can provide a UAV charging function on the basis of existing lighting, thereby realizing the power replenishment of the UAV during the execution of tasks, and further improving the working efficiency of the technical solution of the present application in the process of emergency lighting.

[0026] Using the fog bank area lighting method and system based on a UAV swarm in the technical solution of the present application can collect road site data in real time, timely detect the area of sudden fog banks, then calculate the lighting parameters of the lamps required to meet the illuminance value for safe road passage, and finally realize the lighting of the fog bank area through the UAV swarm. This method can accurately monitor the formation and location of fog banks in real time, respond to the appearance of fog banks in a timely manner, quickly provide lighting for the fog bank area, significantly improve the road visibility in the fog bank area, and effectively reduce the probability of traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0028] Figure 1 Schematic diagram of the structure of a UAV equipped with lighting fixtures provided by an embodiment of the present application;

[0029] Figure 2Schematic flowchart of a method for illuminating a fog area based on a drone swarm provided by an embodiment of the present application;

[0030] Figure 3 Schematic flowchart of a method for obtaining lighting fixture parameters required to meet the road safety passing standard for the fog area according to the fog physical information and environmental information provided by an embodiment of the present application;

[0031] Figure 4 Schematic flowchart of a method for calculating an illuminance value using a lighting algorithm according to the lighting mode, the fog physical information, and environmental information provided by an embodiment of the present application;

[0032] Figure 5 Schematic diagram of the fog physical information and the arrangement of drone lighting fixtures provided by an embodiment of the present application;

[0033] Figure 6 Example flowchart of a method for obtaining lighting fixture parameters required to meet the road safety passing standard for the fog area according to the fog physical information and environmental information provided by an embodiment of the present application;

[0034] Figure 7 Another schematic flowchart of a method for obtaining lighting fixture parameters required to meet the road safety passing standard for the fog area according to the fog physical information and environmental information provided by an embodiment of the present application;

[0035] Figure 8 Schematic diagram of the structure of a lighting system for a fog area based on a drone swarm provided by an embodiment of the present application;

[0036] Figure 9 Another schematic diagram of the structure of a lighting system for a fog area based on a drone swarm provided by an embodiment of the present application.

[0037] Explanation of the reference numerals in the drawings:

[0038] 1. Multi-module lighting fixture; 2. Battery replacement platform. Detailed implementation manners

[0039] It should be noted that the fog mentioned in the embodiments of the present application refers to a denser fog. It is mainly formed by the cooling of the ground radiation, which cools the air close to the ground and causes the saturated specific humidity to decrease, resulting in the condensation of water vapor. The formation of fog requires two main conditions: one is sufficient low-level water vapor and high air humidity, and the other is a large temperature difference between day and night and small wind. Fog is not likely to appear on sunny days. Therefore, fog is likely to appear in suburban and rural areas, especially in some relatively open sections of expressways, which are areas where fog frequently occurs.

[0040] Due to the characteristics of local sudden occurrence, high concentration, and strong mobility of the patchy fog, it is known as the "mobile killer" on the highway. To effectively prevent and control the impact of patchy fog on traffic safety, China has carried out practical explorations at multiple levels, covering national policies, local technological innovations, and public science popularization, etc.

[0041] Among them, in local technological innovations, it includes technical solutions such as image recognition, remote sensing monitoring, and anti-collision warning of patchy fog, mainly focusing on how to accurately and efficiently identify patchy fog, issue early warnings in a timely manner, and conduct safety guidance and prevention for the public. However, there is little mention of technological innovations for actively changing the lighting conditions in the patchy fog area.

[0042] The technical solution of this application mainly provides a lighting method and system for patchy fog areas based on a swarm of drones to overcome the defect that the lighting conditions in the areas where patchy fog appears cannot meet the safety passage conditions of outdoor roads, affecting the safe passage of road vehicles.

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present invention.

[0044] It should also be noted that in the description of the embodiments of this application, the terms "upper", "lower", "front", "rear", "bottom", "top", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or structure referred to must have a specific orientation, or be constructed and operated in a specific orientation, so it cannot be understood as a limitation to the application. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] As Figure 1 shown, the drones used in the technical solution of this application are configured with lighting fixtures. The lighting fixtures can adopt high-power LED fixtures, which are composed of multi-module fixtures, and the multi-module fixtures are equipped with lenses with different beam angles and light sources with different color temperatures. This hardware configuration structure can make the concentration and penetration of light change with the patchy fog environment, providing convenient conditions for improving the lighting conditions of patchy fog. In addition, the drone is also equipped with a charging and battery replacement platform to facilitate the replenishment of the drone's power or even the replacement of the power source before the drone's power runs out.

[0046] A lighting method for patchy fog areas based on a swarm of drones in the technical solution of this application, as Figure 2As shown, it includes the following steps: Step S10: Obtain the physical information and environmental information of the fog bank in the fog bank area; Step S20: Obtain the lighting fixture parameters required to meet the road safety passing standard of the fog bank area according to the physical information and environmental information of the fog bank; Step S30: Configure the drone group according to the lighting fixture parameters, and control the drone group to fly to the fog bank area; Step S40: Control the lighting fixtures of the drone group to achieve lighting.

[0047] The physical information of the fog bank in the technical solution of this application may include the size of the fog bank, including the length, width, height and specific longitude and latitude information of the fog bank. The environmental information includes visibility, transmittance, and illuminance value.

[0048] In step S10, in order to obtain the physical information and environmental information of the fog bank in the fog bank area, the technical solution of this application uses sensor technology and lidar technology. The sensor device and the lidar device are both arranged along the side of the traffic road guardrail. Among them, the sensor device includes a thermal imaging sensor, a visibility sensor, and an illuminance sensor.

[0049] Use lidar to perform real-time laser scanning on the scanning area. When there is a fog bank, by forming a point cloud scanning file, the position and three-dimensional shape data of the fog bank can be obtained through calculation. It is mainly achieved by combining the high-resolution spatial detection ability of lidar and the sensitivity to aerosols. The main principle is: When the light pulse emitted by the laser travels in the atmosphere, it is attenuated by aerosols and air molecules on the transmission path while undergoing elastic scattering (Mie scattering or Rayleigh scattering) with aerosol particles and air molecules in the atmosphere. The scattered signal returns along the original path and is attenuated again by aerosols and air molecules on the transmission path, and finally is received by the laser detector. What the lidar actually receives is the sum of the Mie scattering signal and the Rayleigh signal. Since the laser energy is attenuated both when the laser travels from the laser to a certain distance and when the backscattered light of the particles travels to the receiving system. According to the Beer–Bouguer–Lambert law, the backscattering power of the particles at a certain distance received by the receiving system can be calculated. Finally, by detecting and receiving the echo signal of the lidar and solving the lidar equation, the extinction coefficient profile in the lidar equation can be obtained, and then the position information of the fog bank can be retrieved. The specific solving process will not be elaborated here.

[0050] It should be noted that although the physical information and contour of the patchy fog can be identified using only a lidar, in order to improve the accuracy of patchy fog recognition and reduce the false alarm rate, a recognition scheme of a thermal imaging sensor + lidar is usually used. Since the thermal imaging sensor can identify the temperature boundary layer that cannot be detected by the lidar (a local temperature drop of 0.5 - 2 °C usually accompanies the formation of patchy fog), the reliability of patchy fog detection is significantly improved, and the environmental adaptability is also significantly enhanced. This processing process is mainly achieved through data fusion processing and collaborative judgment of temperature difference and scattering. The specific solution process will not be elaborated here.

[0051] It should also be noted that using a camera equipped with an AI algorithm for pure vision recognition can also achieve the recognition of patchy fog, and it can also be one of the methods for the technical solution of this application to obtain the physical information of patchy fog. However, the recognition rate of patchy fog of this technical solution is relatively low, and it is also easily affected by the intensity of environmental light. Therefore, there are certain limitations in the usage scenarios.

[0052] It should be understood that as long as the technical solution can obtain the physical information of patchy fog, it is one of the technical solutions for this application to obtain the physical information of patchy fog in the patchy fog area, and no specific technical solution for obtaining information is limited here.

[0053] For the acquisition of visibility of environmental information, it mainly relies on a visibility sensor. A multi-spectral imaging + laser scattering sensor can be used to integrate laser scattering and image recognition technologies to obtain the dynamic transmissivity, visibility, and illuminance values of patchy fog. It should be noted that other methods that can obtain transmissivity, visibility, and illuminance values are not limited and can all be applied to the technical solution of this application.

[0054] For the acquisition of illuminance value of environmental information, it mainly relies on an illuminance sensor to collect the illuminance information of the road surface.

[0055] Furthermore, an edge processor can be set to preprocess the above-mentioned patchy fog position data, three-dimensional shape data, illuminance value, visibility, and transmissivity, thereby fusing multi-source sensing data to generate a four-dimensional (XYZ + time) patchy fog field situation map. This further improves the data processing efficiency and provides a data basis for subsequent calculations.

[0056] In step S20, according to the physical information of the patchy fog and environmental information, the step of obtaining the lighting fixture parameters required to meet the road safety passing standard of the patchy fog area. The technical solution of this application provides two different implementation manners. Among them, the lighting fixture parameters include the number of lighting fixtures, the color temperature of the fixtures, and the beam angle of the fixtures.

[0057] As Figure 3 shown, the specific steps of the first implementation manner include:

[0058] Step S201: Determine the lighting mode of the lighting fixture according to the environmental information;

[0059] Step S202: Calculate the illuminance value using the lighting algorithm according to the lighting mode, the physical information of the group fog and the environmental information;

[0060] Step S203: Compare the illuminance value with the standard illuminance value for normal road passage. When the illuminance value is not less than the standard illuminance value for safe road passage, obtain the number of lighting fixtures.

[0061] Among them, the lighting modes in Step S201 include the spotlight mode, the mixed mode and the floodlight mode. The steps of determining the lighting mode of the lighting fixture according to the environmental information specifically include:

[0062] When the visibility is less than the set first visibility threshold value, the lighting mode is the spotlight mode;

[0063] When the visibility is greater than or equal to the set first visibility threshold value and less than the set second visibility threshold value, the lighting mode is the mixed mode;

[0064] When the visibility is greater than or equal to the set second visibility threshold value, the lighting mode is the floodlight mode.

[0065] Among them, as described above, the environmental information includes visibility, transmittance and illuminance value. The spotlight mode is a lighting effect that satisfies concentrating light within a certain range to form a high brightness and strong directivity. The floodlight mode is another lighting effect that satisfies uniformly diffusing light over a large range to form a soft and non - distinct - boundary effect. And the mixed mode is a third lighting effect between the spotlight mode and the floodlight mode.

[0066] To better elaborate the technical solution of this application, one specific embodiment is used to describe it in detail below. It should be noted that the following numerical values are only used to clearly express the technical solution of this application and do not constitute a specific limitation on the technical solution of this application.

[0067] For example, set the first visibility threshold value to 100 meters and the second visibility threshold value to 200 meters. When the visibility is greater than or equal to 0 meters and less than 100 meters, set the lighting fixture to the spotlight mode with color temperature T = 1800k and beam angle θ = 25°. When the visibility is greater than or equal to 100 meters and less than 200 meters, set the lighting fixture to the mixed mode with color temperature T = 2200k and beam angle θ = 35°. When the visibility is greater than or equal to 200 meters, set the lighting fixture to the floodlight mode with color temperature T = 3000k and beam angle θ = 50°.

[0068] In Step S202, the steps of calculating the illuminance value using the lighting algorithm according to the lighting mode, the physical information of the group fog and the environmental information are as Figure 4As shown in the figure, specifically including:

[0069] Step S2021: Obtain the number of initial lighting fixtures according to the lighting mode. As Figure 5 shown, the number N of initial lighting fixtures is equal to the product of the number N x of horizontal lighting fixtures at the geographical location and the number N y of vertical lighting fixtures at the geographical location. The specific calculation method is:

[0070] The number N x of horizontal lighting fixtures at the geographical location = |L / (2H×tan(θ / 2))|; the unit is piece;

[0071] The number N y of vertical lighting fixtures at the geographical location = |D / (2H×tan(θ / 2))|; the unit is piece;

[0072] wherein, L is the numerical value of the length of the fog cluster covering the road extension direction, the unit is meter; D is the numerical value of the width of the road covered by the fog cluster, the unit is meter; the lighting height H is the sum of the height limit of the road and the safe obstacle avoidance height of the unmanned aerial vehicle, the unit is meter; θ is the beam angle of the lighting fixture configured by the unmanned aerial vehicle, the unit is degree; tan is the tangent function in the mathematical calculation formula; the symbol "|" is the absolute value in the mathematical calculation formula. Thus, it can be seen that the number N of the initial lighting fixtures calculated by the above formula realizes the full coverage of the lighting of the lighting fixtures based on the size of the road area affected by the fog cluster coverage.

[0073] In addition, in the actual use process, for the convenience consideration or in the case of missing some parameter acquisition, a simpler method for determining the number N of the initial lighting fixtures can be used. For example, N x = 2, N y = 2, then N at this time = 2 * 2.

[0074] Step S2022: Calculate the illuminance value, and this illuminance value is equal to the product of the power of the lighting fixture, the number of the initial lighting fixtures and the empirical coefficient of the environmental information divided by the road surface area covered by the fog cluster area. Specifically, the illuminance calculation formula of this illuminance value E is as follows:

[0075]

[0076] Among them, Φ is the luminous flux of the lamp (unit: lm), corresponding to the lamp power, and generally takes the luminous flux corresponding to 50% of the maximum power value of the lamp; N is the number of lamps; U is the utilization factor, which is specifically related to the road surface material; K is the maintenance factor; S is the road surface area (unit: square meters) covered by the group fog; B is the width (unit: meters) of the road surface covered by the group fog; D is the lamp spacing (unit: meters); E is the illuminance value, unit: lx. The maintenance factor K usually takes the value of K0×transmittance. Among them, K0 is based on general experience, and at the same time, to avoid insufficient later lighting due to environmental, aging and other problems, usually K0 takes the value of 0.7.

[0077] In step S203: Compare the calculated illuminance value with the normal traffic standard illuminance value of the road. When the illuminance value is not less than the illuminance value of the road safety traffic standard, the number of lighting lamps is obtained.

[0078] When the illuminance value is less than the illuminance value of the road safety traffic standard, the number of lighting lamps is increased and the process returns to step S2022 to continue calculating the illuminance value until the illuminance value is not less than the illuminance value of the road safety traffic standard.

[0079] The national standard "Urban Road Lighting Design Standard" (CJJ 45-2015) requirements for road safety traffic are as follows for motor vehicle road lighting standards:

[0080] Road Class Average Illuminance (lx) Illuminance Uniformity Value (Minimum) Expressway, Arterial Road 20～30 ≥0.4 Sub-arterial Road 15～20 ≥0.35 Branch Road 10～15 ≥0.3

[0081] Table 1: Motor Vehicle Road Lighting Standards in "Urban Road Lighting Design Standard" (CJJ 45-2015)

[0082] According to different road attributes (road grades), the average illuminance E has different standards E 标准 . Taking expressways and arterial roads as examples, the minimum value of the illuminance value E 标准 for their safe traffic is 20 lx, that is, it is required that the calculated illuminance value needs to meet E≥E 标准 = 20 lx. When the illuminance value E≥20 lx, output the number N x of horizontal lamps at the geographical location, the number N y of vertical lamps at the geographical location, and the lamp spacing D. When the illuminance value E<20 lx, it means that the on-site lighting provided by the configured number of lamps at this time will not be able to meet the national standard safety illuminance value. Then, the number N x of horizontal lamps at the geographical location and the number N y of vertical lamps at the geographical location are each increased by 1, that is, N x = N x + 1, N y = N y+1; then return to step S2022 to continue calculating the illuminance value E until the calculated illuminance value E is not less than the illuminance value of the road safety passing standard, that is, 20 lx. For the detailed example process of the above embodiment, please refer to Figure 6 as shown for understanding.

[0083] Further, if the number of lamps is continuously increased and the calculated illuminance value E still cannot be greater than or equal to the illuminance value E of the road safety passing 标准 national standard, or even if it is greater than or equal to the illuminance value E 标准 , but the number of lamps N is greater than the existing number of drones, or the number of vertical lamps N at the geographical location y is greater than the value obtained by dividing the width D of the road covered by the group fog by the safe flight spacing of the drones, an exception handling mechanism is triggered. At this time, an alarm message will be given to inform the management personnel that, on the one hand, the number of lamps N that can meet the road safety passing cannot be solved, and on the other hand, traffic control linkage is triggered, presenting the group fog information and the corresponding geographical location, and at the same time, according to the different concentrations of the group fog, prompting the passing vehicles to reduce the speed (for example, limited to 40 km / h) to pass, or even conducting traffic control in the group fog area. Thus, a system closed-loop is formed to further avoid the occurrence of road safety accidents when the group fog suddenly appears and abnormal situations occur.

[0084] In step S20, the step of obtaining the lighting fixture parameters required to meet the road safety passing standard of the group fog area according to the physical information and environmental information of the group fog. The second implementation method provided by the technical solution of the present application, as Figure 7 shown, specifically includes:

[0085] Step S211: According to the physical information and environmental information of the group fog, use the lighting simulation method to obtain the illuminance value;

[0086] Step S212: Compare the illuminance value with the illuminance value of the road safety passing standard. When the illuminance value is not less than the illuminance value of the road safety passing standard, obtain the number of lighting fixtures.

[0087] In step S211, the physical information of the group fog includes the length, width and height of the group fog, and the environmental information includes the light transmittance and illuminance value at the site of the group fog area.

[0088] Common lighting simulation software includes DIALux, Relux, and AGi32. Lighting simulation methods are generally implemented through the above-mentioned lighting simulation software. Among them, DIALux is a professional lighting design software developed by DIAL GmbH in Germany and is widely used in lighting calculations and simulations in fields such as architecture, landscape, and industry. It provides powerful tools and a flexible interface to help designers, engineers, and planners efficiently complete lighting scheme design, visualization, and document output. It features comprehensive lighting calculations, a rich fixture database, 3D modeling and visualization, standardized output, and dynamic scene simulation, and can also provide computer plug-ins and data interface functions. Relux is another professional lighting design and simulation software developed by Relux Informatik AG in Switzerland and is widely used in the fields of architectural, road, landscape, and industrial lighting design. It is known for its high-precision optical calculations and user-friendly interface and has features such as lighting calculation and analysis, 3D modeling and visualization, dynamic lighting simulation, and intelligent control. Compared with DIALux, Relux has higher calculation accuracy, especially in road lighting calculations, and faster calculation speed. AGi32 is another more professional lighting design and calculation software developed by LightingAnalysts, Inc. in the United States and is widely used in lighting design, simulation, and analysis in fields such as architecture, roads, landscapes, and stadiums. It is known for its high-precision calculations and powerful engineering-level functions and is suitable for projects that require strict optical verification.

[0089] Taking DIALux as an example to describe step S211, according to the physical information and environmental information of the fog, the illuminance value is obtained using the lighting simulation method. The steps include:

[0090] The first step: Create a new project and basic settings, including "Create Project" and setting parameters. Select the "Road" scene to build a geometric model, and then set the spatial parameters, enter the length, width, and height of the physical information of the fog, as well as necessary environmental parameters such as transmittance, road surface emissivity, or utilization coefficient.

[0091] The second step: Build the model and set up the scene. The information of the road can be imported into the software in file form, and after import, appropriately adjust the scale and position of the model.

[0092] Step 3: Select the lighting layout and light source settings. You can select the light fixture attributes according to the type of the light fixture module configured for the drone, select the corresponding type and model in the manufacturer's library in the "Light Fixture" tool, or import the corresponding IES / LDT file (different lenses have different ies files). Then, set the lighting layout method and adjust the light source parameters. Generally, the "even lighting layout" is selected for the lighting layout method, and at the same time, set the number of light fixtures and the row / column spacing. The light source parameters are adjusted in the property bar, including the luminous flux (lm), color temperature (K), beam angle (°), etc. of the light fixture.

[0093] Step 4: Calculate and optimize the illuminance value. After all the above information is set or entered, select the illuminance calculation in the "Calculation Settings", click "Start Calculation" and wait for the calculation to complete, then the corresponding illuminance value E can be obtained.

[0094] In step S212: Compare the illuminance value with the illuminance value of the road safety passing standard. When the illuminance value is not less than the illuminance value of the road safety passing standard, obtain the number of lighting fixtures. This belongs to optimization and adjustment. Taking DIALux as an example, the operation method is as follows:

[0095] Compare the illuminance value E obtained in the above step 4 with the national standard value of road safety passing (see Table 1 of the technical solution of this application: "Urban Road Lighting Design Standard" (CJJ 45-2015) for details). If the illuminance value E is greater than or equal to E in Table 1 标准 then output the number of light fixtures and the light fixture spacing. If the illuminance value E is less than E in Table 1 标准 then jump to step 3, adjust the position / number of light fixtures, or replace the light source type, and then recalculate until the calculated illuminance value is greater than or equal to E in Table 1 标准 until. Finally, output the number of light fixtures and the light fixture spacing.

[0096] In addition, the visualization effect can also be used in the DIALux software. Switch to the 3D view and use the "ray tracing" mode to generate a rendered image of the on-site simulation to further facilitate the inspection and verification of the result accuracy. A report can also be generated, and a simulation report can be generated through the report function in the software.

[0097] In step S30, configure the drone swarm according to the lighting fixture parameters and control the drone swarm to fly to the fog area.

[0098] Through the calculation in the previous step S20, the lighting fixture parameters have been obtained, which include the number of fixtures, the color temperature T of the fixtures, and the beam angle θ. Since the drone is equipped with lighting fixtures, the lighting fixture parameters obtained above can be correspondingly set on the lighting fixtures of the drone, namely the color temperature T and the beam angle θ. Then, the drone control center will dispatch a corresponding number of drones to form a group and plan a route to the fog area. The specific algorithm for planning the route can refer to the general method for drone route planning, which will not be elaborated here.

[0099] In step S40, when the drones arrive at the fog area site, the lighting fixtures of the drone group will be remotely controlled to achieve lighting. It should be noted that the lighting height H of the drone is the sum of the height limit of the road and the safe obstacle avoidance height of the drone. During the lighting process of the drone, the on-site illuminance value will also change due to the change of the fog. Therefore, the technical solution of this application further includes an illuminance compensation mechanism after the step of controlling the lighting fixtures of the drone group to achieve lighting:

[0100] When the illuminance value of the environmental information is less than the illuminance value of the road safety passing standard in the fog area, the power of the lighting fixtures of the drone group is increased until the illuminance value is not less than the illuminance value of the national road safety passing standard.

[0101] The acquisition device continuously acquires the on-site illuminance value E of the fog area 变 , and during the lighting process of the drone, if it is found that the on-site illuminance value E 变 is less than the standard illuminance value E 标准 , then the illuminance needs to be compensated. The specific method is as follows:

[0102] Gradually increase the power P of the lighting fixtures of the drone group 补偿 until the on-site illuminance value E 变 is not less than the illuminance value E 标准 of the road safety passing standard. Assuming that the initial power of the fixture is P 初始 , in actual work, it generally takes P 初始 = 50% of the maximum power value of the lighting fixture, then the compensation power P 补偿 that the fixture needs to execute is:

[0103] P 补偿 = P 初始 ×(1 + |E 变 - E 标准 / E 变 |), where

[0104] E 标准 is the minimum illuminance value standard for road safety passing in Table 1, and "|" is the absolute value operation in mathematical calculations.

[0105] When the lamp power is increased to 100%, the on-site illuminance E still cannot reach E 标准 , at this time, it is necessary to increase the number of drones and recalculate the lighting fixture parameters according to the above calculation method.

[0106] In addition, the technical solution of the present application also provides an exception handling mechanism, which includes not only the abnormality of the number of lamps N (including unsolvable when calculating, greater than the number of drones or exceeding the flight safety distance) mentioned in the foregoing technical solution, but also a gradient fallback strategy when the lamp power cannot meet the on-site safe passage illuminance value condition after being increased to 100%, and the triggered traffic control linkage. Further, it also includes the return strategy when the drone power is insufficient and the supplementary charging method, etc.

[0107] The embodiment of the present application also provides a fog area lighting system based on a drone swarm, as Figure 8 shown, including a data collection subsystem, a decision-making subsystem and an execution subsystem. Among them,

[0108] The data collection subsystem is used to obtain the fog physical information and environmental information in the fog area;

[0109] The decision-making subsystem is used to obtain the lighting fixture parameters required to meet the road safety passage standard of the fog area according to the fog physical information and environmental information;

[0110] The execution subsystem includes a drone control center and a drone swarm equipped with lighting fixtures. The drone control center is used to configure the drone swarm according to the lighting fixture parameters and control the drone swarm to fly to the fog area;

[0111] The drone swarm is used to implement lighting.

[0112] The data collection subsystem includes sensors, lidar and an edge processor. The sensors and lidar are used to collect the fog physical information and environmental information in the fog area and transmit the fog physical information and environmental information to the edge processor. The edge processor is used to preprocess the fog physical information and environmental information and then transmit it to the decision-making subsystem. The sensors include any one or more of the following sensors: thermal imaging sensors, visibility sensors, illuminance sensors.

[0113] The lidar and the above sensors are both arranged on the side of the traffic road guardrail and are both equipped with a satellite positioning system. In order to improve the efficiency of data processing, an edge processor can be set at the proximal end. The edge processor is communicatively connected to the above sensors and lidar. An edge processor can also be separately set in each device, and the preprocessed data is uniformly transmitted to the central processor of the decision-making subsystem.

[0114] In addition, the data collection subsystem may further include a video recognition system, which is equipped with an AI algorithm to implement video and image recognition of the advection fog.

[0115] The decision-making subsystem includes a central processing unit, which is mainly used to calculate the lighting fixture parameters required to meet the road safety passing standards in the advection fog area and to implement the path planning method of the unmanned aerial vehicle (UAV). The central processing unit is communicatively connected to the edge processor of the data collection subsystem. At the same time, the central processing unit is also communicatively connected to the execution subsystem and is used to send the calculated lighting fixture parameters to the execution subsystem.

[0116] The execution subsystem includes a UAV control center and a group of UAVs equipped with lighting fixtures. The UAV control center is communicatively connected to the UAVs. The UAV control center will configure the UAVs according to the received lighting fixture parameters, including the number of lighting fixtures, the color temperature of the fixtures, and the beam angle of the fixtures. At the same time, it will drive the UAVs to the advection fog area to implement lighting according to the path planning method of the UAVs in the decision-making subsystem.

[0117] During the implementation of lighting, the acquisition subsystem and the decision-making subsystem are still working in real time, and the real-time results, such as the on-site illuminance value E 变 and the calculated illuminance value E, are transmitted to the execution subsystem. The specific calculation process and method have been described in the advection fog area lighting system based on a group of UAVs in the technical solution of this application, and will not be elaborated here. The execution subsystem will continuously adjust and optimize in real time according to the received results and the operating conditions of the on-site UAVs to ensure that the on-site illuminance value in the advection fog area meets the road safety communication standards and reduce the occurrence of road safety hazards.

[0118] In addition, the decision-making subsystem is also communicatively connected to the warning system. When an abnormal mechanism as described above in the technical solution of this application is triggered, a warning will be formed through the warning system to cooperate with the traffic management department to form a synergy of traffic information release or even traffic control mechanism.

[0119] As Figure 9 shown, in order to further improve the operation and execution efficiency of the entire system, the execution subsystem further includes a smart road pole. The smart road pole is communicatively connected to the UAV control center. The smart road pole is provided with UAVs and a UAV replacement station, which is used to replace the UAVs in the advection fog area according to the instructions of the UAV control center or to supplement the power of the UAVs when the UAVs are out of power. For example, when the UAV is out of power during the lighting task, the power can be replaced through a smart road pole with the function of a UAV replacement station nearby, without having to return to the UAV base for power replacement, saving time and improving the overall efficiency of the system.

Claims

1. A lighting method for fog clusters based on a drone swarm, where the drone swarm is equipped with lighting fixtures, characterized in that, It includes the following steps: Obtain the fog physical information and environmental information within the fog area; Based on the fog physical information and environmental information, obtain the lighting fixture parameters required to meet the road safety passing standard for the fog area; Configure the drone swarm according to the lighting fixture parameters, and control the drone swarm to fly to the fog area; Control the lighting fixtures of the drone swarm to achieve lighting.

2. The method for illuminating a group fog area according to claim 1, wherein The step of obtaining the lighting fixture parameters required to meet the road safety passing standard for the fog area based on the fog physical information and environmental information specifically includes: Determine the lighting mode of the lighting fixture according to the environmental information; Based on the lighting mode, the fog physical information and environmental information, calculate the illuminance value using a lighting algorithm; Compare the illuminance value with the illuminance value of the normal road passing standard. When the illuminance value is not less than the illuminance value of the road safety passing standard, obtain the number of lighting fixtures.

3. The method for illuminating a group fog area according to claim 2, wherein The lighting mode includes a spotlight mode, a hybrid mode, and a floodlight mode; the environmental information includes visibility; the step of determining the lighting mode of the lighting fixture according to the environmental information specifically includes: When the visibility is less than the set first visibility threshold value, the lighting mode is the spotlight mode; When the visibility is greater than or equal to the set first visibility threshold value and less than the set second visibility threshold value, the lighting mode is the hybrid mode; When the visibility is greater than or equal to the set second visibility threshold value, the lighting mode is the floodlight mode.

4. The method for illuminating a group fog area according to claim 3, characterized in that, The step of calculating the illuminance value using a lighting algorithm specifically includes: According to the lighting mode, obtain the number of initial lighting fixtures; The illuminance value is equal to the product of the power of the lighting fixture, the number of initial lighting fixtures, and the empirical coefficient of the environmental information divided by the road surface area covered by the fog area.

5. The method for illuminating a group fog area according to claim 1, wherein The step of obtaining the lighting fixture parameters required to meet the road safety passing standard for the fog area based on the fog physical information and environmental information specifically includes: Based on the fog physical information and environmental information, obtain the illuminance value using a lighting simulation method; Compare the illuminance value with the illuminance value of the road safety passing standard. When the illuminance value is not less than the illuminance value of the road safety passing standard, obtain the number of lighting fixtures.

6. The method for illuminating a group fog area according to claim 5, wherein, The lighting simulation method includes: Create a project and basic settings; Based on the information of the basic settings, construct a model and set up a scene; Based on the information of the scene, select the lighting fixture layout and light source settings; Calculate the illuminance value.

7. The method for illuminating a group fog area according to any one of claims 1 to 6, characterized in that, After the step of controlling the lighting fixtures of the drone swarm to achieve lighting, there is also an illuminance compensation mechanism: When the illuminance value of the environmental information is less than the illuminance value of the road safety passing standard for the fog area, increase the power of the lighting fixtures of the drone swarm until the illuminance value is not less than the national standard of the road safety passing standard.

8. A fog area lighting system based on a drone swarm, including a data collection subsystem, a decision-making subsystem, and an execution subsystem, characterized in that The data collection subsystem is used to obtain the fog physical information and environmental information within the fog area; The decision-making subsystem is used to obtain the lighting fixture parameters required to meet the road safety passing standard of the fog area according to the fog physical information and environmental information; The execution subsystem includes a UAV control center and a UAV group equipped with lighting fixtures. The UAV control center is used to configure the UAV group according to the lighting fixture parameters and control the UAV group to fly to the fog area; The UAV group is used to achieve lighting.

9. The fog area lighting system according to claim 8, characterized in that, The data collection subsystem includes sensors, lidar, and an edge processor. The sensors and lidar are used to collect the fog physical information and environmental information of the fog area and transmit the fog physical information and environmental information to the edge processor. The edge processor is used to preprocess the fog physical information and environmental information and then transmit it to the decision-making subsystem. The sensors include any one or more of the following sensors: thermal imaging sensors, visibility sensors, and illuminance sensors.

10. The fog cluster area lighting system according to claim 8 or 9, characterized in that, The execution subsystem further includes a smart road pole. The smart road pole is provided with UAVs and a UAV charging and swapping station, which is used to replace the UAVs in the fog area according to the instructions of the UAV control center or replenish the power of the UAVs when the UAVs are out of power.

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