Unmanned aerial vehicle night illumination decision-making method and system suitable for multiple users and multiple illumination points
Through game theory model and convex optimization theory, the problems of lighting point selection and time allocation in the night lighting system of multi-user multi-light point drone are solved, and efficient resource allocation between lighting points and fairness are achieved, which is suitable for engineering practice.
Patent Information
- Application Number
- CN202510686927.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-27
- Publication Date
- 2025-08-29
AI Technical Summary
In a drone night lighting system with multiple users and multiple lighting points, how to efficiently select lighting points and allocate lighting time to meet user needs and drone resource limitations.
The game theory model is adopted to calculate the light intensity and user set of each illumination point, optimize the resource allocation, determine the order and illumination time of the illumination point of the drone, and obtain the optimal solution using convex optimization theory.
It realizes efficient resource allocation between lighting points, ensures fairness of lighting effects and safe flight of drones, and provides a unique and efficient solution suitable for engineering practice.
Smart Images
Figure CN120560296A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and in particular to a nighttime lighting decision-making method and system for UAVs with multiple users and multiple lighting points. Background Art
[0002] Drone nighttime lighting technology has made significant progress in recent years, especially in scenarios with multiple lighting points and diverse user needs, where decision-making methods are becoming increasingly crucial. By integrating high-brightness, dimmable LEDs with sophisticated flight control systems and intelligent sensors, drones can achieve precise lighting at multiple lighting points. The decision-making system first collects environmental data, user commands, and other information, applying advanced algorithms for real-time analysis to determine the optimal lighting strategy and drone flight path. Furthermore, it leverages IoT technology for remote monitoring and management, ensuring that lighting effects are precisely aligned with user needs. Furthermore, with the introduction of big data and artificial intelligence technologies, drone nighttime lighting decision-making methods are becoming increasingly intelligent and adaptive. This paper focuses on a multi-user, multi-lighting point drone nighttime lighting system. A drone can randomly select one or more lighting points to provide nighttime lighting services to multiple users nearby. However, key challenges addressed by this paper are how to select these lighting points and how to allocate lighting durations. This paper conducts in-depth theoretical analysis and application research to address these issues, aiming to provide a unique and efficient solution. Summary of the Invention
[0003] Purpose of the invention: The present invention provides a nighttime lighting decision-making method and system for drones with multiple users and multiple lighting points, which can select and allocate lighting duration among multiple lighting points.
[0004] Technical solution: The present invention provides a nighttime lighting decision-making method for drones with multiple users and multiple lighting points, comprising the following steps:
[0005] Step 1: Initialize the lighting point set {1,…,M} and its coordinates The light intensity I of the UAV's onboard light source, the optical efficiency η of the lens, the standard deviation σ of the light spot, the distance Δh between the bulb and the bottom edge of the housing of the UAV lighting system, the radius Δr of the circular housing, the hovering height H of the UAV, and the coordinates of each user in the network;
[0006] Step 2: Calculate the lighting coverage radius R of the drone based on the distance Δh between the bulb and the bottom edge of the shell of the drone lighting system, the radius Δr of the circular shell, and the hovering height H of the drone. Initialize the user set of each lighting point based on the lighting radius R of the drone. And let the UAV lighting sequence parameter k = 0;
[0007] Step 3: If This means that the mth lighting point has no service users and does not need to participate in the subsequent resource game; if On the contrary;
[0008] Step 4: Calculate the sum of the light intensities of the user sets at each lighting point participating in this resource game. And the lighting points are H is the vertical distance between the light source and the illuminated surface, that is, H is the hovering height of the drone, η is the optical efficiency of the lens, r i Representing a collection The horizontal distance between the i-th user and the lighting point;
[0009] Step 5: Based on the conclusion of the game resource allocation, calculate the optimal lighting time obtained by the drone lighting point with the largest sequence number in the set of drone lighting points participating in this game. As the k+1th lighting hovering time to be selected by the UAV, the UAV intends to select the UAV lighting point as the k+1th target lighting point to be selected by the UAV;
[0010] Step 6: Calculate the flight time of the UAV from the kth illumination point to the k+1th target illumination point to be selected;
[0011] Step 7: Update the user set for each lighting point in Denote the user set served by the target lighting point for the k+1th time, let k=k+1, and return to step 3.
[0012] Furthermore, in step 2, a user exists within the service range of one or more drone lighting points, and in the game relationship, one user provides game benefits for multiple lighting points.
[0013] Furthermore, in step 6, the flight time of the UAV from the kth illumination point to the k+1th target illumination point to be selected is calculated as
[0014]
[0015] Where v is the flight speed of the drone, specifically (X0, x0) = (0, 0).
[0016] Furthermore, if Then the UAV can fly to the k+1th target lighting point to be selected and continue to hover and illuminate at the lighting point. Time, then, update If t=0, the algorithm ends, otherwise, go to step 7; if The lighting point with the next highest sequence number in the set of drone lighting points participating in the game compared to the k+1th drone lighting point to be selected is used as the new k+1th drone lighting point to be selected, and the optimal lighting time obtained is calculated. Return to step 6.
[0017] Furthermore, in step 7, when the UAV has traversed all the lighting points, Make When UAV selects all The lighting point with the shortest UAV approach flight time among the lighting points is selected as the next target lighting point, so that the UAV can obtain higher revenue from selling resources; if the approach flight time of the next target lighting point is still greater than or equal to the remaining flight time of the UAV, the UAV returns.
[0018] Furthermore, in step 7, the optimal lighting time obtained by the lighting points participating in the game is
[0019] (1) If When
[0020]
[0021] M is the number of lighting points available for the drone to hover. is the normalization coefficient of the sum of the light intensities obtained by all users at the l-th UAV lighting point, where the denominator is the sum of the light intensities of the users at each lighting point;
[0022] (2) If When
[0023]
[0024] and
[0025]
[0026] in, is the normalization coefficient of the sum of the illumination intensities obtained by all users at the mth drone lighting point.
[0027] Accordingly, a nighttime lighting decision system for drones with multiple users and multiple lighting points includes: an initialization module, a parameter calculation module, and a lighting decision module; the initialization module initializes various parameters; the parameter calculation module calculates the sum of the light intensities of the user sets of each lighting point participating in this resource game, and arranges the lighting points in ascending order of the sum of the light intensities; the lighting decision module calculates the optimal lighting time obtained by the lighting points participating in the game based on the conclusion of the game resource allocation.
[0028] Furthermore, various parameters are initialized including the lighting point set {1,…,M} and its coordinates The light intensity I of the light source carried by the UAV, the optical efficiency η of the lens, the standard deviation σ of the light spot, the distance Δh between the bulb and the bottom edge of the shell of the UAV lighting system, the radius Δr of the circular shell, the hovering height H of the UAV, and the coordinates of each user in the network.
[0029] Furthermore, the sum of the light intensities of the user sets of each lighting point participating in this resource game is calculated. And the lighting points are Sort in ascending order.
[0030] Furthermore, the lighting decision module calculates the optimal lighting time for the drone lighting point with the largest sequence number in the set of drone lighting points participating in the game based on the game resource allocation conclusion. As the k+1th lighting hovering time to be selected by the UAV, the UAV will select the UAV lighting point as the k+1th target lighting point to be selected by the UAV; calculate the flight time of the UAV from the kth lighting point to the k+1th target lighting point to be selected; update the user set of each lighting point in Denotes the set of users served by the target lighting point for the k+1th time. Let k=k+1 and continue the calculation.
[0031] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: the present invention can conduct a game of lighting time between various lighting points based on the sum of the light intensities of the users at each lighting point, while taking into account the UAV flight time between various lighting points, and the situation that a single user can exist within the service range of one or more UAV lighting points, and gives a closed solution for the optimal value obtained by the game for each lighting point. The present invention aims to provide a unique and efficient solution that physically conforms to the scenarios of real applications and can be effectively applied to actual engineering. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 Schematic diagram of the method scenario of the present invention. DETAILED DESCRIPTION
[0033] like Figure 1 As shown in FIG, a nighttime lighting decision method for a multi-user multi-lighting point UAV includes the following steps:
[0034] Step 1: Initialize the lighting point set {1,…,M} and its coordinates The light intensity I of the UAV's light source, the optical efficiency η of the lens, the standard deviation σ, Δh, Δr of the light spot, the drone's hovering height H, and the coordinates of each user in the network (the coordinate system is established with the drone's initial position as the origin);
[0035] Step 2: Calculate the lighting coverage radius R of the drone based on Δh, Δr and the drone's hovering height H, and initialize the user set of each lighting point based on the drone's lighting radius R. And let k = 0;
[0036] Step 3: If This means that the mth lighting point has no service users and does not need to participate in the subsequent resource game; if On the contrary;
[0037] Step 4: Calculate the sum of the light intensities of the user sets at each lighting point participating in this resource game. And the lighting points are Arrange in ascending order;
[0038] Step 5: Based on the conclusion of the game resource allocation, calculate the optimal lighting time obtained by the drone lighting point with the largest sequence number in the set of drone lighting points participating in this game. As the k+1th lighting hovering time to be selected by the UAV, the UAV intends to select the UAV lighting point as the k+1th target lighting point to be selected by the UAV;
[0039] Step 6: Calculate the flight time of the UAV from the kth lighting point to the k+1th target lighting point to be selected:
[0040]
[0041] Where v is the flight speed of the UAV, specifically (X0,X0)=(0,0).
[0042] 1) If Then the UAV can fly to the k+1th target lighting point to be selected and continue to hover and illuminate at the lighting point. time;
[0043] Then, update If t=0, the algorithm ends; otherwise, go to step 7;
[0044] 2) If The lighting point with the next highest sequence number in the set of drone lighting points participating in the game compared to the k+1th drone lighting point to be selected is used as the new k+1th drone lighting point to be selected, and the optimal lighting time obtained is calculated. Return to step 6;
[0045] Step 7: Update the user set for each lighting point in Denotes the set of users served by the target lighting point for the k+1th time. Let k=k+1 and return to step 3.
[0046] In step 2, the user set of each lighting point is initialized according to the lighting radius R of the drone. It is worth noting that in the present invention, a user may be within the service range of one or more drone lighting points;
[0047] In step 3, each lighting point participating in the resource game is calculated according to the sum of the light intensities of the user set. Arrange in ascending order;
[0048] In step 4, the optimal lighting time obtained by the drone lighting point with the largest sequence number in the set of drone lighting points participating in this game is The UAV selects the UAV lighting point as the UAV's k+1th target lighting point so that the UAV can obtain higher resource sales income;
[0049] In step 5, if The UAV flies to the k+1th target lighting point to be selected and continues to hover and illuminate at the lighting point time; otherwise, the lighting point with the next highest sequence number in the set of drone lighting points participating in the game compared to the proposed k+1th drone lighting point will be used as the new proposed k+1th drone lighting point;
[0050] In step 6, update the user set of each lighting point in represents the set of users served by the k+1th target lighting point. This means that the mth lighting point has no service users and does not need to participate in the subsequent resource game; if On the contrary;
[0051] When the UAV has traversed all the lighting points, Make When UAV selects all The lighting point with the shortest UAV approach time is chosen as the next target lighting point, allowing the UAV to earn higher profits from selling resources. It is worth noting that if the approach time of the next target lighting point is still greater than or equal to the UAV's remaining flight time, the UAV returns.
[0052] In step 5, update If t>0, it is necessary to Otherwise, the lighting point of the drone can be selected using the result of this game.
[0053] The optimal lighting time obtained by the lighting points participating in the game is
[0054] 1) If When
[0055]
[0056] 2) If When
[0057]
[0058] and
[0059]
[0060] in, is the normalized sum of the illumination intensities obtained by all users at the m-th drone lighting point.
[0061] In the network described in the present invention, there are M lighting points for drones to hover and illuminate. The drone can randomly select one or more lighting points to provide nighttime lighting services to multiple users near these points. Since traditional lighting hardware has a protective shell outside the bulb and is generally circular, the present invention assumes that the distance between the bulb and the bottom edge of the shell of the drone lighting system is Δh, and the radius of the circular shell is Δr. Assuming that the drone maintains the same flight and hovering height H during lighting work, the drone can provide lighting services to multiple users within a range of radius R with the hovering point as the center, where
[0062]
[0063] Since the light source of the UAV lighting system is in a uniform medium, the law of how the light intensity (in lux) at the center of the light spot on the illuminated surface changes with distance can be described by the inverse square law, which is:
[0064]
[0065] Where I is the light intensity of the light source (in lux); H is the distance between the light source and the illuminated surface (in meters); and η is the optical efficiency of the lens (between 0 and 1).
[0066] Therefore, the distribution of light intensity of the lighting system on the illuminated surface can be expressed as
[0067]
[0068] Where r is the distance from a point on the illuminated surface to the center of the light spot (in meters); σ is the standard deviation of the light spot, indicating the degree of diffusion of the light spot (in meters), and σ is related to the beam angle and optical design of the lighting system: the smaller the beam angle, the smaller σ and the more concentrated the light spot; the larger the beam angle, the larger σ and the more dispersed the light spot. The σ value can be determined through experiments or simulations.
[0069] Assuming that the initial position of the drone is set as the origin of the coordinate system and the coordinate system is established, the coordinates of the M points available for the drone to illuminate are Combining the coordinates of the user and the coordinates of the lighting point, we can get the set of users for which the mth drone lighting point can provide lighting services: It should be noted that different user sets In the present invention, there may be intersections, as a user can be located in one or more drone lighting coverage areas. In the present invention, the drone can provide lighting services to the corresponding user by flying to each lighting point. However, since the drone's single flight time t is limited (it should be noted that the time resource sold by the drone in the present invention is the remaining flight time after it is guaranteed to return safely), how to select from multiple lighting points and how to allocate the lighting duration are key issues to be addressed in the present invention.
[0070] Game theory offers a good approach to allocating limited resources. In this paper, each lighting point with a drone's lighting time resource requirement is considered a follower in the game, while the drone, which has the decision-making power over the allocation of limited resources, is the leader in the game. This model can be as follows:
[0071] In the present invention, each UAV lighting point hopes to increase its own lighting time. Therefore, the optimization problem of the mth UAV lighting point in the game relationship is modeled as
[0072]
[0073] in, is the normalized sum of the illumination intensities obtained by all users at the mth drone lighting point; t m represents the lighting time required by the mth UAV lighting point; r i Representing a collection The horizontal distance between the i-th user and the lighting point; represents the sum of the light intensities obtained by all users at the mth drone lighting point; β represents the game price per unit lighting time. The revenue constructor for the mth drone lighting point uses the property that the ln(x) function increases with the increase of x. This is because as the lighting time increases, the user experience becomes better and the relative fairness of each drone lighting point is guaranteed. As the lighting time increases, the growth rate of the revenue of the mth drone lighting point also decreases to reflect the marginal diminishing characteristics of resource acquisition. m Indicates that the mth UAV lighting point is used to obtain the lighting time t m And the price to pay.
[0074] In the game relationship, the drone sells limited single flight time to multiple competing drone lighting points. Therefore, the objective function in the game relationship is defined as the total cost paid by all drone lighting points to purchase their respective lighting time, which is
[0075]
[0076] Constraints
[0077]
[0078] Therefore, in the game relationship there is
[0079]
[0080] The two sets of optimization problems above together form a game model. The final equilibrium can be achieved by both parties playing the game according to certain rules. The optimal solution can be obtained through convex optimization theory. The steps are as follows:
[0081] The objective function of the profit of the mth UAV lighting point in the game relationship is optimized for the variable t m Find the first-order partial derivative, and we can get
[0082]
[0083] Therefore, it can be seen that if hour, Otherwise, t m =0.
[0084] Without loss of generality, assume that M UAV lighting points follow Sort in ascending order, so there is
[0085] Next, we will explain how to allocate the optimal lighting time for the mth drone lighting point based on game theory:
[0086] 1) If When
[0087]
[0088] 2) If When
[0089]
[0090] and
[0091]
[0092] From the above optimal lighting time allocation based on game theory, we can know that: in the same time resource sales, The larger the lighting point, the better the time resources Therefore, under the same resource pricing β, the drone can obtain greater benefits from this lighting point. Therefore, in order to obtain higher resource sales revenue, the drone in this invention first selects the Mth drone lighting point in the drone lighting point set as the drone's first lighting point.
[0093] Based on the above conclusions, a multi-lighting point decision method for UAVs is given below:
[0094] Step 1: Initialize the lighting point set {1,…,M} and its coordinates The light intensity I of the UAV's light source, the optical efficiency η of the lens, the standard deviation σ, Δh, Δr of the light spot, the drone's hovering height H, and the coordinates of each user in the network (the coordinate system is established with the drone's initial position as the origin);
[0095] Step 2: Calculate the lighting coverage radius R of the drone based on Δh, Δr and the drone's hovering height H, and initialize the user set of each lighting point based on the drone's lighting radius R. And let k = 0;
[0096] Step 3: If This means that the mth lighting point has no service users and does not need to participate in the subsequent resource game; if On the contrary;
[0097] Step 4: Calculate the sum of the light intensities of the user sets at each lighting point participating in this resource game. And the lighting points are Arrange in ascending order;
[0098] Step 5: Based on the conclusion of the game resource allocation, calculate the optimal lighting time obtained by the drone lighting point with the largest sequence number in the set of drone lighting points participating in this game. As the k+1th lighting hovering time to be selected by the UAV, the UAV intends to select the UAV lighting point as the k+1th target lighting point to be selected by the UAV;
[0099] Step 6: Calculate the flight time of the UAV from the kth lighting point to the k+1th target lighting point to be selected:
[0100]
[0101] Where v is the flight speed of the UAV, specifically (X0,X0)=(0,0).
[0102] 1) If Then the UAV can fly to the k+1th target lighting point to be selected and continue to hover and illuminate at the lighting point. time;
[0103] Then, update If t=0, the algorithm ends; otherwise, go to step 7;
[0104] 2) If The lighting point with the next highest sequence number in the set of drone lighting points participating in the game compared to the k+1th drone lighting point to be selected is used as the new k+1th drone lighting point to be selected, and the optimal lighting time obtained is calculated. Return to step 6;
[0105] Step 7: Update the user set for each lighting point in Denotes the set of users served by the target lighting point for the k+1th time. Let k=k+1 and return to step 3.
[0106] In particular, when the UAV has traversed all the lighting points and still Make When , it indicates that the remaining flight time of the UAV is small, so the UAV cannot fly close to the lighting point m and continue to illuminate it. In this case, the UAV should select all The lighting point with the shortest UAV approach time is chosen as the next target lighting point, allowing the UAV to earn higher profits from selling resources. It is worth noting that if the approach time of the next target lighting point is still greater than or equal to the UAV's remaining flight time, the UAV returns.
Claims
1. A nighttime lighting decision-making method for multi-user and multi-lighting-point unmanned aerial vehicles, characterized in that: The steps include: Step 1: Initialize the lighting point set {1,…,M} and its coordinates The light intensity I of the UAV's onboard light source, the optical efficiency η of the lens, the standard deviation σ of the light spot, the distance Δh between the bulb and the bottom edge of the housing of the UAV lighting system, the radius Δr of the circular housing, the hovering height H of the UAV, and the coordinates of each user in the network; Step 2: Calculate the lighting coverage radius R of the drone based on the distance Δh between the bulb and the bottom edge of the shell of the drone lighting system, the radius Δr of the circular shell, and the hovering height H of the drone. Initialize the user set of each lighting point based on the lighting radius R of the drone. And let the UAV lighting sequence parameter k = 0; Step 3: If This means that the mth lighting point has no service users and does not need to participate in the subsequent resource game; if On the contrary; Step 4: Calculate the sum of the light intensities of the user sets at each lighting point participating in this resource game. And the lighting points are H is the vertical distance between the light source and the illuminated surface, that is, H is the hovering height of the drone, η is the optical efficiency of the lens, r i Representing a collection The horizontal distance between the i-th user and the lighting point; Step 5: Based on the conclusion of the game resource allocation, calculate the optimal lighting time obtained by the drone lighting point with the largest sequence number in the set of drone lighting points participating in this game. As the k+1th lighting hovering time to be selected by the UAV, the UAV intends to select the UAV lighting point as the k+1th target lighting point to be selected by the UAV; Step 6: Calculate the flight time of the UAV from the kth illumination point to the k+1th target illumination point to be selected; Step 7: Update the user set for each lighting point in Denote the user set served by the target lighting point for the k+1th time, let k=k+1, and return to step 3.
2. The nighttime lighting decision-making method for a multi-user, multi-lighting-point UAV according to claim 1, characterized in that: In step 2, a user exists within the service range of one or more drone lighting points, and in the game relationship, one user provides game benefits for multiple lighting points.
3. The nighttime lighting decision-making method for a multi-user, multi-lighting-point UAV according to claim 1, characterized in that: In step 6, the flight time of the UAV from the kth illumination point to the k+1th target illumination point to be selected is calculated as Where v is the flight speed of the UAV, specifically (X0,X0)=(0,0).
4. The nighttime lighting decision-making method for a multi-user, multi-lighting-point UAV according to claim 3, characterized in that: like Where t is the flight time of the drone, the drone can fly to the k+1th target lighting point to be selected and continue to hover and illuminate at the lighting point. Time, then, update If t=0, the algorithm ends, otherwise, go to step 7; if The lighting point with the next highest sequence number in the set of drone lighting points participating in the game compared to the k+1th drone lighting point to be selected is used as the new k+1th drone lighting point to be selected, and the optimal lighting time obtained is calculated. Return to step 6.
5. The nighttime lighting decision-making method for a multi-user, multi-lighting-point UAV according to claim 1, characterized in that: In step 7, when the UAV has traversed all the lighting points, Make When UAV selects all The lighting point with the shortest UAV approach flight time among the lighting points is selected as the next target lighting point, so that the UAV can obtain higher sales resource income; If the approach flight time to the next target lighting point is still greater than or equal to the remaining flight time of the drone, the drone returns.
6. The nighttime lighting decision-making method for a multi-user, multi-lighting-point UAV according to claim 1, characterized in that: In step 7, the optimal lighting time obtained by the lighting points participating in the game is (1) If When M is the number of lighting points available for the drone to hover. is the normalization coefficient of the sum of the light intensities obtained by all users at the l-th UAV lighting point, where the denominator is the sum of the light intensities of the users at each lighting point; (2) If When and in, is the normalization coefficient of the sum of the illumination intensities obtained by all users at the mth drone lighting point.
7. A system based on the nighttime lighting decision-making method for multi-user and multi-lighting-point unmanned aerial vehicles according to claim 1, characterized in that: include: Initialization module, parameter calculation module and lighting decision module; The initialization module initializes various parameters; The parameter calculation module calculates the sum of the light intensities of the user sets of each lighting point participating in this resource game, and arranges the lighting points in ascending order of the sum of the light intensities; The lighting decision module calculates the optimal lighting time for the lighting points participating in the game based on the game resource allocation conclusion.
8. The UAV nighttime lighting decision system applicable to multiple users and multiple lighting points according to claim 7, characterized in that: Initialize various parameters including the lighting point set {1,…,M} and its coordinates The light intensity I of the light source carried by the UAV, the optical efficiency η of the lens, the standard deviation σ of the light spot, the distance Δh between the bulb and the bottom edge of the shell of the UAV lighting system, the radius Δr of the circular shell, the hovering height H of the UAV, and the coordinates of each user in the network.
9. The UAV nighttime lighting decision system applicable to multiple users and multiple lighting points according to claim 7, characterized in that: Calculate the sum of the light intensities of the user sets at each lighting point participating in this resource game And the lighting points are Sort in ascending order.
10. The UAV nighttime lighting decision system applicable to multiple users and multiple lighting points according to claim 7, characterized in that: The lighting decision module calculates the optimal lighting time for the drone lighting point with the largest sequence number in the set of drone lighting points participating in the game based on the game resource allocation conclusion. As the k+1th lighting hovering time to be selected by the UAV, the UAV will select the UAV lighting point as the k+1th target lighting point to be selected by the UAV; calculate the flight time of the UAV from the kth lighting point to the k+1th target lighting point to be selected; update the user set of each lighting point in Denotes the set of users served by the target lighting point for the k+1th time. Let k=k+1 and continue the calculation.